HCV Model Development: Industry Perspective
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Transcript of HCV Model Development: Industry Perspective
1Modeling & SimulationIntegrating knowledge, enhancing decisions
HCV Model Development: Industry Perspective
Larissa Wenning
Quantitative Translational Models to Accelerate Hepatitis C Drug Development
August 2, 2012
2Modeling & SimulationIntegrating knowledge, enhancing decisions
What Do We Want From HCV Models?
Exploration of Knowledge
Gaps
Portable, integrated form of knowledge
EnhancedUnderstanding
Ideas & Scientific Knowledge
Clearly Defined Assumptions
Data
Predictions vs. ObservationsPredictions vs. Observations
EnhancedEnhanced Decision Decision Making
MODELINGMODELING
Integrated, mathematical representation of all inputsIntegrated, mathematical
representation of all inputs
INPUTSOUTPUTS
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What Kind of Questions Might We Answer With Models?
• What is the therapeutic window for my compound?– Is there a dose where we can maximize efficacy and minimize
adverse events?
• What are the optimal combinations of compounds?• What is the optimal dosing regimen and duration of
therapy for each of the many patient populations we are interested in?
• What is the impact of factors that alter the pharmacokinetics of my compound(s) on efficacy and/or safety?– Drug-drug interactions, formulation changes, special populations
4Modeling & SimulationIntegrating knowledge, enhancing decisions
What Will It Take To Get to Enhanced Decision Making?
• Flexible, standardized model structures• Understanding of relationship between in vitro
and in vivo data• Ability to leverage data from the outside world
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HCV Viral Dynamics Model: End Goal
Core viral dynamics model with clear separation between parameters associated with the
biological system (virus, hepatocytes), and those associated with the drug; can “plug and play”
parameter sets to simulate combinations of drugs in different populations of interest
Patient parameters
Patient population (naïve, experienced, null, etc)•PR responsiveness•IL28B genotype•Other factors relevant for IFN-free regimens?
HCV genotype•GT1, 2, 3, etc
Baseline HCV•Pre-existing RAVs from prior treatment or polymorphisms•High vs low baseline viral load
Drug parameters
Efficacy•Dose/exposure response•Against different GTs and RAVs
Resistance•Shift in drug efficacy•Baseline amounts and relative fitness of RAVs selected by drug
Combinations•Efficacy is additive, synergistic, etc?•Resistance with combination
•Rates for infection & production of virus•Rates for clearance of virus & hepatocytes•Regeneration of hepatocytes
System parameters
ClearanceClearance
Drug Inhibits Production of Virus; Higher potency for
Wild-Type than Mutant
ClearanceClearance
ClearanceClearance
ClearanceClearance
InfectionInfection
Regeneration
Wild-TypeVirus
InfectionInfectionNew InfectionNew Infection New InfectionNew Infection
Wild Type Infected
Cells
Resistant Infected
Cells
Wild Type Virus
Resistant Virus
Uninfected Cells
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Complex, Multifactorial Problem
• As a single company, we do not have enough data to address all of the relevant factors in a timely manner!
• Must draw data from the outside world & leverage non-clinical data
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Merck HCV Viral Dynamics Model: Current State
Uninfected cells (T)
Wild-type infected cells (Iwt)
Mutant infected cells (Im)
Wild-type virions (Vwt)
Mutant virions (Vm)
T + Iwt + Im = T0
Vwt T
Iwt
pwt (1-wt) (1 – ζ) Iwt
Im
Vm T pm (1-m) (1 – ζ) Im
c Vm
c Vwt
pwt (1-wt) ζ Iwt
pm (1-m) ζ Im
Uninfected cells (T)
Wild-type infected cells (Iwt)
Mutant infected cells (Im)
Wild-type infected cells (Iwt)
Mutant infected cells (Im)
Wild-type virions (Vwt)
Mutant virions (Vm)
Wild-type virions (Vwt)
Mutant virions (Vm)
T + Iwt + Im = T0
Vwt T
Iwt
pwt (1-wt) (1 – ζ) Iwt
Im
Vm T pm (1-m) (1 – ζ) Im
c Vm
c Vwt
pwt (1-wt) ζ Iwt
pm (1-m) ζ Im
Total # of hepatocytes assumed to remain
constant (T0)
Drug effect only on production of virus
Model applied to several compounds: MK-5172, MK-7009, Peg-IFN, RBV, boceprevir, etc
Not shown in diagram,but RBV treatment assumed to result in production of non-infectious virus, which also decays at rate c; then measured total virus = infectious + non-infectious
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Example 1: MK-7009Developing a Model for Multiple
Compounds & Patient Populations
• M&S objective: improve the understanding of MK-7009 dose and treatment duration needed to cure HCV in combination with SOC treatment of peg-IFN and RBV, accounting for viral dynamics with resistant virus
• Approach: pool data across multiple studies, including MK-7009 monotherapy, MK-7009 + PR and PR alone (IDEAL study)
Poland et al, American Conference of Pharmacometrics, San Diego, CA, April 2011.
