Dr. Tausif Ahmed [Compatibility Mode]

Role of PK/PD in Evidence based Medicine
Tausif Ahmed, PhD
Asst. Director,
Modeling & Simulation, GLP-BA and Met-ID
Piramal Enterprises Ltd, Mumbai
Third Annual Conference, Indian Association for Statistics in Clinical Trials
Agenda
Introduction
Historical perspectives
PK-PD Models
Applications of PK/PD
Challenges in PK/PD modeling
Summary
Drug Development Cycle
Is it safe?
KNOWLEDGE
LEVEL
Does it work?
Does it
work in
double
blind
trials?
Drug Discovery & Development - Attrition Rate
Reasons for Phase 2 Failures 2008-2010
Nature Reviews: Drug Discovery, May 2011
Reasons for Phase 3 Failures 2008-2010
Nature Reviews: Drug Discovery, Feb 2011
Agenda
Introduction
Historical perspectives
PK-PD Models
Applications of PK/PD
Challenges in PK/PD modeling
Summary
Pharmacometrics defined
– Pharmacometrics – still an emerging science
– Science that quantifies drug, disease, and trial information to aid efficient drug
development and/or regulatory decisions
– Pharmacometrics - a collection of model-based approaches used to
• extract from data & organize our understanding of a system’s behavior in a
concise manner
• do so in a language (i.e. mathematics) that allows simulation of the system
output
– Pharmacometric Models - three broad classes:
• Exposure-Response Models- specifically describe the relationships
among dose, drug concentration in blood (or another matrix), and clinical
response (effectiveness and undesirable effects)
• Disease Models- aim to describe disease progression
• Clinical Trial Models- describe patient demographics, adherence, dropout
rates, trial structure, and so on
A Brief History of Pharmacometrics
THE LATE 60’S:
A PREMATURE BIRTH
?
1968: The Birth of PK/PD
• Presence of a delay between norepinephrine concentration-time
profiles and the kinetics of pharmacological response, i.e. blood
pressure-time data
• Gino Segre introduced the concept of a hypothetical effect
compartment to account for this delay
• This allowed an empirical description of time-dissociated kinetics
Segre G. Kinetics of interaction between drugs and biological systems. FarmacoSci. 1968
Oct;23(10):907-18
1968: Was it a Premature Birth?
• NONLIN software only introduced in a year later by Carl Metzler
• It was written in FORTRAN-66 programming language for mainframe
computers
• Long gap of PK/PD publications until 1979
•
CM Metzler. A Users Manual for NONLIN. Technical Report 7297 69 7292 005. Upjohn
Co., Kalamazoo, Michigan (1969)
A Brief History of Pharmacometrics
THE LATE 70’S:
A REBIRTH
1979: Rebirth
•
Lewis Sheiner and coworkers made Segre’s model more popular. They were
the first to formalize this concept into a model to describe hysteresis caused
by distribution to the biophase
•
It was reborn as the ―Link Model
•
SheinerLB, Stanski DR, Vozeh S, Miller RD, Ham J. Simultaneous modeling of
pharmacokinetics and pharmacodynamics: application to d-tubocurarine Clin Pharmacol
Ther. 1979 Mar;25(3):358-71
A Brief History of Pharmacometrics
THE LATE 80’S:
TODDLING
80’s: Growing Application
• Growing use of PK/PD modeling, with applications to diverse
therapeutic areas (mainly cardiovascular)
•
Source: Pubmed search (Key-words: ―pharmacodynamic AND modeling)
80’s: Growing Application
• Growing use of PK/PD modeling, with applications to diverse
therapeutic areas (mainly cardiovascular)
A Brief History of Pharmacometrics
THE LATE 90’S:
A STEEP LEARNING CURVE
90’s: The Advent of Mechanism-Based PK/PD
• In so called – Indirect Physiological Response (IPR) models, the drug
concentration is no longer related to the PD variable itself. Instead, it
is assumed to modulate upstream and/or down stream regulation
mechanisms
90’s: The Advent of Mechanism-Based PK/PD
• Significant increase in the number of publications. Predominantly
theoretical until 1993. Growing number of increasingly complex
applications afterwards
•
Source: Pubmed search (Key-words: ―pharmacodynamic and modeling)
90’s: The Advent of Mechanism-Based PK/PD
A Brief History of Pharmacometrics
TODAY:
A MATURE DISCIPLINE
Today: A Mature Discipline
