On the following pages you will find one of the... Applications and Summary Statements indexed here:

Transcription

On the following pages you will find one of the Sample R01
Applications and Summary Statements indexed here:
http://www.niaid.nih.gov/ncn/grants/app/default.htm
Visit the Web site for the most recent information. We may add more
in the future.
We are truly indebted to the grantees who've allowed us to post their
outstanding applications to help the next generation of investigators
write their applications.
Please note that the application text is copyrighted. It may be
used only for nonprofit educational purposes provided the document
remains unchanged and the PI, the grantee organization, and NIAID
are credited.
PI: Drusano, George Louis
Title: Resistance Suppression for Influenza Virus With Combination Chemotherapy
Received: 11/07/2007
FOA: AI07-025
Competition ID: VERSION-2A-FORMS
FOA Title: PHARMACOLOGICAL APPROACHES TO COMBATING
ANTIMICROBIAL RESISTANCE (R01)
1 R01 AI079729-01
Dual:
IPF: 10000856
Organization: ORDWAY RESEARCH INSTITUTE, INC.
Former Number:
Department: Emerging Infections
IRG/SRG: ZAI1 DDS-M (M1)
AIDS: N
Expedited: N
Subtotal Direct Costs
(excludes consortium F&A)
Year 1:
453,198
Year 2:
464,720
Year 3:
476,777
Year 4:
490,114
Animals: N
Humans: N
Clinical Trial: N
Exemption: 10
HESC: N
New Investigator: N
Senior/Key Personnel:
Organization:
Role Category:
George Drusano MD
Ordway Research Institute
PD/PI
James McSharry PhD
Other (Specify)-Co-Investigator
Paul Kiem PhD
Translational Genomics Institute
David Engelthaler
Translational Genomics Institute
Robert Kulawy
Other (Specify)-Analytical Chemist
Paul Spence PhD
Adamas Pharmaceuticals
Other (Specify)-Consultant
Council: 05/2008
Accession Number: 3041864
Additions for Review
Supplemental Material
Supplemental Data
Submitted by the applicant.
01/07/2008
APPLICATION FOR FEDERAL ASSISTANCE
SF 424 (R&R)
2. DATE SUBMITTED
11/07/2007
Applicant Identifier
3. DATE RECEIVED BY STATE
State Application Identifier
1. * TYPE OF SUBMISSION
❍ Pre-application ❍ Application
● Changed/Corrected Application
4. Federal Identifier
GRANT00360252
5. APPLICANT INFORMATION
* Legal Name: Ordway Research Institute
Department:
* Street1: 150 New Scotland Avenue
* City: Albany
* Organizational DUNS:124361945
Division:
Street2:
County: Albany
* State: NY: New York
Province:
* Country: USA: UNITED STATES
* ZIP / Postal Code: 12208
Person to be contacted on matters involving this application
Prefix:
* First Name:
Middle Name:
Sharon
E.
* Phone Number: 518-641-6410
Fax Number: 518-641-6303
6. * EMPLOYER IDENTIFICATION NUMBER (EIN) or (TIN):
XXXXXXXX
8. * TYPE OF APPLICATION:
❍ Resubmission
❍ Renewal
● New
❍ Continuation
❍ Revision
If Revision, mark appropriate box(es).
❍ A. Increase Award ❍ B. Decrease Award ❍ C. Increase Duration
❍ D. Decrease Duration ❍ E. Other (specify):
* Last Name:
Suffix:
Boswell
CRA
Email: [email protected]
7. * TYPE OF APPLICANT
M: Nonprofit with 501C3 IRS Status (Other than Institution of Higher Education)
Other (Specify):
Small Business Organization Type
❍ Women Owned
❍ Socially and Economically Disadvantaged
9. * NAME OF FEDERAL AGENCY:
National Institutes of Health
10. CATALOG OF FEDERAL DOMESTIC ASSISTANCE NUMBER:
93.856
TITLE: Microbiology and Infectious Diseases Research
* Is this application being submitted to other agencies? ❍ Yes ● No
What other Agencies?
11. * DESCRIPTIVE TITLE OF APPLICANT'S PROJECT:
Resistance Suppression for Influenza Virus With Combination Chemotherapy
12. * AREAS AFFECTED BY PROJECT (cities, counties, states, etc.)
global
13. PROPOSED PROJECT:
14. CONGRESSIONAL DISTRICTS OF:
* Start Date
* Ending Date
a. * Applicant
b. * Project
07/01/2008
06/30/2012
21
21
15. PROJECT DIRECTOR/PRINCIPAL INVESTIGATOR CONTACT INFORMATION
Prefix:
* First Name:
Middle Name:
* Last Name:
George
L.
Drusano
Position/Title: Co-Director
* Organization Name: Ordway Research Institute
Division:
Street2:
* City: Albany
County: Albany
Province:
* Phone Number: 518-641-6434
Tracking Number: GRANT00372521
Fax Number: 518-641-6303
Funding Opportunity Number: RFA-AI-07-025
Suffix:
MD
* Email: [email protected]
Received Date: 2007-11-07 15:21:16.000-05:00 Time
Zone: GMT-5
OMB Number: 4040-0001
Expiration Date: 04/30/2008
SF 424 (R&R) APPLICATION FOR FEDERAL ASSISTANCE
16. ESTIMATED PROJECT FUNDING
a. * Total Estimated Project Funding
$3,116,549.00
b. * Total Federal & Non-Federal Funds $3,116,549.00
c. * Estimated Program Income
$0.00
Page 2
17. * IS APPLICATION SUBJECT TO REVIEW BY STATE EXECUTIVE ORDER 12372 PROCESS?
a. YES
❍ THIS PREAPPLICATION/APPLICATION WAS MADE AVAILABLE TO THE
STATE EXECUTIVE ORDER 12372 PROCESS FOR REVIEW ON:
DATE:
b. NO
● PROGRAM IS NOT COVERED BY E.O. 12372; OR
❍
PROGRAM HAS NOT BEEN SELECTED BY STATE FOR REVIEW
18. By signing this application, I certify (1) to the statements contained in the list of certifications* and (2) that the statements herein are true, complete
and accurate to the best of my knowledge. I also provide the required assurances * and agree to comply with any resulting terms if I accept an
award. I am aware that any false, fictitious, or fraudulent statements or claims may subject me to criminal, civil, or administrative penalties. (U.S.
Code, Title 18, Section 1001)
● * I agree
* The list of certifications and assurances, or an Internet site where you may obtain this list, is contained in the announcement or agency specific instructions.
19. Authorized Representative
Prefix:
* First Name:
Garrett
* Position/Title: Director
Department: Office for Sponsored Research
* City: Albany
Middle Name:
* Last Name:
R.
Sanders
* Organization Name: Ordway Research Institute
Division:
Street2:
County: Albany
Province:
* Phone Number: 518-641-6410
Fax Number: 518-641-6303
Suffix:
* Email: [email protected]
* Signature of Authorized Representative
* Date Signed
Sharon Hanley
11/07/2007
20. Pre-application File Name: Mime Type:
21. Attach an additional list of Project Congressional Districts if needed.
File Name: Mime Type:
Funding Opportunity Number: RFA-AI-07-025
Received Date: 2007-11-07 15:21:16.000-05:00 Time
Zone: GMT-5
Principal Investigator/Program Director (Last, first, middle): Drusano, George, L.
424 R&R and PHS-398 Specific
Table Of Contents
Page Numbers
SF 424 R&R Face Page------------------------------------------------------------------------------------------
1
Table of Contents---------------------------------------------------------------------------------------------
3
Performance Sites---------------------------------------------------------------------------------------------
4
Research & Related Other Project Information------------------------------------------------------------------
5
Project Summary/Abstract (Description)----------------------------------------
6
Public Health Relevance Statement (Narrative attachment)----------------------------------------
7
Facilities & Other Resources----------------------------------------
8
Equipment----------------------------------------
10
Research & Related Senior/Key Person--------------------------------------------------------------------------
11
Biographical Sketches for each listed Senior/Key Person----------------------------------------
15
Research & Related Budget - Year 1----------------------------------------------------------------------------
36
39
42
45
Budget Justification----------------------------------------
48
Research & Related Budget - Cumulative Budget-----------------------------------------------------------------
50
Research & Related Budget - Consortium Budget (Subaward 1)----------------------------------------------------
51
PHS 398 Specific Cover Page Supplement------------------------------------------------------------------------
66
PHS 398 Specific Research Plan--------------------------------------------------------------------------------
68
Specific Aims----------------------------------------
71
Significance and Related R&D----------------------------------------
72
Preliminary Studies/Phase I Final Report----------------------------------------
75
Experimental/Research Design and Methods----------------------------------------
84
Bibliography & References Cited----------------------------------------
97
Consortium/Contractual Arrangements----------------------------------------
105
Resource Sharing Plan (Data Sharing and Model Organism Sharing)----------------------------------------
106
PHS 398 Checklist---------------------------------------------------------------------------------------------
Table of Contents
107
Page 3
RESEARCH & RELATED Project/Performance Site Location(s)
Project/Performance Site Primary Location
Organization Name: Ordway Research Institute
Street2:
* City: Albany
County: Albany
Province:
* Country: USA: UNITED
STATES
* Zip / Postal Code: 12208
Project/Performance Site Location 1
Organization Name: Translational Genomics Institute
* Street1: 445 N. Fifth St.
Street2:
* City: Phoenix
County:
* State: AZ: Arizona
Province:
* Country: USA: UNITED
STATES
File Name
Mime Type
Additional Location(s)
Performance Sites
Page 4
RESEARCH & RELATED Other Project Information
❍
Yes
●
No
❍
Yes
❍
No
3
4
❍
Yes
●
No
❍
Yes
❍
No
3. * Is proprietary/privileged information ❍ Yes
●
No
1. * Are Human Subjects Involved?
1.a. If YES to Human Subjects
Is the IRB review Pending?
IRB Approval Date:
Exemption Number:
1
2
5
6
Human Subject Assurance Number
2. * Are Vertebrate Animals Used?
2.a. If YES to Vertebrate Animals
Is the IACUC review Pending?
IACUC Approval Date:
Animal Welfare Assurance Number
included in the application?
4.a. * Does this project have an actual or potential impact on
❍
●
Yes
No
the environment?
4.b. If yes, please explain:
4.c. If this project has an actual or potential impact on the environment, has an exemption been authorized or an environmental assessment (EA) or
environmental impact statement (EIS) been performed?
❍
❍
Yes
No
4.d. If yes, please explain:
5.a. * Does this project involve activities outside the U.S. or
❍
Yes
●
No
partnership with International Collaborators?
5.b. If yes, identify countries:
5.c. Optional Explanation:
6. * Project Summary/Abstract
5614-Influenza_Grant_Abstract.pdf
7. * Project Narrative
9314-Project_Narrative_Influenza_Virus.pdf
Mime Type: application/pdf
8. Bibliography & References Cited
3605-Literature_Cited.pdf
9. Facilities & Other Resources
4380-Resources.pdf
10. Equipment
3834-Major_Equipment.pdf
Other Information
Page 5
PROJECT ABSTRACT
The advent of H5N1 influenza A Virus is a critical wake up call. We are overdue for a global pandemic of
Influenza Virus caused by H5N1 or some other influenza A virus. Such a pandemic could cause a very large
number of deaths worldwide and major morbidity and economic disruption. It is important to recognize that
optimal chemotherapy directed at such a pandemic virus is critical to reduce the attendant mortality and
morbidity. In Specific Aim #1, we propose to employ our novel hollow fiber infection model (HFIM) to
demonstrate that we can rapidly select Influenza Virus clones that are resistant to either adamantine or
neuraminidase inhibitors and that the mutations conferring resistance will be the same as those of naturallyoccurring strains. Once the system is validated that it is a good surrogate for the clinical selection of resistant
isolates, we can employ our HFIM to pursue Specific Aim #2, and identify the optimal dose and schedule of
administration of these agents given as monotherapy to optimize viral suppression and suppress the
emergence of resistance. This will be accomplished through dose ranging and dose fractionation experiments.
It is important to identify optimal dose ranges for resistance suppression and viral turnover suppression for
drugs alone, as pharmacological differences between agents may allow “pharmacokinetic mismatching” at
certain times within the treatment period. Such mismatched times may be more permissive for resistance
emergence, even in the face of combination chemotherapy. Therefore, it is important for each drug in any
combination to be optimal or near-optimal for resistance suppression on its own. In Specific Aim #3, we will
pursue optimizing the drugs in combination for resistance suppression. Little has been done in this regard. We
have developed a mixture model approach that will allow simultaneous description of the effect of these
antiviral compounds in combination on both the fully wild-type viral population as well as the viral
subpopulation with resistance mutations. As previous work from our laboratory with bacteria has shown, these
different pathogen populations will be differentially affected by the drug pressure in combination. Our approach
will be to design combination therapy experiments from data developed in the monotherapy experiments of
Specific Aim #2. We will then perform combination therapy experiments with sixteen different combinations of
drug doses. All these data (drug concentrations over time for both drugs, the effect on the total viral population
over time, and the effect on the mutant viral population over time) will be simultaneously co-modeled
employing our completely novel mathematical population mixture model. Obtaining robust point estimates of
system parameters will allow design of regimens that are optimized in the combination for Influenza Virus
resistance suppression.
Project Description
Page 6
Project Narrative
We are well overdue for a global pandemic of Influenza virus that could wreak havoc, causing
considerable mortality, morbidity and economic dislocation. Anti-influenza chemotherapy is
critical in protecting ourselves from such a pandemic. The goals of this proposal are to 1)
demonstrate that our in vitro hollow fiber system produces resistant Influenza Virus that reflect
the clinical circumstance when suboptimal drug exposures are given 2) identify optimal drug
exposures that suppress resistance by Influenza Virus to a neuraminidase inhibitor and the
adamantine amantadine 3) identify the best ways to use these agents in combination to prevent
Influenza virus from emerging resistant.
Public Health Relevance Statement
Page 7
RESOURCES – ORDWAY RESEARCH INSTITUTE
Laboratory:
Dr. McSharry’s laboratory at the Ordway Research Institute is 1,000 sq. ft. Within the main lab are two 120 sq.
ft. BSL-2 labs. The main laboratory contains a laminar flow hood, two CO2 incubators, a refrigerator, 2 Metler
balances, an inverted light microscope, an epifluorescence microscope, 2 Fisher microfuges, one IBM
computer, a BeckmanCoulter FC500 four color analytical flow cytometer with computers, an autoclave, a liquid
nitrogen tank for storing cells, and a Bio-Tek Synergy II plate reader and washer with computer. Each BSL-2
lab has an eight foot laminar flow hood, an inverted light microscope, a refrigerator, 4 CO2 incubators and a
refrigerated centrifuge. One -20 degrees C and two -70 degrees C freezers are available to the lab. All
freezers are locked. The BSL-2 labs have doors that lock. The Ordway Research Institute has an Agilent
Technologies LC/MSD SL apparatus and an Applied Biosystems API 5000 LC/MS/MS instrument for
measuring drug concentrations in medium. Both of these pieces of equipment are situated in Dr. McSharry’s
laboratory.
Computer:
Dr. Drusano has 3 fast PCs (>2GHz) with 512 Megabytes or more of fast RAM. In addition, for computationally
intensive problems he has a 128 mode Beowulf cluster. Modeling tools available include both “Big” and Little
NPAG and NPOD, the ADAPT II package of programs and SYSTAT for windows (v10.2). Dr. McSharry and
his technicians have access to networked PCs in the main lab. The laboratory contains computers that control
the hollow fiber systems, the Bio Tek Synergy II plate reader and washer, and the flow cytometer. Software
that meets the needs of the laboratories for word processing, data analysis and molecular biology are
available.
Office:
Dr. Drusano has his own 220 sq. ft. office with networked computer for internet access and a phone. Dr.
McSharry has his own office with phone and networked PC. The Ordway Research Institute has research
coordinators to assist with paper work and other administrative functions. Their offices have high speed copy
machines that are networked to the PCs in the labs and a fax machine.
Other:
Ordway Research Institute investigators have electronic and physical access to libraries at the nearby David
Axelrod Institute, Wadsworth Center, New York State Department of Health and Albany Medical College.
RESOURCES - TGEN - NORTH
Laboratory:
The Translational Genomics Research Institute (TGen), founded in July of 2002, is a not-for-profit research
institute whose mission is to make and translate genomic discoveries into advances for human health. TGen
North is a new branch of TGen, located in Flagstaff, Arizona. The new TGen North Laboratory facility is
approximately 4,500 sq. ft. with over 2,000 sq. ft. of laboratory space, and is currently conducting pathogen
genomics research activities, including projects related to influenza, valley fever, MRSA, community acquired
pneumonia, and several biothreat agents.
Computer:
TGen North has numerous desktop and high-performance computers necessary to support ongoing real-time
PCR, sequencing and micro-array analyses. TGen North also has a Bioinformatics coordinator on site that will
help support this project. Additionally, TGen North’s researchers are supported by state-of-the-art scientific
support cores at the primary TGen facility in Phoenix, which include the following:
Bioinformatics Core: The Bioinformatics Core provides, under an integrated data warehousing environment
and smart data mining tools, a powerful resource in modern biomedical research. They provide researchers
ways to extract and use information from vast amounts of knowledge and data collected from wide-ranging
sources such as public and commercial databases (e.g. genome sequences, chromosome, SNP, genetic
diseases, EST cluster, gene mapping, gene expression, proteomic, molecular biology, and clinical and
literature databases). The Bioinformatics Core also provides a scientific data warehousing environment that
Facilities
Page 8
integrates and delivers a variety of data for both global and specialized local data views and analyses. The
scientific data warehousing environment is built upon integrating a variety of relational databases (e.g. IBM
DB2, MySQL, and Oracle), robust database query tools (e.g. IBM Discovery Link and XML embedded Query),
secure Web, IBM WebSphere, data encryptions, J2EE, XML, and multi-tier client-server technologies. The
Core works closely with the TGen scientific staff and other collaborating researchers to push the research
forward.
Center for Computational Bioscience: The Center for Computational Bioscience provides computational
resources, biomedical informatics support and knowledge-based data management systems to the scientific
staff. The following are some of the Center’s resources: access to Linux, Deep Blue, Knowledge Data
Management Resources such as Discovery Links, Application/Business Logic/Web resources such as
WebSphere Application Server, IBM XML Zone, security software such as Tivoli Management Environment,
Rational software such as Rational Suite Development/Enterprise, Lotus Software, Application Development
resources such as APL2, Operating System Software such as AIX Operating Systems, Cluster/Scalable
Parallel Resources such as Cluster Systems Management (CSM).
In collaboration with IBM and Arizona State University, TGen’s Center for Computational Bioscience provides
researcher access to a high performance scientific supercomputing facility. This 1,024 CPU facility comprising
of IBM Linux cluster computers, IBM SMP high performance servers, and SAN storage units makes this
supercomputer one of the top 160 in the world (according to floating point estimates). The facility provides
over 1.75 Tera-flops of computing power and over 12 Tera-bytes of high-speed disk storage units. TGen
scientific staff have access to a large number of high performance computational tools (e.g. sequence
alignment, linkage analysis, gene clustering and classification, multivariate analysis, etc.) and data
management systems (e.g. LIMS, Expression DB, Gene Ontology, etc.) as well as customized application
software (e.g. genomic regulatory network simulations) on these powerful machines. With high-speed LANs
and WANs, this high performance scientific computing facility eliminates the “plumbing” bottlenecks of the
research system architecture and provides researchers reliable access.
Office:
Researchers on this project have over 500 sq. ft. of office space in the two TGen facilities.
Facilities
Page 9
MAJOR EQUIPMENT
Major Equipment – Ordway Research Institute, Inc.
There are four dual pumps to perform HFIM studies with eight arms simultaneously. There are six
computerized pumps to deliver antiviral drugs and media to the hollow fiber units yielding one complete set-up
in one BSL-2 lab to perform pharmacodynamic/pharmacokinetic analyses. An Agilent Technologies LC/MSD
SL apparatus and an Applied Biosystems APT 5000 LC/MS/MS apparatus are available to this study. A
Beckman Coulter Cytomics FC 500 series flow cytometer is available in the lab for analyzing virus infected
cells to determine the number of infected cells to add to the HFIM systems.
Major Equipment – TGen North
TGen North has two AB7900HT real-time PCR instruments with both 96 well and 364 well high-throughput
capacity, and a robotic arm allowing for 24/7 operations. TGen North has an AB3130xl gene sequencer that
will be used for the primary sequencing activities in this grant. Additionally, we have full access to the robust
sequencing capabilities of the TGen DNA Sequencing Core facility, which is capable of up to one million
sequencing reactions per year and produces consistent reads of on average 800 base pairs. The following
major equipment is located within the DNA Sequencing Core: 8453 spectrophotometer, ABI Prism 3730 XL
DNA Analyzer, Dual 384-Well Geneva PC System 9700, ABI Prism® 3100 Genetic Analyzer, Dual 384-Well
GeneAmp PCR System, and ABI Prism 3730XL-96 Cap DNA Analyzer. Additionally, TGen North has a Class
IIA biosafety cabinet, two PCR cabinets, four thermocyclers (including 384 well format), two 96 well plate
capable centrifuges ultra-cold freezers, a high capacity (96 channel) automated plate loader, and several
electronic and manual multi-channel pipettors.
The following major equipment is located within the primary TGen Laboratory in Phoenix, which we will have
access to, if needed, for this project: two ABI Prism® 3730xl DNA Analyzers capable of analyzing
approximately 800,000 sequencing reactions per year; one ABI Prism® 3100 Genetic Analyzer; four ABI
GeneAmp® 9700 Thermocyclers (Dual-well 384); 1 MJ Research PTC200 thermocycler; one TomTec Quadra
3 Automated Assay Workstation for routine small-volume liquid handling such as assembly of sequencing
reactions; one Beckman Multimek 96 Automated 96-channel Pipettor for routine liquid-handling; one Beckman
FX Laboratory Workstation; one Genetix Q-PixII Automated Colony Picker for arraying shotgun library
subclones into 96-well growth plates; one GeneMachines Higro® High-capacity Bacterial Growth System
capable of growing 48 96-well microtiter plates of bacterial cultures; one GeneMachines HydroShear® for
kinetically shearing DNA during the shotgun library construction process; one Robbins Scientific Hydra96
Pipetting Station for routine liquid handling; two Jouan GR422 centrifuges; one Beckman Avanti J-25i highspeed centrifuge; one Revco -80 degree freezer; one Life Technologies Cell-Porator E. coli pulser; four Biorad
agarose gel apparatus with power supplies; one LabLine shaking incubator (floor model).
Equipment
Page 10
RESEARCH & RELATED Senior/Key Person Profile (Expanded)
PROFILE - Project Director/Principal Investigator
Prefix
* First Name
Middle Name
* Last Name
Suffix
George
L.
Drusano
MD
Position/Title: Co-Director
Division:
Street2:
* City: Albany
County: Albany
* State: NY: New York Province:
*Phone Number
Fax Number
* E-Mail
518-641-6434
518-641-6303
[email protected]
Credential, e.g., agency login: XXXXXXX
* Project Role: PD/PI
Other Project Role Category:
File Name
3494-biosketch_Drusano10-09-07.pdf
*Attach Biographical Sketch
Mime Type
application/pdf
Attach Current & Pending Support
PROFILE - Senior/Key Person
Prefix
* First Name
Middle Name
* Last Name
Suffix
James
J.
McSharry
PhD
Position/Title: Senior Scientist & Professor
Division:
Street2:
* City: Albany
County: Albany
*Phone Number
Fax Number
* E-Mail
518-641-6462
518-641-6304
[email protected]
Credential, e.g., agency login:
* Project Role: Other (Specify)
Other Project Role Category: Co-Investigator
File Name
3221-biosketch_McSharry.pdf
Mime Type
application/pdf
Prefix
* First Name
Middle Name
* Last Name
Suffix
Paul
S.