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Model structure is flexible enough to represent range of behaviors in viral load
Illustrative example showing fit to 3 individual subjects in an MK-7009 Phase II study (MK-7009+PR x 4 wks followed by PR x 44 wks):
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Can account for different patient populations using different parameter
distributions• Example: response to treatment with PR using data
from IDEAL study• IDEAL study is in treatment naïve population, but
contains those who will in the future be treatment experienced.
Subgroup Proportion R0 δ ED50peg
SVR 40% 1.86 0.245 0.389
Null responder 20% 3.07 0.184 1.60
Partial responder 10% 2.56 0.193 0.683
Relapser 10% 2.52 0.227 0.454
Other 20% 2.46 0.214 0.771
All 100% 2.36 0.219 0.743
Subgroup Proportion R0 δ ED50peg
Null responder 33% 3.07 0.184 1.60
Partial responder 17% 2.56 0.193 0.683
Relapser 17% 2.52 0.227 0.454
Other 33% 2.46 0.214 0.771
All 100% 2.69 0.203 0.978
“Treatment Naïve”“Treatment Experienced”
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Final Model Can Be Used to Simulate Many Scenarios
Example: Simulated MK-7009 Dose-Response with PR in Treatment-Naïve Patients
MK-7009+PR through treatment period
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Example 1 Conclusions
• A relatively simple viral dynamics model can predict short-term and longer-term response to HCV treatment with peg-IFN+RBV, protease inhibitor monotherapy, and triple combination therapy, in patients with little or no prior treatment.
• With a very small estimated ED50, MK-7009 BID administered with Peg-IFN and RBV is predicted to sharply improve SVR over Peg-IFN and RBV alone.
• Simulations show that proportion of patients cured increases with treatment time and continues to increase long after proportion with undetectable virus plateaus
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Example 2: MK-5172Leveraging in vitro data
• M&S objective: use existing viral dynamics model (developed for MK-7009+/-PR), clinical data from a monotherapy study, and in vitro data to project clinical response for MK-5172 in a number of scenarios
• Challenge: Monotherapy data includes patients infected with GT1 and GT3. GT3 data shows dose response, but GT1 does not (all doses appear maximally efficacious)
• Approach: Fit monotherapy data for GT3 and GT1 simultaneously and assume relative potency observed in vitro (24-fold more potent for GT1 vs GT3) translates directly in vivo; leverage existing model for PR to simulate combination of MK-5172 +PR
Nachbar et al., EASL 2012 & 7th International Workshop on Hepatitis C Resistance & New Compounds
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Data & Model FitGT1 GT3
0 1 0 2 0 3 0 4 0 5 0 6 0 8
6
4
2
0
2
tim e d
log 10V 0
50 m g Q D
0 1 0 2 0 3 0 4 0 5 0 6 0 8
6
4
2
0
2
tim e dlo
g 10V 0
400 m g Q D
0 1 0 2 0 3 0 4 0 5 0 6 0 8
6
4
2
0
2
tim e d
log 10V 0
100 m g Q D
0 1 0 2 0 3 0 4 0 5 0 6 0 8
6
4
2
0
2
tim e d
log 10V 0
600 m g Q D
0 1 0 2 0 3 0 4 0 5 0 6 0 8
6
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tim e d
log 10V 0
200 m g Q D
0 1 0 2 0 3 0 4 0 5 0 6 0 8
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2
tim e d
log 10V 0
800 m g Q D
0 1 0 2 0 3 0 4 0 5 0 6 0 8
6
4
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0
2
tim e d
log 10V 0
50 m g Q D
0 1 0 2 0 3 0 4 0 5 0 6 0 8
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log 10V 0
400 m g Q D
0 1 0 2 0 3 0 4 0 5 0 6 0 8
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tim e dlo
g 10V 0
100 m g Q D
0 1 0 2 0 3 0 4 0 5 0 6 0 8
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tim e d
log 10V 0
600 m g Q D
0 1 0 2 0 3 0 4 0 5 0 6 0 8
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log 10V 0
200 m g Q D
0 1 0 2 0 3 0 4 0 5 0 6 0 8
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g 10V 0
800 m g Q D
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Monotherapy Predictions
• Dose differentiation for GT1 predicted to be at or below 10 mg
• 50 mg dose for GT3 predicted to be no different than placebo
