HCV Model Development: Industry
Perspective
Larissa Wenning
Quantitative Translational Models to Accelerate Hepatitis C
Drug Development
August 2, 2012
Modeling & Simulation
Integrating knowledge, enhancing decisions
1
What Do We Want From HCV Models?
INPUTS
OUTPUTS
Data
Portable,
integrated form
of knowledge
Clearly Defined
Assumptions
MODELING
Ideas & Scientific
Knowledge
Integrated, mathematical
representation of all inputs
Exploration
of Knowledge
Gaps
Enhanced
Understanding
Predictions vs. Observations
Enhanced Decision Making
Modeling & Simulation
Integrating knowledge, enhancing decisions
2
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
Modeling & Simulation
Integrating knowledge, enhancing decisions
3
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
Modeling & Simulation
Integrating knowledge, enhancing decisions
4
HCV Viral Dynamics Model: End Goal
System parameters
•Rates for infection & production of virus
•Rates for clearance of virus & hepatocytes
•Regeneration of hepatocytes
Patient parameters
Patient population (naïve,
experienced, null, etc)
•PR responsiveness
•IL28B genotype
•Other factors relevant for
IFN-free regimens?
New Infection
Infection
Clearance
Wild Type
Infected
Cells
Uninfected
Cells
New Infection
Infection
Regeneration
Resistant
Infected
Cells
Baseline HCV
•Pre-existing RAVs
from prior treatment
or polymorphisms
•High vs low
baseline viral load
Modeling & Simulation
Integrating knowledge, enhancing decisions
Clearance
Drug Inhibits Production of
Virus; Higher potency for
Wild-Type than Mutant
Efficacy
•Dose/exposure
response
•Against different GTs
and RAVs
Clearance
HCV genotype
•GT1, 2, 3, etc
Wild
Wild-Type
Type
Virus
Virus
Drug parameters
Resistant
Virus
Clearance
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
Resistance
•Shift in drug efficacy
•Baseline amounts and
relative fitness of RAVs
selected by drug
Combinations
•Efficacy is additive,
synergistic, etc?
•Resistance with
combination
5
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 nonclinical data
Modeling & Simulation
Integrating knowledge, enhancing decisions
6
Merck HCV Viral Dynamics Model:
Current State
Drug effect only on
production of virus
Iwt
Vwt T
Wild-type
infected
cells (Iwt)
c Vwt
pwt (1-wt) (1 – ζ) Iwt
Wild-type
virions (Vwt)
Not shown in diagram,
but RBV treatment
assumed to result in
production of noninfectious virus, which
also decays at rate c;
then measured total
virus = infectious +
non-infectious
pwt (1-wt) ζ Iwt
Uninfected
cells (T)
pm (1-m) ζ Im
T + Iwt + Im = T0
Vm T
Mutant
infected
cells (Im)
Im
Total # of hepatocytes
assumed to remain
constant (T0)
Modeling & Simulation
Integrating knowledge, enhancing decisions
pm (1-m) (1 – ζ) Im
Mutant
virions (Vm)
c Vm
Model applied to several compounds: MK-5172,
MK-7009, Peg-IFN, RBV, boceprevir, etc
7
Example 1: MK-7009
Developing 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.
Modeling & Simulation
Integrating knowledge, enhancing decisions
8
Model structure is flexible enough to
represent range of behaviors in viral load
5. Example
Individualsubjects
Predictions
Illustrative example Figure
showing
fit to 3 individual
in an MK-7009
Study) by PR x 44 wks):
Phase II study (MK-7009+PR x(Phase
4 wks 2A
followed
Obs.
Obs. Unused in Fit
Ind. Pred.
Observations, Predictions (log10 IU/mL)
0
Patient 1
ID:72965
5
Pop. Pred.