Kiem
PhD
Position/Title: Director
Department: Pathogen Genomics
Division:
* Street1: 445 N. Fifth Street
Street2:
* City: Phoenix
County:
Key Personnel
Province:
Page 11
*Phone Number
Fax Number
* E-Mail
602-343-8400
602-343-8440
[email protected]
File Name
9706-Biosketch.Keim.pdf
Mime Type
application/pdf
Prefix
* First Name
Middle Name
* Last Name
David
Suffix
Engelthaler
Position/Title: Director of Programs
Department: TGen North
Division:
* Street1: 445 N. Fifth Street
Street2:
* City: Phoenix
County:
Province:
*Phone Number
Fax Number
* E-Mail
602-343-8400
602-343-8440
[email protected]
File Name
983-Biosketch_Egenthaler.pdf
Mime Type
application/pdf
Prefix
* First Name
Middle Name
* Last Name
Robert
Suffix
Kulawy
Position/Title: Analytical Chemist
Department:
Division:
Street2:
* City: Albany
County: Albany
*Phone Number
Fax Number
* E-Mail
518-641-6457
518-641-6304
[email protected]
Other Project Role Category: Analytical Chemist
File Name
1839-biosketch.Kulawy.pdf
Mime Type
application/pdf
Key Personnel
Page 12
Prefix
* First Name
Middle Name
Paul
Position/Title: Sr. V.P. Research
Department:
Organization Name: Adamas Pharmaceuticals
Division:
* Street1: 1900 Powell Street
Street2: Suite 1050
* City: Emeryville
County:
* Last Name
Suffix
Spence
PhD
* State: CA: California Province:
*Phone Number
Fax Number
* E-Mail
510-450-5313
510-428-0519
[email protected]
Other Project Role Category: Consultant
File Name
8826-biosketch.Spence.pdf
Mime Type
application/pdf
Key Personnel
Page 13
RESEARCH & RELATED Senior/Key Person Profile (Expanded)
Additional Senior/Key Person Form Attachments
When submitting senior/key persons in excess of 8 individuals, please attach additional senior/key person forms here. Each additional form attached here, will provide you with the ability to identify another 8 individuals, up to a maximum of 4 attachments (32 people).
The means to obtain a supplementary form is provided here on this form, by the button below. In order to extract, fill, and attach each additional
form, simply follow these steps:
•
•
•
•
•
•
•
Select the "Select to Extract the R&R Additional Senior/Key Person Form" button, which appears below.
Save the file using a descriptive name, that will help you remember the content of the supplemental form that you are creating. When assigning a name to the file, please remember to give it the extension ".xfd" (for example, "My_Senior_Key.xfd"). If you do not name your file
with the ".xfd" extension you will be unable to open it later, using your PureEdge viewer software.
Using the "Open Form" tool on your PureEdge viewer, open the new form that you have just saved.
Enter your additional Senior/Key Person information in this supplemental form. It is essentially the same as the Senior/Key person form that
you see in the main body of your application.
When you have completed entering information in the supplemental form, save it and close it.
Return to this "Additional Senior/Key Person Form Attachments" page.
Attach the saved supplemental form, that you just filled in, to one of the blocks provided on this "attachments" form.
Important: Please attach additional Senior/Key Person forms, using the blocks below. Please remember that the files you attach must be Senior/
Key Person Pure Edge forms, which were previously extracted using the process outlined above. Attaching any other type of file may
result in the inability to submit your application to Grants.gov.
1) Please attach Attachment 1
Filename
ADDITIONAL SENIOR/KEY
PERSON PROFILE(S)
MimeType
Filename
Additional Biographical
Sketch(es) (Senior/Key Person)
MimeType
Filename
Additional Current and
Pending Support(s)
MimeType
Key Personnel
Page 14
BIOGRAPHICAL SKETCH
Provide the following information for the key personnel and other significant contributors in the order listed on Form Page 2.
Follow this format for each person. DO NOT EXCEED FOUR PAGES.
NAME
POSITION TITLE
Drusano, George Louis (MD)
Professor of Medicine & Pharmacology
Director, Division of Clinical Pharmacology
Co-Director, Ordway Research Institute
eRA COMMONS USER NAME
XXXXXXX
EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, and include postdoctoral training.)
INSTITUTION AND LOCATION
Boston College, Chestnut Hill, MA
U. of Maand School of Medicine, Baltimore,
MD
DEGREE
(if applicable)
B.S.
M.D.
YEAR(s)
1971
1975
FIELD OF STUDY
Physics
Medicine
A. Positions and Honors
1975-1976
Straight Medical Internship, University of Maryland Hospital, Baltimore, MD
1976-1977
Jr. Assistant Resident in Medicine, University of Maryland Hospital, Baltimore, MD
1977-1978
Assistant Resident in Medicine, University of Maryland Hospital, Baltimore, MD
1978-1979
Fellow in Medicine in Infectious Diseases, Univ. of Maryland Hospital, Baltimore, MD
1979-1980
Chief Resident in Medicine, University of Maryland Hospital, Baltimore, MD
1979-1981
Instructor in Medicine, University of Maryland Hospital, Baltimore, MD
1980-1981
Fellow in Medicine in Infectious Diseases, Univ. of Maryland Hospital, Baltimore, MD
1981-1986
Assistant Prof. of Medicine, Div. Of Infectious Dis., Univ. of Maryland School of Medicine
1985-1987
Assistant Prof. of Pharmacy, University of Maryland School of Pharmacy
1986-1992
Associate Prof. of Medicine, Div. Of Infectious Dis., Univ. of Maryland School of Medicine
1987-1992
Associate Prof. of Pharmacy, University of Maryland School of Pharmacy
1992-1992
Prof. of Medicine, Div. Of Infectious Dis., University of Maryland School of Medicine
1992-Present Prof. of Medicine & Pharmacology and Director, Clinical Pharmacology, Albany Medical Center
2001-Present Co-Director, Clinical Research Institute, Albany Medical College & NYS Dept. of Health
2002-Present Co-Director, Ordway Research Institute, Inc., Albany, NY
Boston College: Scholar of the College (A&S), Honors Program, Magna Cum Laude φΒΚ
University of Maryland School of Medicine: Cum Laude, Alpha Omega Alpha
Rhone-Poulenc Award at International Congress of Chemotherapy, Berlin, 1991
Editor, Section of Pharmacology, Antimicrobial Agents and Chemotherapy, 1/1989-1/1998
Interscience Conference on Antimicrobial Agents and Chemotherapeutics (ICAAC) Program Committee
National Human Retroviruses and Related Infections Conference Program Committee
Distinguished Investigator – American College of Clinical Pharmacology 2003
IDSA Annual Meeting Program Committee Member – Appointed December, 2006
Ad Hoc Member NIAID Council (DMID), September 18, 2006
B. Selected peer-reviewed publications (of 207 cited).
1. Drusano, G.L., Forrest, A., J. A. Yuen, and K.I. Plaisance (1994) Optimal Sampling Theory: Effect of
error in a nominal parameter value on bias and precision of parameter estimation. Journal of Clinical
Pharmacology; 34: 967 - 974.
2. Drusano, G.L., Aweeka, F., Gambertoglio, J., Jacobson, M., Polis, M., Lane, H.C., C. Eaton and S.
Martin-Munley. (1996) Relationship between foscarnet exposure, baseline Cytomegalovirus blood
culture and the time to progression of Cytomegalovirus retinitis in HIV-positive patients. AIDS; 10: 1113
- 1119.
Biosketches
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3. Stein, D., Fish, D., Bilello, J. A., Chodakewitz, J., Emini, E., Hildebrand, C., Preston, S.L., G.L.
Martineau, and G.L. Drusano. (1996) A 24 week open label phase I evaluation of the HIV protease
inhibitor MK-639. AIDS; 10: 485 -492.
4. Drusano, G.L., Prichard, M.N., P.A. Bilello and J.A. Bilello (1997) Modeling combinations of
antiretroviral agents in vitro with integration of pharmacokinetics: Guidance of regimen choice for
clinical trial evaluation. Antimicrobial Agents and Chemotherapy; 41: 1143 - 1147.
5. Kashuba, A.D., Nafziger, A.N., G.L. Drusano and J.S. Bertino, Jr. (1999) Optimizing aminoglycoside
therapy for nosocomial pneumonia caused by Gram-negative bacteria. Antimicrobial Agents
Chemotherapy; 43: 623 - 629.
6. Rybak, M.J., Abate, B.J., Kang, S.L., Ruffing, M.J., S.A. Lerner and G.L. Drusano (1999) Prospective
evaluation of the effect of an aminoglycoside dosing regimen on rates of observed nephrotoxicity and
ototoxicity. Antimicrobial Agents Chemotherapy; 43: 1549 - 1555.
7. Drusano, G.L., D’Argenio, D.Z., Preston, S.L., Barone, C., Symonds, W., LaFon, S., Rogers, M., Prince,
W., A Bye and J.A. Bilello (2000) Use of drug effect interaction modeling with Monte Carlo simulation
to examine the impact of dosing interval on the projected antiviral activity of the combination of abacavir
and amprenavir. Antimicrobial Agents Chemotherapy; 44: 1655 - 1659.
8. Snyder, S., D’Argenio, D.Z., Weislow, O., J.A. Bilello and G.L. Drusano (2000) The triple combination of
indinavir, zidovudine plus lamivudine is highly synergistic. Antimicrobial Agents Chemotherapy; 44:
1051 - 1058.
9. Drusano, G.L., Bilello, J.A., Preston, S.L., Omara, E., Kaul, S., S. Schnittman and R Echols (2001)
Hollow fiber unit evaluation of a new Human Immunodeficiency Virus (HIV) –1protease inhibitor, BMS
232632, for determination of the linked pharmacodynamic variable. J Infec. Dis.; 183: 1126 - 1129.
10. Drusano, G.L., Preston, S.L., Hardalo, C., Hare, R., Banfield, C., Vesga, O., D. Andes and W.A. Craig
(2001) Use of preclinical data for the choice of a Phase II/III dose for evernimicin with application to
decision support for identification of a preclinical MIC breakpoint. Antimicrobial Agents Chemotherapy;
45: 13 - 22.
11. Drusano, G.L., Bilello, P.A., W.T. Symonds, Stein, D.S., McDowell, J., A. Bye, and J.A. Bilello (2002)
Pharmacodynamics of abacavir in an in vitro hollow fiber model system. Antimicrob Agents
Chemother;
46: 464 -470.
12. Drusano, G.L., Moore, K. H. Kleim, J. P., W. Prince, and A. Bye (2002) Rational dose selection for a
non-nucleoside reverse transcriptase inhibitor through use of population pharmacokinetic modeling and
Monte Carlo simulation. Antimicrob Agents Chemother; 46:913-916.
13. Jumbe, N., Louie, A., Leary, R., Liu, W., Deziel, M.R., Tam, V.H., Bachhawat, R., Freeman, C., Kahn,
J.B., Bush, K., Dudley, M.N., M.H. Miller, and G.L. Drusano (2003) Application of a mathematical
model to prevent in vivo amplification of antibiotic-resistant bacterial populations during therapy. J Clin
Invest; 112: 275 - 285.
14. Drusano GL. Antimicrobial pharmacodynamics: the interactions between bug and drug. Nature
Reviews: Microbiology. 2004;2:289-300.
15. Gumbo, T. Louie, A, Deziel, MR, Parsons, LM, Salfinger, M, Drusano, GL. (2004) Selection of a
Moxifloxacin Dose that Suppresses Mycobacterium tuberculosis Resistance Using an In Vitro
Pharmacodynamic Infection Model and Mathematical Modeling. J Infect Dis; 190:1642-1651
16. Blumer, JL, Reid, MD, Kaplan, EL, Drusano, GL. Explaining the poor bacteriological eradication rate of
single-dose ceftriaxone in Group A streptococcal tonsillopharyngitis: A reverse engineering solution
using pharmacodynamic modeling, Pediatrics 2005;116:927-932.
17. Gumbo T, A Louie, MR Deziel, GL Drusano. Pharmacodynamic Evidence that Ciprofloxacin Failure
against Tuberculosis is Not due to Poor Microbial Kill But to Rapid Emergence of Resistance.
Antimicrob. Agents Chemother. 2005;49:3178-3181.
18. Tam VH, A Louie, MR Deziel, W Liu, R Leary, GL Drusano. Bacterial Population Responses to Drug
Selective Pressure: Examination of garenoxacin against Pseudomonas aeruginosa. J Infect Dis.
2005;192:420-428.
19. Tam VH, A Louie, TR Fritsche, M Deziel, W Liu, DL Brown, L Deshpande, R Leary, RN Jones, GL
Drusano. Drug Exposure Intensity and Duration of Therapy’s Impact on Emergence of Resistance of
Staphylococcus aureus to a Quinolone Antimicrobial. J Infect Dis. 2007;195:1818-1827.
Biosketches
Page 16
20. M Deziel, H. Heine, A Louie, M Kao, WR Byrne, J Bassett, L Miller, K Bush, M Kelley, GL Drusano.
Identification of effective antimicrobial regimens for use in humans for the therapy of Bacillus anthracis
infections and post-exposure prophylaxis. Antimicrob Agents Chemother 2005;49:5099-5106.
21. Hope WW, PA Warn, A Sharp, P Reed, B Keevil, A Louie, DW Denning, GL Drusano. Surface
response modeling to examine the combination of amphotericin B and 5-fluorocytosine for invasive
candidiasis. J Infect Dis. 2005;192:673-680.
22. Lodise T, B Lomaestro, GL Drusano. Piperacillin/tazobactam for Pseudomonas aeruginosa infections:
Clinical implications of an extended infusion dosing strategy. Clin Infect Dis. 200744:357-363.
23. Heine HS, A Louie, F Sorgel, J Bassett, L Miller, LJ Sullivan, M Kinzig-Schippers, GL Drusano.
Comparison of 2 antibiotics that inhibit protein synthesis for the treatment of Yersinia pestis delivered
by aerosol in a mouse model of pneumonic plague. J Infect Dis. 2007;196:782-787.
24. Gumbo T, A Louie, W Liu, P Ambrose, S Bhavnani, D Brown, GL Drusano. Isoniazid Bactericidal
Activity Ceases due to Resistance Emergence, Not Depletion of Log-Phase Growth Mycobacterium
tuberculosis. J. Infect. Dis. 2007;195:194-201.
25. Gumbo T, A Louie, D Brown, PG Ambrose, SM Bhavnani, GL Drusano. Isoniazid bactericidal activity
and resistance emergence: integrating pharmacodynamics and pharmacogenomics to predict efficacy
in different ethnic populations. Antimicrob Agents Chemother. 2007;51:2329-2336.
26. XXXXXXX
Drs. Louie and Drusano mentored Dr. Gumbo on the use of hollow fiber systems and mathematical modeling,
respectively, for TB research. They also mentored Dr. Gumbo in study design and manuscript preparation.
Their work culminated in 5 publications in anti-TB drug pharmacodynamic optimization for improving treatment
outcome and for preventing amplification of resistance (see manuscripts 15, 17, 24, 25, & 26).
C. Research support
Ongoing
1 PO1 AI060908-01A1.
G.L. Drusano, PI
Choosing Drug Doses for Biodefense Pathogens.
7/15/05-6/30/10
The major goal of this project is to develop and validate regimens for B anthracis and Y pestis in the hollow
fiber model system and in an inhalational mouse model. There is no overlap between this grant and the current
proposal.
$5.12 million Direct Cost
$9.1 million Total Cost. 50% Effort.
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Biosketches
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Selected projects completed within the past 3 years:
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Pending:
None , other than current submitted proposals
Biosketches
Page 18
BIOGRAPHICAL SKETCH
NAME
POSITION TITLE
McSharry, James J. (Ph.D.)
eRA COMMONS USER NAME X
XXXXXX
Senior Scientist and Professor
Virologist
Manhattan College, Bronx, NY
University of Virginia, Charlottesville, VA
University of Virginia, Charlottesville, VA
Rockefeller University, New york, NY
DEGREE
(if applicable)
B.S.
M.S.
Ph.D.
Postdoc
YEAR(s)
1965
1967
1970
1970-1973
FIELD OF STUDY
Biology
Microbiology
Microbiology
Virology
PROFESSIONAL EXPERIENCE:
1973-1976
Assistant Professor, Dept. of Microbiology, Albany Medical College, Albany, NY
1976-1983
Associate Professor, Dept. of Microbiology, Albany Medical College, Albany, NY
1983-2003
Professor, Dept. of Microbiology, Albany Medical College, Albany, NY
1983-present
Professor, Program in Biomedical Sciences, School of Public Health, SUNY, Albany, NY
1995-1997
Chair, Virology/Immunology SWAT Team – ACTU
2003-Present
Senior Scientist and Professor, Ordway Research Institute, Inc. Albany, NY
HONORS: Sigma Xi, Harvey Society, Fellow of the American Academy of Microbiology
NIH STUDY SECTIONS: Microbicides Review Panel - April 26, 2002; April, 2004. SBIR March 2004.
BIBLIOGRAPHY: (SELECTED PUBLICATIONS)
McSharry, J.J., and R. Benzinger. 1970. Concentration and purification of vesicular stomatitis virus by
polyethylene glycol “precipitation.” Virology 40:745 - 746.
McSharry, J.J., and R.R. Wagner. 1971. Lipid composition of purified vesicular stomatitis virus. J Virol. 7: 59 70.
McSharry, J.J., and R.R. Wagner. 1971. Carbohydrate composition of vesicular stomatitis virus. J. Virol. 7:412 415.
McSharry, J.J., R.W. Compans, and P.W. Choppin. 1971. Proteins of vesicular stomatitis viruses and of
phenotypically-mixed vesicular stomatitis virus-simian virus 5 virions. J. Virol 8: 722 - 729.
McSharry, J.J., R.W. Compans, H. Lackland, and P.W. Choppin 1975 Isolation and characterization of the nonglycosylated protein and a nucleocapsid complex from the paramyxovirus SV5. Virology 67: 265 - 374.
McSharry J.J. 1977. The effect of chemical and physical treatments on the lactoperoxidase-catalyzed iodination
of vesicular stomatitis virus. Virology 83:482 - 485.
McSharry, J.J., and P.W. Choppin. 1978. Biological properties of the VSV glycoprotein. I. Effects of the
isolated glycoprotein on host macromolecular synthesis. Virology 84:172 - 182.
McSharry, J.J., Ledda, C.A., H. Freiman, and P.W. Choppin 1978. Biological properties of the VSV
gylcoproteins. II. Effects of host cell and glycoprotein carbohydrate composition on hemagglutination.
Virology 83: 183 - 188.
McSharry, J.J., L.A Caliguiri, and H.J. Eggers.1979. Inhibition of uncoating of poliovirus by arildone, a new
antiviral drug. Virology 97: 307 - 315.
Goodman-Snitkoff, G.W., and J.J. McSharry. 1980. Activation of mouse lymphocytes by vesicular stomatitis
virus. J Virol. 35:757-765.
Biographical Sketches for each listed Senior/Key Person 2
Page 19
Goodman-Snitkoff, G.W., R.J. Mannino, and J.J. McSharry. 1981. The glycoprotein isolated from vesicular
stomatitis virus is mitogenic for mouse B lymphocytes. J. Expt. Med. 153: 1482 - 1502.
McSharry, J.J., R. Pickering, and L.A. Caliguiri. 1981. Activation of the alternative complement pathway by
enveloped viruses containing limited amounts of sialic acid. Virology 114: 507 - 515.
Goodman-Snitkoff, G.W., and J.J. McSharry 1982. Mitogenic activity of Sindbis virus and its isolated
glycoproteins. Infection and Immunity 38: 1242 - 1248.
Elmendorf, S.L., McSharry, J.J., Laffin, J.A., D. Fogelman, and J.M. Lehman. 1988. Detection of an early
cytomegalovirus antigen with two-color quantitative flow cytometry. Cytometry 9: 254 - 260.
McSharry, J.J., Costantino, R., Robbiano, E., Echols, R., R. Stevens, and J.M. Lehman. 1990. Detection and
quantitation of human immunodeficiency virus-infected peripheral blood mononuclear cells by flow
cytometry. J. Clin. Microbiol. 28: 724 - 733.
McSharry, J.J., N. Lurain, GL Drusano, A Landay, J Manischewitz, M Nokta, M O’Gorman, HM Shapiro, A
Weinberg, P. Reichelderfer and C. Crumpacker. 1998. Flow Cytometric Determination of Ganciclovir
Susceptibility of Human Cytomegalovirus (HCMV) Clinical Isolates. J. Clin. Microbiol. 36: 958 - 964.
McSharry, JJ, N Lurain, GL Drusano, A Landay, M Notka, MRG O’Gorman, A Weinberg, HM Shapiro, P
Reichelderfer and C. Crumpacker 1998. Rapid ganciclovir susceptibility assay using flow cytometry for
human cytomegalovirus clinical isolates. Antimicrob. Agents Chemother. 42: 2326 – 2331.
McSharry, JJ., BA Olson, A. McDonough, S Hallenberger, J.,Reefschlaeger, W. Bender and G.L. Drusano.
2001. Comparison of drug susceptibilities of human cytomegalovirus clinical isolates for BAY38-4766,
BAY43-9695, and ganciclovir. Antimicrob. Agents Chemother. 45: 2995 - 2997.
McSharry, JJ, A McDonough, BA Olson, C Talarica, M Davis and K.K. Biron. 2001. Inhibition of ganciclovir
susceptible and ganciclovir resistant human cytomegalovirus (HCMV) clinical isolates by a benzimidazole
L-riboside 1263W94. Clin. Diag. Lab. Immunol. 8: 1279 -1281.
Chutkowski, C., Olson, B., McDonough, A., J. Mahoney, and J.J. McSharry. 2002. Use of a single monoclonal
antibody to determine the susceptibilities of herpes simplex virus type 1 and type 2 clinical isolates to
acyclovir. Clin. Diag. Lab Immunol. 9: 1379 - 1381.
McSharry, J.J., McDonough, A.C., B,A., Olson, and G.L. Drusano. 2004. Phenotypic drug susceptibility assay
for influenza virus neuraminidase inhibitors. Clin. Diag. Lab. Immunol. 11:21 - 28.
Reviews
JJ McSharry. 1994. Uses of flow cytometry in virology. Clinical Microbiology Reviews 7: 576 - 604.
JJ McSharry. 1995. Flow cytometry-Based Antiviral Resistance Assays. Clinical Immunology News Letter
15(9): 115 -119.
JJ McSharry 1999. Flow cytometric antiviral drug susceptibility assays. Clinical Immunology News Letter 19:114.
JJ McSharry. 1999. Antiviral drug susceptibility assays: going with the flow. Antiviral Research 43:1-21.
JJ McSharry. 2000. Flow cytometric analysis of virus-infected cells. Res. Adv. Microbiol. 1:37-42.
JJ McSharry. 2000. Analysis of virus-infected cells by flow cytometry. Methods 21:249-257.
Abstracts
McSharry, JJ, M Deziel, A Louie, K Zager, B Olson, P Savina, K Adkinson, C Labranche, J Demarest , S
Piscitelli, GL Drusano. Pharmacodynamics of 873140, an inhibitor of CCR5, in an in vitro hollow fiber
model. Abstracts of 44th Interscience Conference on Antimicrobial Agents and Chemotherapy. Washington,
DC October 30-November 2, 2004. Abstract # H-212.
McSharry, JJ. In vitro hollow fiber system for determining the Pharmacodynamics of cidofovir for vaccinia virus.
Abstracts of 2005 ASM Biodefense Research Meeting. Baltimore, MD March 20-23, 2005. Abst #170
McSharry, JJ, K Zager, Q Weng, MR Deziel, A Louie, and GL Drusano. Pharmacodynamics of cidofovir, an
inhibitor of poxvirus replication, in an in vitro hollow fiber model system. Abstracts of the 18th International
Conference on Antiviral Research, Barcelona, Spain, April 10-14, 2005. Abstract # 78
McSharry, JJ, K Zager, Q Weng, R Jordan, D Hruby and GL Drusano. Comparison of the pharmacodynamics
of cidofovir and ST-246. Abstracts of 45th ICAAC, Washington, DC, December 16-19, 2005, #A-420.
Page 20
McSharry, JJ, K Zager, Q Wang, and GL Drusano. Pharmacodynamics of neuraminidase inhibitors for an
influenza A virus clinical isolate. Abstracts of 46th ICAAC, San Francisco, CA Sept 27-30, 2006 #A-388.
C. RESEARCH SUPPORT
Ongoing
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XXXXXXXX
Completed grant support for the past three years
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Page 21
BIOGRAPHICAL SKETCH
NAME
POSITION TITLE
Keim, Paul S.
