Integrating Predictive and Experimental Tools to Capture

Integrating Predictive and Experimental Tools
to Capture Degradation Knowledge in the Early
Development Phase of a Drug’s Lifetime
Tasneem H Patwa
13 August 2013
Outline
• The Drug Development Pipeline
• The Degradation Workflow – Overview
• Predictive Degradation
• Forced Degradation and Method Development – Drug
Substance
• ASAP- Drug Product
• Degradation Milestones and Data Archiving
• Metrics to date
• Examples
• Summary/Conclusion
Drug Development Timeline
Space supported
by research
analytical group
(second species
ETS to POC)
Primary Activities in Early Stages of Drug
Development
• Route development analytical support – process related
impurity potential and initial drug substance stability
assessment
• Formulation development analytical support – ASAP for
formulation compatibility and drug product stability
• Potential degradation products highlighted in this stage
• Initial GMP manufacture to support regulatory toxicology
studies and FIH (IND submission).
• Further large scale manufactures to support studies
leading up to POC
Where does drug degradation knowledge fall and how much time
can/should be dedicated to it?
The Degradation Workflow
Prior to a harmonized degradation workflow:
•  Degradation process was not harmonized
•  Significant variation among projects
•  Inefficient model in place especially during project
transfers
Team put together to identify major milestones in
degradation knowledge over lifetime of drug development
with a goal to implement a workflow that will ensure
consistent practices from project to project
Compound Enters
Development
Predict Degradants Using
in-Silico Tools
Design and Execute
Forced Degradation
Experiments
Define KPSS
and
Optimize Method
Prepare Degradation
Report
DEG GATE MEETING:
Develop Stability Strategy
ASAP for Initial
Formulation Selection
POC
DEG GATE MEETING:
R to D Transition
Assess degradants for Mutagenicity
ETS Purity Method Screen
Why Predictive Degradation?
• Understand degradation and consequently design better forced
degradation experiments
• Correlate predicted degradant molecular weights with unknown peaks
in real samples (tentative identification in early space)
• Influence stability of API
• Predict excipient compatibility
• Explain unusual behavior
Predictive Degradation
• Degradant prediction achieved using Zeneth:
•  LHASA Inc. https://www.lhasalimited.org/
•  Non profit organization
•  Experts in developing software tools for the pharmaceutical
industry.
•  Maintains DEREK used by pharmaceutical companies and the
FDA to determine the potential toxicity of new chemical
entities
•  Maintains METEOR used by pharmaceutical companies and
the FDA to determine the potential metabolites of new
chemical entities
• Sets of conditions assessed (pH 1 and 13 in water, oxygen, metal,
radical initiator, peroxide and light conditions at 80oC)
• Software that can be improved by entering new data
Query Compound
Knowledge base
of
transformations
Literature
Pharma
Knowledge
Expertise
Heat
pH
Hydrolysis
Molecular O2
Peroxides
Radical Initiator
Metals (Fe(III) and Cu(II))
Editor
Can incorporate new
transformations into
knowledge base
Result
Likelihood
Chemical formula
Exact Mass
Degradation pathway
One can predict interactions of query compound with excipients and
impurities in excipients as well as contaminants and potential for
dimerization
Reaction of query compound with acetaldehyde
What zeneth can help with and what it cannot do
• Designing useful forced degradation experiments
• Determining likelihood of degradant formation
• Shelf-life and use periods
• Degradation rates
• Accelerated degradation studies
Compound Enters
Development
Method Screens
Predict Degradants Using
in-Silico Tools
Design and Execute
Forced Degradation
Experiments
Define KPSS
and
Optimize Method
Prepare Degradation
Report
DEG GATE MEETING:
Develop Stability Strategy
ASAP for Initial
Formulation Selection
POC
DEG GATE MEETING:
R to D Transition
Assess degradants for Mutagenicity
ETS Purity Method Screen
• 4 method UPLC screen that covers
multiple stationary phases (HSS T3,
BEH C8 and RP C18) and pHs
(Perchloric Acid, MSA and
Bicarbonate).
