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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?. OUTPUTS. INPUTS. Data. Portable, integrated form of knowledge. Clearly Defined Assumptions.

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Hcv model development industry perspective

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
What Do We Want From HCV Models?




Portable, integrated form of knowledge

Clearly Defined


Exploration of Knowledge



Integrated, mathematical representation of all inputs

Ideas & Scientific



Predictions vs. Observations

EnhancedDecision Making

What kind of questions might we answer with models
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
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
HCV Viral Dynamics Model: End Goal

System parameters

  • Rates for infection & production of virus

  • Rates for clearance of virus & hepatocytes

  • Regeneration of hepatocytes

Drug parameters

Patient parameters

  • Efficacy

  • Dose/exposure response

  • Against different GTs and RAVs

  • Patient population (naïve, experienced, null, etc)

  • PR responsiveness

  • IL28B genotype

  • Other factors relevant for IFN-free regimens?

  • Resistance

  • Shift in drug efficacy

  • Baseline amounts and relative fitness of RAVs selected by drug

  • HCV genotype

  • GT1, 2, 3, etc

  • Baseline HCV

  • Pre-existing RAVs from prior treatment or polymorphisms

  • High vs low baseline viral load

  • Combinations

  • Efficacy is additive, synergistic, etc?

  • Resistance with combination

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

Complex multifactorial problem
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
Merck HCV Viral Dynamics Model: Current State

Drug effect only on production of virus

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

Total # of hepatocytes assumed to remain constant (T0)

Model applied to several compounds: MK-5172, MK-7009, Peg-IFN, RBV, boceprevir, etc

Example 1 mk 7009 developing a model for multiple compounds patient populations
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
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
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.

“Treatment Naïve”

“Treatment Experienced”

Final model can be used to simulate many scenarios
Final Model Can Be Used to Simulate Many Scenarios different parameter distributions

Example: Simulated MK-7009 Dose-Response with PR in Treatment-Naïve Patients

MK-7009+PR through treatment period

Example 1 conclusions
Example 1 Conclusions different parameter distributions

  • 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 5172 leveraging in vitro data
Example 2: MK-5172 different parameter distributionsLeveraging 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 fit
Data & Model Fit different parameter distributions

Monotherapy predictions
Monotherapy Predictions different parameter distributions

Dose differentiation for GT1 predicted to be at or below 10 mg

50 mg dose for GT3 predicted to be no different than placebo

Setting up simulation of combination therapy simulation for efficacy against ravs
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

Simulation of combination therapy percent below limit of detection
Simulation of Combination Therapy for Efficacy Against RAVsPercent below limit of detection

Very high percentage of patients are expected to become undetectable quickly, and remain so while on therapy

Simulation of combination therapy projected breakthrough relapse and svr
Simulation of Combination Therapy for Efficacy Against RAVsProjected % Breakthrough, Relapse, and SVR

Example 2 conclusions
Example #2 Conclusions for Efficacy Against RAVs

  • 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
Conclusions & Wrap-Up for Efficacy Against RAVs

  • 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 for Efficacy Against RAVs

  • 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
Back-Up Slides for Efficacy Against RAVs

Combination therapy model
Combination Therapy Model for Efficacy Against RAVs