FUTURE CONSIDERATIONS FOR PKPD RESEARCH

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FUTURE CONSIDERATIONS FOR PK/PD RESEARCH

• Terrence F. Blaschke, M.D.
• Professor of Medicine and Molecular Pharmacology
• Stanford University

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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?

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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

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PK/PD Modeling

• What is meant by this expression?

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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
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• 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)

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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)
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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
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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
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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.
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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.

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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 PIs 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
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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

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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!)
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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

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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!)
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In Vitro Pharmacokinetic-Pharmacodynamic System
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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

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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!)
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A simple PK/PD relationship to help understand
the potential consequences of changes in dose
regimens or formulations
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Dosing Times
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99Inhibition _at_ trough
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Dosing Times
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(Note that the overall antiviral response is the
integrated response over time)
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Dosing Times
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98Inhibition _at_ trough
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Dosing Times
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Dosing Times
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96Inhibition _at_ trough
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Dosing Times
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0
Dosing Times
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90Inhibition _at_ trough
Dosing Times
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0
Dosing Times
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85Inhibition _at_ trough
Dosing Times
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0
Dosing Times
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72Inhibition _at_ trough
Dosing Times
24
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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

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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

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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

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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

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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)