Quantitative Clinical Pharmacology:
Applications of Modeling and Simulation in
Clinical Development
Rajesh Krishna, PhD, FCP
October 9, 2006
Program in Integrative Information, Computer and Application Sciences
(PICASso), Princeton University
1
Typical Drug Development Program
Prior Knowledge
-Analogues
-Disease
-Competitors
-Patients
-Discovery/Pre-clinical
Drug Molecule
O
H3C
O
HN
CH3
N
N
N
O
S
O
CH3
N
Clinical Development Plan
Trial
1
Trial
2
Trial
…
N
H3C
Learn
Efficacy
Tox
PK/PD
Mechanistic
Learn / Confirm
MTD, Efficacy D-R
PK/PD, (P)PK/PD
Biomarker, Surrogate
2
Drug Product
Trial
N
-indication
-patients
-formulation
-dose
-safety/ efficacy
Confirm
Therapeutic Benefit
PPK/PD
Clinical Endpoint
Reality of Present Day New Drug Development
High NME attrition
–
High failure rate before IND
–
NME IND = NDA <20% of time
–
Reported >50% failure rate in
Phase 3 (Carl Peck, CDDS)
–
Decreased NME NDAs despite
increased INDs
–
Cost per NME approved
estimated at >$800M (Tufts)
Success Rate (%)

100
90
80
70
60
50
40
30
20
10
0
Phase I
Phase II
Phase III
NDA
Stage of Development
Adapted from: Kola and Landis, Nature Review Drug Discovery, 2004
(3):711-715
3
Approval
Probability of Success for New Mechanisms ~11%
Adapted from: Kola and Landis, Nature Review Drug Discovery, 2004
(3):711-715
4
New Mechanisms Often Fail Because of Lack of Efficacy
or Demonstrated Benefit
Adapted from: Kola and Landis, Nature Review Drug Discovery, 2004
(3):711-715
5
Drivers For Change

Escalating R&D costs despite flat growth
–
R&D expenditure not proportional to number and quality of NMEs
–
Need to  productivity (do more with same amount of investment)

Low POS of NME’s entering Phase I - ~11% (Kola and Landis, 2004)

Phase II as stage for decision making often the default today

–
High attrition given low POS for new mechanisms without evidence of
pharmacological benefit in Phase I
–
R&D costs required to support decision-making for new mechanisms this
late, given POS, prohibitive
–
Lack of resources to create tools to facilitate early decision-making
Knowledge management and integration
–

New tools to link outcomes, predict hazards, reduce uncertainty in
risk/benefit
Quick win-quick kill paradigm
6
FDA Critical Path Initiative
Goals
1. Develop new predictive “tools”
2. Improve the productivity and
success of drug development
3. Speed approval of innovative
products
Adapted from: http://www.fda.gov/oc/initiatives/criticalpath/
7
Areas for Change

Key objective:
–

Need to dramatically improve predictions of efficacy and safety in
clinical development
Enablers:
–
Biomarkers ~ target validation
• Biomarker qualification, qualifying disease specific biomarkers
–
M&S ~ effective knowledge management leveraging bioinformatics
• Drug disease models, clinical trial simulation
–
Clinical trials ~ better decision making, improving efficiency
• Adaptive trial designs, seamless trials
8
Pulse Check - Terminology
4
6
Time (h)
8
10
12
probality(DLT)
0.2 0.4 0.6 0.8
2
Mixed Effects Modeling
0 2 4 6 8
E xpos ure
0
0.0
0
1
2
3
4
Time (h)
9
0.0
0
1.0
0.0
Safety
Drug Concentration
0.05 0.10 0.15 0.20
0 20 Efec4t0(%) 60 80 10
Efficacy
Drug Concentration
0.02
0.04
0.06
PK
5
6
2
4
6
8
Exposure
10
12
Model Based Drug Development

Hypothesis based drug development emphasizing integrating
information and improving the quality of decision making in drug
development
–
Preclinical and clinical biomarkers
–
Dose-response and/or PK-PD relationships
–
Mechanistic or empirical disease models
–
Novel clinical trial designs
–
Clinical trial simulations and probabilities of success
–
Baseline-, placebo- and dropout-modified models
–
Outcome models
10
Roadmap for Model Based Drug Development
Capture
Prior knowledge
Model
Clinical trial
Simulate
Optimize
11
Case Example 1: Meta-Analysis of Statin Efficacy