10
Ind. Pred. Mutant
15
Patient
2
ID:72966
Patient 3
ID:73242
6
4
2
0
-2
-4
0
5
10
15
0
5
10
15
Time (weeks)
Modeling & Simulation
Integrating knowledge, enhancing decisions
9
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”
Proportion
R0
δ
ED50peg
SVR
40%
1.86
0.245
0.389
Subgroup
Null responder
20%
3.07
0.184
1.60
Partial responder
10%
2.56
0.193
Relapser
10%
2.52
Other
20%
All
100%
Subgroup
Modeling & Simulation
Integrating knowledge, enhancing decisions
Proportion
R0
δ
ED50peg
Null responder
33%
3.07
0.184
1.60
0.683
Partial responder
17%
2.56
0.193
0.683
0.227
0.454
Relapser
17%
2.52
0.227
0.454
2.46
0.214
0.771
Other
33%
2.46
0.214
0.771
2.36
0.219
0.743
All
100%
2.69
0.203
0.978
10
Final Model Can Be Used to Simulate
Many Scenarios
100%
100%
90%
90%
80%
70%
60%
50%
40%
30%
At 48 Wks Treatment
At 24 Wks Treatment
At 12 Wks Treatment
At 4 Wks Treatment
20%
10%
Proportion of Patients Reaching SVR Threshold
Proportion of Patients with Undetectable HCV RNA
Example: Simulated MK-7009 Dose-Response with PR in Treatment-Naïve Patients
80%
70%
60%
50%
SVR After 48 Wks Tx
SVR After 24 Wks Tx
SVR After 12 Wks Tx
SVR After 4 Wks Tx
40%
30%
Bars: 90% Prediction Interval
20%
10%
Bars: 90% Prediction Interval
0%
0%
0
200
400
600
800
1000
MK-7009 Dose (mg BID)
Modeling & Simulation
Integrating knowledge, enhancing decisions
MK-7009+PR through treatment period
0
200
400
600
800
1000
MK-7009 Dose (mg BID)
11
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
Modeling & Simulation
Integrating knowledge, enhancing decisions
12
Example 2: MK-5172
Leveraging 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
Modeling & Simulation
Integrating knowledge, enhancing decisions
13
Data & Model Fit
GT1
GT3
2
2
2
2
0
2
V0
0
2
V0
0
2
V0
0
2
4
6
4
6
log10
4
6
log10
400 mg QD
log10
50 mg QD
V0
400 mg QD
log10
50 mg QD
4
6
8
8
0 10 20 30 40 50 60
time d
time d
time d
600 mg QD
100 mg QD
600 mg QD
0
2
4
0
2
4
0
2
4
time d
Modeling & Simulation
Integrating knowledge, enhancing decisions
6
8
0 10 20 30 40 50 60
time d
log10
2
4
6
8
800 mg QD
V0
V0
V0
0 10 20 30 40 50 60
2
4
2
0
0 10 20 30 40 50 60
time d
0 10 20 30 40 50 60
time d
200 mg QD
log10
6
8
2
0
0 10 20 30 40 50 60
6
8
time d
800 mg QD
log10
2
4
log10
time d
200 mg QD
2
0
0 10 20 30 40 50 60
6
8
log10
0 10 20 30 40 50 60
6
8
V0
0
2
4
V0
2
V0
2
6
8
0 10 20 30 40 50 60
time d
2
time d
V0
8
0 10 20 30 40 50 60
2
log10
log10
V0
100 mg QD
log10
8
0 10 20 30 40 50 60
2
0
2
4
6
8
0 10 20 30 40 50 60
time d
14
Monotherapy Predictions
GT 1
GT 3
1
1
0.01
4
50. mg QD
5. mg QD
10. mg QD
V0
V0
30. mg QD
10
0.01
5. mg QD
10. mg QD
30. mg QD
10
4
50. mg QD
100. mg QD
100. mg QD
200. mg QD
10
6
400. mg QD
200. mg QD
10
6
400. mg QD
600. mg QD
600. mg QD
800. mg QD
10
8
0
10
20
30
40
time d
• Dose differentiation for GT1
predicted to be at or below 10
mg
Modeling & Simulation
Integrating knowledge, enhancing decisions