• Integrated with ReactArray platforms
for degradation experiments
Forced Degradation Experiments
And
Method Optimization
• Acid, base, Oxidative conditions probed in solution
• Temperature, humidity and photostability probed in solid state
• 15 to 20% degradation sought to achieve degradation knowledge space and
observe realistic degradants (minimize secondary and tertiary degradants)
Condition
Reagant
Acid Hydrolysis
0.1M HCl (pH~1)
Base Hydrolysis
0.8 M KOH (pH~11)
Oxidative – Radical
Chain Initiators
ACVA at 30 mole% of API at
60oC
Oxidative – Radical
Hydrogen Peroxide
0.3% H2O2 at room
temperature
Solid State – Thermal
and Humidity
70oC/75%RH – 1 wk
Photostability
2X ICH (1.2 Million lux hrs)
• Solid state forced deg – more likely formed degradants seen
• Solution state forced deg – less likely formed degradants may be
seen; great for generating samples that challenge method during
development. Only major degradants pursued further.
• At least 5-10% degradation targeted. Samples not stressed for more
than 3 days in early space.
Drug Substance
Drug Product
% of largest
degradant
25
10
% of total
degradation
10
10
Drug Product Stability
• Common formulations for early phase clinical studies:
• Extemporaneous preparations: oral solutions/suspensions –
typically prepared and used quickly hence only in-use
stability needed
• Powder in a capsule – stability is basically the drug substance
stability
• Material Sparing Tablets – Some preliminary work needs to
be done to ensure excipients used will not interfere with
active ingredient
• Primary focus of the stability work done on MSTs is prediction of
shelf life, assessment to narrow down to most stable formulation,
some understanding about effect of water and temperature on
formulation.
• Zeneth has the capability to predict interactions of drug
substance with excipients and their impurities – can help guide
formulation development and design focused experiments around
understanding the chosen formulation’s stability
ASAP – Accelerated Stability Assessment Protocol
− Ea 1
ln (k) = ln (A') +
+ B ∗ RH
R T
• 
• 
• 
Two terms:
–  Activation Energy (Ea / R)
–  Humidity Factor (B)
Ea
–  10 – 45 kcal/mol
–  Lower = faster degradation
B
–  -0.01 – 0.1
–  Moderate effect = 0.04 –
0.06
Modeling with ASAP
Sample
Pk 1
Pk 2
Pk 3
Pk 4
Pk 5
5C/5% RH control
0.08%
0.08%
0.15%
99.54%
0.15%
50C/75% RH 14
days
0.18%
0.08%
0.15%
99.45%
0.14%
60C/40%RH 14
days
0.33%
0.08%
0.15%
99.30%
0.14%
70C/5%RH 14 days
0.16%
0.08%
0.14%
99.48%
0.14%
Condition
#
Temp
(oC)
RH
(%)
T (days)
Control
5
5
T0
(initial)
1
50
75
14
2
60
40
14
70C/75%RH 1 day
0.36%
0.08%
0.15%
99.27%
0.13%
3
70
5
14
4
70
75
1
80C/40% RH 2 days
0.34%
0.07%
0.15%
99.30%
0.14%
5
80
40
2
Drug Substance
0.03%
0.08%
0.15%
99.59%
0.15%
Deg Gate Milestones
Milestones resulting in degradation data package:
• Collaborative with input from formulator, solid state expert and
chemist
•  Method discussion – LC-MS friendly? Will aid degradant ID efforts
• Summarize predicted degradants
• Summarize forced degradation results and highlight major
degradants
• Discussion around potential structural alerts
• Drug substance-excipient compatibility
• Mitigation of potential degradation issues: need for antioxidants, desiccants, packaging compatibility, stability study
strategy
PharmaD3
• Database that can be used as a repository for all degradation
knowledge
• Drug substance structures can be linked to forced degradation
experiments, resulting major degradants, and any reports/documents
associated with compound of interest
• Substructure searches possible
Degradation Database for Knowledge Capture
Metrics
Zeneth Predictions
Stability Strategy Meeting
25
4
Cpd
Prediction
Report
Experimental
Forced
Degradation
report
Accurate
Mass of
Major
Degradants
Known
Degradants
Submitted
for Derek
Any PGI
issues
Highlighted
by DEREK
Solid
State
Stability
Issues
Links
W
Yes
Yes
No
No
No
Yes
X
Yes
Yes
No
No
No
No
Exampl
e
Below
Y
Yes
Yes
In
Progress
No
No
No
Z
Yes
Yes
Yes
Yes
No
No
Summary of Degradation report (Example)
From:
Stress testing results in more detail:
Subject: Forced Degradation Report for Cmp XYZ
Structure: AxByCz, MW = 123 (Exact Mass)
Predicted Degradants:
STRESS TESTING RESULTS: Total Degradation as determined by
peak area % of main band
DEG.
COND.