Accumulated data from 25 trials (~9500 patients)
–
5 Pfizer sponsored trials for Lipitor
–
7 AstraZeneca summary basis trials for Crestor
–
9 Merck summary basis trials for Zetia
–
4 Pfizer sponsored trials for an investigational non-statin
Epidemiology Trials
–
Wilson et al, Framingham risk equations,
–
(Prediction of Coronary Heart Disease Using Risk Factor Analysis, Circulation, 1998, 97:1837-1847)
–
Riker et al, C-Reactive Protein and LDL
–
(Comparison of C-Reactive Protein and Low-Density Lipoprotein Cholesterol Levels in the Prediction of First Cardiovascular Events, NEJM
2002, 347:1557-1565)
Adapted from: Mandema and Hartman. Model-Based Development of Gemcabene
a New Lipid-Altering Agent. AAPS Journal. 2005; 7(3): E513-E522.
12
Pharmacodynamic Model Development
•Trials looked at the following alone or in
combination with Ezetamibe or
gemcabine:
–Atorvastatin
–Rosuvastatin
–Simvastatin
–Lovastatin
–Pravastatin
Adapted from: Mandema and Hartman. Model-Based Development of Gemcabene
a New Lipid-Altering Agent. AAPS Journal. 2005; 7(3): E513-E522.
13
Dose vs LDL lowering Response: Population, Mixed
Effects Response Meta-Analysis:
LDL % change  E0  Estatin  Enonstatin    Estatin  Enonstatin
0
20 40
60
Dose (mg)
80
0
20
40
60
Dose (mg)
Lovastatin
80
0
20
40
60
Dose (mg)
80
0
20 40 60
Dose (mg)
Ezetimibe
80
0
10 20 30
Dose (mg)
40
LDL % change from base
-60
-40
-20
0
80
LDL % change from base
-60
-40
-20
0
20 40
60
Dose (mg)
Pravastatin
LDL % change from base
-60
-40
-20
0
0
LDL % change from base
-60
-40
-20
0
Rosuvastatin
LDL % change from base
-60
-40
-20
0
Simvastatin
LDL % change from base
-60
-40
-20
0
Atorvastatin
14
Statin Dose-Response Relationship: Absence (E 0) and
Presence of 10 mg Ezetimibe (E 10)
Adapted from: Mandema and Hartman. Model-Based Development of Gemcabene
a New Lipid-Altering Agent. AAPS Journal. 2005; 7(3): E513-E522.
15
Dose-Response Relationship for Non-Statin Without and
With Statin
With Atorvastatin
Alone
Adapted from: Mandema and Hartman. Model-Based Development of Gemcabene
a New Lipid-Altering Agent. AAPS Journal. 2005; 7(3): E513-E522.
16
Prediction of Simvastatin Risk Reduction vs Dose
Using a Model Based Approach
Adapted from D. Stanski
17
Case Example 2: Gabapentin Approval and Label

Gabapentin was approved by FDA for post-herpetic neuralgia

Approved label states under clinical studies: “Pharmacokineticpharmacodynamic modeling provided confirmatory evidence of
efficacy across all doses”

Model and Data Provided with Submission
–
FDA reviewers used model to test various scenarios
–
Supported doses and conclusions of Pfizer
–
Provided confidence to eliminate need for replicate doses
–
FDA proposed language in the label on PK-PD modeling and clinical
trials
Adapted from: Miller, J Pharmacokinet Pharmacodynamics, Vol. 32, No. 2, April 2005
18
Gabapentin PHN Study Designs
le 1. Overview of PHN Controlled Studies: Double-Blind Randomized/Target Dose and ITT Population
Duration of Double-Blind Phase
Fixed
Titration
Dose
4 Weeks 4 Weeks
Number of Patients
Final Gabapentin Dose, mg/day
Overall
Duration
8 Weeks
Placebo
116
600
--
1200
--
1800
--
2400
--
All
Any
3600 Gabapentin Patients
113
113
229
30) 3 Weeks
4 Weeks
7 Weeks
111
--
--
115
108
--
223
334
4 Weeks
4 Weeks
8 Weeks
152
379
-0
-0
-115
153
261
-113
153
489
305
868
ts
p not included in study design
ion = All randomized patients who received at least one dose of study medication.