Director of Pathogen Genomics – TGEN
The Cowden Endowed Chair – NAU
Arizona Reagents Professor - NAU
XXXXXXX
DEGREE
(if applicable)
YEAR(s)
FIELD OF STUDY
B.S.
1975-77
Biology and Chemistry
Northern Arizona University, Flagstaff AZ
Ph.D.
1977-81
Plant Biochemistry
University of Kansas, Lawrence KS
Postdoctoral
1981-87
Micro. & Mol. Genetics
University of Utah, Salt Lake City UT
Postdoctoral
1987-88
Pop. & Quant. Genetics
Iowa State University, Ames IA
A. Positions and Honors.
Research and Professional Experience
1981-4
Research Associate, Dept of Biology, University of Utah (K.G. Lark, mentor)
1984-7
Research Assistant Professor of Biology, University of Utah
1987-8
Research Associate, Iowa State University, Dept. of Genetics
1989-2
Assistant Professor of Biology, Northern Arizona University
1992-5
Associate Professor of Biology with Tenure, Northern Arizona University
1995-6
Professor of Biological Sciences, Northern Arizona University
1992-pres Affiliate Researcher (Q), BioSciences - Los Alamos National Laboratory
1998-pres Project Director - Howard Hughes Medical Institute: Project BioConnect
1998-pres Adjunct Faculty - College of Veterinary Medicine - Louisiana State University
1997-pres The E. Raymond and Ruth Cowden Endowed Chair in Microbiology, NAU
2002-pres Arizona Regents Professor, Northern Arizona University
2003-pres Director of Pathogen Genomics, TGen (Phoenix AZ)
Honors and Awards
1977 Bachelor of Science, magna cum laude Biology & Chemistry
1977 Phi Kappa Phi
1981 Ph.D. Dissertation awarded Honors in Plant Biochemistry
1990 “Hot Paper” selection by The Scientist
1995 Phi Kappa Phi - NAU Faculty Scholar of the Year
1998 The Centennial Distinguished Professor- NAU College of A&S
2001 The Betty Klepper Honorary Scholar – Crop Science Society
2002 Fellow, American Academy of Microbiology
B. Selected peer-reviewed publications
1. Farlow, J., K.L. Smith, J. Wong, M. Abrahms, M. Lytle, & P. Keim. Fransicella tularensis strain typing using
multiple-locus variable number tandem repeat analysis. Journal of Clinical Microbiology 2001;39:3186-3192.
2. Vogler, A.J., J.D. Busch, S. Percy-Fine, C.M. Tipton-Hunton, K.L. Smith, & P. Keim. Molecular analysis of
rifampicin resistance in Bacillus anthracis and B. cereus. Antimicrobial Agents and Chemotherapy
2001;46:511-513.
3. Keim, P, K.L. Smith, C. Keys, H. Takahashi, T. Kurata, & A. Kaufmann. Molecular investigation of the Aum
Shinrikyo anthrax release in Kameido, Japan. Journal of Clinical Microbiology 2001;39:4566-4567.
4. Read, T.D., S.L. Salzberg, M. Pop, M. Shumway, L. Umayam, L. Jiang, E. Holtzapple, J. Busch, K.L. Smith,
J.M. Schupp, D. Solomon, P. Keim, & C.M. Fraser. Comparative genome sequencing for discovery of novel
polymorphisms in Bacillus anthracis. Science 2002; 296:2028-2033.
5. Fouet, A., K.L. Smith, C. Keys, J. Vaissaire, C. Le Doujet, M. Lévy, M. Mock, & P. Keim. Diversity among
French Bacillus anthracis isolates. Journal of Clinical Microbiology 2002;40: 4732–4734.
Page 22
6. Farlow, J., D. Postic, K.L. Smith, Z. Jay, G. Baranton, & P. Keim. Strain typing of Borrelia burgdorferi, B.
afzelii, and B. garinii by using multiple-locus variable-number tandem repeat analysis. Journal of Clinical
Microbiology 2002;40:4612–4618.
7. Price, L.B., A. Vogler, T. Pearson, J.D. Busch, J.M. Schupp, & P. Keim. In vitro selection and
characterization of Bacillus anthracis mutants with high-level resistance to ciprofloxacin. Antimicrobial Agents
and Chemotherapy 2003; 47:2362-2365.
8. Takahashi, H., P. Keim, A.F. Kaufmann, K.L. Smith, C. Keys, K. Taniguchi, S. Inouye, & T. Kurata. Bacillus
anthracis incident, Kameido, Tokyo, 1993. Emerging Infectious Diseases. 2004;10:117-120.
9. Hill, K.K., L.O. Ticknor, M. Asay, H. Blair, K. Bliss, M. Laker, P.E. Pardington, A.P. Richardson, M. Tonks,
J.D. Kemp, A-B. Kolstø, A.C.L. Wong, P. Keim, & P.J. Jackson. Fluorescent amplified fragment length
polymorphism analysis of Bacillus anthracis, Bacillus cereus, and Bacillus thuringiensis isolates. Applied
Environmental Microbiology 2004; 70:1068-1080.
10. Keim, P., M. Van Ert, T. Pearson, A. Vogler, L. Huynh, & D. Wagner. Anthrax molecular epidemiology and
forensics: using the appropriate marker for different evolutionary scales. Infection, Genetics and Evolution
2004;4:205-213.
11. Girard, J.M., D.M. Wagner, A.J. Vogler, C. Keys, C.J. Allender, L.C. Drickamer, & P. Keim. Differential
plague transmission dynamics determine Yersinia pestis population genetic structure on local, regional, and
global scales. PNAS (USA) 2004; 101:8408-8413. (cover photo).
12. Johansson, A., J. Farlow, P. Larsson, M. Dukarich, E. Chambers, M. Byström, J. Fox, M. Chu, M. Forsman,
A. Sjöstedt, & P. Keim. Worldwide genetic relationships among Francisella tularensis isolates determined by
multiple-locus variable-number tandem repeat analysis. Journal of Bacteriology 2004;186:5808-5818.
13. Pearson, T., J. Busch, J. Ravel, T. Read, S. Rhoton, J. U’Ren, T. Simonson, S. Kachur, R. Leadem, M.
Cardon, M. Van Ert, L. Huynh, C. Fraser & P. Keim. Phylogenetic discovery bias in Bacillus anthracis using
single-nucleotide polymorphisms from whole-genome sequencing. PNAS (USA) 101:13536-13541.
14. XXXXXXX
15. Achtman, M., G. Morelli, P. Zhu, T. Wirth, I. Diehl, A.J. Vogler, D.M. Wagner, C.J. Allender, W.R. Easterday,
V. Chenal-Francisque, P. Worsham, N.R. Thomson, J. Parkhill, L.E. Lindler, E. Carniel, & P. Keim. 2004.
Microevolution and history of the plague bacillus, Yersinia pestis. Proc Natl Acad Sci U S A. 2004; Dec 21;
101(51):17837-42. Epub 2004 Dec 14.
16. Easterday, W.R., M.N. Van Ert, T.S. Simonson, L.J. Kenefic, C.J. Allender, D.M. Wagner, & P. Keim. Specific
detection of Bacillus anthracis using SNPs in the plcR gene. Journal of Clinical Microbiology 2005: 43:19951997.
17. Farlow, J., D.M. Wagner, M. Dukerich, M. Stanley, M. Chu, K. Kubota, J. Petersen, and P. Keim. Population
structure, genetic diversity and evolution of Francisella tularensis in the United States. Emerging Infectious
Diseases, 2005; 11:1835-1841.
18. Han CS, Xie G, Challacombe JF, Altherr MR, Bhotika SS, Bruce D, Campbell CS, Campbell ML, Chen J,
Chertkov O, Cleland C, Dimitrijevic M, Doggett NA, Fawcett JJ, Glavina T, Goodwin LA, Hill KK, Hitchcock P,
Jackson PJ, Keim P, Kewalramani AR, Longmire J, Lucas S, Malfatti S, McMurry K, Meincke LJ, Misra M,
Moseman BL, Mundt M, Munk AC, Okinaka RT, Parson-Quintana B, Reilly LP, Richardson P, Robinson DL,
Rubin E, Saunders E, Tapia R, Tesmer JG, Thayer N, Thompson LS, Tice H, Ticknor LO, Wills PL, Brettin
TS, Gilna P. Pathogenomic sequence analysis of Bacillus cereus and Bacillus thuringiensis isolates closely
related to Bacillus anthracis. J Bacteriol. 2006 May; 188(9):3382-90.
19. Vogler AJ, Keys C, Nemoto Y, Colman RE, Jay Z, Keim P. Effect of repeat copy number on variable-number
tandem repeat mutations in Escherichia coli O157:H7. J Bacteriol. 2006 Jun;188(12):4253-63.
20. Maho A, Rossano A, Hachler H, Holzer A, Schelling E, Zinsstag J, Hassane MH, Toguebaye BS, Akakpo AJ,
Van Ert M, Keim P, Kenefic L, Frey J, Perreten V. Antibiotic susceptibility and molecular diversity of Bacillus
anthracis strainsin Chad: detection of a new phylogenetic subgroup. J Clin Microbiol. 2006 Sep;44(9):3422-5.
21. Bangert RK, Allan GJ, Turek RJ, Wimp GM, Meneses N, Martinsen GD, Keim P, Whitham TG. From genes
to geography: a genetic similarity rule for arthropod community structure at multiple geographic scales. Mol
Ecol. 2006 Nov;15(13):4215-28.
22. Okinaka, R, T Pearson, P Keim. 2006. Anthrax but not Bacillus anthracis? PLoS – Pathogens. Pathog. 2006
Nov;2(11):e122.
Page 23
23. Van Ert, MN., WR Easterday, TS Simonson, JM U’Ren, T Pearson, LJ Kenefic, JD Busch, LY Huynh, M
Dukerich, CB Trim, J Beaudry, A Welty-Bernard, T Read, CM Fraser, J Ravel, and P Keim. 2006. Strainspecific single nucleotide polymorphism assays for the Bacillus anthracis Ames strain. J Clinical Microbiology
2007 Jan;45(1):47-53. Epub 2006 Nov 8.
C. Research Support.
Ongoing Research Support
0425908
(Paul Keim, PI)
2004-2009
NSF FIBR
Community Genetics, Heritability & Evolution: Consequences of Extended Phenotypes
This is a multi investigator project that is examining the genetic basis of community structure in a cottonwood
tree population. This is an extension of the previous NSF award to Dr. Tom Whitham. There is no overlap with
this proposal.
Role: Co-PI
Grant
(Paul Keim, PI)
2005-2008
Dept. of Homeland Security – HSARPA: BioForensics BAA
High resolution and highly sensitive assays for bacterial biothreat agents
This project has just been funded but is a continuation of the work previously funded by DOE, DHS and ONR.
We will establish high resolution signatures for biothreat pathogens (e.g. B. anthracis) and develop assays for
their routine use in identification and subtyping. There is no overlap with the current proposal.
Role: PI
U01 AI066581-01
(Paul Keim, PI)
07/01/2005 - 06/30/2010
NIH/NIAID
Real-Time PCR Assays for the Direct Detection of Sepsis and CAP Pathogens
This project will develop advanced diagnostic assays for pathogens causing sepsis and community acquired
pneumonia. These assays will be rapid, sensitive and very specific for the pathogens and their virulence genes.
Role: Principal Investigator
XXXXXXX
U54 AI065359
(Alan Barbour, PI)
2005 - 2007
NIH/NIAID
Population and ecological analysis of Burkholderia pseudomallei pathogenesis”,
This study will develop assays for the detection of specific virulence genes in B. pseudomallei and examine its
genetic population structure at several spatial scales in Australia. There is no overlap.
Role: Co-PI
Completed Research Support
XXXXXXXX
DBI-0321344
(Paul Keim, PI)
NSF – Major Research Instrumentation program
Acquisition of a High Capacity Genotyping Facility
2003-2006
Page 24
This proposal was for the purchase of an ABI3730xl 96-capillary electrophoresis instrument and supporting
robotics, computers, and data analysis software. Its primary purpose will be for high capacity DNA fingerprinting
via PCR fragment sizing (e.g. STRs, SSRs, VNTRs). There is no overlap with this proposal.
Role: PI
Award
(Paul Keim, PI)
2001-2004
Department of Justice – Federal Bureau of Investigation
DNA Analysis of Bacterial Samples
This work improved the identification and analysis of B. anthracis strains through the development of SNP and
STR assays. We also developed SOP’s and QA/QC systems for diagnostic assay applications in the forensic
analysis of samples from the Amerithrax Case.
Role: PI
R01 GM060795
(Paul Keim, PI)
2000-2005
NIH-NIGMS
Molecular Epidemiology and Evolution of Bacillus anthracis
This project is characterizing the genetic diversity patterns of B. anthracis in North America and Africa in a
comparative fashion to understand the evolution and ecology of this pathogen. A competitive renewal is in
progress. There is no overlap.
Role: PI
Grant
Fraser (PI)
2002-2005
NIH-NIAID subcontract from The Institute for Genomic Research (TIGR).
Comparison of B. anthracis Genomes from Diverse Strains
This project will complete the genomic sequences for 14 B. anthracis strains. Dr. Keim is a subcontractor
supplying DNA, biological material, and developing SNP assays for B. anthracis. There is no overlap with this
proposal.
Role: PI on the subcontract portion.
Grant
(Paul Keim, PI)
2002-2005
DOE: NNSA-CBNP
BDAP: Biological Demonstration Application Project
This project is coordinating, transferring, and demonstrating BioForensic capabilities at the National Labs,
military (Army), law enforcement (FBI) and NAU. The funding is now at DHS and is being routed through Los
Alamos National Lab in FY04, There is no overlap with this proposal.
Role: PI
Grant
(Paul Keim, PI)
2003-2005
Department of Homeland Security
MLVA: A High Resolution Approach for Molecular Typing Bacterial Pathogens
This project is developing high resolution DNA signatures for several bacterial pathogens including Yersinia
pestis, Francisella tularensis, Brucella spp., Burkholderia spp. and Coxiella burnetti. There is no overlap with this
proposal.
This contract was transferred from the NN20 - CBNP program Dept. of Energy and will be administered by the
Office of Naval Research in FY04-05.
Role: PI
PA# 03169
(Paul Keim, PI)
2003-2005
CDC-HHS “Centers for Public Health Preparedness”
In conjunction with the CDC, NAU has been funded to establish and maintain a “Center for Pathogen Genomic
Analysis and Biodefense.” A significant portion of this project is devoted to training biodefense workers. There is
no overlap with this proposal.
Role: PI and Director
Page 25
BIOGRAPHICAL SKETCH
NAME
POSITION TITLE
Engelthaler, David M.
Director of Programs, TGen North
The Translational Genomics Research Institute
eRA COMMONS USER NAME XXXXXXX
Northern Arizona University, Flagstaff, Arizona
DEGREE
(if applicable)
YEAR(s)
B.S.
1987 - 1991
University of Arizona, Tucson, Arizona
Colorado State University, Ft. Collins, Colorado
1996 - 1997
M.S.
Arizona State University, Tempe, Arizona
1997 - 1999
2003 - 2004
FIELD OF STUDY
Wildlife Biology
Public
Health/Epidemiology
Microbiology
Public Health
Leadership
A. Positions and Honors
Research and Professional Experience
1990 - 1993
Biological Technician, USDA Forest Service
1994 - 1997
Epidemiology Specialist, Arizona Department of Health Services
1997 - 1999
Visiting Fellow, Division of Vector-Borne Infectious Disease, Centers for Disease Control
and Prevention
1999 - 2000
Biologist, Division of Vector-Borne Infectious Disease, Centers for Disease Control and
Prevention
2000 - 2001
Coordinator, Bioterrorism Epidemiology Program, Arizona Department of Health
Services
2000-2006
Member, Joint Terrorism Task Force, Arizona
2002-2004
Chief, Emergency Preparedness and Response Office, Arizona Department of Health
Services
2000-2005
State Bioterrorism Coordinator, Arizona Department of Health Services
2004-2006
State Epidemiologist, Arizona Department of Health Services
2006-Present
Director of Programs and Operations, TGen North
Honors and Awards
2001
Outstanding Service Award, U.S. Postal Service
2001
Outstanding Assistance Recognition Award, Federal Bureau of Investigation
2002-2003
Public Health Leader of the Year, Arizona Department of Health Services
B. Peer-reviewed Publications
1. Mills, JN; Ksiazek, TG; Ellis, BA; Rollin, PE; Nichol, ST; Ellis, BA; Yates, TL; Gannon, WL; Levy, CE;
Engelthaler, DM; Davis, T; Tanda, D; Frampton, W; Nichols, C; Peters, CJ; Childs, JE. Patterns of
association with host and habitat: antibody reactive with Sin Nombre virus in small mammals in the
major biotic communities of the southwestern United States. Am.J.Trop.Med.Hyg. 1997;56:273-284.
2. Engelthaler, DM; Levy, CL; Fink, TM; Leslie, MJ. The Re-emergence of Aedes aegypti in Arizona.
Emerg. Infect. Dis. 1997;3:241-242.
3. Engelthaler, DM; Levy, CE; Tanda, D; Davis T. Decrease in seroprevalence of antibodies to hantavirus
in rodents from 1993-94 hantavirus pulmonary syndrome case sites. Am.J.Trop.Med.Hyg. 1998;
58:737-738.
4. Engelthaler, DM; Mosley, DG; Cheek, J; Levy, MS; Komatsu, K; Ettestad, P; Davis, T; Tanda, D; Miller,
L; Frampton, W; Porter, R; Bryan, RB. Climatic and environmental patterns associated with hantavirus
pulmonary syndrome cases in the Four Corners region. Emerg. Inf. Dis. 1999; 5(1):87-94.
Page 26
5. Engelthaler, DM; Gage, KL; Montenieri, JA; Chu, M; Carter LG. PCR detection of Yersinia pestis in
fleas: comparison with mouse inoculation. J. Clin. Microbiol. 1999;37:1980-1984.
6. Engelthaler, DM and KL Gage. Quantities of Yersinia pestis in fleas (Siphonaptera: Pulicidae,
Ceratophyllidae, and Hystrichopsyllidae) collected from areas of known or suspected plague activity. J
Med Entomol 2000;37:422-426.
7. Engelthaler, DM; Rittner, C; Hinnebusch, J; Gage, KL. Quantitative competitive PCR as technique for
exploring flea - Yersinia pestis dynamics. Am J Trop Med Hyg. 2000;62:552-560.
8. Enscore RE; Biggerstaff BJ; Brown TL; Fulgham RE; Reynolds PJ; Engelthaler DM; Levy CE;
Parmenter RR; Montenieri JA; Cheek JE; Grinnell RK; Ettestad PJ; Gage KL. Climate-based Poisson
regression models as predictors of human plague outbreaks within enzootic sylvatic foci of the
southwestern United States. Am J Trop Med Hyg 2002;66(2):186-96
9. Huang, X-Z; Chu, MC; Engelthaler, DM; Lindler, LE. Genotyping of a homogeneous group of Yersinia
pestis strains isolated in the United States. J Clin. Microbiol. 2002;40:1164-1173.
10. Engelthaler, DM; and KL Gage. Cover Photograph (Rat flea (Xenopsylla cheopis) infected with strain
of Yersinia pestis that expresses GFP.) Proc. Nat. Acad. Sciences. 2004;01(22).
11. Arboleda N, Fleischauer AT, Sejvar J, Diggs A, Schumacher M, Santana S, Engelthaler DM, Komatsu
K, Hughes S, Jones G, Hutwagner L. 2006. An Emergency Department Based Syndromic Surveillance
System for Meningitis and Encephalitis, Maricopa County, AZ, 2004. Adv Dis Surv. 1:4
12. LeVecchio, F; Stapczynski, S; Hill, J; Haffer, AF; Skindlov, JA; Engelthaler, DM, Mrela, C; Luber, GE;
Straetemans, M; Duprey, Z. Heat-Related Mortality --- Arizona, 1993--2002, and United States, 1979-2002; 2005 MMWR. 54(25);628-630.
13. Engelthaler, DM; Anderson, SM; Lewis, KL; et al.. Vibrio Illnesses After Hurricane Katrina — Multiple
States, August–September 2005. MMWR. 2005; 54(Dispatch):1-4.
14. Engelthaler, DM; Anderson, SM; Lewis, KL; et al. 2005. Vibrio Illnesses After Hurricane Katrina —
Multiple States, August–September 2005. MMWR. 54(Dispatch):1-4.
15. Engelthaler, DM; Levy, C; Ettestad, P, et al. Update: Hantvirus Pulmonary Syndrome – Five States,
2006. MMWR. 2006;55(22);627-629.
16. Eisen, J; Enscore, R; Biggerstaff, B; Reynolds, Ettestad, P; Brown, T; Pape, J; Tanda, D; Levy C;
Engelthaler, D; Cheek, J; Bueno, R; Targhetta, J; Montenieri, J; Gage, K. 2007. Human plague in the
Southwestern United States, 1957-2004: Spatial models of elevated risk of human exposure to Yersinia
pestis. J Med Entomol. 44:53-537.
17. XXXXXXX
18. XXXXXXX
C. Research Support
Ongoing Research Support
XXXXXXX
XXXXXXX
Page 27
Completed Research Support
Centers for Disease Control and Prevention
2000-2004
“Public Health Emergency Preparedness Grant”
This project developed public health emergency preparedness responses systems and capacity in Arizona,
including the development of surveillance, laboratory, and communications infrastructure.
Role: PI
Page 28
BIOGRAPHICAL SKETCH
NAME
POSITION TITLE
Kulawy, Robert
Analytical Chemist
Rensselaer Polytechnic Institute. Troy, NY
DEGREE
(if applicable)
YEAR(s)
B.S.
1985
FIELD OF STUDY
Chemistry
Professional Experience:
06/86 to 12/89
Chemist, Industrial Mass Spectrometry Group, Oneida Research Services, Inc., Whitesboro, NY
01/90 to 10/92
Assistant Staff Scientist, Industrial Mass Spectrometry Group, Oneida Research Services, Inc.,
Whitesboro, NY
10/92 to 02/94
Assistant Staff Scientist, Bioanalytical Group, Oneida Research Services, Inc., Whitesboro, NY
02/94 to 06/97
Project Leader, Bioanalytical Group, Oneida Research Services, Inc., Whitesboro, NY
06/97 to 05/99
Associate Director of Operations, Oneida Research Services, Inc., Whitesboro, NY
05/99 to 06/02
Project Manager, Bioanalytical Group, Oneida Research Services, Inc., Whitesboro, NY
07/02 to 10/03
Project Manager, Bioanalytical Sciences Group, Prevalere Life Sciences, Inc., Whitesboro, NY
10/03 to 11/05
Manager, Physical/Chemical Analysis Group, Prevalere Life Sciences, Inc., Whitesboro, NY
11/05 to Present Analytical Chemist, Ordway Research Institute, Inc.
Continuing Professional Training:
11/7-9/06
Applied Biosystems API 5000 Operators Training Course, Applied Biosystems, Framingham, MA
10/14/99
ORS Training Session: "Ethics Training", Dr. Lee Schrader, Dr. Mario Rocci, and Barry McArdle,
02/29/00
ORS Training Session: “Notebook Documentation” Debra Beck, Debra O’Neil, and Thomas Willette
06/11-15/00 The 48th American Society of Mass Spectrometry Conference Long Beach, CA
03/16/01
Micromass Quattro Ultima Operation Training Course, Micromass, Beverly, MA, Thierry D. Mann,
PhD, Instructor
04/02-03/02 Waters Millenium32 Version 4.0 Fundamentals Software Training, Held at Oneida Research
Services Kenneth Conroe and Linda Cunningham, Waters Corp., Instructors
Professional Membership:
Member, American Association of Pharmaceutical Scientists
Member, American Society of Mass Spectrometry
Member, American Society of Microbiology
Publications and Presentations:
1. Gumbo T, Drusano GL, Liu W, Kulawy RW, Fregeau C, Hsu Y, Louie A. Once-weekly micafungin
therapy is as effective as daily therapy for disseminated candidiasis in mice with persistent neutropenia.
Antimicrob. Agents Chemother. 2007;51:968-974.
