A lifecycle approach to bioassay validation · 2019-04-19 · A lifecycle approach to bioassay validation TIM SCHOFIELD OWNER & CONSULTANT CMC SCIENCES, LLC CASSS DC DISCUSSION GROUP

A lifecycle approach to bioassay validation

T I M SCHOFI ELDOWN ER & CON SULTAN T

CM C SC I EN CES, LLC

CA SSS DC D I SCUSS ION GROUPROCK VI LLE , MD, A PR I L 5 , 2019

Outline

Quality by design for analytical methods

The analytical target profile

Fitness for use

Lifecycle stages:

1. Design and development of a bioassay

2. Bioassay qualification

3. Continued performance verification

CMC Sciences, LLC DCDG APRIL 5, 2019 2

Validation

Quality by Design for analytical methods (AQbD)

Industry and regulators have begun to recognize that analytical methods generate a product – measurements

Like pharmaceutical products, measurements should have adequate quality to meet their intended use –making a decision

The fundamental goals of product development are:◦ Safety and efficacy (hitting the clinical target)

◦ Variance reduction

The fundamental goals of analytical development are:◦ Accuracy (hitting the analytical target)

◦ Variance reduction


AQbDFundamentals

◦ “Validation” is a continuous process

◦ “Validation” is a demonstration of fitness-for-use

◦ “Demonstration” should include consideration of risks

◦ Risk of making decisions from an “invalid” procedure

◦ Risk of invalidating a procedure which is in control

◦ Risk is directly related to “uncertainty”


Risk

Uncertainty

The analytical target profile (ATP)

The list of requirements throughout the bioassay lifecycle (from method selection to method retirement)

◦ Performance requirements – specificity, precision, accuracy, LOQ . . .

◦ Business requirements – throughput, cost, ease of implementation

◦ Regulatory requirements (ICH Q2)

The ATP is being viewed as an opportunity to achieve regulatory flexibility as the established conditions for analytical methods (ala ICH Q12)


Fitness for useFitness for use can be viewed as meeting the ATP

◦ Typically considered in the context of batch release

◦ A release paradigm:


Life

Release

Specifications

Shelf-

Control

Lim its

-6 0 6 12 18 24 30 36

Minimum Requirement

Maximum Requirement

Requirements

– Scientifically/clinically justified minimum and maximum quality requirements

– Release limits determined to ensure (with specified confidence) that the CQA meets its quality requirements at release and throughout shelf-life

– Control limits associated with process control

Target specifications

Having a target for commercial specifications facilitates process, formulation and analytical development – including development of a lifecycle management program

◦ “Budget” allocation

◦ The “analytical budget” is the basis of the ATP

◦ The “lifecycle management budget” is the basis of post approval change management


Formulation budget

Process and lifecycle management budget

Analytical budget

Lifecycle stages (ala USP)Stage 1 – Method design, development and understanding

◦ Method selection and optimization

Stage 2 – Procedure performance qualification

◦ “. . . confirms the analytical procedure is capable of delivering reproducible data that consistently meet the performance criteria defined in the ATP while operated subject to the noise variables that may be experienced.”

Stage 3 – Procedure performance verification

◦ “. . . routine monitoring of the analytical procedure's performance and evaluation to determine if the analytical procedure, as a result of any change, is still fit for purpose.”


Stage 1: Method design, development & understanding

Selection of method(s)

◦ Bases – release, stability, process/formulation development, characterization, lifecycle management

◦ Potency assay methods

◦ In vivo bioassay (mouse potency or challenge assays)

◦ In vitro bioassay (antigencity in vitro – IVRP; cell based bioassay)

◦ Physical/chemical – properties related directly to potency (e.g., o-acetyl content in vaccines; fucose content in monoclonals)

Design – relative potency bioassay◦ Parallel curve bioassay (4PL)

◦ Range should support its uses

◦ Robust to changes in sensitivity


0

20

40

60

80

100

120

140

160

180

200

220

1 10 100 1000 10000

Re

sp

on

se

Concentration

Relative Potency Determination

Test

Standard

RP

0

50

100

150

200

1 10 100 1000

Re

sp

on

se

Concentration

Dilutional Linearity

10

Stage 1: Method design, development & understanding (cont.)

Optimization using DOE◦ Screening design and results

0

50

100

150

200

250

1 10 100 1000

Concentration

Resp

on

se

0

5

10

15

20

25

30

35

40

%C

V

– Precision

profiles

Exp. Capt. Biot. EnzCult Vol. Incub. NBCl

1 250 250 750 100 1 1

2 250 600 300 100 1 4

3 250 600 750 50 3 1

4 750 600 300 50 1 1

5 250 600 750 50 3 1

6 750 600 300 50 1 1

7 750 250 300 100 3 1

8 250 250 750 100 1 1

9 750 600 750 100 3 4

10 750 250 300 100 3 1

11 250 250 300 50 3 4

12 750 250 750 50 1 4

13 750 600 750 100 3 4

14 250 600 300 100 1 4

15 250 250 300 50 3 4

16 750 250 750 50 1 4

– 26-2 fractional factorial

(Resolution IV)

– Factors and

levels

– Factor effects

10


The method operates reproducibly at the optimum, leaving room to determine a range which ensures quality measurements (adequate range)

Using Bayesian analysis to determine the design space (MODR):

CMC Sciences, LLCDCDG APRIL 5, 2019 11

There is room left for the factors to vary while ensuring adequate performance

𝑓(𝑋1, 𝑋2,…)