Dissolving
Solvent
40°C, 24h
Oxidation/
Pending
R• (ACVA)
40°C, 24h
Dissolving
Solvent
RT, 24h
High
Reactivity
>10%
Moderate
Reactivity
1-10%
Low
Reactivity
<1%
1N-HCl
H2O/ACN
40C, 24h
0.8N-KOH
H2O/ACN
40C, 24h
H2O2
H2O/ACN
40C, 24h
< 1%
TBD
< 1%
Main
Band
Area (%)
Total
Impurities
(%)
Major
Degradant
s (%)
In Solution
99.5%
N/A
Dissolving
Solvent, RT, 24
hr
Dissolving
Solvent, 40C,
24 hr
1N HCl, 40C,
24 hr
0.8N KOH,
40C, 24 hr
H2O2, 40C, 24
hr
Oxidation R•
(ACVA), 40C,
24 hr
99.5%
0.48%
0.48%
Accurate
Mass of
Major
Degradant
N/A
N/A
N/A
99.5%
0.48%
N/A
N/A
91.37%
5.39%
MW = 326.15
MW = 171.01
62.85%
37.15%
4.53%
3.98%
35.24%
98.17%
1.83%
Pending
Pending
Pending
Pending
98.6%
1.37% (vs
1.14% for
control)
1.03% (vs
0.94% for
control)
0.23%
unknown
N/A
N/A
Solid State
Light Exposed,
1X ICH
37%
8.5%
Exposure
Condition
1.8%
Thermal/
Humidity
(70C/75% RH,
1 wk)
99.0%
MW = 326.15
MW = 291.08
Summary of Degradation report (Example) – Cont’d
II.Sample preparation
I. UPLC METHOD:
Acquity UPLC
Acquity UPLC
photodiode Array
Solvent A
Solvent B
Column
type
Column
size
Temperatur
e
UV @
Waters BEH Shield RP18
100mm x 2.1mm
45°C
210 nm (PDA detector)
(10pts/sec)
Flow Rate
0.35 mL/min
Enter Solvent
A here e.g. 10mM
Ammonium Acetate
Injection
2.0 µL
Vol.
Acetonitrile
Dissolving
50% H2O/50% MeCN.
solvent
Gradient Table
Time (min)
% solvent A
% solvent B
00.0
95
5
2.0
95
5
12.0
5
95
13.0
9
95
13.1
95
5
15
95
5
Note: Stress Testing Performed using conditions above.
Solid state work (thermal /photo) run using release method.
API dissolving
solvent
50% H2O/50% MeCN referred to as dissolving
solvent.
PF-06273340
stock sol.
ACVA stock
solution
1.95 mg of Enter compound number here (lot xxxxx)
was dissolved in 5.0 mL of dissolving solvent.
5.0 mg of ACVA was dissolved in 10 mL of 50%
H2O/50% MeCN.
API, 60°C
500 µL of Enter compound number here stock sol.
was treated with 500 µL of dissolving solvent
500 µL of Enter compound number here stock sol.
was treated with XX (this value will come from the
ACVA calculator) µL of ACVA stock solution,
followed by 466 µL of dissolving solvent.
500 µL of Enter compound number here stock sol.
was treated with 500 µL of dissolving solvent
500 µL of Enter compound number here stock sol.
was treated with 400 µL of dissolving solvent
followed by 100 µL of concentrated-HCl solution
(~12N) to give a 1.2N HCl soln.
500 µL of Enter compound number here stock sol.
was treated with 400 µL of dissolving solvent
followed by 100 µL of 8N-KOH solution to give a
0.8N KOH solution.
500 µL of Enter compound number here stock
solution was treated with 475 µL of dissolving
solvent, followed by 25 µL of 3% H2O2 solution.
API + ACVA
API RT
API + HCl
API + KOH
API + H2O2
III. DATA LOCATION:
Empower Data Path: Enter empower data path here e.g. Projects/
2012Q4_GRO/RA-API
Empower Sample Set ID: Enter empower sample set ID here
Empower Results Set ID: Enter empower results set ID here
Symyx eln experiment ID: Enter experiment ID from symyx here
Summary of Degradation report (Example) – Cont’d
IV. SUMMARY:
Highlight the conditions at which API is significantly unstable and add information about recommended KPSS. Any other significant output from the initial DEG
GATE meeting or the R to D transition meeting can also be included here.