2. A.C. Hermann, A.N. Nafziger, J. Victory, R.W. Kulawy, M.L. Rocci, Jr., J.S. Bertino, Jr. “Over-theCounter Progesterone Cream Produces Significant Drug Exposure Compared to an FDA-Approved,
Oral Progesterone Product” Journal of Clinical Pharmacology, 2005 Jun; 45(6):614-9
Page 29
3. J.S. Kim, A.N. Nafziger, S.M. Tsunoda, E.F. Choo, D.S. Streetman, A.D.M. Kashuba, R.W. Kulawy,
D.J. Beck, M.L. Rocci, Jr., G.R. Wilkinson, D.J. Greenblat, J.S. Bertino, Jr. “Limited Sampling Strategy
to Predict AUC of the CYP3A Phenotyping Probe Midazolam in Adults: Application to Various Assay
Techniques” Journal of Clinical Pharmacology, 42, 376-382, 2002
4. D.S. Streetman, A.D.M. Kashuba, J.S. Bertino, Jr., R.W. Kulawy, M.L. Rocci, Jr., A.N. Nafziger “Use of
a Midazolam Urinary Metabolic Ratio for Cytochrome P450 3A (CYP3A) Phenotyping”
Pharmacogenetics, 11, 349-355, 2001.
5. A.D.M. Kashuba, Pharm.D., A.N. Nafziger, M.D., M.H.S., G.L. Kearns, Pharm.D., F.C.P., J.S. Leeder,
Pharm.D., Ph.D., R. Gotschall, M.S., M.L. Rocci, Jr., Ph.D., R.W. Kulawy, B.S., D.J. Beck, B.S., J.S.
Bertino, Jr., Pharm.D. “Effect of Fluvoxamine Therapy on the Activities of CYP1A2, CYP2D6, and
CYP3A, as Determined by Phenotyping” Clinical Pharmacology and Therapeutics 64, 257-268, 1998.
6. A.D.M. Kashuba, J.S. Bertino, Jr., M.L. Rocci, Jr., R.W. Kulawy, D.J. Beck, A.N. Nafziger. “Quantitation
of 3-month Intraindividual Variability, and the Influence of Sex and Menstrual Cycle Phase on CYP3A
Activity as Measured by Phenotyping With Intravenous Midazolam” Clinical Pharmacology and
Therapeutics 1998 Sep; 64(3):269-77
7. J.F. Rogers, A.N. Nafziger, A.D.M. Kashuba, D.S. Streetman, R.W. Kulawy, M.L. Rocci, Jr., E.F. Choo,
G.R. Wilkinson, J.S. Bertino, Jr. “Prediction of Midazolam (MDZ) Clearance (CL) Using Minimized
Sampling of 1-OHM and 1-OHM/MDZ in Healthy Subjects (HS)” (2001) Clinical Pharmacology and
Therapeutics, Volume 69, 89.
Abstracts:
1. S.K. Gotzkowsky, A.D.M. Kashuba, B.S. Hall, R.W. Kulawy, D.J. Beck, M.L. Rocci Jr. “Poor
Correlation Between 24-Hour Urinary 6β-Hydroxy Cortisol: Cortisol Molar Ratios (CMR) and
Plasma Midazolam Clearance (MDZ CL) as Measures of Hepatic CYP3A Activity.” Clinical
Pharmacology and Therapeutics Volume 65, No. 2, 167, 1999.
2. D.S. Streetman, J.S. Bertino, Jr., A.D.M. Kashuba, R.W. Kulawy, M.L. Rocci, Jr., M.D. Nafziger
“Use of Midazolam (MDZ) Urinary Metabolic Ratio (MR) for CYP3A Phenotyping (PT).” Submitted
for Presentation: 101st Annual Meeting of the American Society for Clinical Pharmacology and
Therapeutics, Los Angeles, CA, March 15-17, 2000.
3. R.W. Kulawy, D.J. Beck, M.L. Rocci, Jr., “Analysis of Midazloam, 1-Hydroxymidazolam and 4Hydroxymidazolam in Human plasma by LC/MS/MS” (1998) Proceedings of the American Society
for Mass Spectrometry.
4. A.D.M. Kashuba, A.N. Nafziger, G.L. Kearns, J.S. Leeder, B. Gotschall, M.L. Rocci Jr., R.W.
Kulawy, D.J. Beck, J.S. Bertino. “Dextromethorphan, (DM) N-Demethylation (N-D) Does Not
Accurately Reflect Hepatic CYP3A Phenotype.” Clinical Pharmacology and Therapeutics
Volume 65, No. 2, 170, 1999.
5. J.S. Kim, A.D.M. Kashuba, A.N. Nafziger, M.L. Rocci, Jr., R.W. Kulawy, D.J. Beck, J.S. Bertino.
“Optimal Plasma Sampling to Predict AUC of the CYP3A Probe Midazolam (MID).” Clinical
Pharmacology and Therapeutics Volume 65, No. 2, 185, 1999.
6. R.M. Matsumato, D.G. Fellows, J.I. Usansky, R.W. Kulawy, G.S. Rahn, D. Tang-Liu “Determination of
Tazarotene and its Metabolite, Tazarotenic acid, in Human Plasma by GC/MS and GC/MS/MS” American
Association of Pharmaceutical Scientists Annual Meeting (1997).
7. R.W. Kulawy, M. Jemal, D.J. Beck, R.A. Morrison, S.H. Weinstein, M.L. Rocci, Jr. “Determination of Free
Captopril in Human Whole Blood by GC/MS/MS” American Association of Pharmaceutical Scientists
Annual Meeting (1996).
Page 30
Research Support
On-going projects:
1 P01 AI060908-01A1
GL Drusano, PI
NIH/NIAID
7/15/2005-6/30/2010
“Choosing Drug Doses for Biodefense Pathogens”
Mr. Kulawy is the analytical chemist for the P01 Study: The program project uses hollow fiber systems,
mathematical algorithms, and animal model validation to derive dosages and frequencies of administration for
candidate compounds that will maximize treatment efficacies and will prevent emergence of resistance during
the treatment of Bacillus anthracis and Yersinia pestis infection. Mr. Kulawy is responsible for developing
and validating the LC-MS and LC-MS/MS assays for measuring 10 antibiotics in Mueller-Hinton II broth and in
mouse serum following Good Laboratory Practice standards. He is also responsible for conducting studies to
determine the extent that these antibiotics bind to mouse and human serum proteins and for determining the
stability of these drugs in broth, agar, and serum.
XXXXXXX
XXXXXXX
XXXXXXX
Selected projects completed within the past 3 years:
XXXXXXX
Page 31
XXXXXXX
XXXXXXX
Page 32
(3)
Spence,R.P., Eley,S.M., Nuttall,P.A., Pullin,J.S.K. & Moore,N.F. (1985). Replication and
polypeptide synthesis of Mill Door/79, an orbivirus isolated from ticks from a seabird colony in
Scotland. Journal of Virology 53, 705-707.
(4)
Spence,R.P., Nuttall,P.A. & Moore,N.F. (1986). A comparison of the induced polypeptides and
RNAs of three orbiviruses isolated from seabird colonies on the Isle of May, Scotland. Acta
Virologica 30, 19-24.
(5)
Spence,R.P., Pullin,J.S.K. & Moore,N.F. (1986). Proteins expressed by Mill Door/79 virus, a
kemerovo serogroup orbivirus transmitted by the ticks Ixodes uriae. Archives of Virology 90,
53-62.
(6)
Nuttall,P.A., Carey,D., Moss,S., Green,B. & Spence,R.P. (1986). Hughes group viruses
(Bunyaviridae) from Ixodes ticks Ixodes (Ceratixodes) uriae (Acari:Ixodidae). Journal of
Medical Entomology 23, 437-440.
(7)
Matlashewski,G., Banks,L., Wu-Liao,J., Spence,P., Pim,D. & Crawford,L.(1986). The
expression of human papillomavirus type 18 E6 protein in bacteria and the production of
anti-E6 antibodies. Journal of General Virology 67, 1909-1916.
8)
Banks,L., Spence,P., Androphy,E., Hubbert,N., Matlashewski,G., Murray,A. & Crawford,L.
(1987). Identification of human papillomavirus type 18 E6 polypeptide
in cells derived from
human cervical carcinomas. Journal of General Virology 68, 1351-1359.
(9)
Spence,R.P., Murray,A., Banks,L., Kelland,L.R. & Crawford,L. (1988). Analysis of human
papillomavirus sequences in cell lines recently derived from cervical cancers. Cancer
Research 48, 324-328.
(10)
Highfield,P.E., Duncan,R.J.S., Parker,D. and Spence,R.P. (1988). Immunoassay and
biological constructs for use therein. European Patent Application.
(11)
Spence,R.P., Jarvill,W.M., Ferns,R.B. Tedder,R.S. and Parker,D. (1989). The cloning and
expression in E.coli of sequences coding for P24, the core protein of human immunodeficiency
virus (HIV) and the use of the recombinant protein in characterising a panel of monoclonal
antibodies against the viral P24 protein. Journal of General Virology 70, 2843-2851.
(12)
Spence,R.P., Walker J., Jarvill,W.M., Ferns,R.B., Tedder,R.S., Sattentau,Q., Weber,J.,
Parry,N. and Highfield,P.E. (1989). The expression in E.coli of sequences coding for the P18
protein of human immunodeficiency virus (HIV) and the use of the recombinant protein in
characterising a panel of monoclonal antibodies against the viral P18 protein. Journal of
General Virology 70, 2853-2863.
(13)
Ferns R.B., Partridge J.C., Spence R.P., Hunt N. and Tedder R.S. (1989). Epitope location of
thirteen anti-gag human immunodeficiency virus 1 (HIV1) monoclonal antibodies using
oligopeptides and their cross reactivity with HIV2. AIDS 3, 829-834.
(14)
Tersmette,M., Winkel,I.N., Groenink,M., Gruters,R.A., Spence,P., Saman,E., van den
Groen,G., Miedema,F. and Huisman,J.G. (1989) Detection and subtyping of HIV-1 isolates
Page 34
with a panel of characterized monoclonal antibodies to HIV-p24gag. Virology 171, 149-155.
(15)
Peakman,T.C., Reynolds,C.H., Willson,M.G., Moore,J.D., Spence,P., Sydenham,M.,
Linstead,D.J., Gewert,D.R. & Page,M.J. (1992). Expression of the mouse c-abl type IV protooncogene product in the insect cell baculovirus system. Biochimica et Biophysica Acta.
1138(11), 68-74.
(16)
Spence, R.P. (1993) Inhibitors of tyrosine kinase activity as cancer therapeutics recent
advances. Current Opinion in Therapeutics Patents. 3, 3-9.
(17)
Gusterson, B., Crompton, M., Mitchell, P., Barker, K., Kamalati, T. Page, M. and Spence,
P.Wellcome Foundation Patent: BRK (PTK23) a non-receptor tyrosine kinase, its discovery
and application in cancer therapy. Great Britain Patent GB9314233.9 1993.
(18)
Gusterson, B., Crompton, M., Mitchell, P., Barker, K., Martindale, J., Page, M. and Spence, P.
(1993) Wellcome Foundation Patent: DDR (PTK22) a receptor tyrosine kinase, its discovery
and application in cancer therapy. Great Britain Patent GB-9314271.9.
(19)
Page,M.J., Crompton,M.R., Affleck,K. & Spence,R.P. (1994) Tyrosine Kinases as Targets for
Therapy in Breast Cancer. in Cancer Therapy in the 21st Century Ed. B. Huber.
(20)
Spence, P., Franco, R., Wood, A. and Moyer, J. (1996) Mechanisms of apoptosis as drug
targets in the central nervous system. Current Opinion in Therapeutic Patents 6(4), 345-366.
(21)
Spence, P. (1998) Obtaining value from the human genome: a challenge for the
pharmaceutical industry. Drug Discovery Today 3 (4) 179-188.
(22)
Spence, P., Bard, J., Jones, P. and. Betty, M. (1998) The identification of G-Protein Coupled
Receptors in Sequence Databases. Expert Opinion in Therapeutic Patents 8 (3) 235-247.
(23)
Spence, P. (1999) Genomics: the race is on (editorial). Drug Discovery Today 4 (3), 103-104.
(24)
Spence, P. (1999) From genome to drug: optimizing the drug discovery process. Progress in
Drug Research 53 157-191.
(25)
Spence, P and Aurora, R (1999). From Reductionist to Constructionist: But only if we
integrate Trends in Biotechnology (bioinformatics supplement).
(26)
Spence, P. (2002) Toxicoproteomics, learning to walk before it can run. Drug Discovery Today
7, 597.
(27)
Spence, P (2003) Maximizing the value of genomics in the drug discovery and development
process. Progress in Drug Discovery 60, 161-170.
Page 35
RESEARCH & RELATED BUDGET - SECTION A & B, BUDGET PERIOD 1
* ORGANIZATIONAL DUNS: 1243619450000
* Budget Type:
● Project
❍ Subaward/Consortium
Enter name of Organization: Ordway Research Institute
* Start Date: 07-01-2008
* End Date: 06-30-2009
Budget Period: 1
A. Senior/Key Person
Prefix
* First Name Middle Name
* Last Name
Suffix
* Project Role
Base Salary
($)
1.
2.
George
James
L.
Drusano
McSharry
MD
PhD
PD/PI
Co-Investigator
Cal.
Acad.
Sum.
Months Months Months
XXXXXXX 2.4
XXXXXXX 6.
* Requested
* Fringe
Salary ($)
Benefits ($)
37,320.00
69,224.00
* Funds Requested ($)
8,957.00
16,614.00
46,277.00
85,838.00
Total Funds Requested for all Senior Key Persons in the attached file
Additional Senior Key Persons:
File Name:
Mime Type:
Total Senior/Key Person
132,115.00
B. Other Personnel
* Number of
* Project Role
Personnel
Cal.
Acad.
Sum.
1
1
Post Doctoral Associates
Graduate Students
Undergraduate Students
Secretarial/Clerical
Senior Research Tech
Research Tech
2
Total Number Other Personnel
6.00
6.00
* Requested
* Fringe
* Funds Requested
Salary ($)
Benefits
($)
23,981.00
16,631.00
5,755.00
3,991.00
29,736.00
20,622.00
Total Other Personnel
50,358.00
Total Salary, Wages and Fringe Benefits (A+B)
182,473.00
RESEARCH & RELATED Budget {A-B} (Funds Requested)
Detailed Budget - Year 1
Page 36
RESEARCH & RELATED BUDGET - SECTION C, D, & E, BUDGET PERIOD 1
* Budget Type:
● Project
* Start Date: 07-01-2008
* End Date: 06-30-2009
Budget Period: 1
C. Equipment Description
List items and dollar amount for each item exceeding $5,000
Equipment Item
Total funds requested for all equipment listed in the attached file
Total Equipment
Additional Equipment:
File Name:
Mime Type:
D. Travel
Funds Requested ($)
1. Domestic Travel Costs ( Incl. Canada, Mexico, and U.S. Possessions)
2. Foreign Travel Costs
3,600.00
Total Travel Cost
E. Participant/Trainee Support Costs
3,600.00
Funds Requested ($)
1. Tuition/Fees/Health Insurance
2. Stipends
3. Travel
4. Subsistence
5. Other:
Number of Participants/Trainees
Total Participant/Trainee Support Costs
RESEARCH & RELATED Budget {C-E} (Funds Requested)
Page 37
RESEARCH & RELATED BUDGET - SECTIONS F-K, BUDGET PERIOD 1
* Budget Type:
● Project
* Start Date: 07-01-2008
* End Date: 06-30-2009
Budget Period: 1
F. Other Direct Costs
Funds Requested ($)
1. Materials and Supplies
2. Publication Costs
3. Consultant Services
4. ADP/Computer Services
5. Subawards/Consortium/Contractual Costs
6. Equipment or Facility Rental/User Fees
7. Alterations and Renovations
8. Hollow Fiber Units
9. LC/MS/MS Core
15,000.00
2,000.00
226,863.00
84,400.00
24,816.00
Total Other Direct Costs
G. Direct Costs
353,079.00
Funds Requested ($)
Total Direct Costs (A thru F)
539,152.00
H. Indirect Costs
Indirect Cost Type
Indirect Cost Rate (%)
1. MTDC
66
Cognizant Federal Agency
Indirect Cost Base ($)
337,289.00
222,611.00
Total Indirect Costs
222,611.00
DHHS, Jeffrey Warren, 212-264-2069
(Agency Name, POC Name, and POC Phone Number)
I. Total Direct and Indirect Costs
Funds Requested ($)
Total Direct and Indirect Institutional Costs (G + H)
J. Fee
K. * Budget Justification
761,763.00
Funds Requested ($)
File Name: 1845-Budget_Justification.pdf
(Only attach one file.)
RESEARCH & RELATED Budget {F-K} (Funds Requested)
Page 38
* Budget Type:
● Project
* Start Date: 07-01-2009
* End Date: 06-30-2010
Budget Period: 2
Prefix
* Last Name
Suffix
* Project Role
Base Salary
($)
1.
2.
George
James
L.
Drusano
McSharry
MD
PhD
PD/PI
Co-Investigator
Cal.
Acad.
Sum.
XXXXXXX 2.40
XXXXXXX 6.00
* Requested
* Fringe
Salary ($)
Benefits ($)
38,440.00
71,300.00
9,226.00
17,112.00
47,666.00
88,412.00
File Name:
Mime Type:
136,078.00
B. Other Personnel
* Number of
* Project Role
Personnel
Cal.
Acad.
Sum.
1
1
Graduate Students
Research Tech
2
6.00
6.00
* Requested
* Fringe
* Funds Requested
Salary ($)
Benefits
($)
24,700.00
17,129.00
5,928.00
4,111.00
30,628.00
21,240.00
51,868.00
187,946.00
Page 39
* Budget Type:
● Project
* Start Date: 07-01-2009
* End Date: 06-30-2010
Budget Period: 2
Equipment Item
Total Equipment
File Name:
Mime Type:
D. Travel
Funds Requested ($)
3,600.00
Total Travel Cost
3,600.00
Funds Requested ($)
2. Stipends
3. Travel
4. Subsistence
5. Other:
Page 40
* Budget Type:
● Project
* Start Date: 07-01-2009
* End Date: 06-30-2010
Budget Period: 2
Funds Requested ($)
9. LC/MS/MS
15,450.00
2,060.00
230,507.00
86,932.00
25,560.00
G. Direct Costs
360,509.00
Funds Requested ($)
552,055.00
H. Indirect Costs
Indirect Cost Type
1. MTDC
66
321,548.00
212,222.00
212,222.00
Funds Requested ($)
J. Fee
764,277.00
Funds Requested ($)
Page 41
* Budget Type:
● Project
* Start Date: 07-01-2010
* End Date: 06-30-2011
Budget Period: 3
Prefix
* Last Name
Suffix
* Project Role
Base Salary
($)
1.
2.
George
James
L.
Drusano
McSharry
MD
PhD
PD/PI
Co-Investigator
197,964.00
148,304.00
Cal.
Acad.
Sum.
2.40
6.00
* Requested
* Fringe
Salary ($)
Benefits ($)
39,593.00
74,152.00
9,502.00
17,797.00
49,095.00
91,949.00
141,044.00
File Name:
Mime Type:
B. Other Personnel
* Number of
* Project Role
Personnel
Cal.
Acad.
Sum.
1
1
Graduate Students
Research Tech
2
6.00
6.00
* Requested
* Fringe
* Funds Requested
Salary ($)
Benefits
($)
25,688.00
17,815.00
6,165.00
4,276.00
31,853.00
22,091.00
53,944.00
194,988.00
Page 42
* Budget Type:
● Project
* Start Date: 07-01-2010
* End Date: 06-30-2011
Budget Period: 3
Equipment Item
Total Equipment
File Name:
Mime Type:
D. Travel
Funds Requested ($)
3,600.00
Total Travel Cost
3,600.00
Funds Requested ($)
2. Stipends
3. Travel
4. Subsistence
5. Other:
Page 43
* Budget Type:
● Project
* Start Date: 07-01-2010
* End Date: 06-30-2011
Budget Period: 3
Funds Requested ($)
9. LC/MS/MS
15,914.00
2,122.00
232,300.00
89,540.00
26,327.00
G. Direct Costs
366,203.00
Funds Requested ($)
564,791.00
H. Indirect Costs
Indirect Cost Type
1. MTDC
66
332,491.00
219,444.00
219,444.00
Funds Requested ($)
J. Fee
784,235.00
Funds Requested ($)
Page 44
* Budget Type:
● Project
* Start Date: 07-01-2011
* End Date: 06-30-2012
Budget Period: 4
Prefix
* Last Name
Suffix
* Project Role
Base Salary
($)
1.
2.
George
James
L.
Drusano
McSharry
MD
PhD
PD/PI
Co-Investigator
203,903.00
154,237.00
Cal.
Acad.
Sum.
2.40
6.00
* Requested
* Fringe
Salary ($)
Benefits ($)
40,781.00
77,118.00
9,787.00
18,508.00
50,568.00
95,626.00
146,194.00
File Name:
Mime Type:
B. Other Personnel
* Number of
* Project Role
Personnel
Cal.
Acad.
Sum.
1
1
Graduate Students
Research Tech
2
6.00
6.00
* Requested
* Fringe
* Funds Requested
Salary ($)
Benefits
($)
26,716.00
18,527.00
6,412.00
4,447.00
33,128.00
22,974.00
56,102.00
202,296.00
Page 45
* Budget Type:
● Project
* Start Date: 07-01-2011
* End Date: 06-30-2012
Budget Period: 4
Equipment Item
Total Equipment
File Name:
Mime Type:
D. Travel
Funds Requested ($)
3,600.00
Total Travel Cost
3,600.00
Funds Requested ($)
2. Stipends
3. Travel
4. Subsistence
5. Other:
Page 46
* Budget Type:
● Project
* Start Date: 07-01-2011
* End Date: 06-30-2012
Budget Period: 4
Funds Requested ($)
9. LC/MS/MS
16,391.00
2,185.00
235,541.00
92,226.00
27,117.00
G. Direct Costs
373,460.00
Funds Requested ($)
579,356.00
H. Indirect Costs
Indirect Cost Type
1. MTDC
66
343,815.00
226,918.00
226,918.00
Funds Requested ($)
J. Fee
806,274.00
Funds Requested ($)
Page 47
Budget Justification
Personnel
George L. Drusano, M.D., 20% effort (2.4 calendar months) Dr. Drusano is the Co-Director of
the Ordway Research Institute. His specialties are pharmacodynamics/pharmacokinetics and
mathematical modeling. He has spent over 20 years developing the HFIM system for bacteria,
fungi and viruses. As PI on the project, Dr. Drusano will direct all facets of the proposed
research. In addition, he will design the pharmacodynamic and pharmacokinetic studies,
perform all statistical analysis required for the various aspects of the study, and be involved in
all of the discussions related to planning experiments, writing reports and manuscirpts. He will
be one of the two scientists who will participate in meetings with NIH program managers
James J. McSharry, Ph.D., 50% effort (6 calendar months) Dr. McSharry is head of the
Virology Laboratory at the Ordway Research Institute. He has had over 30 years experience
studying animal viruses with emphasis on antiviral drug development. He will direct all
laboratory work associated with the proposed research, participate in the hollow fiber
pharmacodynamic and pharmocokinetic studies, meet daily with the technicians involved in the
project and the PI, and, along with the PI, present findings at scientific meetings including those
at NIH with program managers.
Kris Zager, BS, 50% effort (6 calendar months) She is the head technician in the Virology
Laboratory at the Ordway Research Institute. She was trained as a medical technologist and
performed as such at Memorial Sloan Kettering Medical Center and the Albany Medical Center
for over 20 years. For the past three years she has been performing antiviral drug studies in Dr.
McSharry’s laboratory. She performs all of studies that involve the hollow fiber units and is
involved in assaying virus output from the hollow fiber units.
Qingmei Wang, MS, 50% effort (6 calendar months), is a level two technician is the laboratory.
Qingmei has been in Dr. McSharry’s laboratory for over two years where she has learned basic
virology and cell culture. She maintains all of the tissue culture cells and viruses needed for the
studies. She assists Kris Zager in setting up and running the hollow fiber experiments and
performing analyses on the virus output.