𝑒(𝑏0, 𝑏1, … )

X 10,000

±e

𝑓(𝑋1, 𝑋2, … ) = 𝑏0 + 𝑏1𝑋1 +∙∙∙ +𝑒

DSp=0.65


In vivo/in vitro concordance◦ In vivo methods are inherently more

variable than in vitro assays, and thus less effective controls of product quality

◦ Strategic studies can be performed to establish the concordance between methods

◦ Concordance: direct proportionality between methods – y = bx

◦ Arrhenius analysis of accelerated stability data used to target potency levels

◦ Accelerated stability, manufacturing & clinical data show direct proportionality

◦ Relationship used to “calibrate” in vitro specification


-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2.8 2.9 3 3.1 3.2 3.3 3.4 3.5

ln(b

)

1000/Absolute Temperature

Arrhenius Plot of Recombivax IVRP

GM

T

ED

50

IVRP:

Manufactured Lots

Stability Experiment

Unit Slope Line

Clinical Experience

3000

1500

750

375

0.10

0.20

0.40

0.80

1.60

3.20

0.10 0.20 0.40 0.80 1.60

188

90

Stability, manufacturing & clinical concordance

ED

50

IVRP

Old Seed (n=6)

Redemo (n=12)

New Seed (n=158)

0.25 0.50 1.00 2.00

0.25

0.50

1.50

2.00

`

IVRP limit (0.50) corresponding to in vivo specification (1.5)

from Schofield, T. (2002) in vitro versus in vivo concordance: A case study of the replacement of an animal potency test with an immunochemical method, Dev. Biol., 111, 295-304

Stage 2: Procedure performance qualification

USP <1033> Biological Assay Validation◦ Acceptance criteria for a release assay

(ATP)

◦ Utilizes a process capability index (Cpm)

◦ CPM is related to the probability of out-of-specification (OOS)


y.variabilitassay release is and

bias, relative is RB y,variabilit product of estimate an is ˆ where

RBˆ6

Limit ionSpecificat LowerLimit ionSpecificat UpperCpm

2leaseRe

2oductPr

2leaseRe

22oductPr

++

−=

Stage 2: Procedure performance qualification (cont.)Requirement to meet specifications can be represented as isobars of constant probability (acceptable region)

◦ Allows the lab to balance accuracy and precision (e.g., can tolerate more variability for a very accurate procedure)


Hoffman & Kringle, 2007

Stage 2: Procedure performance qualification (cont.)

Total error approach

◦ Tolerance interval on combined accuracy and precision


Method design, development &understanding (Revisited)

The “reportable value” is the composite of assays performed in replicate and compared to the acceptance criterion

◦ Reportable values are associated with uncertainty

◦ Managed through design &

Variance components (VC)

◦ Runs x reps

◦ Other design (assay) factors:

◦ Analysts

◦ Instruments

◦ VC’s can be used to design studies for other uses of bioassays in the most effective/efficient manner (e.g., method transfer, standard qualification/calibration, process development, etc.)


%𝐺𝐶𝑉 = 100 ∙ 𝑒 Τ𝑉𝑎𝑟 𝑅𝑢𝑛 𝑛+ Τ𝑉𝑎𝑟(𝑅𝑒𝑝) 𝑛𝑘 − 1

Reps (n) 1 2 3 6

1 7.2% 5.1% 4.1% 2.9%

2 6.4% 4.5% 3.6% 2.6%

3 6.0% 4.2% 3.4% 2.4%

6 5.7% 4.0% 3.3% 2.3%

Number of Runs (k)

%GCV<5.0%

Stage 3: Procedure performance verification

Statistical process control on system suitability parameters which are related to bioassay performance (e.g., precision)◦ Hill coefficient (sometimes called slope) of the reference curve

◦ Together with the upper asymptote; or as effective range (A-D)

◦ Independent control sample◦ Holistic measure of method performance

◦ Response at a selected dose◦ Geometric mean titer of mice in a bioassay

◦ Preferably close to the ED50


Life

Release

Specifications

Shelf-

Control

Lim its

-6 0 6 12 18 24 30 36

Minimum Requirement

Maximum Requirement

Requirements

Stage 3: Procedure performance verification (cont.)

Method changes (transfer, technology, new standard)

◦ Using the release model . . .

◦ The “lifecycle management budget” () can be established which is associated with low risk of an OOS if the margin is reached (manufacturer's risk) while the patient is protected by the release limit

◦ An equivalence test (or noninferiority) is performed to demonstrate conformance to the equivalence margin (upper confidence bound falls within the equivalence margin)

◦ Note: The “comparability study” design addresses the risks of making a bad decision (width of confidence bound)


-1 0 1

)

)

Equivalent

Not equivalent

SummaryBioassay validation follows the stages defined for process validation, and represents a method of ensuring fitness for use(s) throughout the bioassay lifecycle

Strategic design and development of the bioassay, together with appropriate system suitability parameters, combine to ensure quality of potency measurement

Potency may be measured in vivo, in vitro, or chemically; careful method selection during development and QC facilitates process development and patient outcomes

Statistical approaches to validation help reduce and communicate risks of bioassay performance


Thank you


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Documents

A lifecycle approach to bioassay validation · 2019-04-19 · A lifecycle approach to bioassay validation TIM SCHOFIELD OWNER & CONSULTANT CMC SCIENCES, LLC CASSS DC DISCUSSION GROUP