“FUTURE CONSIDERATIONS FOR PK/PD RESEARCH”

1. “FUTURE CONSIDERATIONS FOR PK/PD RESEARCH” Terrence F. Blaschke, M.D. Professor of Medicine and Molecular Pharmacology Stanford University

2. Issue for discussion: Can PK/PD modeling help to devise dosage regimens that will have better efficacy and/or safety without adding time/cost to drug development?

3. Premise: • There is a need for alternative dose-finding methods since all reasonable regimens cannot be studied using the current standard of a 48 week controlled study of efficacy and safety • Patient resources are limited • Time requirements would be excessive, and delay patient access to alternative regimens • HIV therapeutics is a fast-moving field, and approved regimens may not be acceptable as controls to patients or investigators

4. Combinatorials: the numbers problem IF: n! M = p!(n-p)! FOR: n=31 M=4495 n=23 M=1771 n=14 M=364 n=6 M=20 (p=3)

5. “PK/PD Modeling” What is meant by this expression?

6. Pharmacokinetics (PK) describes the time course of drug concentrations in plasma (and sometimes in other fluids and tissues) resulting from a particular dosing regimen Pharmacodynamics (PD) expresses the relationship between drug concentrations in plasma (and sometimes in other fluids and tissues) and a resulting pharmacological effect

7. A PK/PD Model combines • A model describing drug concentrations vs. time (PK) with • A model describing the relationship of effect vs. concentration (PD), and • A statistical model describing variation in intra- and inter-individual PK/PD models • to predict the time-course and variability of effect vs. of time. • Note: Only mechanistic PK/PD models can be relied upon for extrapolation (I.e., for prediction vs. description)

8. Process: • Build PK Model • Build PD Model • Link PK and PD models • Simulate treatment regimens or trials for useful predictions An Example: (Next few slides courtesy of Abbott Laboratories and Pharsight Corporation)

9. This simple model links adherence, pharmacokinetics, and viral pharmacodynamics to treatment outcome in a patient population. Antiretroviral Experience, Disease Severity Prescribed PI Doses Actual Dose Plasma Conc Adherence Pharmaco- kinetics Pharmaco- dynamics Viral Load In-vitro data, Data Source Two multiple-dose Phase I studies, One Phase II study MEMS data, Public literature Two one-comp. PK models with enzyme inhibition and induction Model Random, (beta distribution) fractional adherence rate Standard two-strain viral model

10. Pharmacokinetic Modeling: The PK model accounts for dose-dependent bioavailability, competitive inhibition, and exposure-dependent enzyme induction. Enzyme induction when applicable Absorption Site Plasma Fraction Absorbed When Applicable Time PI Competitive Enzyme Inhibition E l i m i n a t i o n Absorption Site Plasma Fraction Absorbed RTV Enzyme Induction Time

11. r. t. inhibitors r. t. inhibitors infect infect mutate: m1 mutate: m2 T T V2 V1 virus: 2 = mutant virus: 1 = wild-type release 1–m1 1–m2 release protease inhibitors protease inhibitors TA1 TA2 actively infected TL1 TL2 long-lived cells Pharmacodynamic ModelingThe model was previously published.# This simple PD model includes two viral strains (wild type and a pre-existing mutant), long-lived infected and actively infected cells, and different sites of action by PIs and NRTIs. # Hsu A, Wada DR, Liu M et al., PK/PD Modeling of ABT378/Ritonavir Clinical Trials, Including an Adherence Factor. Seventh European Conference on Clinical Aspects and Treatment of HIV Infection, 1999, Oct 23-27.

12. Simulation • For assessing the effect of PK and adherence variability, 400 subjects were simulated for 48 weeks for each of the six regimens, for a dose-time perturbation of 1.6 hr. Adherences with a beta distribution and with a mean of 81% and SD of 0.20 were used for BID regimens, and a mean of 84% and SD of 0.19 were used for QD regimens.

13. Abbott used this approach to compare various combinations PI dosing regimens which included low and moderate dose ritonavir and were able to predict: • The range of peak and trough concentrations for each of the PI’s in the regimen, and the ratio of trough concentrations to IC50 values • The effect of varying degrees of nonadherence on the fraction of patients who were likely to experience virological failure The PK/PD model and the simulations done with it were observed to be consistent with data from several actual trials carried out by Abbott

14. Building and Evaluating PK/PD Models • PK models • As part of conventional PK studies, information on inter- and intra-subject variability is needed • For drug combinations, interactions should be evaluated at steady-state with dose regimens that include/bracket those likely to be used • Consider measuring binding proteins such as 1 acid glycoprotein and unbound drug concentrations

15. This simple model links adherence, pharmacokinetics, and viral pharmacodynamics to treatment outcome in a patient population. Prescribed PI Doses Actual Dose Plasma Conc Adherence Pharmaco- kinetics Pharmaco- dynamics Viral Load Two one-comp. PK models with enzyme inhibition and induction Model Single-coin model, beta distribution of fractional adherence Standard two-strain viral model Antiretroviral Experience, Disease Severity Data Source Two multiple-dose Phase I studies, One Phase II study MEMS data, Public literature In-vitro data, DATA NEEDED TO CREATE PK/PD MODELS (Much of it is pre-existing scientific knowledge!)