• Application of integrated drug-disease-trial models to optimize clinical
development programs with respect to therapeutic potential, R&D
productivity and commercial value
Preclinical
Phase
Post-NDA Phase
Clinical Phase
eIND preIND IND
VGDS
EOP2a
Drug Model:
PK/PD
Human PK/PD Prediction
S
PK/PD
Dose-escalation
POP
S
Dose S
Ranging
EOP2
NDA
6 mo safety
Efficacy/Safety
Benefit/Risk
Confirming
Label Update
Approval
• Drug
• Label
Simulate (S)
• Dosing
Cross-trial analysis:
dose-response
(efficacy/safety)
• Human proof of principle
Individual
Dosing
PK/PD Bridging
• Phase 3 trial design
• Value
Target Product Profile
Benefit
Risk
• Pediatrics
Quantitative Analysis
&/or Simulation
• Elderly
• Dosage forms
Disease Model: detect change, qualify new biomarkers, simulate trial design
Predict, Learn
Confirm, Save
Safety Model: learn ‘at risk’ population, detect early or avoid risk
Predict, Learn
Confirm, Save
Today: A Mature Discipline
1999
2003
Today: A Mature Discipline
• ...............as well as regulatory decisions about labeling and approval
Operation of M&S
DMPK
Biomarkers
Imaging
Pharmacology
Modeling & Simulation
Clinical Research
Data Management &
Biostatistics
Confidential
Software's: WinNonlin,
NONMEM, R, S-plus,
ADAPT, SAS
IT
Domain
•
•
•
•
•
•
•
•
Preclinical & in-vitro studies
•
Pharmacokinetic (PK) Modeling
•
In vitro-in vivo correlation (IVIVC)
•
Pharmacodynamic (PD) Modeling
•
Population Pharmacokinetic (PopPK) Modeling
•
Pharmacokinetic/ Pharmacodynamic (PK/PD) Modeling
•
Physiology Based Pharmacokinetic (PBPK) Modeling
•
Clinical Trial Simulation
Single and multiple dose pharmacokinetics
Absolute bioavailability & dose proportionality
Metabolism and drug interactions
Food effects studies; Bioequivalence studies
Special population studies – age, gender, race
Pharmacokinetics in the target population
Disease states such as renal and liver impairment
Agenda
Introduction
Historical perspectives
PK-PD Models
Applications of PK/PD
Challenges in PK/PD modeling
Summary
Concentration
Effect
PK/PD Modeling
Concentration
Time
Pharmacodynamics
(PD)
What the body does to
the drug
Effect
Pharmacokinetics
(PK)
What the drug does to the body
Time
MODEL
Simplified description of
some aspect of reality
HELPS IN PREDICTION
Pharmacokinetics/Pharmacodynamic Modeling
(PK/PD)
Pharmacodynamic Models
- Linear model
- Log-linear model
- Emax - model
- Sigmoid Emax – model
- Inhibitory models
- Effect Compartment
- Indirect Models
- Tolerance Models
PD Models: Basic Principles
Drug must “interact” with a “receptor substance” to elicit an
activity
Drug(D)+Receptor(R)↔ [DR] →Effect
Rearrangement leads to Michaelis-Menten Equation
E max• D
Effect =
KD + D
- D - Free drug concentration, Emax - Maximal effect,
- KD - Binding constant
Maximum Effect Model
Inhibitory Effect Model
Direct Effect Model
Biophase Distribution Model
Indirect Link Model
Analgesic effect of 400 mg oral
ibuprofen quantified by
subjective pain intensity rating
ka
k10
D
D Dose
C Plasma concentration
Ce Effect compartment
concentration
k10, ka, k1e, ke0
First-order rate constants
C
k1
ke
Suri et al., Int J Clin Pharmacol Ther 1997, 35, 1-8
b
0
Ce e E
max-model
Effect
Indirect Link Models
Distributional delay between plasma and effect site
concentration
Dissociated time courses of concentration and effect
−
−
−
Concentration maximum before effect maximum
Effect intensity increasing despite decreasing plasma
concentrations
Effects persist beyond the time plasma concentrations
are detectable
Counterclockwise hysteresis loop
Indirect Response Models
Production
Degradation
Response (R)
ki
kout
n
The general indirect response model assumes that the change in the response
parameter, which is related to the effect, is governed by an input or production
process (zero-order rate constant kin) and an output or degradation process (firstorder rate constant kout)
Temporal dissociation between the concentration time course and the effect-time
course (hysteresis) due to synthesis of a protein, reduction in a synthesis rate
(reduction in hormonal levels)
Thus, the rate of change in response (R) is described by
dR
=kin - kout ×R
