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

Alan HartfordAgensys

Tim SchofieldBiologics Consulting Group, Inc.

The 32nd Annual Midwest Biopharmaceutical Statistics Workshop

May 18 – 20, 2009, Muncie, Indiana

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.

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.

Outline

Introduction Bioequivalence (BE) bounds PK/PD Models Predict effect of process variation on

clinical outcome Required clinical information Collaborative modeling

nonclinical/clinical Summary

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

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)

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 M&S 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%†

{

Range of exposure for patient populationdue to variability in PK parameters

Increase dose to ensure mean exposure high enough (~38) to conclude statistical significantly 40% effect

†For this example, 40% was chosen completely arbitrarily and not generally a target chosen for most drugs.

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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 mgAnd should have been confirmedin clinical development

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

Dose:Exposure:Response

Perform simulations1. Assume manufacturing variability limits2. Using dose linear relationship and incorporating PK

model with inter-subject variability, determine effect of additional variability due to manufacturing process on exposure

3. 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 >40% 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

This clinical information provides an upper limit for manufacturing variability on dose.

In clinical development, there is a maximum dose studied or maximum dose found to be safe

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

Required Clinical Information

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

Required Clinical Information (cont.)

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

Modeling approaches are used widely across drug development

These different modeling efforts can be linked across nonclinical and clinical

Additional Modeling Opportunities

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Expanded Problem statement 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

Pro: Can directly study impact of process parameters on patient outcome

Con: Too many combinations to study

Process Parameters (x’s)

Patient Outcomes (z’s)

Potential paths (cont.)

Process Parameters (x’s)

Patient Outcomes (z’s)

PreclinicalModels (ẑ’s)

Pro: Can study many combinations of process parameters in a homogeneous population

Con: Uncertain relationship of response in animals to response in humans

Potential paths – nonclinical and preclinical

Process Parameters (x’s)

Patient Outcomes (z’s)

PreclinicalModels (ẑ’s)

Pro: Can study many combinations of process parameters in a homogeneous population

Con: Uncertain relationship of response in animals to response in humans

Exposure (ž)

Allometric Scaling

Potential paths – nonclinical

Process Parameters (x’s)

QualityAttributes (y’s)

Patient Outcome (z’s)

Pro: Can study many combinations of process parameters in vitro

Con: Less certain relationship of response in vitro to response in humans

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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

-6 0 6 12 18 24 30 36

Specifications

Shelf-Life

Release

Limits

Capability

Limits

Minimum Specification

2Assay

2b̂df

Assaydf

sstb̂Minimum

ty VariabiliCombinedLossMinimum

)Var,Loss,Minimum(fleaseRe Minimum

st-Maximum

abilityAssay Vari-Maximum

Var)f(Maximum,Release Maximum

The piecesSpecifications

Maximum Specification

z)y(g

31

LSL

USL

Dave ChristopherPhRMA CMC SET

X 2

X1

UCL

LCL

Design

Space

Knowledg

eSpace

NOR

The piecesDesign Space

)y(fx

y)x(f1

y

32

Posterior Predicted Reliability with

Temp=20 to 70, Catalyst=2 to 12, Pressure=60, Rxntime=3.0

Catalyst

Tem

p

20

30

40

50

60

70

2 4 6 8 10 12

PressureRxntime

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

such that Prob( is in | , ) 1Y A data x x

= Design Space

Contour plot of p(x) equal toProb (y is in Agiven x & data).

The region inside thered ellipse is thedesign space.

x1=

x2=

John Peterson, GSKJune 11, 2008, Graybill Conference, Ft. Collins, CO

The piecesDesign Space (cont.) )y(fx 1

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

The piecesIVIVC

Potential paths – IVIVC

Process Parameters (x’s)

QualityAttributes (y’s)

PK Profile (ž’s)

Patient Outcome (z’s)

DesignSpace

IVIVC

PK/PDModeling

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

IVIVC

Fdiss=fraction dissolvedFabs=fraction absorbed

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

The piecesIVIVC (cont.)

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

0E

T0ET

TCdtK

CdtKCF

010

CTpT010T

TCdtK

V)X(CdtKCF

The Full Cascade of Information

Processing Parameters (x’s) Quality Attributes (y’s) PK (exposure) Parameters (ž’s) PD or Clinical Outcome (z’s)

Potential paths (cont.)y)x(f

z)y(g

z)z(h

)z(hz Limit1

Limit

)z(gy Limit1

Limit

)y(fx Limit1

Limit

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

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

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