Used all daily pain scores

Exposure-Response analysis utilized titration data for withinsubject dose response
Adapted from: Miller, J Pharmacokinet Pharmacodynamics, Vol. 32, No. 2, April 2005
19
Gabapentin PHN Data Fits
945-211
0.0
0.0
Placebo (Observed)
1800 mg Daily (Observed)
2400 mg Daily (Observed)
Placebo (Predicted)
1800 mg Daily (Predicted)
2400 mg Daily (Predicted)
-0.2
-0.2
-0.4
-0.4
-0.6
-0.8
Placebo (Observed)
Placebo (Predicted)
3600 mg Daily (Observed)
3600 mg Daily (Predicted)
-1.0
-1.2
-1.4
-1.6
Mean Pain Score
-0.6
Mean Pain Score
945-295
-0.8
-1.0
-1.2
-1.4
-1.6
-1.8
-1.8
-2.0
-2.0
-2.2
-2.2
-2.4
-2.4
-2.6
-2.6
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46
Time (Days)
Time (Days)
Time Dependent Placebo Response, Emax Drug Response and
Saturable Absorption
Adapted from: Miller, J Pharmacokinet Pharmacodynamics, Vol. 32, No. 2, April 2005
20
Model Predicted Gabapentin Effect (Less Placebo)
Plot of Model Predicted Gabapentin Effect by
Total Daily Dose and Estimated Dose Absorbed
1.6
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
Total Daily Dose
Estimated Dose Absorbed
0.4
0.3
0.2
0.1
0.0
0
500
1000
1500
2000
2500
3000
3500
4000
Gabapentin Dose (Total Daily or Total Daily Absorbed)
Adapted from: Miller, J Pharmacokinet Pharmacodynamics, Vol. 32, No. 2, April 2005
21
Case Example 3: Optimizing Dose Selection for an ACE
Inhibitor

A 2-compartment PK model with first order absorption and first
order output

Daytime variation of ACE is described with a cosine function with
time period tp, amplitude A and shift
ACE(t)=ACEo+ A cos(2π (t+S)/tp)

An Emax model and a sigmoidal Emax model are tested to
describe the relationship between concentrations and plasma
ACE activity
Adapted from: Pfister. Dose selection of M100240. J Clin Pharmacol 2004 Jun;44(6):621-31
22
Simulation Scenarios

Target:
–

> 90% inhibition of plasma ACE activity in at least 50% of patients
Simulations at steady state:
–
For comparison of oral daily doses ranging from 25 to 150 mg
–
PK and plasma ACE activity profiles (n=500) under these dosage
regimens are simulated with parameters drawn from the population
PK and PD distribution
23
Model Based Simulations of BID Regimens
PK
ACE activity
Fraction of patients achieving
target; horizontal lines denote
50 and 80%
24h
24
24h
Model Based Simulations of TID Regimens
25
Simulations at Steady State

Simulations are used to evaluate candidate QD and BID dose
regimen to achieve >90% plasma ACE inhibition at 24 hours

For comparison of oral daily doses ranging from 25 to 250 mg,
PK and plasma ACE activity profiles (n=500) under these dosage
regimens are simulated with parameters drawn from the
population PK and PD distribution
26
Fraction of Patients Achieving 90% ACE Inhibition at Trough
27
Case Example 4: Tipranavir (TPV) Approval and Label

Protease inhibitor for experienced patients or patients with viral
resistance to other PIs

Plasma TPV levels are a major driver of efficacy and toxicity,
boosted with ritonavir (RTV)

HIV-1 protease mutations represent a major driver of resistance
and decreased efficacy

500/200 TPV/RTV dose employed in Phase III
–
Plasma TPV levels > IC50 to suppress viral load and avoid
development of resistance
Adapted from: FDA Antiviral Drug Advisory Committee Meeting, May 19, 2005
28
Inhibitory Quotient (IQ) As A Predictor of Efficacy

Protein Binding Correction Factor (PBCF = 3.75x)
–
TPV is highly bound in plasma (99.96 - 99.98%)
–
Cell culture media only contains 6% fetal bovine serum (99.88%)
–
PBCF estimated using 2 methods:
• Method 1: Equilibrium Dialysis: 0.120% free / 0.034% free = 3.5x
• Method 2: Addition of 75% human plasma to antiviral assay
resulted in a 4x shift