Drug Product (SDD) – One significant degradation product is observed under accelerated thermal conditions: (succinic acid adduct = proposed deg)
Structure below:
V. APPENDICES: (Please add any other details that you think may be useful to someone to whom the project is transferred in the future)
Representative Chromatograms:
This is an optional section where one may insert representative chromatograms for the forced degradation conditions run. Please ensure that the chromatograms
are sufficiently zoomed in to be useful
Additional Supporting Docs:
purp deg eLN
report.pdf
SEG2012-1477.doc
Example 1: Unexpected precipitate
• A precipitate was seen in a drug product development (Product will
be an IV and precipitate was seen at high concentrations of API in
solution under stress/heat conditions)
• Sample was analyzed on a single quad mass spectrometer and a MH
+ of 669 was seen
• This data was compared to the Zeneth prediction results and a
degradant of a degradant with a VERY LIKELY likelihood was
highlighted.
Elemental composition was confirmed
by high resolution mass spectrometry
Example 1: Unexpected precipitate - Mechanism
Since precipitate is seen at high concentration under
heated conditions venues such as filtration may need to
be explored to ensure IV is suitable for use
Example 2: Excipient compatibility of drug
substance with lactose
• Lactose is a commonly used excipient which may contain reactive
impurities such as formaldehyde, formic acid, hydroxymethylfurfural,
galactose and acetic acid
• Zeneth was used to predict possible degradants due to reactivity with
excipients and its impurities in order to correlate with results from an ASAP
studies and compare with data seen with di cal phosphate (another
excipient).
Example 3: Enriching degradant for identification
• Structure confirmation of filed impurity was needed
• MS data consistent with N-oxidation but fragmentation cannot pinpoint
location of N-oxidation
• Oxidative degradation sample was run on purity method and a major
degradant aligned with impurity of interest
• Enrichment by degradation followed by isolation and characterization
pursued
Example 4: Enriching a drug product degradant
• During an ASAP study on an SDD tablet an impurity was seen to grow
to 1% after 2 days at 70oC/75%RH
• LC-MS data suggested a succinic acid adduct (succinic acid is an
impurity in HPMCAS, the polymer in the SDD). Zeneth prediction
assigned a likelyhood of VERY LIKELY to the transformation.
• HRMS supported the succinic acid adduct hypothesis. Fragmentation
data could not confirm position at which the acid attaches.
• Enrichment by synthesis was pursued and a screen initiated to find
suitable conditions for enrichment
Screen with Na2CO3
4.737
0.20
4.20
4.40
4.60
60 mg SA for hrs
4.80 5.00
Minutes
5.20
5.40
5.60
4.735
0.20
60 mg SA for 1 day
0.00
4.00
20 hrs
0.40
4.238
0.40
AU
0.60
5.741
0.60
4.00
60 mg SA for 1 day
4.20
4.40
4.24 min
4.75 min
5.01 min
5.74 min
0.08%
0.03%
0.91%
99.0%
DMAP 60 mg SA ~ 5hrs
1.8%
2.6%
41.1%
51.7%
DMAP 60 mg SA ~1day
4.8%
2.7%
60.5%
26.6%
ND
0.03%
1.4%
98.6%
60 mg SA Na2CO3 ~5hrs
4.5%
9.1%
40.3%
44.6%
60 mg SA Na2CO3 ~ 1day
12.1%
11.5%
51.7%
20.8%
DMAP ~5 mol eqv 20 hrs
Na2CO3 25hrs
25 hrs
60 mg SA for hrs
0.00
5.80
Drug Substance - 5.741
0.80
5.017
0.80
Target peak - 5.013
1.00
4.243
AU
Screen with DMAP
4.60
4.80
5.00
Minutes
5.20
5.40
5.60
Molecular ions of
peaks:
4.24 min MH+ = 680
4.75 min MH+ = 580
5.01 min MH+ = 580
5.74 min MH+ = 480
This can be further screened to optimize
excess SA addition and then scaled up for
isolation and characterization
5.80
Summary
• A degradation workflow ensures consistent practices across
projects which facilitates data mining and transfer in an efficient
manner
• Bringing together experts from each area of drug development
early in the process helps design well thought-out experiments.
•  This in turn provides useful information that will guide the
development of a drug and its formulation, with the ultimate
goal of avoiding or minimizing degradation related issues
• Predictive degradation is a developing area that will enable us to
cleverly design initial stress testing studies
• Storing data/information in a repository that is searchable will
reduce situations where we re-invent the wheel
Acknowledgements
Deg Workflow LDT:
Karen Alsante, Chris Foti, Greg Sluggett, Todd Zelesky, Joel
Hawkins, Zhaohui Lei, Janice Ensing, Brian Marquez, Dave
DeAntonis
Research Analytical Management
Charanjeet Jassal
Jared Van Haitsma
Doug Farrand
Duc Vuong
Audience