Using PK/PD Modeling to Simulate Impact of Manufacturing Process Variability

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Using PK/PD Modeling to Simulate Impact of
Manufacturing Process Variability

• Alan Hartford
• Agensys
• Tim Schofield
• Biologics Consulting Group, Inc.
• The 32nd Annual Midwest Biopharmaceutical
  Statistics Workshop
• May 18 20, 2009, Muncie, Indiana

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Introduction

• The manufacturer has the responsibility of
  keeping manufacturing process variability of the
  dose in control.
• One method for assuring that a product, after a
  re-formulation, is viable is to perform a
  clinical trial showing bioequivalence (BE) of
  exposure endpoints.

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Investigate with Modeling

• PK/PD models can be used to simulate the impact
  of variations of dose on the response cascade of
• dose ? exposure ? pharmacodynamics ? clinical
  outcome
• These models can address the appropriateness of
• the BE study
• choice of bounds
• Specific information from clinical development is
  needed as input for this PK/PD modeling.
• Important information from nonclinical
  development can also be incorporated to save
  clinical resources.

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Outline

• Introduction
• Bioequivalence (BE) bounds
• PK/PD Models
• Predict effect of process variation on clinical
  outcome
• Required clinical information
• Collaborative modeling nonclinical/clinical
• Summary

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Current Practice

• When a new formulation is developed, the current
  practice is to perform a clinical study to show
  the new formulation is bioequivalent to a
  previously studied formulation.
• This allows for inference of conclusions from
  earlier studies for the new formulation.
• i.e., efficacy results from a Ph III study can be
  inferred to a new formulation

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BE Requirements

• Strict bioequivalence (BE) bounds are used for
  exposure endpoints (AUC and Cmax)
• The geometric mean for AUC and Cmax is calculated
  for both formulations.
• If the formulations are similar, the ratio of
  exposure for new formulation / old formulation
  1.
• For BE, AUC and Cmax of new formulation compared
  to approved formulation must have 90 CI of GMRs
  to be within (0.80, 1.25)

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BE Requirements (cont.)

• This strict BE requirement is standard for many
  clinical comparisons (e.g., interaction studies,
  elderly/young studies, insufficiency studies)
• But (0.80, 1.25) may not be appropriate for
  clinical reasons
• (0.80, 1.25) is standard for when no clinical
  justification can be given for other bounds
• If victim drug has wide therapeutic window, then
  wider bounds are appropriate

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BE Requirements (cont.)

• For drug interaction studies, FDA suggests that
  boundaries can be justified by a sponsor based on
  population average dose, concentration-response
  relationships, PK/PD models, or other
• So the onus is on the sponsor to justify other
  comparability bounds

FDA Guidance Drug Interaction Studies--Study
Design, Data Analysis, and Implications for
Dosing and Labeling (draft 2006)
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Example of Using Alternate Comparability Bounds

• In the case of testing if a new antibody has an
  effect on the exposure of a standard of care
  chemotherapy
• Not ethical to sample many patients with cancer
  for this interaction trial
• Variability of the chemo (AUC, Cmax) was high
• FDA accepted plan with small N which required GMR
  to be within (0.80, 1.25) but that the 90 CI to
  be within (0.70, 1.43)

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FDA Guidance to Clinical

• FDA Guidance for some studies (e.g., interaction
  studies) allows for some leeway for sponsor to
  clinically justify alternative bounds
• PK/PD modeling can be used to justify different
  bounds for comparing formulations

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Modeling Simulation

• PK/PD MS and Clinical Trial Simulation can
  provide insight to
• Effect of variation in dose on exposure
• Effect of variation in exposure on PD
• Effect of PD on clinical endpoint

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Example Selection of Dose to Achieve 40 Effect
Example Assume Emax model is appropriate for a
drug
Target of 40 Response
This target of exposure not appropriate. Does not
allow for variability in exposure or effect.
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Suppose Clinical Selection of Dose was to Achieve
40
For this example, 40 was chosen completely
arbitrarily and not generally a target chosen
for most drugs.
Range of exposure for patient population due to
variability in PK parameters

Increase dose to ensure mean exposure high enough
(38) to conclude statistical significantly ? 40
effect
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Assume the following dose-linear relationship is
observed/verified
Need exposure of 38 for desired clinical effect
Needed dose is then 15 mg And should have been
confirmed in clinical development
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Manufacturing Variability

• Every manufacturing process has specification
  limits
• Product from this process is allowed to vary
  within these limits
• In this manner, the dosage of drug is not
  constant across a batch
• The effect of the manufacturing variability is
  what we need to understand

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Incorporating Manufacturing Variability

• To determine effect of manufacturing variability
  on the sequence of
• DoseExposureResponse
• Perform simulations
• Assume manufacturing variability limits
• Using dose linear relationship and incorporating
  PK model with inter-subject variability,
  determine effect of additional variability due to
  manufacturing process on exposure
• Using PK/PD model (e.g., Emax), determine effect
  of compounded variability in exposure in step 2
  on clinical or PD effect

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Effect on Exposure due to Manufacturing
Variability and Subject-to-Subject Variability in
PK
May need to increase dose beyond 15 mg to ensure
exposure for all above 38, but only if safe
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Increase Effect Target to Account for Variability
in Exposure
Mean target for response is now gt40 to account
for variability in exposure
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Limits for Effect

• Note that the 40 effect size was determined from
  Ph III development and not an effect size
  targeted in earlier studies
• Likewise, an upper limit for effect to be
  determined by the safety profile observed
  throughout clinical development
• Simulations using PK/PD models will help to
  determine acceptable limits of manufacturing
  variability

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Including Safety Information from Clinical
Development
In clinical development, there is a maximum dose
studied or maximum dose found to be safe
This clinical information provides an upper limit
for manufacturing variability on dose.
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Required Clinical Information

• The information needed from clinical development
  includes
• Upper limit on exposure due to safety
• Target response for efficacy

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Required Clinical Information (cont.)

