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


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


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


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


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


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


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)






Mutant virions (Vm)


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


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.


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):


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”


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


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


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


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

4

2

0

2

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

6

4

2

0

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

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 d

log 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 dlo

g 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

4

2

0

2

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

6

4

2

0

2

tim e dlo

g 10V 0

800 m g Q D


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


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


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


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


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


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


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


Back-Up Slides


Combination Therapy Model

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HCV Model Development: Industry Perspective

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