IQ = Cmin / (IC50 fold WT ● mean WT HIV IC50 ● 3.75)
PK
standardized
TPV susceptibility
susceptibility in
patient isolate
PBCF
Adapted from: FDA Antiviral Drug Advisory Committee Meeting, May 19, 2005
29
Percent of Responders at Week 24
0%
20%
40%
60%
80% 100%
TPV Cmin, IC50, T20 Parameters and Viral Response
phase 3 without T20 (n=200)
phase 3 with T20 (n=91)
phase 2 (n=160)
Control+T20
Control
0
200
400
600
Inhibitory Quotient
800
1000
For IQ ≥ 100, 54% responded to TPV and 73% responded to TPV+T20
For IQ < 100, 21% responded to TPV and 52% responded to TPV+T20
30
Adapted from: FDA Antiviral Drug Advisory
Committee Meeting, May 19, 2005
Risk vs. Benefit: Impact of IQ on 24-Week Viral Load
Response and Cmin on Liver Toxicity
Risk: Grade 3-4 ALT,
AST or Bilirubin
Percent of Patients with Grade 3/4 ALT Toxicity
0%
20%
40%
60%
80% 100%
Percent of Responders at Week 24
20%
40%
60%
80% 100%
Benefit: Viral Load Change
From Baseline (log10)
0%
phase 3 without T20 (n=200)
phase 3 with T20 (n=91)
phase 2 (n=160)
0
200
400
600
Inhibitory Quotient
800
1000
10
20
30
Cmin in ug/mL
40
50
Adapted from: FDA Antiviral Drug Advisory Committee Meeting, May 19, 2005
31
TPV Label Statements

“Among the 206 patients receiving APTIVUS-ritonavir without
enfuvirtide…..the response rate was 23% in those with an IQ
value < 75 and 55% with an IQ value > 75.”

“Among the 95 patients receiving APTIVUS-ritonavir with
enfuvirtide, the response rate in patients with an IQ < 75 vs. those
with IQ > 75 was 43% and 84% respectively.”
Adapted from: TPV Label, under “Pharmacodynamics”.
32
Case Example 5: Drug Disease Model

Mechanistic disease model for HIV/AIDS

Pharmacodynamic model incorporating dose, concentration, HIV
viral load time course

Biomarkers of efficacy – viral RNA time course

Biomarkers of safety – GIT events time course

Dose response relationships or PK/PD model

Outcome analysis
33
Components of Drug Disease Models
34
Viral Dynamics
Adapted from: Bonhoeffer (1997) Proc. Natl. Acad. Sci. USA 94, 6971-6976
35
Drug Disease Models
p
d
2
PI
l
Active
Infected
fAbVT
(N)NRTI
CD4+ Cells
+
(N)NRTI
d1
a
Virus
fLbVT
Latent
Infected
c
d3
l: production rate
of target cell
d1: dying rate of
target cell
c: dying rate of virus
b: infection rate
constant
d2: dying rate of
active cells
d3: dying rate of latent
cells
p: production rate of
virus
Adapted from: J Acquir Immun Defic Syndr 26:397, 2001, FDA EOP2A Slides
36
Case Example 5: Applying Drug HIV Disease Model

Maraviroc (MVC;UK-427,857)

Novel CCR5 antagonist in development
for the treatment of HIV

Blocks the CCR5 receptor, which is
used by HIV to enter CD4+ cells

Simulate decline of HIV-1 RNA plasma
levels for 400 patients per treatment arm

Dosing regimens simulated were as
follows: 150 mg twice daily fed, 150 mg
twice daily fasted, and 300 mg once
daily fasted

HIV-1 RNA measurements were
performed daily for 40 days after the
start of treatment
37
HIV-1 RNA Log10 Time Course
Adapted from Rosario. J Acquir Immune Defic Syndr, Volume 42(2). 2006.183-191
38
Measured and Predicted HIV-1 RNA log10
Adapted from Rosario. J Acquir Immune Defic Syndr, Volume 42(2). 2006.183-191
39
Measured and Model Simulated HIV-1 RNA log10
Adapted from Rosario. J Acquir Immune Defic Syndr, Volume 42(2). 2006.183-191
40
Predicted Inhibition Fraction in Function of Mean Viral
Load Drop
Adapted from Rosario. J Acquir Immune Defic Syndr, Volume 42(2). 2006.183-191
41
Summary: QCP Enabled Drug Development Program
Prior Knowledge
-Analogues
-Disease
-Competitors
-Patients
-Discovery/Pre-clinical
Drug Molecule
O
H3C
O
HN
CH3
N
N
N
O
S
N
O
CH3
Clinical Development Plan
Trial
1
Trial
2
Trial
…
Drug Product
Trial
N
N
H3C
-indication
-patients
-formulation
-dose
-safety/ efficacy
QCP Enablers: Reducing Uncertainty in Risk/Benefit
Drug and Disease Modeling
Dose Response, PK-PD and Dosing
Targeted Label Information Optimal Use
Adaptive Trial Design
“The best way to predict the future is to create it”
42
– Peter F. Drucker
Acknowledgements

John Wagner (Merck)

Gary Herman (Merck)

Marc Pfister (BMS)

Joga Gobburu (FDA)
43
Questions
44
Modeling and Simulation in Drug Development
45