Supplies
These are the actual expenses incurred while running these hollow fiber experiments. The
costs of the hollow fiber units are self evident. Several liters of medium for growing MDCK cells
(MEM, FBS, and Pen/Strep) and medium for growing influenza viruses in flasks and hollow fiber
units (MEM, BSA, TPCK-treated trypsin, Pen/Strep) are required for each experiment. We are
planning to study two viruses for each of the 4 drugs each year. Each set of experiments (dose
ranging experiment and dose fractionation experiment) will be performed in its entirety two
times.
Duet pumps. Dr. McSharry’s laboratory currently has four duet pumps to pump medium and
drugs through eight hollow fiber units. If this grant application is funded, his laboratory will
require four additional duet pumps so this work can be carried out within the time frame of the
Page 48
grant proposal. With these four additional duet pumps the second BSL-2 lab would be
completely equipped to perform the proposed studies.
Hollow fiber units
Each dose ranging experiment uses six to eight hollow fiber units and each dose fractionation
study uses five to six hollow fiber units. In addition to the experimental units, one unit is used to
determine if the compound understudy binds to the hollow fiber unit. Therefore, approximately
16 hollow units are required for each drug under study. As the number of drugs and the number
of viruses under study increase, so does the number of hollow fiber units.
PK analysis
Our institution charges institute members $47 per sample. We expect to generate 10 samples
per experimental arm per experiment (8 arms X 10 samples X $65 = $5200). Two drugs will be
assayed for six viruses each year for a total pk analysis expense of $31,2000.
Travel
Monies for one trip for the PI to meet with program personnel at NIH is requested and money to
help defray the cost of attending a national meeting to present data to the scientific community.
Budget increase in years 2, 3, and 4. The salary and supply budgets were increased by 3%
each year in an attempt to keep up with inflation.
Page 49
RESEARCH & RELATED BUDGET - Cumulative Budget
Totals ($)
Section A, Senior/Key Person
555,431.00
Section B, Other Personnel
212,272.00
8
767,703.00
Section C, Equipment
14,400.00
Section D, Travel
14,400.00
1. Domestic
2. Foreign
Section E, Participant/Trainee Support Costs
2. Stipends
3. Travel
4. Subsistence
5. Other
6. Number of Participants/Trainees
1,453,251.00
Section F, Other Direct Costs
62,755.00
8,367.00
925,211.00
8. Other 1
353,098.00
9. Other 2
103,820.00
10. Other 3
Section G, Direct Costs (A thru F)
2,235,354.00
881,195.00
Section H, Indirect Costs
Section I, Total Direct and Indirect Costs (G + H)
3,116,549.00
Section J, Fee
Cumulative Budget
Page 50
* Budget Type:
❍ Project
● Subaward/Consortium
Enter name of Organization: Translational Genomics Research Institute
* Start Date: 07-01-2008
* End Date: 06-30-2009
Budget Period: 1
Prefix
* Last Name
Suffix
* Project Role
Base Salary
($)
1.
2.
Paul
Dave
Keim
Engelthaler
PhD
PD/PI
Co-Investigator
Cal.
Acad.
Sum.
XXXXXXX 0.60
XXXXXXX 1.20
* Requested
* Fringe
Salary ($)
Benefits ($)
9,330.00
9,305.00
1,539.00
1,535.00
10,869.00
10,840.00
File Name:
Mime Type:
21,709.00
B. Other Personnel
* Number of
* Project Role
Personnel
Cal.
Acad.
Sum.
1
1
1
Graduate Students
Bioinformatician
Research Coordinator
Research Technician
3
1.20
1.20
6.00
* Requested
* Fringe
* Funds Requested
Salary ($)
Benefits
($)
12,100.00
6,200.00
20,000.00
1,997.00
1,023.00
3,300.00
14,097.00
7,223.00
23,300.00
44,620.00
66,329.00
Subaward 1
Page 51
* Budget Type:
❍ Project
* Start Date: 07-01-2008
* End Date: 06-30-2009
Budget Period: 1
Equipment Item
Total Equipment
File Name:
Mime Type:
D. Travel
Funds Requested ($)
2,000.00
Total Travel Cost
2,000.00
Funds Requested ($)
2. Stipends
3. Travel
4. Subsistence
5. Other:
Subaward 1
Page 52
* Budget Type:
❍ Project
* Start Date: 07-01-2008
* End Date: 06-30-2009
Budget Period: 1
Funds Requested ($)
8. Service Contracts
68,700.00
3,880.00
G. Direct Costs
72,580.00
Funds Requested ($)
140,909.00
H. Indirect Costs
Indirect Cost Type
1. MTDC
61
140,909.00
85,954.00
85,954.00
DHHS, Kitty Unti, 415-437-7820
Funds Requested ($)
J. Fee
226,863.00
Funds Requested ($)
File Name: 2000-Keim_Budget_Justification.pdf
Subaward 1
Page 53
* Budget Type:
❍ Project
* Start Date: 07-01-2009
* End Date: 06-30-2010
Budget Period: 2
Prefix
* Last Name
Suffix
* Project Role
Base Salary
($)
1.
2.
Paul
Dave
Keim
Engelthaler
PhD
PD/PI
Co-Investigator
Cal.
Acad.
Sum.
XXXXXXX 0.60
XXXXXXX 1.20
* Requested
* Fringe
Salary ($)
Benefits ($)
9,330.00
9,584.00
1,539.00
1,581.00
10,869.00
11,165.00
File Name:
Mime Type:
22,034.00
B. Other Personnel
* Number of
* Project Role
Personnel
Cal.
Acad.
Sum.
1
1
1
Graduate Students
Bioinformatician
Research Technician
3
1.20
1.20
6.00
* Requested
* Fringe
* Funds Requested
Salary ($)
Benefits
($)
12,463.00
6,386.00
20,600.00
2,056.00
1,054.00
3,999.00
14,519.00
7,440.00
24,599.00
46,558.00
68,592.00
Subaward 1
Page 54
* Budget Type:
❍ Project
* Start Date: 07-01-2009
* End Date: 06-30-2010
Budget Period: 2
Equipment Item
Total Equipment
File Name:
Mime Type:
D. Travel
Funds Requested ($)
2,000.00
Total Travel Cost
2,000.00
Funds Requested ($)
2. Stipends
3. Travel
4. Subsistence
5. Other:
Subaward 1
Page 55
* Budget Type:
❍ Project
* Start Date: 07-01-2009
* End Date: 06-30-2010
Budget Period: 2
Funds Requested ($)
8. Service Contracts
68,700.00
3,880.00
G. Direct Costs
72,580.00
Funds Requested ($)
143,172.00
H. Indirect Costs
Indirect Cost Type
1. MTDC
61
143,172.00
87,335.00
87,335.00
Funds Requested ($)
J. Fee
230,507.00
Funds Requested ($)
Subaward 1
Page 56
* Budget Type:
❍ Project
* Start Date: 07-01-2010
* End Date: 06-30-2011
Budget Period: 3
Prefix
* Last Name
Suffix
* Project Role
Base Salary
($)
1.
2.
Paul
Dave
Keim
Engelthaler
PhD
PD/PI
Co-Investigator
Cal.
Acad.
Sum.
XXXXXXX 0.60
XXXXXXX 1.20
* Requested
* Fringe
Salary ($)
Benefits ($)
9,330.00
9,871.00
1,539.00
1,629.00
10,869.00
11,500.00
File Name:
Mime Type:
22,369.00
B. Other Personnel
* Number of
* Project Role
Personnel
Cal.
Acad.
Sum.
1
1
1
Graduate Students
Bioinformatician
Research Technician
3
1.20
1.20
6.00
* Requested
* Fringe
* Funds Requested
Salary ($)
Benefits
($)
12,837.00
6,578.00
21,218.00
2,118.00
1,085.00
3,501.00
14,955.00
7,663.00
24,719.00
47,337.00
69,706.00
Subaward 1
Page 57
* Budget Type:
❍ Project
* Start Date: 07-01-2010
* End Date: 06-30-2011
Budget Period: 3
Equipment Item
Total Equipment
File Name:
Mime Type:
D. Travel
Funds Requested ($)
2,000.00
Total Travel Cost
2,000.00
Funds Requested ($)
2. Stipends
3. Travel
4. Subsistence
5. Other:
Subaward 1
Page 58
* Budget Type:
❍ Project
* Start Date: 07-01-2010
* End Date: 06-30-2011
Budget Period: 3
Funds Requested ($)
8. Service Contract
68,700.00
3,880.00
G. Direct Costs
72,580.00
Funds Requested ($)
144,286.00
H. Indirect Costs
Indirect Cost Type
1. MTDC
61
144,286.00
88,014.00
88,014.00
Funds Requested ($)
J. Fee
232,300.00
Funds Requested ($)
Subaward 1
Page 59
* Budget Type:
❍ Project
* Start Date: 07-01-2011
* End Date: 06-30-2012
Budget Period: 4
Prefix
* Last Name
Suffix
* Project Role
Base Salary
($)
1.
2.
Paul
David
Keim
Engelthaler
PhD
PD/PI
Co-Investigator
Cal.
Acad.
Sum.
XXXXXXX 0.60
XXXXXXX 1.20
* Requested
* Fringe
Salary ($)
Benefits ($)
9,330.00
10,167.00
1,539.00
1,678.00
10,869.00
11,845.00
File Name:
Mime Type:
22,714.00
B. Other Personnel
* Number of
* Project Role
Personnel
Cal.
Acad.
Sum.
1
1
1
Graduate Students
Bioinformatician
Research Technician
3
1.20
1.20
6.00
* Requested
* Fringe
* Funds Requested
Salary ($)
Benefits
($)
13,222.00
6,775.00
22,067.00
2,182.00
1,118.00
3,641.00
15,404.00
7,893.00
25,708.00
49,005.00
71,719.00
Subaward 1
Page 60
* Budget Type:
❍ Project
* Start Date: 07-01-2011
* End Date: 06-30-2012
Budget Period: 4
Equipment Item
Total Equipment
File Name:
Mime Type:
D. Travel
Funds Requested ($)
2,000.00
Total Travel Cost
2,000.00
Funds Requested ($)
2. Stipends
3. Travel
4. Subsistence
5. Other:
Subaward 1
Page 61
* Budget Type:
❍ Project
* Start Date: 07-01-2011
* End Date: 06-30-2012
Budget Period: 4
Funds Requested ($)
8. Equipment Service Contracts
68,700.00
3,880.00
G. Direct Costs
72,580.00
Funds Requested ($)
146,299.00
H. Indirect Costs
Indirect Cost Type
1. MTDC
61
146,299.00
89,242.00
89,242.00
Funds Requested ($)
J. Fee
235,541.00
Funds Requested ($)
Subaward 1
Page 62
RESEARCH & RELATED BUDGET - Cumulative Budget
Totals ($)
88,826.00
Section A, Senior/Key Person
187,520.00
Section B, Other Personnel
12
276,346.00
Section C, Equipment
8,000.00
Section D, Travel
8,000.00
1. Domestic
2. Foreign
Section E, Participant/Trainee Support Costs
2. Stipends
3. Travel
4. Subsistence
5. Other
6. Number of Participants/Trainees
290,320.00
Section F, Other Direct Costs
274,800.00
15,520.00
8. Other 1
9. Other 2
10. Other 3
Section G, Direct Costs (A thru F)
574,666.00
Section H, Indirect Costs
350,545.00
Section I, Total Direct and Indirect Costs (G + H)
925,211.00
Section J, Fee
Subaward 1
Page 63
Personnel
Paul Keim, Ph.D., Co-Investigator, (.6 calendar months ) – Dr. Paul Keim will be responsible for direction of
TGen’s involvement in the project. He will direct the science and oversee the conduct of the research. Dr.
Keim has a joint appointment at NAU as a Regents Professor and at TGen as an investigator and the Director
of Pathogen Genomics. As such, he will be overseeing research efforts at both institutions. Salary
commensurate with .6 calendar months effort in all years of funding is requested.
David M. Engelthaler, M.S., Co- Investigator, (1.2 calendar months ) – David Engelthaler is the Director of
Programs for TGen North and will be the Co-Investigator for this project. He has considerable experience in
laboratory research, organizational management and project management. He will oversee all activities and
personnel at TGen North, will coordinate the administration of this grant and will coordinate efforts between the
collaborating institutions. Salary commensurate with 1.2 calendar months effort in all years of funding is
requested.
Josh Colvin, Bioinformatics Coordinator, (1.2 calendar months ) – Josh Colvin, a computation biologist and
software engineer recently hired by TGen, will assist the researchers with needed bioinformatics analysis,
especially in regards to sequencing. Josh will be responsible for adapting the TGen North informatics system
to ensure multi-institution data sharing and collaboration on this project. Salary commensurate with 1.2
calendar months effort in all years of funding is requested.
Elizabeth Driebe, Research Coordinator, (1.2 calendar months) – Elizabeth Driebe will be responsible for
coordinating the daily aspects of molecular assay testing and validation within TGen North, including activities
related to this project. She will be responsible for ordering and receiving reagents and coordinating the
technicians in the laboratory. She is highly experienced in RT-PCR and other pertinent laboratory techniques,
developed the TGen influenza quantitative PCR assay, and has laboratory supervisory experience and a MS
degree in microbiology. Elizabeth will work on assay implementation and sample extraction protocols and will
specifically direct the activities of the Research Associate on this project. Salary commensurate with 1.2
calendar months effort in all years of funding is requested.
To be Named, Research Technician, (6 calendar months) – This position will be responsible for daily
molecular biology activities. They will work with the Research Coordinator on assay development, validation,
and sample extraction protocols. They will work closely with the Ordway and NYSL researchers and NPI staff.
We may use and existing research technician or hiring a new staff member. The candidate will have a B.S.
and molecular biology lab experience. Salary commensurate with 6 calendar months effort in all years of
funding is requested.
Budgeted salaries are based on actual, existing salaries, with the exception of the one technician
position, which will be filled only if this proposal is funded. However, the salaries listed for the
technician position is congruent with those for similar, existing positions at TGen. Fringe benefits are
calculated for each individual according to the existing rate at TGen: 16.5% of salaries. The indirect
costs are associated with total direct costs at a rate that is TGen’s DHHS negotiated rate of 61%.
Supplies
To perform the proposed analyses, we will require funding to cover expenses associated with purchasing
reagents and expendable supplies. We are requesting $28,700 each year to cover these expenses, with the
following breakdown in costs: RNA extraction kits - $4,700 (each year); Taq DNA polymerase - $6,000 (each
year); primers and probes - $3,500 (each year); sequencing supplies - $45,000 (each year) and miscellaneous
lab supplies and small equipment - $9,500 (each year).
Subaward 1 Budget Justification Attachment
Page 64
Travel
In each year, TGen requests a total of $2,000 to support out of state travel, to annual collaborator meeting in
New York. We estimate 1 trip @$1000/ trip ($550 airfare, $300 hotel, $75-ground transportation and $75meals) X two people, per year.
Other
In each year, TGen request $3,880 to cover appropriate costs (20%) of service and maintenance on the rtPCR and sequencing instruments: $1,405 for the 7900HT real-time PCR and $2,475 for the 3130xl sequencer.
Subaward 1 Budget Justification Attachment
Page 65
PHS 398 Cover Page Supplement
1. Project Director / Principal Investigator (PD/PI)
Prefix:
* First Name: George
Middle Name: L.
* Last Name:
Suffix:
Drusano
MD
* New Investigator?
Degrees:
● No
❍Yes
M.D.
2. Human Subjects
Clinical Trial?
● No
❍Yes
* Agency-Defined Phase III Clinical Trial?
❍No
❍Yes
3. Applicant Organization Contact
Person to be contacted on matters involving this application
Prefix:
* First Name: Sharon
Middle Name: E.
* Last Name:
Boswell
CRA
Suffix:
* Phone Number: 518-641-6410
Fax Number: 518-641-6303
Email: [email protected]
* Title:
Sponsored Research Administrator
* Street1:
150 New Scotland Avenue
Street2:
* City:
County:
* State:
Albany
Albany
NY: New York
Province:
* Country:
USA:
* Zip / Postal Code:
Clinical Trial & HESC
12208
Page 66
PHS 398 Cover Page Supplement
4. Human Embryonic Stem Cells
* Does the proposed project involve human embryonic stem cells?
●No
❍Yes
If the proposed project involves human embryonic stem cells, list below the registration number of the
specific cell line(s) from the following list: http://stemcells.nih.gov/registry/index.asp . Or, if a specific
stem cell line cannot be referenced at this time, please check the box indicating that one from the registry will be used:
Cell Line(s):
❏
Specific stem cell line cannot be referenced at this time. One from the registry will be used.
Clinical Trial & HESC
Page 67
PHS 398 Research Plan
1. Application Type:
From SF 424 (R&R) Cover Page and PHS398 Checklist. The responses provided on these pages, regarding the type of application being submitted, are repeated for your reference, as you attach the appropriate sections of the research plan.
*Type of Application:
● New
❍ Resubmission
❍ Renewal
❍ Continuation
❍ Revision
2. Research Plan Attachments:
Please attach applicable sections of the research plan, below.
1. Introduction to Application
(for RESUBMISSION or REVISION only)
2. Specific Aims
1800-2007_flu_grant_specific_aims.pdf
3. Background and Significance
3852-Background_and_Significance.pdf
4. Preliminary Studies / Progress Report
381-Preliminary_data.pdf
5. Research Design and Methods
1166-Research_Design_and_Methods.pdf
6. Inclusion Enrollment Report
7. Progress Report Publication List
Human Subjects Sections
Attachments 8-11 apply only when you have answered "yes" to the question "are human subjects involved" on the R&R Other Project Information
Form. In this case, attachments 8-11 may be required, and you are encouraged to consult the Application guide instructions and/or the specific
Funding Opportunity Announcement to determine which sections must be submitted with this application.
8. Protection of Human Subjects
9. Inclusion of Women and Minorities
10. Targeted/Planned Enrollment Table
11. Inclusion of Children
Other Research Plan Sections
12. Vertebrate Animals
13. Select Agent Research
14. Multiple PI Leadership
15. Consortium/Contractual Arrangements
531-Keim_Consortium_Statement.pdf
16. Letters of Support
17. Resource Sharing Plan(s)
4822-Data_Sharing_Plans.general.pdf
18. Appendix
List of Research Plan Attachments
Page 68
Attachments
IntroductionToApplication_attDataGroup0
File Name
Mime Type
SpecificAims_attDataGroup0
File Name
1800-2007_flu_grant_specific_aims.pdf
Mime Type
application/pdf
BackgroundSignificance_attDataGroup0
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3852-Background_and_Significance.pdf
Mime Type
application/pdf
ProgressReport_attDataGroup0
File Name
381-Preliminary_data.pdf
Mime Type
application/pdf
ResearchDesignMethods_attDataGroup0
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1166-Research_Design_and_Methods.pdf
Mime Type
application/pdf
InclusionEnrollmentReport_attDataGroup0
File Name
Mime Type
ProgressReportPublicationList_attDataGroup0
File Name
Mime Type
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File Name
Mime Type
InclusionOfWomenAndMinorities_attDataGroup0
File Name
Mime Type
TargetedPlannedEnrollmentTable_attDataGroup0
File Name
Mime Type
InclusionOfChildren_attDataGroup0
File Name
Mime Type
VertebrateAnimals_attDataGroup0
File Name
Mime Type
SelectAgentResearch_attDataGroup0
File Name
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MultiplePILeadershipPlan_attDataGroup0
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531-Keim_Consortium_Statement.pdf
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application/pdf
Page 69
Appendix
File Name
Mime Type
Page 70
Specific Aims.
The long term goal of this research project is to identify the optimal dose and schedule of
administration of drugs active against influenza viruses that will prevent and/or cure people with
influenza without causing the emergence of resistant viruses. The adamantanes and neuraminidase
inhibitors have been used for the prevention and/or treatment of influenza. However, they often fail
because treatment with these drugs leads to the emergence of resistant viruses in the treated
population.
Adamantanes have historically been used in the treatment and prevention of influenza A virus
infections (1). Recently, viruses that are resistant to these inexpensive drugs have emerged,
rendering them less useful for the therapy of influenza (2, 3). Neuraminidase inhibitors represent a
new class of agents for use against type A and type B influenza virus infections (1). While shown to
be effective, there have been instances of emergence of resistance or reduced sensitivity during
therapy with neuraminidase inhibitors (4-6). This has been seen especially in children where high
clearances for these agents in general and oseltamivir in specific are the norm (5).
The hollow fiber infection model (HFIM) system has been used to determine the optimal dose and
schedule of administration of antibacterial, antifungal and antiviral compounds for use in the treatment
of individuals infected with bacteria, fungi, and viruses (7-16). We propose to use the HFIM system to
study the effects of amantadine and the neuraminidase inhibitor, oseltamivir carboxylate, on the
replication of influenza viruses and to identify the pharmacodynamically-linked variables for these
antiviral drugs, alone and in combination, with respect to inhibition of virus replication. We also
propose to identify whether a different pharmacodynamically-linked variable is present for
suppression of emergence of resistance. We hypothesize that the HFIM system can be used to
provide information on resistance selection in humans and that the HFIM system can be used
to determine the dose and administration schedule of antiviral compounds and combinations
of antiviral compounds that will inhibit the replication of influenza viruses while preventing the
emergence of resistance.
Specific Aim #1. Validate the HFIM system as a model for antiviral drug-induced resistance in
humans by demonstrating that: 1) influenza viruses that are resistant to amantadine and oseltamivir
carboxylate can be generated in the HFIM system when these antiviral compounds are delivered as
monotherapy using the recommended human doses and PKs; and 2) that the resistant strains
generated in the HFIM system have similar characteristics as those isolated from clinical settings.
Several influenza virus clinical isolates and laboratory strains including the recombinant H5N1
influenza virus, rgA/Vietnam/1203/2004xA/PR/8/34 (a surrogate for H5N1 influenza virus), will be
used in these studies.
Specific Aim #2. Use these viruses in the HFIM system to optimize the dosing strategy of
amantadine and oseltamivir carboxylate to minimize the emergence of drug resistance by performing
dose ranging and dose fractionation studies of these drugs in influenza virus-infected cells under
monotherapy conditions.
Specific Aim #3. Use the HFIM system to determine the pharmacodynamically-linked variables of
combinations of amantadine and oseltamivir carboxylate with the aim of suppressing or preventing
the emergence of resistance to these drugs in cells infected with these influenza viruses.
The results of these studies will help establish protocols for the use of these antiviral compounds
for the treatment of patients during influenza epidemics and pandemics.
Our research strategy involves a multifaceted, translational collaboration designed to optimize the
move from research discovery to clinical application. The collaborators in this activity include a nonprofit research institute (Ordway Research Institute. Albany, NY), a non-profit genomics research
institute (Translational Genomics Research Institute, Flagstaff, AZ), and a private biotech company
(Adamas Pharmaceuticals, Inc, Emeryville, CA). This strategy has proven successful in other
activities including a current and ongoing research project involving the above partners.
Specific Aims
Page 71
Background and Significance.
Influenza type A viruses, H3N2 and H1N1, and influenza type B virus cause yearly infections in
people leading to considerable morbidity and, in the very young and the very old, mortality (17).
These annual influenza epidemics are caused by changes in the amino acid composition of the
hemagglutinin (HA) glycoprotein and/or the neuraminidase (NA) glycoprotein that are found on the
surface of the virus particle and are targets of the humoral immune response. The amino acid
changes are caused by point mutations in the nucleotide sequences of the genes that encode the HA
and NA proteins. These mutations occur all of the time and are termed antigenic drift (18).
Occasionally, major changes occur in the antigenic make up of type A influenza viruses when two
unrelated type A influenza viruses infect the same cell and produce viruses that contain genes from
each of the infecting viruses (18). This process is termed antigenic shift. Thus, there are two distinct
mechanisms for producing new influenza viruses that can cause yearly epidemics and occasional
pandemics in the human population. Over the past hundred years several influenza pandemics have
occurred via these mechanisms. Often viruses that originate through antigenic shift contain HA
and/or NA antigens that the human population has not encountered in the recent past and for which
they have little or no immunity (19). If these new viruses have the ability to spread easily from person
to person they will have the potential for causing influenza pandemics. Such pandemics occurred in
1918, killing 40 to 50 million people, and again in 1957 and 1968 when thousands of people were
killed throughout the world (20). Recent evidence suggests that the 1918 influenza pandemic was
caused by an avian influenza virus that gained the ability to infect humans through point mutations,
antigenic drift, in the HA gene (21-24), where as the 1957 and 1968 influenza pandemics were
caused by influenza viruses that arose through the reassortment of genes, antigenic shift, obtained
from avian and mammalian viruses (25). Very recent evidence suggests that the original 1918
influenza virus, which arose from point mutations, may have reassorted with human influenza A
viruses during the course of the pandemic leading to the waves of more serious infection in the winter
of 1918-1919 (A.S. Monto, ICAAC, 2007).