5 . m g Q D
1 0 . m g Q D
3 0 . m g Q D
5 0 . m g Q D
1 0 0 . m g Q D
2 0 0 . m g Q D
4 0 0 . m g Q D
6 0 0 . m g Q D
8 0 0 . m g Q D
0 1 0 2 0 3 0 4 01 0 8
1 0 6
1 0 4
0 .0 1
1
tim e d
V 0
G T 1
5 . m g Q D
1 0 . m g Q D
3 0 . m g Q D
5 0 . m g Q D
1 0 0 . m g Q D
2 0 0 . m g Q D
4 0 0 . m g Q D
6 0 0 . m g Q D
8 0 0 . m g Q D
0 1 0 2 0 3 0 4 01 0 8
1 0 6
1 0 4
0 .0 1
1
tim e d V 0
G T 3
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Setting up Simulation of Combination Therapy: Simulation for Efficacy Against RAVs
• Simulate total viral load for a range of ED50,m/ED50,wt ratios to determine reasonable range for ED50,m
– Sizable breakthrough on treatment in simulations for 30- and 100-fold shift in potency against resistant virus
– Fold shift in potency against resistant virus therefore not greater than 10
ED 50 ,m 3 ED 50 , wt ED 50 ,m 10 ED 50 , wt ED 50 ,m 30 ED 50 , wt ED 50 ,m 100 ED 50 , wt
GT1
2 0 2 4 6 8 1 01 0 8
1 0 6
1 0 4
0 .0 1
1
tim e d
V 0
2 0 2 4 6 8 1 01 0 8
1 0 6
1 0 4
0 .0 1
1
tim e d V 0 2 0 2 4 6 8 1 0
1 0 8
1 0 6
1 0 4
0 .0 1
1
tim e d
V 0
2 0 2 4 6 8 1 01 0 8
1 0 6
1 0 4
0 .0 1
1
tim e d
V 0
GT3
2 0 2 4 6 8 1 01 0 8
1 0 6
1 0 4
0 .0 1
1
tim e d
V 0
2 0 2 4 6 8 1 01 0 8
1 0 6
1 0 4
0 .0 1
1
tim e d
V 0
2 0 2 4 6 8 1 01 0 8
1 0 6
1 0 4
0 .0 1
1
tim e d V 0
2 0 2 4 6 8 1 01 0 8
1 0 6
1 0 4
0 .0 1
1
tim e d
V 0
5 0 m g Q D1 0 0 m g Q D2 0 0 m g Q D4 0 0 m g Q D6 0 0 m g Q D8 0 0 m g Q D
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Simulation of Combination TherapyPercent below limit of detection
• Very high percentage of patients are expected to become undetectable quickly, and remain so while on therapy
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Simulation of Combination TherapyProjected % Breakthrough, Relapse, and SVR
12 we e ks of M K 5172 PR Followe d by 36 We e ksof PR Alone 48 We e ks Tota l Tre atme nt
dose mg A ssumed ED 50 ,m shift from ED 50 ,w t
3 fold 10 foldbreakthrough relap se SVR breakthrough relap se SVR
10 3 6 80 1 6 7430 4 4 88 3 5 8050 4 3 91 3 4 84
100 3 2 94 3 4 89200 3 2 95 3 3 92400 3 3 95 3 2 94
12 we e ks of M K 5172 PR Followe d by 12 We e ksof PR Alone 24 We e ks Tota l Tre atme nt
dose mg A ssumed ED 50 ,m shift from ED 50 ,w t
3 fold 10 foldbreakthrough relap se SVR breakthrough relap se SVR
10 2 14 72 1 15 6330 2 11 83 2 14 7150 2 10 86 2 12 78
100 2 8 89 2 11 83200 2 8 91 2 9 88400 2 7 92 2 7 90
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Example #2 Conclusions
• Monotherapy study:– In vitro data has been used successfully to bridge efficacy
between genotypes in a viral dynamics model– This tactic may have broader utility to inform relative potency for
genotypes and RAVs in these models for early clinical response prediction
– For GT1: 10 mg QD dose is predicted to be noticeably less efficacious compared to higher doses
– For GT3: 50 mg QD dose is predicted to be similar to placebo in terms of viral load decline
• Subsequent studies:– Simulations with the 2-species combination treatment model
predict high SVR rates with low viral breakthrough due to RAVs – Comparison of future clinical results to such prospective
predictions is planned to further evaluate this early response prediction approach
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Conclusions & Wrap-Up
• HCV viral dynamics models have the potential to be very useful tools for enhancing decision making by development teams
• Flexible, standardized model structures & ability to leverage outside and non-clinical data are critical in the current fast-moving, ever-changing development environment for HCV
21Modeling & SimulationIntegrating knowledge, enhancing decisions
Acknowledgments
• The M&S Network at Merck• Merck’s HCV M&S team: Bob Nachbar, Luzelena Caro,
Julie Stone, many others!• Project teams for MK-7009 and MK-5172• Bill Poland; Pharsight• John Tolsma, Haobin Luo, Jonna Seppanen; RES Group
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Back-Up Slides
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Combination Therapy Model
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