dt
Dayneka et al., J Pharmacokinet Biopharm 1993, 21, 457-78
Indirect Response Models
Modulation of Input or Production Process
dR 0  Smax ⋅ C 
= kin ⋅ 1+
− kout ⋅ R
+

dt
D
 SC50 + C 
-
ka
k10
k21
C
+
dR 0  Imax ⋅ C 
= kin ⋅ 1−
− kout ⋅ R

dt
 IC50 + C 
Smax > 0 0 < I max ≤ 1
-
+
-
k0in
Response
Pharmacokinetics
Pharmacodynamics
kout
Agenda
Introduction
Historical perspectives
PK-PD Models
Applications of PK/PD
Challenges in PK/PD modeling
Summary
Example 1: Dose-response Relationship
Example 1: Dose-response Relationship
Artemisinin derivatives commonly used to treat drug resistant falciparum malaria
Doses of artesunate used in mono therapy and combination treatment- derived
empirically
PD end point- PCT (Parasite clearance time)
47 adult patients with acute uncomplicated falciparum malaria and parasitemia were
randomized to receive a single oral dose of artesunate: 0 - 250 mg
Inhibitory sigmoid Emax model fitted to dose vs shortening of PCT
Emax was estimated as 28.6 h, and the 50% effective dose was 1.6 mg/kg bw
No reduction in PCTs with the use of single oral doses of artesunate higher than 2 mg/kg,
and this reflects the average lower limit of the maximally effective dose
Example 1: Dose-response Relationship
Example 2: PK-PD Models
in Preclinical Drug Development- Antimicrobials
Plasma Concentration
Efficacy of Aminoglycosides
Peak
AUC / MIC
AUC
Efficacy of New Quinolones
Trough
MIC
Time above MIC
0
4
8
Safety of Aminoglycosides
12
16 Time (hr)
Efficacy of b-lactam, macrolides, glycopeptides
Example 2: PK/PD Models
in Preclinical Drug Development- Antimicrobials
Example 2: PK/PD Models
in Preclinical Drug Development- Antimicrobials
Telithromycin (Tel)- belongs to the class of antimicrobial, ketolides
Effective against penicillin and macrolide resistant gram +ive Streptococcus pneumoniae
Thigh infection model: CD-1 mice rendered neutropenic by ip injection of cyclophosphamide
Colonies of S. pneumoniae (106- 107 CFU/mL) appx. 0.1 mL inocculum injected in thigh of
mice, 2h prior to initiation of antimicrobial therapy
2h post-infection, Tel 50 or 100 mpk dose administered
PK- blood collected at regular intervals till 24 hours post-dose
PD- 2h- post infection, Tel doses ranging from 25-200 mg/kg administered at different
dosing regimens
After 24h of treatment, mice were sacrificed and thighs removed and CFU counted vs control
PK-PD analysis done using Inhibitory Emax model
Example 2: PK/PD Models
in Preclinical Drug Development- Antimicrobials
AUC/MIC- strong determinant of the response against S. pneumoniae
Maximal efficacy and bacterial inhibition against S. pneumoniae strains were predicted
by AUC/MIC and Cmax/MIC ratios of appx 1000 and 200, respectively
Example 3: Preclinical DevelopmentDiabetes
xx (antidiabetic drug)
PK-PD Analysis of data from the study in rat, hamsters and ob/ob mice
Key results:
PD Analysis: IC50 for xx ranged from 100-300 ng/mL across 3
different preclinical disease models (concn. vs biomarker levels
Similar concn. expected in clinic for efficacy
Good correlation between biologic response and biomarker (r = 0.85)
Biomarker focus right from preclinical stage
Compare and combine with data from all the preclinical studies : Build
knowledge-base for extrapolation to clinical trials
Confidential
Preclinical Data
xx in Hamsters
140
y = 0.0218x + 103.38
R2 = 0.40
PK-PD
(Biomarker)
model
Day 21 Body Wt
130
120
110
100
90
80
0
200
400
600
800
1000
Response
vs
Biomarker
model
Day 21 TG Level
PK-PD
(Response)
model
Disease
Progression
model
Confidential
Prediction of FIH Dose
Use of allometric scaling in predicting FIH dose
Other alternative approaches for FIH dose prediction: NOAEL from preclin. Species
FIH dose prediction based on efficacious doses in preclin. disease models
In vitro-In vivo correlation: Prediction human clearance from human hepatocyte intrinsic
clearance data
Simulated human PK profiles and correlation with efficacy/toxicity- Integrated approach
CL*MLP
y = 1.3113x + 1.6455
R2 = 0.9746
NOAEL Cmax= 11, 000 ng/mL
Toxic dose Cmax: 42, 000 ng/mL
log CL (mL/min)
5.00
4.00
3.00
2.00
1.00
Efficacious conc.