In addition to the three strains of influenza virus that normally infect people, numerous other
strains including H5N1, H7N7, and H9N2 avian influenza A viruses infect domestic and wild birds (26,
27). The H5N1 strain of avian influenza virus has been shown to infect a wide range of animals
including chickens, water fowl, tree sparrows, pigs, dogs, tigers, and cats (28). H5N1 spreads to
humans more often and kills more of the infected people than other strains of avian influenza A virus
(29, 30). People become infected with H5N1 when the virus gets access to the lower portions of the
human respiratory tract where the α2,3-linked sialic acid receptors for avian influenza viruses are
predominately located in the human respiratory tract (31, 32).
The H5N1 strain was originally isolated from patients and poultry in Hong Kong in 1997 (33, 34).
The original infection was contained by culling all poultry in Hong Kong. However, since H5N1 is
endemic in birds in Southeast Asia, culling was not successful and more epidemics occurred in birds
and humans in Southeast Asia in 2002 and 2004 (35-37). Over the past two years, H5N1 has spread
from Southeast Asia through Europe to India and into Africa. Although this has not yet occurred, the
H5N1 virus has the potential to enter the North American continent from Asia during the spring
migration of birds along the Pacific Americas flyway (38) or by the illegal importation of infected birds.
To date, although millions of birds have died from H5N1 infection or culling of infected flocks,
as of October, 2007 only 329 people have been infected by direct contact with infected birds or close
family members and 201 of the infected people have died, making H5N1 infection of humans a very
serious problem. The low infection rate suggests that the spread of H5N1 from birds to people is
inefficient. Since influenza viruses mutate rapidly there is the potential for this virus to gain the ability
to spread more easily from birds to people. At the current time, there is little evidence that infected
people readily spread H5N1 virus to other people. However, this mode of spread may have occurred
several times as clusters of family members have been infected with H5N1 virus (39). Through
natural mutation, the virus could attain the ability to spread more easily from person to person. If
efficient human to human spread of H5N1 occurs and the virus retains its ability to kill more than half
Background & Significance
Page 72
of the infected people, a devastating H5N1 influenza virus pandemic would occur. Is there any way to
avert this calamity? Yes, if effective antiviral compounds and vaccines are available for the
prevention and treatment of influenza virus infections, pandemics could be prevented.
Historically, inactivated vaccines for H1N1, H2N2 and H3N2 influenza A viruses and influenza B
virus have been used to prevent influenza virus infection in people. These vaccines are reasonably
effective if they are used and the strains included in the vaccine are a good match for the virus
circulating in the population at the time. More recently, a live attenuated cold adapted influenza
vaccine has been used with good effect (40). However, there is no evidence that these effective
vaccines will be useful for prevention of H5N1 infection in people. There are several experimental
vaccines for H5N1 influenza virus in development. One of these is an inactivated, subunit vaccine
against H5N1 influenza virus, but, at the highest doses used (two 90 μg doses given 28 days apart), it
produced a level of neutralizing antibody thought to be protective in only 57% of the vaccinees (41).
The United States government and others are trying to use adjuvants, such as MF59, to boost the
effectiveness of influenza vaccines (42). In addition, influenza vaccines that rely on the influenza A
virus M2 ion channel protein or the nucleocapsid protein are under development (43-46). However,
at present, no effective vaccine is available to protect people from infection with H5N1 influenza virus.
There are several licensed antiviral compounds for the prevention and treatment of influenza virus
infections (47). In the past amantadine and rimantadine have been effective for the prevention and
treatment of influenza caused by type A influenza viruses (1). Unfortunately, the majority of strains of
H1N1, H3N2 and H5N1 influenza A viruses that circulate in the world today are resistant to these two
relatively inexpensive drugs (2, 3, 48). Resistance of H5N1 avian influenza A viruses to amantadine
varies from country to country with 95% of recent isolates from Vietnam and Thailand being resistant
where as less than 10% of the isolates from China are resistant to amantadine (49). Thus, if the
clinical isolate is known to be susceptible to the adamantanes on the basis of genotypic and
phenotypic assays, then these inexpensive and widely available drugs could be used for the
prevention and treatment of influenza A virus infections. Another possibility is to use adamantanes in
combination with other antiviral drugs for the prevention and/or treatment of influenza virus infections
(50, 51). The neuraminidase inhibitors, oseltamivir carboxylate and zanamivir, are FDA approved for
the prevention and treatment of infections caused by H1N1 and H3N2 influenza A viruses and
influenza B virus (52-54). These neuraminidase inhibitors also are effective against H5N1 virus
infections in vitro and in vivo and could be used in the event of a pandemic caused by H5N1 (55).
Most isolates of H1N1 and H3N2 influenza A viruses and influenza B virus are susceptible to
neuraminidase inhibitors (56-58); however, resistance to oseltamivir carboxylate was reported in
pediatric patients with influenza A virus infections (4, 5) and in one child with an influenza B virus
infection (59). Three experimental neuraminidase inhibitors, Peramivir, CS8958, and A-315675, are
under development for the prevention and treatment of influenza A and B virus infections (60-65). It
has been reported that these newer neuraminidase inhibitors are effective for influenza viruses that
are resistant to zanamivir or oseltamivir carboxylate (66). In addition to the adamantanes and the
neuraminidase inhibitors, the attachment inhibitor, DAS181 (67), and the viral RNA polymerase
inhibitor, T-705 (68, 69), are under development for the treatment of infections due to influenza
viruses. Thus, effective antiviral compounds for the prevention and therapy of epidemic and pandemic
influenza are currently available and additional compounds may be on the horizon. However, the
long-term effectiveness of each drug in terms of its ability to suppress treatment-induced resistance
must be evaluated.
Mathematical modeling of pandemic influenza suggest that such a pandemic could be controlled
with the judicious use of antiviral drugs, wide spread vaccination against pandemic influenza strains,
and non-pharmaceutical measures such as school closing and working from home, etc (70-75).
Thus, with the appropriate use of nonpharmaceutical interventions and antiviral drugs in the short
term and vaccination in the long term, it should be possible to contain epidemics and pandemics
caused by avian or human influenza viruses. Now the questions that remain are: how much drug to
give and how often does one have to give that much drug to prevent infection or cure a patient
infected with epidemic or pandemic strains of influenza virus without allowing resistant viruses to
Page 73
emerge during therapy? We hypothesize that there is an optimal dose of each of these influenza
virus drugs or combinations of drugs and an optimal schedule of administration of these drugs and
combinations of drugs that will prevent and/or cure infection with avian or human influenza viruses
without leading to the emergence of drug resistant viruses during therapy.
Since it will not be possible to determine the effect of these antiviral compounds on H5N1 or other
epidemic and pandemic influenza virus infections in people in the standard phase II – III clinical trials,
we shall use our in vitro HFIM system, developed by Dr. Drusano, the PI of this grant application (716), to determine the optimal dose and administration schedule for amantadine (for type A viruses)
and oseltamivir carboxylate for type A and type B viruses. Several H1N1 and H3N2 human influenza
A viruses, the recombinant virus, rgA/Vietnam/1203/2004 X A/PR/8/34, (a surrogate for H5N1
influenza virus), and type B viruses will be tested. Once we have determined the
pharmacodynamically-linked variable for each of these antiviral compounds given as monotherapy for
these viruses, we will determine the effects of combinations of these compounds on virus replication
in the HFIM system. Since it is known that treatment of influenza virus-infected individuals with the
amantadine or oseltamivir carboxylate can lead to the emergence of drug resistant viruses during
therapy (1-6), a major aim of this proposal will be to determine the dose and schedule of
administration of these drugs that will suppress the emergence of resistant viruses when these drugs
are delivered as monotherapy or in combination therapy.
Page 74
Preliminary data
Pharmacodynamics is the area of science that links drug exposure to response. A key element of
pharmacodynamics investigation is to identify the true pharmacodynamically (PD)-linked variable.
This idea operates under the hypothesis that the shape of the drug concentration-time curve may
impact drug effectiveness (76). For example, the time that free drug concentrations remain above the
measure of potency of the drug for the virus in question (EC50, EC95) may be most closely linked to
the effect. In this case, relatively short dosing intervals lead to maximal effects. Alternatively, peak
concentrations relative to the measure of potency (Peak/EC50 ratio) may be linked to outcome. Here,
infrequent dosing with high peak concentrations result in the best effect. There are times when the
mode of administration does not alter the effect produced. Here, the Area Under the concentrationtime Curve (AUC) relative to the measure of potency (AUC/EC50 ratio) is linked to effect. We will use
the in vitro hollow fiber infection model (HFIM) pharmacodynamic system to determine the
pharmacodynamically-linked variables of compounds active against influenza A and B viruses.
We have used the HFIM pharmacodynamic system to prospectively predict the optimal dose and
schedule of administration for a number of antibacterial, antifungal and antiviral compounds required
to positively affect the outcome in patients infected with these agents (7-16). Clinical validation of the
HFIM system exists for the predictions that the HFIM produces. In the HIV arena there have been a
number of prospective validations that are listed in Table 1.
Table 1
Drug
Prediction
Validation
Stavudine
Predicted dose and schedule (7)
Clinical Trial (NDA)
Atazanavir
Predicted dose and Schedule (10)
Amprenavir
Predicted dose (12)
(Boosted)
Abacavir
Predicted schedule (Daily – 9)
GW420867X
Predicted dose
Clinical Trial (11)
Consequently, it may be safely stated that the HFIM system has more prospective predictions
regarding dose and schedule validated by clinical trials than any other in vitro or in vivo system.
Those studies listed in Table 1 are in the realm of HIV, but a similar list could be assembled for
antibacterial and anti-TB chemotherapy (13-16). The real issue, however, is whether the HFIM can
give proper predictions regarding therapy of influenza.
The in vitro Hollow Fiber Infection Model (HFIM) Pharmacodynamic System.
Figure 1 illustrates the workings of the HFIM system. For our studies, we use 4300-C2011
cartridges (FiberCell Systems, Inc, Frederick, MD) containing high molecular weight cut off (20 kd)
polysulfone hollow fibers (HF) with a surface area of 2100 cm2 and a 15 ml extracapillary space
(ECS) giving a surface area to volume ratio of 140. The high surface area to volume ratio guarantees
that the drug exposures in the ECS and the central reservoir rapidly come to equilibrium. The HFIM
system allows uninfected and virus-infected cells to grow in the ECS of the HF cartridge where cell to
cell spread of virus is very efficient. The ECS is separated from the central reservoir by semipermeable HFs with pore sizes that are large enough to allow nutrients, low molecular weight drugs,
and cellular metabolites to freely transverse into and out of the ECS, but too small for viruses and
virus-infected cells to leave the ECS. At specific times, drugs are administered by computer
controlled pumps into the system through a port in the central reservoir to simulate any schedule of
drug delivery. The effect of drug on virus replication can be determined by sampling the contents of
the ECS through the sampling ports and determining virus yield over time. The concentration of
antiviral drug in the reservoir and the ECS can also be measured by LC/MS/MS over time. As the
virological endpoint (inhibition of viral replication, prevention of cell-to-cell spread of viruses) is
measured sequentially and the drug concentration is later validated by direct measurement of
Preliminary Studies/Progress
Page 75
achieved drug concentrations by PK analysis, two measured outcomes (antiviral effect and drug
exposure) are obtained that allow construction of an exposure-response relationship. Since up to 8
drug exposure evaluations are performed simultaneously, a robust exposure-response curve is
generated with these experiments.
Figure 1: Diagram of the in vitro hollow fiber infection model (HFIM) pharmacodynamic system
The HFIM pharmacodynamic system for amantadine and oseltamivir carboxylate for influenza
viruses.
The long term goal of these investigations is to clearly identify the pharmacodynamically (PD)linked variable for amantadine and the oseltamivir carboxylate for influenza viruses. To identify the
PD-linked variable, we first perform a dose range study in the HFIM system using a continuous
infusion profile. This identifies a daily AUC that will have a known effect. We perform the dose range
as a function of multiples of the EC50. Therefore, the AUC for the chosen degree of suppression (we
usually choose between 50%-80% suppression) is: AUC = X times the EC50 times 24 hours =
XEC50*24 nM*h. This is the exposure target. In a separate experiment, this exposure is administered
in a dose fractionated manner as follows: 1) one unit receives a continuous infusion of drug at the
desired concentration, usually 2X the EC50 value; 2) in another unit the total dose is given as a single
administration over a I hour period resulting in a peak concentration followed by decline with the
correct half-life to achieve the same 24 hour AUC as the continuous infusion; 3) in a third unit, half the
dose is administered every 12 hours with a lower peak, but with the same half-life of decline and a
matching 24-hour AUC; and 4) in a fourth unit, the total dose is broken up into three equal parts and
administered every 8 hours with a still lower peak concentration, with the correct half-life and a
matching 24 hour AUC. If peak concentration (Peak/EC50) is linked to outcome, the once-daily dose
will have the best antiviral effect. If trough concentration (surrogate for Time > EC50) is linked to
antiviral effect then the most fractionated schedule or continuous infusion will have the best antiviral
effect. Finally, if AUC (AUC/EC50) is linked to the best antiviral effect, all four modes of administration
will provide the same effect.
For influenza virus studies, a mixture of 108 uninfected MDCK cells and 102 influenza virusinfected MDCK cells are placed in the ECS outside the semi-permeable membranes. If no antiviral
agent is administered, the system produces virus growth to high titer with subsequent death of virusinfected cells. Medium containing virus released from virus-infected cells is removed from the ECS
through sampling ports in the cartridge for quantitative analysis of cell-free virus by virus yield assays
(plaque assay or TCID50 assay). Viruses are contained within the ECS and they are not diluted
because they are too large to pass through the 20 kd average pore size of the hollow fibers. For
dose ranging studies, drug is placed in the central reservoir and, with circulation, this produces a
continuous infusion drug concentration profile. By adding different concentrations of antiviral
compounds to the central reservoirs of different HF systems operated under continuous infusion, an
Page 76
EC50/EC95 can be determined. For dose fractionation (pharmacodynamic) studies, drug is introduced
into the central reservoir in an infusion given over a period of time followed by a washout with drugfree medium and an equivalent volume of drug-containing medium removed from the central
reservoir. This produces a continuously diluted, iso-volumetric system. The ratio of the dilution rate to
the total system volume generates a true, first order rate of decline. Therefore, any drug half-life can
be simulated simply by changing the dilution rate. Drug can be administered on any schedule. As
this is done by computer-controlled pumps, any drug administration schedule can be attained in the
HFIM system. In so doing, the effect of differing schedules of administration on outcome can be
delineated. Since there is no immune system, the HFIM system measures the effect of compounds
on virus replication without the aid of products of T and B cells, yielding a true measure of the effect
of drug on virus replication.
Growth of virus stocks.
Flasks containing one day old confluent MDCK cell monolayers were washed 2X with virus growth
medium (MEM + 0.2% BSA + 2 µg/ml of TPCK-treated trypsin + pen/strep) and influenza viruses
obtained from the ATCC or other sources were diluted 1:1000 in virus growth medium and 0.5 ml of
diluted virus was added to the monolayer. After a 2 hr adsorption period at 35oC, 5% CO2, the
inoculum was removed, 5 ml of virus growth medium was added and the flask was incubated at 35oC,
5% CO2 until the monolayer exhibited substantial cytopathic effect. At that time, the medium
containing released virus, infected cells, and cell debris was collected, the infected cells and debris
were removed by centrifugation at 1500 rpm for 10 min, the supernatant containing released virus
was collected, dispensed into 1 ml aliquots, and frozen at -80oC until the virus titer was determined by
plaque assay and TCID50 assay as described in the experimental methods section. If this initial
attempt to grow virus stocks with a titer of at least 106 plaque forming units/ml failed, the procedure
was performed using 1:10 or 1:100 dilution of the virus until stocks containing 106 to 108 plaque
forming units per ml were produced. This procedure has been successful in producing virus stocks
from over 20 clinical isolates and laboratory strains of influenza A or B viruses.
EC50 values for amantadine and oseltamivir carboxylate for A/Albany/1/98, a wild type
influenza A virus clinical isolate.
To use the HFIM system one needs to know the EC50/EC95 value of a compound for the viruses
understudy. To that end, MDCK cell monolayers in 25 cm2 flasks were washed 2X with virus growth
medium and pretreated with various concentrations of amantadine or oseltamivir carboxylate in virus
growth medium for 1 hr at 35oC, 5% CO2. The virus growth medium was decanted and the cell
monolayers were infected with a low passage clinical isolate of influenza A virus (A/Albany/1/98) at a
multiplicity of infection (MOI) of 0.001 to 0.0001 pfu/cell. After a 2 hr incubation period at 35oC, 5%
CO2 the inoculum was removed and 5 ml of virus growth medium supplemented with various
concentrations of amantadine or oseltamivir carboxylate were added to appropriate flasks. The flasks
were incubated at 35oC, 5% CO2 for 24 to 48 hr. The medium was collected into 15 ml centrifuge
tubes, and virus-infected cells and debris were removed from the medium by centrifugation at 1500
rpm for 10 min. The supernatants were dispensed into 1 ml aliquots and frozen at -80oC for further
analysis. The effect of these antiviral drugs on virus replication was determined by plaque assay and
TCID50 assay on MDCK cell monolayers. The EC50 values for these drugs for this clinical isolate are
0.1±0.01 µg/ml for amantadine and 0.31±0.05 ng/ml for oseltamivir carboxylate. Since these results
are similar to published data from other clinical isolates for these compounds, these results suggest
that this isolate is susceptible to all of these antiviral drugs (77).
Growth of influenza A virus in the HFIM system.
The HFIM system has been used to determine the pharmacodynamically-linked variable for
bacteria, fungi and viruses (7-16). To determine if influenza virus can replicate in the HFIM system,
102 or 103 virus-infected MDCK cells were mixed with 108 uninfected MDCK cells and placed into two
HF systems. Virus growth medium was continuously infused through each HF unit for 168 hours with
Page 77
daily medium changes. Every 24 hr, the ECS of each HF unit was sampled and the number of
infectious viruses produced over time was determined by plaque assay (78).
Figure 2
Figure 3
EFFECT OF M OI ON INFLUENZA A VIRUS GROWTH IN HF
8
1 0 3 INFECTED CELLS + 1 0
UNINFECTED CELLS
EFFECT OF M OI ON INFLUENZA A VIRUS GROWTH IN HF
1 0 2 INFECTED CELLS + 1 08 UNINFECTED CELLS
900
PFU/ML X 1 0 ^3
4000
PFU/ML X 10^3
V I RUS Y I E L D AS S AY
3000
2000
1000
800
V I RUS Y I E L D AS S AY
700
600
500
400
300
200
100
0
0
0
34
68
102
136
HOURS
170
0
34
68
102
136
170
HOURS
The data in Figure 2 show that when the HF was infected with 102 virus-infected cells and 108
uninfected cells greater than 3 X 107 pfu/ml were produced at 48 hr after infection, the peak of virus
production, where as when the HF was infected with 103 infected cells and 108 uninfected cells
approximately 4 fold fewer infectious viruses were produced at 48 hr post infection, the peak of virus
production. Furthermore, initiation of the infection with 102 virus-infected cells increased the time
over which the HFIM system produced measurable infectious virus. In Figure 2 and Figure 3, the
decline in the number of plaque forming units per ml from the peak of virus production at 48 hr after
infection is due to the temperature sensitivity of the virus and the lose of uninfected MDCK cell targets
available to infect due to death of the cells. These studies demonstrate that the HFIM system can
mimic the time course of influenza virus infection in man (79). This study showed that the amount of
infectious virus in nasal washes peaked between 48 and 72 hr after infection of human volunteers
with wild-type influenza A/Hong Kong/123/77 (H1N1). On the basis of these results, we used an
inoculum of 102 virus-infected cells per 108 uninfected cells to determine the effect of drugs on virus
replication in the HFIM system.
One of the biggest challenges facing the effort to treat and contain influenza A infection is the
emergence of resistant viruses during treatment with amantadine and oseltamivir carboxylate (1-6).
For instance, it is well documented that resistance to amantadine arises rapidly during the course of
treatment due to mutations in the M2 protein. Given the widespread baseline resistance to
amantadine and the growing number of documented cases of resistance to oseltamivir carboxylate, it
is critical to develop experimental systems that can accurately model the selection of resistance
under drug pressure in humans (1-6).
To that end, we used the HFIM system to examine the effects of amantadine treatment on viral
load and resistance selection for A/Albany/1/98 (H3N2), an amantadine sensitive clinical isolate of
influenza virus. To initiate the infection, each HF unit was charged with a mixture of 108 uninfected
MDCK cells and 102 influenza virus-infected MDCK cells. This low MOI was used to allow the
infection to progress over seven days. Since the half-life of amantadine in humans is about 17.5 ± 4
hours, amantadine was delivered daily as continuous infusions of 0, 0.3, 0.8, 2 and 6 µg/ml.
Duplicate samples were removed from both the extracapillary space (ECS) and intracapillary space
(ICS) at 24, 48, 72, 96, 120, 144, and 168 hours and analyzed for drug concentrations by LC/MS/MS
(Figure 4) and viral load by plaque assay (Figure 5).
Page 78
Figure 4. Pk analysis of amantadine concentrations in the extracapillary (ECS) and
intracapillary (ICS) spaces of the HF units treated with 0.3, 0.8, 2 and 6 µg/ml amantadine.
0.3 ug/mL
0.8 ug/mL
1
0.3
Theoretical
0.25
ICS-1
0.2
ICS-2
0.15
ECS-1
0.1
ECS-2
0.05
ug/mL NPI-5291
ug/mL NPI-5291
0.35
0.8
Theoretical
0.6
ICS-1
ICS-2
0.4
ECS-1
0.2
ECS-2
0
0
0
50
100
150
200
0
50
Time (hr)
2 ug/mL
150
200
6 ug/mL
2.5
7
2
Theoretical
ICS-1
1.5
ICS-2
1
ECS-1
ECS-2
0.5
0
ug/mL NPI-5291
ug/mL NPI-5291
100
Time (hr)
6
Theoretical
5
ICS-1
4
ICS-2
3
ECS-1
2
ECS-2
1
0
0
50
100
150
200
Time (hr)
0
50
100
150
200
Time (hr)
The data in Figure 4 show that LC/MS/MS analysis of both ICS and ECS samples from the drug
arms showed that the drug concentrations in both the ECS and ICS matched targeted concentrations
throughout the course of the study, demonstrating that the hollow fiber could be used to reliably
deliver targeted doses of drugs under conditions of continuous infusion.
Figure 5. Dose range study of the effect of different doses of amantadine on the replication of
influenza A virus in MDCK cells in the HFIM system
The data, presented in Figure 5, show the effect of various concentrations of amantadine on the
yield of infectious virus in the HFIM system. The viral load quantitation demonstrated that the kinetics
of influenza virus infection in the HFIM system approximated the kinetics of infection in humans, with
a peak in viral load at 48 hours in the no drug control arm followed by loss of infectious virus. At 48 hr
post infection, all drug concentrations effectively suppressed virus replication compared to the nodrug control. However, at 72, 96 and 120 hr post infection, virus replication was no longer
suppressed as virus titers in the drug-treated arms were greater than those in the no drug control arm
Page 79
suggesting that amantadine resistant viruses were being produced at these times. This inverted “U”
curve is exactly what one would expect with increasing concentrations of drug where at low drug
concentrations there is little selection of resistant viruses whereas at high drug concentrations
amantadine-resistant mutations emerged (80). Interestingly, the peak viral titer at 96 hr was seen at
an intermediate concentration of amantadine (0.8 µg/mL), suggestive that there may be an optimal
concentration for the selection of drug resistant mutants. The decline in virus titers after each arm
peaked is due to the lack of new target (uninfected) cells and to the sensitivity of infectious virus to
35oC.