• Additionally, clinical information is needed to
  build the PK/PD model
• Need to sample responses across wide range of
  exposure values to understand what model is
  appropriate
• Note that this can be at odds with goals of
  adaptive designs

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Dose-Exposure Relationship

• Earlier, we assumed a linear dose-exposure
  relationship
• However, this relationship might not be known for
  patients for a new formulation
• Nonclinical and preclinical modeling could be
  used to provide this information

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Additional Modeling Opportunities
delete

• Modeling approaches are used widely across drug
  development
• These different modeling efforts can be linked
  across nonclinical and clinical

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Expanded Problem statement
delete

• How can nonclinical development collaborate with
  clinical development to demonstrate that a
  manufacturing process is delivering product to
  the patient that is safe and effective?

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Potential paths
Process Parameters (xs)

• Pro Can directly study impact of process
  parameters on patient outcome
• Con Too many combinations to study

Patient Outcomes (zs)
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Potential paths (cont.)
Process Parameters (xs)

• Pro Can study many combinations of process
  parameters in a homogeneous population
• Con Uncertain relationship of response in
  animals to response in humans

Preclinical Models (?s)
Patient Outcomes (zs)
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Potential paths nonclinical and preclinical
Allometric Scaling
Process Parameters (xs)

• Pro Can study many combinations of process
  parameters in a homogeneous population
• Con Uncertain relationship of response in
  animals to response in humans

Preclinical Models (?s)
Patient Outcomes (zs)
Exposure (ž)
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Potential paths nonclinical
Process Parameters (xs)

• Pro Can study many combinations of process
  parameters in vitro
• Con Less certain relationship of response in
  vitro to response in humans

Quality Attributes (ys)
Patient Outcome (zs)
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The piecesSpecifications
Maximum Specification

• Starts with clinically supportable maximum and
  minimum limits (specifications)
• Maximum release calculated from release assay
  variability
• Minimum release calculated from shelf life,
  stability, and release assay variability's

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The piecesDesign Space
y
UCL
USL
LCL
LSL
Design Space
NOR
X1
X2
Knowledge Space
Dave Christopher PhRMA CMC SET
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The piecesDesign Space (cont.)
Posterior Predicted Reliability with
Temp20 to 70, Catalyst2 to 12, Pressure60,
Rxntime3.0
Rxntime
Pressure
70
0.7
0.6
60
Design Space
0.5
Contour plot of p(x) equal to Prob (y is in
A given x data). The region inside the red
ellipse is the design space.
50
0.4
x2
Temp
0.3
40
0.2
30
0.1
0.0
20
2
4
6
8
10
12
x1
Catalyst
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The piecesIVIVC

• An in-vitro in-vivo correlation (IVIVC) has been
  defined by the FDA as a predictive mathematical
  model describing the relationship between an
  in-vitro property of dosage form and an in-vivo
  response
• Main objective is to serve as a surrogate for in
  vivo bioavailability and to support biowaivers
• Might also be used to bridge in vitro and in vivo
  activity along the pathway from manufacturing
  process to patient outcome
• IVIV relationship (IVIVR) more appropriate to the
  goal g(y)ž

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Potential paths IVIVC
Design Space
IVIVC
PK/PD Modeling
Process Parameters (xs)
Quality Attributes (ys)
PK Profile (žs)
Patient Outcome (zs)
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IVIVC

• FDA guidance offers 5-levels of correlation
• Level A correlation comes closest to defining
  IVIVR the purpose of level A correlation is to
  define a direct relationship between in vivo data
  such that measurement of in vitro dissolution
  rate alone is sufficient to determine the
  biopharmaceutical rate of the dosage form

Fdissfraction dissolved Fabsfraction absorbed
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The piecesIVIVC (cont.)

• IVIVR established from link model among in
  vitro dissolution, in vivo plasma levels, and in
  vivo absorption

• Fraction absorbed is obtained in one of 3-ways
• Wagner-Nelson method
• CT plasma C at time T
• KE elimination rate constant
• Loo-Riegelman method
• (XP)T C in peripheral comp. after oral
• VC volume in central compartment
• K10 elimination rate constant after IV
• Numerical deconvolution

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The Full Cascade of Information

• Processing Parameters (xs)
• Quality Attributes (ys)
• PK (exposure) Parameters (žs)
• PD or Clinical Outcome (zs)

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Potential paths (cont.)
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Summary

• A process has been outlined for using information
  from different stages of drug development to
  determine process limits
• Process will inform decision about needing
  additional clinical trials for new formulations

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Summary (cont.)

• Clinical information is needed for successful
  modeling
• Target for efficacy
• Safety
• In total, the therapeutic window
• IVIVC or IVIVR models needed to inform about
  exposure

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Summary (cont.)

• Both PK/PD Modeling and IVIVC modeling are
  time-consuming and tedious and must be integrated
  early into development
• Designs of clinical trials must be designed so
  that information needed for building models is
  available