0.00
-2.0
-1.0
-1.00
0.0
1.0
2.0
3.0
log BW (kg)
Confidential
Disease Progression Model- Diabetes
• Change in fasting plasma glucose (FPG) concentrations modeled as a function of Cp via an
indirect-effect model on the assumption that drug xx reduces glucose by increasing the
removal rate of glucose from the plasma compartment
Models developed based on phase I/IIa data help make valid predictions for larger phase II/III trials
Confidential
Disease Progression Model- Diabetes
K in
FPG
E ⋅C
)
K out ⋅ (1 + max
EC50 + C
1st order Oral Absorption
Cmt 1
HbA1c
K 'out
FPG
dFPG
E ⋅C
= K in − K out (1 + max
) ⋅ FPG
dt
EC 50 + C
HbAlc
Drug Conc.
K 'in
Cmt 2
dHbA1c
= K 'in ⋅FPG − K 'out ⋅HbA1c
dt
Time (Week)
Disease Progression Model- Diabetes
Design of Phase I RMD Study- QD Dosing
Simulation of multiple dose profile of drug x based on single dose PK
Correlate exposure to efficacy in deciding the proposed doses for RMD study
Decide dosing regimen (QD vs BID) based on efficacy and safety
Target Cmax= 80900 ng/mL
SS reached by day 4
Confidential
Agenda
Introduction
Historical perspectives
PK-PD Models
Applications of PK/PD
Challenges in PK/PD modeling
Summary
Current Problem in PK/PD
Today, we do not have an adequate understanding of the clinical
efficacy/ MOA for most disease states
Do not have an adequate understanding of the MOA for clinical toxicity
This is the reason for the lack of suitable biomarkers and surrogate
markers
Validation of PD biomarkers
Correlation of PK/PD model with safety or efficacy outcomes- Need to
develop disease progression models
Validated Assay (reproducible, high precision….)
Understanding of pharmacologic behavior of the drug and
pathophysiology of the disease
Future research needs to address above areas
(Colburn, Washington, 1999)
Practical Aspects
CORPORATE PRESSURES
• mergers, consolidation,
small start-up
• Changing corporate
philosophy and structure is
changing the development
process
• Increased Productivity
More drug candidates in shorter period of
time
challenges and opportunities in the
PK/PD area
THUS
Develop Innovative Drugs
faster with reduced risk, more
effective cheaper
New Discovery
Approaches:
e.g combinatorial chemistry
computational approaches
robotic systems
Pharmacometrics Status in India
Search of website http://www.ctri.nic.in (clinical trials registry- India, Indian Council
of Medical Research) with key words “phase II/III trials 2010” reveals: None of the
trials have pharmacometrics (PM) component
PM is at infancy stage in India
Efforts are made to impart trainings in the fields of PK/PD data analysis and clinical
protocol writing
Preconference workshop entitled “Pharmacokinetics: protocol development, conduct
and analysis” organized by South Asian chapter of ACCP at PLSL in Aug, 2011
Trained (hands-on/didactic lectures) about 50 medical and pharmacy grad and post
graduates
PAGIN formed in 2008: Formed to provide PM training in India
ICMR grant to Sponsor “Research Methodology Workshop on PK/PD” held at ACTREC
(Advanced Centre for Training Research and Education in Cancer), Navi Mumbai from
May 1-7, 2012 to train post graduate students in the field of PM (co hosted by
Piramal)
Pharmacometrics Status in India
Summary
Increasing awareness and understanding of PK-PD in drug discovery and dev
Emphasis on biomarkers relating to target modulation for novel targets
Strong collaboration between discovery DMPK, biology, IT, statistician and clinical
biomarker groups
Translation of preclinical biomarkers to the clinical setting
Start early and transfer PK-PD knowledge from discovery to development; refine model
as more data becomes available
PK-PD provides a scientific basis for optimal FIH dose
Optimal use of PK-PD modeling and simulation- fewer failed compounds, fewer study
failures and smaller number of studies needed for registration: Save time and money
Pharmacokinetics/Pharmacodynamics an
uphill climb – but nice view!
Thanks