Once the data shown in Figure 5 was available, a plaque assay was performed on a separate
unthawed sample of each ECS sample in the shaded area in Figure 6. After incubation at 37oC, 5%
CO2 for 48 hr, the plates were stained with neutral red and 10 plaques were picked from each
sample, placed in AVL buffer containing carrier RNA and sent to TGen for gene sequencing.
Figure 6. Selection of drug resistant viruses grown in the presence of amantadine in the HFIM
system.
The data in Figure 7 show that sequencing the M2 gene at selected time points confirmed that
resistant mutants were being generated as a function of drug pressure. While no mutations in the M2
gene were identified in the no-drug control arm, mutations in the M2 gene appeared in all of the
amantadine arms within 48 to 72 hours of drug treatment. Most of the mutations were identical to
those that have been identified in the clinic and that result in amantadine resistance (e.g. V27A,
A30T, and S31N). Interestingly, we found that the type of mutation was strongly affected by the dose
of the drug. For instance, at the 0.3 µg/ml dose, 100% of the mutants identified were S31N; at 0.8
µg/ml, there was a mixture of V27A and A30T; at 2 µg/ml, 100% were I32S; and at 6 µg/ml, 100%
were V27A.
Page 80
Figure 7. Genotypes of the viruses at the selected drug concentrations
Viral Load (PFU/ml)
1000000.0
Control
0.3 ug/mL CI
0.8 ug/mL CI
2 ug/mL CI
6 ug/mL CI
800000.0
600000.0
400000.0
200000.0
0.0
0
24
48
72
96
120
144
168
Time
Arm
Control
0.3 ug/mL CI
0.8 ug/mL CI
2 ug/mL CI
6 ug/mL CI
Timepoint
48
120
48
72
96
120
48
72
96
120
48
72
96
120
48
72
96
120
Percent WT
100
100
100
80
70
80
100
100
40
60
90
80
60
70
100
90
100
70
Genotype
S31N
S31N
S31N
20% V27A; 40% A30T
20% V27A; 20% A30T
I32S
I32S
I32S
I32S
V27A
V27A
The data in Figure 8 show that there was a good correlation between viral load and the fraction of
mutants in the population. At the 96 hour time point, the order in terms of highest to lowest viral load
was 0.8>2.0>0.3>6 µg/ml. This order was also seen in terms of percentage of mutants in the
population, with the 0.8 µg/ml arm having the highest (60%) and the 6 µg/ml arm having the lowest
(0%). However, this was not the case at 120 hr where each drug concentration has about the same
fraction of mutants. Taken together, these data suggest that the HFIM is a good model for influenza
infection and resistance generation in humans. The HFIM has the advantage of being a highly
controlled system where multiple parameters can be directly and accurately measured.
Figure 8. Correlation between viral load and the fraction of mutants in the population
Viral Load (PFU/ml)
1000000.0
Control
0.3 ug/mL CI
0.8 ug/mL CI
2 ug/mL CI
6 ug/mL CI
800000.0
600000.0
400000.0
200000.0
0.0
0
24
48
72
96
120
144
168
Time
Page 81
Generation of oseltamivir carboxylate resistant influenza A virus in the HFIM system.
The HFIM system was used to determine the effect of oseltamivir carboxylate on the
replication of A/Sydney/5/97 in MDCK cells. Six hollow fibers were charged with102 virus-infected
MDCK cells and 108 uninfected MDCK cells in the presence of various concentrations of the Dtartrate salt of oseltamivir carboxylate. The HF units were continuously infused with various
concentrations of the compound for 144 hr. At various times post infection, each HF unit was
sampled for the presence of cell-free virus and the concentration of oseltamivir carboxylate. The
number of infectious viruses in the clarified supernatants was determined by plaque assay and the
drug concentrations were determined by LC/MS/MS. The effects of oseltamivir carboxylate on
influenza virus replication in this dose ranging study are presented in Figure 9.
Figure 9. Dose ranging study of D-tartrate of oseltamivir carboxylate for influenza A virus.
INFLUENZA A VIRUS-R2 9 2 VS OSEL-D-TARTRATE
DOSE RANGING STUDY
0
0. 625
ng/ ml
1. 25
ng/ ml
2. 5
ng/ ml
5
ng/ ml
10
ng/ ml
70
PFU/ML X 10^5
60
VI RUS YI ELD ASSAY
50
40
30
20
10
0
0
24
48
72
96
120
144
HOURS
At 48 hr after initiation of the infections, with the exception of the 5 ng/ml sample, all doses of
oseltamivir carboxylate inhibited virus replication to the same extent. However, by 72 hr, all of the HF
units were producing at least as much virus as the untreated HF unit. In fact, the HF unit treated with
10 ng/ml produced the most released virus. At later time points, the amount of released infectious
virus declined in all HF units due to the temperature sensitivity of influenza virus. PK analysis of the
drug concentrations in each hollow fiber unit at 48 and 72 hr showed that the drug concentrations
were at least 80% of the intended dose (data not shown). A plaque assay was performed on the ECS
samples from all six arms at 72 hr post infection. Ten plaque forming units were picked from each
arm, dissolved in AVL extraction buffer (Qiagen, Inc.) and sent to TGen for sequencing of the M2, HA
and NA genes by Sanger sequencing. The results showed that the virus isolated from the 10 ng/ml
arm had an S31N mutation in the M2 gene and multiple mutations in the HA and NA genes. Virus
isolated from all of the other arms, including the no drug control arm, had no M2 mutations, but had
the same multiple mutations in the HA and NA genes. None of these mutations in the NA gene were
known to be associated with oseltamivir carboxylate resistance. These results suggest that the
viruses produced in the presence of various concentrations of oseltamivir carboxylate produce
phenotypically resistant viruses, but not genotypically resistant viruses. The neuraminidase inhibition
assay (81) confirmed that these viruses that appeared resistant when grown in the presence of
oseltamivir carboxylate in MDCK cells were not resistant to oseltamivir carboxylate. Further study
will be required to determine if the HFIM system can produce oseltamivir carboxylate-resistant
influenza virus mutants.
Page 82
Page 83
Research Design and Methods:
The first specific aim of this grant application is to use the HFIM system to show that monotherapy
with amantadine or oseltamivir carboxylate will lead to the emergence of resistance in influenza virusinfected cells and to demonstrate that the resistant viruses produced in the HFIM system under these
conditions have the same mutations as those that emerge when people are treated with these drugs.
In the second specific aim, we will use the HFIM system to optimize the dose and schedule of
administration of current antiviral compounds effective against influenza viruses, delivered as
monotherapy, to minimize the emergence of resistance. Finally, in the third specific aim we will
determine the optimal dose and administration schedule of these anti-influenza virus drugs
administered in combination therapy to prevent virus infection and the emergence of resistance.
Specific Aim #1. Validate the HFIM as a model experimental system for influenza virus infection and
the generation of drug resistant mutants.
A. Introduction.
Treatment of patients infected with type A influenza viruses with amantadine/rimantadine is
known to lead to the rapid emergence of resistant viruses in the treated population (1-3).
Treatment of patients with influenza with the neuraminidase inhibitors, oseltamivir carboxylate
or zanamivir, usually does not lead to the emergence of resistant viruses (48). However,
recent data have shown that treatment of children with influenza with oseltamivir carboxylate
has led to the emergence of neuraminidase inhibitor-resistant influenza viruses (4-6). Data
presented in the preliminary results section of this grant application showed that treatment
of MDCK cells infected with a clinical isolate of influenza A virus in the HFIM system with
amantadine can lead to the emergence of resistant viruses within two to three days of initiation
of treatment. Phenotypic, but not genotypic, resistance was demonstrated when influenza
virus-infected MDCK cells were treated with the D-tartrate salt of oseltamivir carboxylate in the
HFIM system. The purpose of this portion of the grant application is to confirm these
observations with A/Albany/1/98 influenza virus and to expand that observation for amantadine
to additional influenza A viruses and for oseltamivir carboxylate to additional influenza A and B
viruses.
B. Experimental Design.
We will examine the effect of amantadine and oseltamivir carboxylate on the replication of wild
type rgA/Vietnam/1203/2004xA/PR/8/34 (a surrogate for avian H5N1 influenza virus),
A/Texas/36/91(H1N1), A/Sydney/5/97(H3N2), and A/Victoria/3/75(H3N2) in the HFIM system.
For comparison, we will also include our original clinical isolate, A/Albany/1/98(H3N2), to be
certain that our original observations are reproducible for amantadine and oseltamivir
carboxylate. Oseltamivir carboxylate will be tested against B/Lee/40 and B/Memphis/20/96
viruses. First, we will determine the EC50/EC95 values of amantadine HCl and oseltamivir
carboxylate for these viruses in monolayers of MDCK cells grown in flasks as described below
in Experimental Methods. Second, we will perform dose ranging studies with these viruses
in the HFIM system to test the system for emergence of resistance. To this end, 108 uninfected
MDCK cells will be mixed with 102 virus-infected cells. The cell mixtures will be added to six
50 ml centrifuge tubes, the cells pelleted by centrifugation at 1500 rpm for 5 min to remove the
cell growth medium, and the pelleted cells will be suspended in 30 ml of virus growth medium
supplemented with various concentrations of amantadine or oseltamivir carboxylate. The
suspended cells will be added to six different HF units and infused continuously with different
concentrations of amantadine or oseltamivir carboxylate for seven days. At various times post
infection, the medium containing virus-infected cells and released virus in the extracapillary
space (ECS) will be sampled from each port on the HF unit, the cells in each sample will be
removed by pelleting the sample at 1500 rpm for 10 min, the supernatant will be collected into
Research Design & Methods
Page 84
a fresh tube, mixed well, dispensed into several tubes, and frozen at –80oC until assayed for
infectious virus by plaque assay or TCID50 assay. A separate sample of the supernatant will
be mixed with AVL extraction buffer (Qiagen, Inc.) containing carrier RNA for analysis of
genomic equivalents by quantitative real-time PCR (see Experimental Methods below). Both
ECS and ICS will be sampled for determination of drug concentrations by LC/MS/MS to insure
that the intended drug concentrations were present during the virus infection. The results of the
plaque assay, the TCID50 assay and the qPCR assay will determine the effect of amantadine
or oseltamivir carboxylate on the production of virus. Once the plaque assay results have
been determined, one of the two frozen tubes containing those ECS samples will be thawed
and plated on MDCK cell monolayers to form plaques. Then 10 plaques will be picked from
each time point at each drug concentration, suspended in AVL buffer containing carrier RNA
and shipped to TGen for Sanger sequencing of the M2, HA and NA genes to determine the
genotype of the viruses produced at each time point under each drug concentration.
A similar approach will be taken for cells infected with B/Lee/40 or B/Memphis/20/96
except that only oseltamivir carboxylate will be used since amantadine does not inhibit
influenza B viruses.
C. Expected results.
Resistance will emerge under monotherapy. Amantadine resistant strains will have
mutations in the M2 gene (residues 26, 27, 30, 31); neuraminidase inhibitor resistant strains
will have mutations in the NA gene (residues 274 and 292) and/or HA genes (multiple
residues).
D. Potential problems.
It is often difficult to generate mutations in vitro in the neuraminidase genes in the presence
of neuraminidase inhibitors that resemble the mutations identified in the clinic. This may be
due to the use of MDCK cells which have inappropriate cell surface receptors for influenza
viruses. To address this potential problem, we will use a variety of other cell lines which more
closely reflect the surface characteristic of lung epithelial cells such as A549 pulmonary
alveolar epithelial cells (82), St Jude porcine lung (SJPL) cells (83), ST6Gal I cells (84) or
SIATI cells (85) which express cell surface receptors with more terminal sialic acid, and Mink
lung cells (86) to perform these dose ranging studies aimed at producing resistant viruses in
the HFIM system. It is expected that by using the appropriate cell lines, resistant strains will be
produced that more accurately reflect the neuraminidase inhibitor-resistant strains that have
been identified in the clinic.
E. Time frame.
If this grant application is funded we will be able to purchase 4 additional duet pumps for
the hollow fiber experiments thus doubling our capacity to perform these experiments. We plan
to perform dose ranging experiments for amantadine and oseltamivir carboxylate on
A/Victoria/3/75, A/Texas/36/91, rgA/Vietnam/1203/2004xA/PR/8/34, and A/Albany/1/98 and
oseltamivir carboxylate for B/Lee/40 and B/Memphis/20/96 in the HFIM system. Each
experiment will be repeated at least 1 time. One hollow fiber experiment takes approximately
two weeks to perform from setup to take down. Analysis of virus yield (plaque assay, TCID50
assay and real time quantitative PCR) will take an additional two weeks. Therefore, each
experiment, including a repeat, will take approximately 2 months. We plan to study at least the
four type A and two type B viruses listed above for two drugs for a total of 24 hollow fiber
experiments. Since we can study two viruses at a time for one drug or one virus for two drugs,
Specific Aim 1 will take at least one year to complete.
Specific Aim #2. Examine the effect of amantadine on influenza A viruses and oseltamivir
carboxylate on influenza A and B viruses in the HFIM with regard to dose range and dose
Page 85
fractionation. We will use the laboratory strains and clinical isolates used for Specific Aim 1. This will
allow delineation of the correct pharmacodynamically-linked variable for influenza viruses for the
amantadine and oseltamivir carboxylate when they are administered as monotherapy.
A. Introduction.
The correct delineation of the pharmacodynamically-linked variable is critically important.
As an example, Oseltamivir is currently administered twice daily. If AUC/EC50 ratio is the
correct PD-linked variable, it indicates that the agent could be administered once-daily (e.g.
150 mg daily versus 75 mg Q 12 h) and attain the same antiviral effect. This would have a
salutary impact on adherence. It would also provide clear guidance for dose sparing with the
addition of probenecid, as the probenecid regimen would only need to regenerate the AUC0-24
of the 75 mg Q12h regimen.
Delineation of the PD-linked variable requires three sequential experiments. In the first
experiment, the EC50 values of the neuraminidase inhibitor for the isolates under study are
determined. This is described below in “Detailed Methods”. The EC50 provides a starting
point for the second experiment, a Dose Ranging experiment. Here, the HFIM is employed.
The inhibitor under study (amantadine or oseltamivir carboxylate) is administered into the
central reservoir via computer-controlled pumps in a continuous infusion mode to produce a
dynamic continuous infusion, as antiviral-containing medium is removed from the efferent part
of the system and is replaced volume-for-volume with fresh antiviral-containing medium. The
range of continuous infusion concentrations is from the EC50 through 16 x EC50 in two-fold
steps. A no-treatment control is included totaling 6 independent regimens being evaluated
simultaneously. The system output from this experiment is virus yield (PFU/ml) and genomic
copies as determined by real time qPCR. In addition to viral load (PFU/ml and qPCR) for each
drug and each regimen the frequency and types of resistance mutations will be monitored. For
analysis, the output (PFU/ml or genomic copies) at the evaluation time of maximal effect will be
modeled with a sigmoid-Emax effect model where the system output is the dependent variable
and regimen drug concentration is the independent variable. This will have the form: pfu/ml =
Effectcontrol – (Effectmaxx(drug conc)H/((drug conc)H + EC50H)), where Effect control is the
estimated value of the system output in the absence of therapy, Effectmax is the maximal
reduction in the system output caused by therapy, EC50 is the drug concentration that results in
50% of the maximal response and H is Hill’s constant. These data will be fit through the use of
the ADAPT II package of programs of D’Argenio and Schumitzky (87). Where possible, inverse
observation variance will be employed as the best approximation to the homoscedastic
assumption. In general, we will use the parameter values to calculate the drug concentration
that results in approximately 80% of the maximal effect. This information will be used to design
the third experiment. This area (circa 80% of maximal effect) is chosen because this area
allows substantial effect to be observed, but also allows more effect to be seen as a function of
schedule of administration. In the third experiment, we perform a Dose Fractionation
evaluation. The drug concentration mediating approximately 80% of the maximal effect is
multiplied times 24 to provide the 24 hour Area Under the concentration-time Curve [AUC0-24]
(e.g. 1nM x 24 hrs = 24 nM*h = AUC0-24). In this experiment we employ six different regimens:
1) a no-treatment control, 2) the AUC0-24 administered in a continuous infusion mode, 3) the 2X
AUC0-24 administered in a continuous infusion mode, 4) the AUC0-24 administered once daily,
giving a higher peak concentration, but lower trough concentration, declining with the “correct”
human half life but getting the same AUC0-24 as the continuous infusion, 5) the AUC0-24
administered as two equal doses on an every 12 hour schedule that attain peak concentrations
circa half that seen in Q24 arm, with a slightly higher trough concentration, declining with the
“correct” human half life but with the AUC0-24 again being equivalent to the other regimens, 6)
the AUC0-24 administered as three equal doses on an every 8 hour schedule that attain peak
Page 86
concentrations circa one third that seen in the Q24 arm, with a slightly higher trough
concentration than in Q24, declining with the “correct” human half life but with the AUC0-24
again being equivalent to the other four regimens. If Peak Concentration /EC50 ratio is most
closely linked to the antiviral effect, then the once daily administration will produce the greatest
effect. If Time > EC50 (or trough concentration/EC50 ratio) is most closely linked to antiviral
effect, then continuous administration or every 8 hour dosing will give the greatest effect. If
AUC0-24/EC50 ratio is most closely linked to effect, then all active drug regimens will provide the
same effect, as all AUC0-24 exposures will be equivalent. We will perform endpoint evaluations
in triplicate. Outcomes will be tested for significant differences using Student’s t-test. Another
analysis will be performed on the third experiment. As we have a considerable amount of
endpoint data collected over time, we will fit the influenza model developed by Perelson’s lab
(79) to the data. Because this is an in vitro system, we will choose the “Target Cell-Limited
Model with Delayed Virus Production”, as there is no interferon generation in this model. The
Model System is displayed below:
(1)
(2)
(3)
(4)
dT/dt= -βTV
dI1/dt= βTV – kI1
dI2/dt = kI1 – δI2
dV/dt = pI2 – cV
where T is the number of uninfected target cells, β is the rate constant characterizing infection,
V is a measure of virus present (e.g. PFU/ml), I1 is the number of infected but not yet virally
producing target cells, k is the rate constant for transition to state I2, where I2 is the number of
productively-infected target cells, δ is the rate constant of death for productively-infected target
cells, p is the rate of increase of viral titer per cell and c is the viral clearance rate. As these
investigators (79) also did, we will examine the utility of a logistic carrying function as an
addition to the model. V0, although not displayed in the equations is the estimate of the viral
population size at baseline. We will incorporate neuraminidase concentration explicitly into the
model using a sigmoid-Emax function that affects p, the viral production rate, where p will be
replaced by equation (5): Viral Production Rate in the presence of drug =p x (1([drug]H/([drug]H+EC50H) which will allow changing neuraminidase concentrations over time to
enter the model. We will also add a differential equation to account for the changing NAI
concentration over time in the system and that will generate the estimate of NAI concentration
[NAI] for equation 5. This is shown below.
(6) d[drugAmount]/dt = R(1) – (SCL/V)x[drugAmount]; where R(1) is the input function, SCL is
the estimate of drug clearance and V is the estimate of volume of distribution. [drug Amount] is
the amount of drug in the system at a specific time. The drug concentration-time profile will be
documented by sampling 6 times over the course of the experiment. Drug concentrations will
be assayed by a sensitive and specific LC/MS/MS assay as indicated in Detailed Methods.
Again, the ADAPT II package of programs will be employed for the modeling process. Inverse
observation variance weighting will be employed. In addition to modeling each of the regimens
individually, we will also model all the regimens simultaneously in a population model
approach, as we have published previously in the antibacterial realm (13, 15). We will use the
Non-Parametric Adaptive Grid population modeling program of Leary, Jelliffe and Schumitzky
(88) as the modeling tool. Again, inverse observation variance weighting will be employed.
Bayesian estimates will be obtained for each regimen using the “population of 1” utility within
NPAG. These Bayesian estimates will be compared for fidelity with those obtained modeling
single regimens with ADAPT II. In the circumstance where AUC/EC50 is not the PD-linked
variable, we will only model individual regimens with ADAPT II.
C. Expected Results. This set of three experiments plus the modeling process will allow us to
identify the true PD-linked variable for amantadine and each of the neuraminidase inhibitors,
Page 87
with the important practical applications of once a day dosing. In addition, the modeling
process will allow good insight into the biology of the process of spread of infection and will
allow good estimates (because of the no-treatment control) of the time constant of spread
throughout the system and the latest point at which NAI introduction could be expected to
make a difference.
D. Anticipated Problems and Alternative Strategies. One potential problem that we have
observed occasionally with other agents examined in the HFIM (antibiotics, anti-HIV agents) is
binding of the compound to the plastic of the hollow fibers themselves. The neuraminidase
inhibitors that we have used do not bind to the hollow fiber units. Nevertheless, we routinely
check the drug concentrations entering the peripheral chamber, in the peripheral chamber
where the cells and viruses are exposed to drug, and exiting the peripheral chamber to insure
that the expected concentration of compound is present in the peripheral chamber. If there is a
problem we have found that pre-conditioning of the HFIM cartridge often obviates this difficulty.
In those circumstances where drug binds, it is possible to obtain cartridges with fibers of other
materials which are readily available.
E. Time Frame: This set of experiments will depend on data derived in Specific Aim 1. Since the
EC50/95 and the dose range studies for each virus/drug combination will have been performed
in Specific Aim 1, only the dose fractions studies are unique to this Specific Aim. The dose
fractionation studies will take approximately as long as the dose range studies but they can
begin as soon as some of the dose range studies are completed in Specific Aim 1. With that in
mind, Specific Aim 2 should take about two years some of which can be done at the same time
that Specific Aim 1 is being completed.
Specific Aim #3. Use the HFIM system to determine the PD-linked variables for antiviral drug
combinations involving amantadine and oseltamivir carboxylate.
A.
Introduction. Production of resistant isolates early in a pandemic could be catastrophic,
particularly if the isolates were relatively biofit, as it would markedly alter the probability of
effectively interrupting transmission with therapy. Consequently, it is vital to learn from
previous data developed in the HIV setting, where combinations of agents have been shown to
be crucial to suppress emergence of resistance for the long haul (89). Likewise, similar lessons
have been learned from tuberculosis chemotherapy (14, 16).
It is important to differentiate viral suppression from suppression of amplification of preexisting, less-susceptible subpopulations. Our group has pioneered the identification of
therapeutic regimens that suppress this resistant population amplification in standard bacteria
as well as in tuberculosis (13-16). In our latest publication, we were able to demonstrate that
two regimens that had the exact same kill rate for the total bacterial population differed in that
on day 4 of therapy, there was loss of control over the less susceptible population (90). We
came to recognize the implication that suppression is a monotonic function (the higher the
therapy intensity, the greater the suppression), while resistance suppression is not and the
resultant from increasing therapeutic exposure is a non-monotonic function that resembles an
inverted “U”, as we have recently published (80). This phenomenon has been demonstrated
with amantadine monotherapy (see Preliminary Results). It is the aim here to identify doses
and schedules that suppress the amplification of the less susceptible population and thereby
protect both drugs in the combination from resistance emergence.
To optimize chemotherapy for resistance suppression, we need to
be able to demonstrate resistance with monotherapy, which we have done for amantadine and
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oseltamivir carboxylate (see Preliminary Results). Certainly, emergence of resistance has
occurred during therapy (1-6).
B1. To identify combination regimens to suppress resistance, it is necessary to develop full
exposure–response curves for each agent (which we will have from Specific Aim #2) and to
test for emergence of resistance in each instance. Once we have a sigmoid-Emax effect model
written for each drug, we can employ optimal sampling approaches [D-optimality] (91) to
identify the three most information-laden parts of the exposure range. These will be performed
with the ADAPT II package of programs of D’Argenio and Schumitzky (87). Weighting will be
as inverse of the estimated variance of the observations to approximate the homoscedastic
assumption. This will lead to identification of a 16-regimen (3 single-agent arms for two drugs
[6 regimens] plus all possible combination regimens from each of the 3 optimally informative
regimens [3x3 or 9 regimens] plus a no-treatment control [total 16 regimens]) experiment that
will lead to the ability to model an effect surface as well as an emergence of resistance
surface. For the former, we will employ the Universal Response Surface Approach of Greco
(92) which employs Loewe additivity as the null reference model. We have employed this
approach in a murine model of Candida infection (93). For each regimen, we will employ the
qPCR assay (see Detailed Materials and Methods) to identify the total viral population and
the effect of each regimen on it over time. We will also delineate the presence and amount of
resistant mutant populations over time and how each regimen has an effect on both total and
less-susceptible (resistant) populations over time.
