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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
6
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)
7
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)
10
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
11
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
12
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.
13
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.
14
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
16
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
17
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
18
Increase Effect Target to Account for Variability in Exposure
Mean target for response is now >40% to account for variability in exposure
19
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
20
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
delete
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?
delete
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
30
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