800. mg QD
10
8
0
10
20
30
40
time d
• 50 mg dose for GT3 predicted
to be no different than placebo
15
Setting up Simulation of Combination
Therapy: Simulation for Efficacy Against
RAVs
ED50,m
10 ED50, wt
0.01
0.01
0.01
0.01
10
4
10
6
10
8
2
0
2
4
6
8
10
10
4
10
6
10
8
2
0
2
4
6
8
10
V0
1
V0
1
10
4
10
4
10
6
10
6
10
8
10
8
2
0
2
time d
4
6
8
10
2
1
0.01
0.01
0.01
0.01
10
10
6
10
8
2
0
2
4
6
8
10
10
10
6
10
8
V0
1
4
2
0
2
4
time d
2
6
8
10
10
4
10
4
10
6
10
6
10
8
10
8
2
0
2
4
time d
4
6
8
10
6
8
10
50 mg QD
100 mg QD
200 mg QD
400 mg QD
600 mg QD
800 mg QD
time d
1
4
0
100 ED50, wt
time d
1
V0
V0
ED50,m
1
time d
•
30 ED50, wt
1
time d
GT3
ED50,m
V0
GT1
3 ED50, wt
V0
V0
ED50,m
6
8
10
2
0
2
4
time d
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
Modeling & Simulation
Integrating knowledge, enhancing decisions
16
Simulation of Combination Therapy
Percent below limit of detection
•
Very high percentage of patients are expected to become undetectable
quickly, and remain so while on therapy
Modeling & Simulation
Integrating knowledge, enhancing decisions
17
Simulation of Combination Therapy
Projected % Breakthrough, Relapse, and SVR
12 weeks of MK 5172 PR Followed by 36 Weeks
of PR Alone 48 Weeks Total Treatment
dose mg
Assumed ED50,m shift from ED50,wt
breakthrough
10
30
50
100
200
400
3
4
4
3
3
3
3 fold
relapse
6
4
3
2
2
3
SVR
80
88
91
94
95
95
10 fold
breakthrough
relapse
1
3
3
3
3
3
6
5
4
4
3
2
SVR
74
80
84
89
92
94
12 weeks of MK 5172 PR Followed by 12 Weeks
of PR Alone 24 Weeks Total Treatment
dose mg
Assumed ED50,m shift from ED50,wt
breakthrough
Modeling & Simulation
Integrating knowledge, enhancing decisions
10
30
50
100
200
400
2
2
2
2
2
2
3 fold
relapse
14
11
10
8
8
7
SVR
72
83
86
89
91
92
10 fold
breakthrough
relapse
1
2
2
2
2
2
15
14
12
11
9
7
SVR
63
71
78
83
88
90
18
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
Modeling & Simulation
Integrating knowledge, enhancing decisions
19
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
Modeling & Simulation
Integrating knowledge, enhancing decisions
20
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
Modeling & Simulation
Integrating knowledge, enhancing decisions
21
Back-Up Slides
Modeling & Simulation
Integrating knowledge, enhancing decisions
22
Combination Therapy Model
wt
m
wt
m
wt,ni
m,ni
healthy hepatocytes
hepatocytes infected with wildtype virus
hepatocytes infected with mutant virus
wildtype virus
mutant virus
wildtype noninfectious virus
mutant noninfectious virus
T
0
c
pwt
pm
wt
Modeling & Simulation
Integrating knowledge, enhancing decisions
m
total number of hepatocytes
hepatocyte infection rate
infected hepatocyte death rate
viral clearance rate
mutation rate
wildtype viral production rate
mutant viral production rate
combined drug and IFN effectiveness on wildtype virus
combined drug and IFN effectiveness on mutant23
virus
RBV effectiveness