B2. To robustly identify regimens to suppress the mutants, we will employ a mixture model, in
which a group of 5 parallel inhomogeneous differential equations will simultaneously describe
the time course of each drug over time (2 equations), and the impact of the regimens on the
susceptible population (1 equation), the rimantadine-resistant population (1 equation) and the
oseltamivir-resistant population (1 equation). The system outputs will be the measured drug
concentrations (measured by LC-MS/MS by GLP in our laboratory), the total viral burden, the
rimantadine-resistant population and the oseltamivir-resistant population. The effect model will
employ the Greco model for combination therapy. However, each set of parameter values will
be independent for each population. Since the same regimen will have differential effects on
each population, the mixture model (summation of the effects from each population) will allow
a 3-dimensional inverted “U”-type surface to be generated.
New Differential Equations to Describe Combination Suppression of Resistance. The first two
differential equations will be those required to describe the concentration-time profiles for each of the
drugs in the combination. This will require 4 parameters, as the pumps will be set to describe a
monoexponential decline profile. The parameters are Volume (V – Liters) for drug1 (V1) and Drug2
(V2) and Clearance for the 2 drugs, CL1 and CL2. These differential equations are displayed below:
(1)
dX1/dT = R1 – (CL1/V1) x X1; where R1 is the piecewise input function for Drug1 and X1 is the
Drug1 amount in the central compartment.
(2)
dX2/dT = R2 – (CL2/V2) x X2; where R2 is the piecewise input function for Drug2 and X2 is the
Drug2 amount in the central compartment.
The next two differential equations describe the growth and death of the drug susceptible
populations for Drug1 and Drug2. Differently from previous modeling we have done, the kill function
will be the equation of the Universal Response Surface Approach (URSA) of Greco. This approach
has been employed by our lab in the past for cell kill analysis. It is appropriate to employ this
combination approach here, as the differential equation describes the growth (front part of the
equation) and kill (back part) of the susceptible population.
(3)
dNS/dT = Kgmax-S x NS x E – Kkmax-S x MS x NS; where NS is the number of organisms
susceptible to Drug1 and Drug2, Kgmax-S is the maximal growth rate constant for the population
sensitive to both Drug1 and Drug2, E is a logistic carrying function, which allows the population
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to achieve stationary phase, Kkmax-S is the maximal kill rate constant for Drug1 and Drug2 in
combination for the susceptible population and MS incorporates the URSA equation of Greco
for the Drug1 and Drug2-Susceptible population. Because the Greco equation is not in closed
form, the parameters must be estimated via a bi-directional root finder. This has been
implemented in the BigNPAG program, along with code to allow simultaneous handling of two
agents by Van Guilder, Schumitzky and Jelliffe.
(4)
dNR1/dT = Kgmax-R1 x NR1 x E – Kkmax-R1 x MR1 x NR1; where NR1 is the number of organisms
resistant to Drug1 and sensitive to Drug2, Kgmax-R1 is the maximal growth rate constant for the
Drug1-resistant organisms, E is a logistic carrying function, which allows the population to
achieve stationary phase, Kkmax-R1 is the maximal kill rate constant for Drug1 and Drug2 in
combination for the Drug1-resistant population and MR1 incorporates the URSA equation of
Greco for the Drug1-resistant, Drug2-sensitive population.
(5)
dNR2/dT = Kgmax-R2 x NR2 x E – Kkmax-R2 x MR2 x NR2; where NR2 is the number of organisms
resistant to Drug2 and sensitive to Drug1, Kgmax-R2 is the maximal growth rate constant for the
Drug2-resistant organisms, E is a logistic carrying function, which allows the population to
achieve stationary phase, Kkmax-R2 is the maximal kill rate constant for Drug1 and Drug2 in
combination for the Drug2-resistant population and MR2 incorporates the URSA equation of
Greco for the Drug2-resistant, Drug1-sensitive population.
Normally, there would be a requirement for a sixth differential equation, describing the population
resistant to both drug1 AND Drug2. However, we have not found such strains so far in our preliminary
experiments. We will, of course, check for such isolates and add the sixth simultaneous
inhomogeneous differential equation to describe this population if they exist.
E = (1-(NS + NR1 + NR2)/ POPMAX)
M = (1 - Fractional Effect) as derived from Greco URSA model; in this circumstance, Econ is set to 1.0.
For the Greco URSA model:
[Drug1]
[Drug2]
1= ------------------------------ + -----------------------------E50D1ψ x (E/Econ – E)1/HD1ψ
E50D2ψ x (E/Econ – E)1/HD2ψ
+
αψ x [Drug1] x [Drug2]
-------------------------------------------------------E50D1ψ x E50D2ψ x (E/Econ – E)(1/2HD1ψ+1/2HD2ψ)
Where ψ refers to the different organism populations (ψ=1 is sensitive to both Drugs; ψ=2 is resistant
to Drug1 and sensitive to Drug2; ψ=3 is sensitive to Drug1 but resistant to Drug2). [Drug1] is the
concentration of drug1; [Drug2] is the concentration of Drug2; E50D1ψ is the concentration for which
effect is half maximal for Drug1 for population ψ; E50D2ψ is the concentration for which effect is half
maximal for Drug2 for population ψ; HD1ψ and HD2ψ are Hill’s constants for Drug1 and Drug2,
respectively for the different organism populations ψ; Econ is the effect for the control (set to 1.0 here);
α is the interaction parameter; E is the calculated fractional effect.
System Outputs:
The system outputs associated with differential equations 1 and 2 are the measured Drug1 and
Drug2 concentrations in the central compartment (Output 1 = X1/V1; Output 2 = X2/V2).
System Output 3 = Total Organism Number = Population sensitive to Drug1 and Drug2 + population
resistant to Drug1, sensitive to Drug2 + population resistant to both Drug1 and Drug2 (as above, the
last population has not yet been observed.
System Output 4 = Population resistant to Drug1 and sensitive to Drug2.
System Output 5 = Population resistant to Drug2 and sensitive to Drug1.
System Output 6 = Population resistant to Drug1 and to Drug2 (if needed).
This approach to modeling combination chemotherapy with a mixture model and the URSA
equation will allow the “inverted U” mountain type of response to be modeled. This is, to our
knowledge, a completely innovative approach to modeling resistance emergence and not cell kill
and is wholly novel. Because of the fully parametric nature of this approach, it still allows Monte
Carlo simulation to be conducted and allows powerful bridging to man.
B3. The full model will be fit to the data employing the population modeling program Big NPAG (NonResearch Design & Methods
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Parametric Adaptive Grid) program of Leary, Jelliffe, Van Guilder and Schumitzky (88). As above,
weighting will be as the inverse of the estimated variance to approximate the homoscedastic
assumption. Bayesian parameter estimates will be obtained for each regimen employing the
“population of one” utility within BigNPAG. The Bayesian estimates will be employed to perform
simulations with the ADAPT II package to identify regimens predicted to allow resistant mutant
amplification and suppression of all mutants. Because of the large dimensionality of the problem,
we will also employ the fully parametric modeler in the new ADAPT V package of programs. The nonparametric modeler may have issues with parameter estimate precision because of the system’s
dimensionality unless the number of grid points gets very large, where we may also have
computational tractability problems. The fully parametric approach obviates this difficulty, but run
times may also be a problem. Having the non-parametric parameter estimates available to guide the
search space will be helpful in this regard, even if the original parameter estimates from the nonparametric run do not have optimal precision. It is also important to recognize that the fully parametric
modeler requires an explicit choice for distributions, which is not the case for the non-parametric
modeler, and one of its major strengths. The optimal information will be garnered from the
combinations of both parametric and non-parametric analyses.
B4. Once we have identified regimens that are predicted to amplify or suppress mutants, we will run a
prospective validation study to directly demonstrate that the regimens intended to amplify or suppress
the mutants perform as was predicted. The correlation between predicted and observed values for
the different population will be tested for statistical significance and will be the final proof of the
validation experiment.
C.
Expected Results. We expect to be able to identify regimens that will allow amplification of
resistant mutants to one drug, the other drug and both. We have a lot of experience employing
mixture models for this purpose (13-16). The Bayesian parameter estimates allow direct regimen
identification to attain the goal of resistance suppression and amplification.
D.
Anticipated Problems and Alternative Strategies. We have identified (see Preliminary Data)
a regimen that allows resistance emergence for an adamantine. There are several problems that may
arise: 1) We cannot identify a regimen that allows resistant mutant amplification for oseltamivir. While
possible, this is unlikely, as we have observed some breakthrough growth in some of our
neuraminidase inhibitor experiments at lower drug exposures. Further, it is possible to increase the
number of target cells or decrease the multiplicity of infection to provide the virus with more rounds of
replication to increase the probability of amplifying the resistant population. 2) We cannot identify a
two drug combination regimen to suppress resistance to both drugs. If this is, indeed the case, even
after increasing the drug doses to exposure above those currently licensed (something done easily in
the hollow fiber model and a strength of the in vitro system), then this will be an incredibly important
finding and will have direct impact on therapy.
Most of this Specific Aim revolves around allowing simultaneous modeling of two different agents
in a population pharmacokinetic approach. Our collaborators (Drs. Schumitzky, Van Guilder and
Jelliffe on the non-parametric side and Dr. D’Argenio on the parametric side) have already solved the
most serious problem of handling two drugs simultaneously in the population pharmacokinetic
situation. The addition of a bi-directional root finder should not pose serious problems, as the code is
“off the shelf”, allowing solution of the Greco URSA equation, which is not in closed form. Both nonparametric and parametric approaches for combination therapy modeling are being pursued for a
simple reason. The number of system parameters grows relatively large for the combination case.
Non-parametric population modeling may suffer from a precision problem unless the number of initial
grid points gets very large, which may cause exceptionally long run times. The parametric approach
does not suffer from this problem, but suffers other theoretical problems of distributional assumptions.
In the worst case, the non-parametric modeler will provide reasonable ranges of point estimates,
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even if imprecise with the number of starting grid points employed to allow reasonable run times.
These will guide the parametric modeler for the final parameter value estimation.
E.
Time Frame. As data from Specific Aim 2 accumulates, we will begin to address combination
chemotherapy in the hollow fiber system. This portion of the grant application will take approximately
two and one half years.
Detailed Materials and Methods
Reagents available for this Influenza Virus Study.
Viruses
PR/8/34 (H1N1)
A/WS/33 (H1N1)
A/NWS/33 (H1N1)
A/Hong Kong/8/68
B/Lee/40
A/Albany/1/98 (H3N2) WT
A/Victoria/3/75 (H3N2) (WT)
A/Victoria/3/75 (H3N2) R2A
A/Sydeny/5/97 (H3N2) R929
B/Memphis/20/96 (WT)
B/Memphis/20/96 Lys152
A/Texas/36/91 H1N1 (WT)
A/Texas/36/91 H1N1 (35.9 NAI reistant)
rgA/Vietnam/1203/2004xA/PR/8/34
Source
ATCC
ATCC
ATCC
ATCC
ATCC
Albany Medical Center
Dr. Robert Sidwell – Utah State Univ.
Dr. Robert Sidwell – Utah State Univ.
Rr. Nick Cammack – Roche Pharma
Dr. Larisa V Gubareva - CDC
Dr. Larisa V Gubareva – CDC
Dr. Richard Webby – St. Jude Children’s
Research Hospital
All of these viruses can be studied under the BSL-2 conditions that are extant in Dr. McSharry’s
laboratory at the Ordway Research Institute. There are two separate BSL-2 labs each with a
Bioguard laminar flow hood, 4 CO2 incubators, microscopes, and automatic pipetters. One of the
labs has a complete set of hollow fiber pumps and syringe pumps to perform pharmacodynamic
studies. If the grant is funded, then we will purchase 4 duet pumps for hollow fiber studies and then
both labs will be complete. Personnel wear gowns and gloves while in the BSL-2 labs and leave them
in the BSL-2 lab when finished. None of the influenza viruses that we use are select agents. To
further protect the personnel, they are offered a yearly influenza vaccination in the fall and, do far, all
personnel have been vaccinated against influenza A and influenza B.
Antiviral compounds on site
Neuraminidase inhibitors
D-tartrate salt of Oseltamivir carboxylate Oseltamivir carboxylate
Zanamivir Peramivir –
a gift of 0.5 grams from Roche Pharmaceuticals, Inc
a gift of 1 gram from Adamas Pharmaceuticals, Inc
a gift of 5 grams from GlaxoSmithKline
a gift of 0.1 gram Johnson and Johnson
Ion Channel Blockers
Amantadine HCl – 25 grams purchased from Sigma Chemical Company.
Cells
MDCK cells (ATCC CCL-34) were obtained from the American Type Culture Collection and
maintained in minimal essential medium (MEM) supplemented with 10% fetal bovine serum (FBS),
1% sodium pyruvate, 1% MEM nonessential amino acids, 1% penicillin-streptomycin solution, and 1%
Page 92
glutamine. The cells were grown as monolayers in 75 cm2 or 25 cm2 cell culture flasks (Corning) or in
6 well tissue culture plates (Corning) or 96 well flat bottom tissue culture plates at 37oC, 5% CO2.
SJPL cells obtained from Dr. Webster at St. Jude’s Children’s Research Hospital, Mink lung cells
obtained from Dr. Hinshaw at U of Wisconsin, and A549 alveolar lung epithelial cells obtained from
the ATCC, are also available to this study. ST6Gal 1 cells were obtained from Dr. Kawaoka at the U
of Wisconsin. These cells were grown in MEM supplemented with 5 to 10% FBS and pen/strep.
ST6Gal1 cells were grown in the presence of puromycin to maintain the plasmid.
Viruses
The viruses listed above are available to this study. To prepare stocks of virus, each virus was
diluted in virus growth medium (MEM supplemented with 0.2% bovine serum albumin (BSA) (Sigma
Chemical Co, St. Louis, MO), 2 μg/ml of TPCK-trypsin (Sigma Chemical Co., St. Louis, MO) and
penicillin/ streptomycin) so that 10 to 20 plaque forming units would be present in 0.5 ml of medium.
The diluted virus (0.5 ml) was added to MDCK cell monolayers in 6 well tissue culture plates in the
presence of virus growth medium. After a 2 hr adsorption period at 35oC, 5% CO2 a 0.6% agar
overlay containing virus growth medium and 1% DEAE-dextran was added to each well. After 48 to
72 hr of incubation at 35oC, 5% CO2, the wells were overlayed with 0.5% agar in MEM containing
neutral red. After 6 hr of incubation, well separated plaques were picked and suspended in virus
growth medium to be used to infect MDCK cell monolayers. This process was repeated two more
times to make sure that the plaque morphology bred true. Viruses originating from these plaques
were used to produce virus stocks for use in the experiments described in this grant application. To
produce virus stocks, MDCK cells were infected with virus diluted in virus growth medium at a
multiplicity of infection of 0.001 to 0.0001 pfu/cell. After 48 hr of incubation at 35oC and 5% CO2, the
medium containing released virus was removed, clarified at 1500 rpm for 5 min to remove virusinfected cells and cell debris, dispensed into 1 ml samples, and frozen at – 80oC. The number of
infectious units in each virus stock was determined by plaque assay on MDCK cell monolayers.
Clinical isolates are typed as A or B by treating virus-infected MDCK cells with fluorochrome-labeled
monoclonal antibodies to the type specific nucleocapsid antigen (Chemicon International, Inc) and
type A viruses are subtyped by treating virus-infected MDCK cells with fluorochrome-labeled
monoclonal antibodies specific for H1 or H3 antigens (Chemicon International, Inc).
Antiviral drug
Stocks of drugs were prepared by suspending the powder in water to yield a final concentration of
10 mg/ml, filter sterilized through a sterile 0.2 micron filter, and stored at -80oC. Fresh stocks of drugs
were made every three months.
EC50/95 determination
To determine the EC50 /EC95 values for amantadine and oseltamivir carboxylate for influenza
viruses, MDCK cell monolayers were prepared in 25 cm2 plastic tissue culture flasks. The following
day, the monolayers were pretreated with drug in virus growth medium for 1 hr at 35oC, 5% CO2, the
drug was removed and influenza A virus, diluted in virus growth medium to yield a multiplicity of
infection (MOI) of 0.001 to 0.0001 pfu/cell, was added to monolayers of MDCK cells. After a 2 hr
incubation period at 35oC, the inoculum was removed and 5 ml of virus growth medium supplemented
with various concentrations of amantadine or oseltamivir carboxylate were added to the appropriate
flasks. The monolayers were incubated at 35oC under an atmosphere of 5% CO2 for 24 to 48 hr.
The medium containing released virus was collected, clarified by centrifugation at 1500 rpm for 5
minutes to remove floating cells and cell debris, the clarified medium was divided into 1 ml samples
and frozen at –80oC. The effect of different concentration of amantadine or neuraminidase inhibitors
on the yield of influenza A virus was determined by plaque assay (78).
Virus yield assay (plaque assay)
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Ten fold dilutions (10-1 to 10-8) of samples of influenza A virus from EC50 assays or the HF
experiments were made in virus growth medium. MDCK cell monolayers were washed 2X with virus
growth medium and 0.5 ml of each virus dilution was placed on MDCK cell monolayers in 6 well
plates. After a 2 hr adsorption period at 35oC under an atmosphere of 5% CO2, the inoculum was
removed, an 0.6% agar overlay containing MEM, 0.2% BSA, 2 μg/ml TPCK-trypsin, 0.5% DEAEdextran and penicillin/streptomycin was added to each well, and the plates were incubated at 35oC,
5% CO2 for 48 to 72 hr. Then the monolayers were stained with neutral red and the plaques were
counted with the naked eye.
Hollow Fiber Infection Model (HFIM) System. (See Figure 1)
Method Development and Analysis by LC/MS/MS.
The analytical laboratory at the Ordway Research Institute is run by Dr. Robert Kulawy using GLP
conditions. Pk analysis of amantadine and oseltamivir carboxylate in virus growth medium (MEM,
0.2% BSA, 10 ug/ml TPCK-treated trypsin, and Pen/Strep solution) will be performed as follows:
each compound will be placed in an appropriate solution and infused into our LC/MS/MS system.
Parent and product ion spectra will be acquired and the instrument parameters optimized for each
individual compound. Available literature will be reviewed for any current available HPLC or
LC/MS/MS methods (94-96). Chromatographic methods for each compound will be developed or
modified from existing procedure using the standard solutions. Possible internal standards for the
assays will also be examined (for example, memantine for amantadine).
It is anticipated that two extraction methods will be examined for each compound. A liquid-liquid
extraction and a solid-phase extraction method of the media will be assessed for recovery. The
extraction methods will be modified as needed to provide the cleanest extract and highest recovery.
Once an extraction method is established, the linearity of the assay as well as the precision and
accuracy of method will be determined. A standard curve in duplicate and quality control samples in
replicates of three at three levels (low, medium, and high levels in the range of the standard curve)
will be analyzed. If additional compounds will be administered for combination therapy, interference
checks will be performed to verify the specificity of the assay.
Quantative real-time PCR (qPCR) analysis and sequencing of influenza viruses generated in
the HFIM system. (TGen collaboration)
Preliminary Studies
The TGen Collaborators have recently developed and validated a quantitative real-time PCR
(qPCR) assay for the quantitation of influenza A virus in research and clinical samples. The
quantitative real-time PCR assay standards were constructed by ligation of a PCR amplified product
of segment 7 (matrix gene) into a plasmid vector. The plasmid DNA was amplified in E. coli strain
TOP10 and purified using a Qiagen plasmid purification kit. The plasmid insert sequence was
confirmed with sequencing in both directions using six different primers. The concentration and purity
of the plasmid DNA was calculated by measuring the OD260/280. These data were used to calculate
target copy number in the standard. Additionally, the standards were run on a 16S real-time Taqman
assay to check for contaminating DNA and were found to have insignificant levels. Our standard
curves were created using 10-fold dilutions from 109 to 101 target copies/reaction (9 logs) and had a
typical R2 of > 0.99. The assay is able to quantify accurately down to 100 target copies/run. This is
equal to 7 x 104 target copies/ml, or roughly 70 pfu/ml (note: It has been previously reported that
approximately 103 copies of virus per ml should correspond to 7 TCID50/ml or pfu/ml (97).). Below
the 100 target copies/reaction level, our qPCR assay will detect virus RNA, but the accuracy of viral
load estimation is not yet determined in that range.
Validation studies
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To establish specificity of the assay, we conducted both in silico and in vitro analyses. In silico,
we ran a BLASTn search of the assay primers and probe against all DNA and RNA sequences in
GenBank and observed homology only with published influenza A strains. We further validated the
specificity in vitro and found the primers were specific to common influenza A strains (H3N2, H1N1,
and H5N1), while we found no cross-reactivity with influenza B strains (unpublished data). To
establish the accuracy and dynamic range of the TGen qPCR assay, we tested 16 blinded influenza
A (H3N2) samples, provided by Adamas Pharmaceutical Inc., containing various dilution quantities of
influenza virus, based on PFU counts. Two runs were conducted for all samples, each in triplicate.
The qPCR assay appropriately and consistently quantified the 10-fold serial dilutions. Our results
found a consistent 103-fold increase in the qPCR results over the known quantity amount of the
blinded samples, as determined by PFU, again consistent with previous reports (96). This suggests
an increase in sensitivity in the qPCR assay over virus titration assays (see “Comparison of PFU,
TCID50 and qPCR Assays” below). Using dilution standards in all experiments, the assay
consistently measures viral copy numbers over 9 logs. The assay has a lower quantitation level of
102 copies per reaction, equivalent to 104 pfu per ml, although the assay can detect viral RNA at
much lower levels.
Comparison of CCID50 and qPCR Assays
We conducted a series of cross-validation studies of the TGen qPCR assay with viral titer
reduction assays (CCID50). The experiments consistently resulted in a standard 103 - 104 fold
increase in sensitivity between qPCR and both PFU and titer reduction assays (unpublished data).
Figure 10. Comparison of CCID50 and qPCR Assays
QPCR vs. CCID50
Viral quantitaion in the presence of antiviral compounds
12
10
8
CCID50
6
qPCR
4
2
0
0.01
0.1
1
10
100
a ma n t a d in e [ a mt ] u M
These results were also replicated in a series of experiments comparing the ability of these
assays to monitor viral load changes in the presence of antiviral compounds (Figure 10). These
studies provide evidence that the TGen real time qPCR assay has a dynamic range of over several
logs (nine), is more sensitive than viral titer assays, and is useful for monitoring viral load changes in
antiviral drug studies.
Quantitive real-time PCR (qPCR) analysis
The qPCR assays will be used by the TGen collaborator to provide beginning point and end point
quantitation of viral load in all samples in the study. These assays are run on 384-well plates on the
AB 7900 HT Real-Time PCR Machine (Applied Biosystems). All quantitation experiments will be run
under GLP conditions with numerous quality control conditions in place. Each sample and standard
is run in triplicate in each 384-well plate. We have determined that we get consistent results when we
use the following subset of dilutions: 109, 105, 103 and 102 to create our standard curve on each 384well plate. Samples with significant value variation (greater than 1 Ct) between replicates will be reassayed. Plates with standard curves that have an R2 less than 0.95 will be re-assayed to ensure
accurate quantitation.
Page 95
Influenza RNA Sequencing
To identify the development of mutations related to selective pressure from the use of antiviral
drugs, we will sequence the M2, HA, and NA genes for all viral populations, including experiment
beginning and endpoint populations. Sequencing primers for these genes have been developed,
using standard techniques, based off of 2000+ sequences published on GenBank, to ensure capture
of entire regions of interest across H3N2 and H1N1. These regions contain all previously identified
mutations that have been related to antiviral resistance: M2: residues 26-34 (98); NA: residues 118294 (99); and HA: residues 119-227(100). As these areas contain the known functional and/or
catalyic residues for their respective genes, it is likely that previously undescribed resistance-related
mutations, if present in this study, will be identified through sequencing with the above primers (100).
Page 96
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3. Bright, RA, DK Shay, B Shu, NJ Cox, and AI Klimov. 2006. Adamantane resistance among
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4. De Jong, MD, TT Thanh, TH Khanh, VM Hien, GJD Smith, NV Chau, B Van Cam, PT Qui,
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