16. Building and Evaluating PK/PD Models • PD models • Require a combination of in vitro and in vivo data incorporated into a mechanistic model of viral dynamics (which incorporates baseline CD4, HIV RNA copy number, possibly prior treatment as well) • Relate in vitro and in vivo sensitivities using early monotherapy data from naïve subjects with wild-type virus • Expand model to pretreated patients using additional in vitro data using various resistant mutants found in vivo

17. This simple model links adherence, pharmacokinetics, and viral pharmacodynamics to treatment outcome in a patient population. Prescribed PI Doses Actual Dose Plasma Conc Adherence Pharmaco- kinetics Pharmaco- dynamics Viral Load Two one-comp. PK models with enzyme inhibition and induction Model Single-coin model, beta distribution of fractional adherence Standard two-strain viral model Antiretroviral Experience, Disease Severity Data Source Two multiple-dose Phase I studies, One Phase II study MEMS data, Public literature In-vitro data, DATA NEEDED TO CREATE PK/PD MODELS (Much of it is pre-existing scientific knowledge!)

18. In Vitro Pharmacokinetic-Pharmacodynamic System

19. Building and Evaluating PK/PD Models • Evaluate PK/PD model by comparing outcome of trial simulations to actual data from trials in experienced patients • Response variables: treatment failure and/or presence of genotypic or phenotypic resistance • Must incorporate realistic estimates of drug-taking behavior into the simulation • For the clinical trial used for comparison, actual measures of adherence would be preferable since the effect of different adherence patterns on resistance development is not known

20. This simple model links adherence, pharmacokinetics, and viral pharmacodynamics to treatment outcome in a patient population. Prescribed PI Doses Actual Dose Plasma Conc Adherence Pharmaco- kinetics Pharmaco- dynamics Viral Load Two one-comp. PK models with enzyme inhibition and induction Model Single-coin model, beta distribution of fractional adherence Standard two-strain viral model Antiretroviral Experience, Disease Severity Data Source Two multiple-dose Phase I studies, One Phase II study MEMS data, Public literature In-vitro data, DATA NEEDED TO CREATE PK/PD MODELS (Much of it is pre-existing scientific knowledge!)

21. A simple PK/PD relationship to help understand the potential consequences of changes in dose regimens or formulations

23. 0 Dosing Times 8 16 24

24. 99%Inhibition @ trough 0 Dosing Times 8 16 24 (Note that the overall antiviral response is the integrated response over time)

25. 0 Dosing Times 12 24

26. 98%Inhibition @ trough 0 Dosing Times 12 24

27. 0 Dosing Times 24

28. 96%Inhibition @ trough 0 Dosing Times 24

30. 0 Dosing Times 8 16 24

31. 0 90%Inhibition @ trough Dosing Times 8 16 24

32. 0 Dosing Times 12 24

33. 0 85%Inhibition @ trough Dosing Times 12 24

34. 0 Dosing Times 24

35. 0 72%Inhibition @ trough Dosing Times 24

36. PK/PD modeling for AIDS: Where do we stand today? • PK models for antivirals are generally well-defined • Several good models of viral dynamics have been developed • For PIs and NNRTIs, plausible mechanistic relationships between drug concentrations in plasma and inhibition of viral replication have been proposed

37. General PK/PD modeling: Where do we stand today? • Although simulations using full, mechanistic PK/PD models are consistent with observed data, the robustness of such models in a variety of settings and dosing regimens has not yet been demonstrated • It is too soon to conclude that PK/PD modeling can substitute for confirmatory trials

38. PK/PD modeling: Where do we go from here? • Continue to improve and refine mechanistic PK/PD models, using in vitro and in vivo data • for individual drugs, in vitro data needs to be related to in vivo data, including the effect of protein binding, early in development when monotherapy data are being generated • Generate concentration-response data in early development

39. PK/PD modeling: Where do we go from here? • Use PK/PD models to plan trials, limiting dosing regimens and drug combinations to those likely to demonstrate acceptable efficacy/toxicity, and be robust to non-adherence • Measure adherence as part of the trial

40. PK/PD modeling: Where do we go from here? • Consider whether PK/PD modeling based on short term (e.g.,  24 weeks) studies can be used as surrogate evidence of long term efficacy • Differences in outcome between 24 and 48 weeks are more likely due to non-adherence rather than regimen failure (use-effectiveness vs. method effectiveness)