© 2015 Envigo envigo.com

Laura Coch, PhDStudy Director, BBC21 November 2018

Assay overkill: Practical solutions for development and validation of fit-for-purpose pre-clinical immunogenicity assays

Pre-clinical ADA assay assessment

2

• To support PK analysis within GLP Tox studies to ensure that the correct dose/risk strategy is taken forward to future studies and consequently to ensure patient safety.

Objective

• Unusual PK data is indicative of ADA response and often suggests immunogenicity in a pre-clinical study.

• Current pre-clinical ADA assessments are costly and time-consuming.

Current situation

• It is not necessary to fully validate a pre-clinical immunogenicity assay up to the standard required for clinical trials, where patient safety is the driving force.

• Approaches proposed herein suffice to interpret pre-clinical PK data.

How to optimize development and validation ADA studies?

Pre-clinical ADA assay assessment

3

Optimization of study design to obtain cut-point

Change of % false positive rate whendetermining screening cut-point

Singlicate versus duplicate sampleanalysis

Simplifying ADA

assessment

Study design optimization

4

SAS JMP

Design-Expert®

Design of Experiments

Powerful statistical approach to improve methoddesign

Optimization of cause-effect relationshipbetween factors with minimum sample size

To understand which factors have a significantimpact on method design

What

How

Why

Study design optimization: Design Expert

5

Number of assays

• Biotinylated antibodyconcentration

• SULFO-TAG antibodyconcentration

• Positive control curve range

• Percentage of matrix(minimum requireddilution)

Factors

• Lower response• Top response• Background• Signal to noise (low

and high responses)• Prozone• Drug Tolerance

Responses

OPTIMAL CONDITION FOR EACH FACTOR

Graph Examples

6

Study design optimization to obtain cut-point

7

+ Does having a larger number of plates affect the conclusions/cut-point?

Study Phase Method % False rate for

SCP

Plateswith positive

curves

CorrectionFactor

Cut-point

Sensitivity

Study 8Pre-clinical

developmentMSD

bridging

0.1% 8 5.87 58.61 RLU

12.7 ng/mL

Pre-clinicalvalidation

MSD bridging

0.1% 18 6.34 57.91 RLU

17.7 ng/mL

Pre-clinical ADA assay assessment

8

Optimization of study design to obtain cut-point

Change of % false positive rate whendetermining screening cut-point

Singlicate versus duplicate sampleanalysis

Simplifying ADA

assessment

Change of % false positive rate

9

Sample

Screening assay

Negative Positive

Confirmatory assay

Negative Positive

Titer, affinity, isotype assays

Neutralization assay

Tier 1 - screening

Tier 2 - confirmation

Tier 3 - characterization

+ Tier 3 for clinical ADAs mainly

5% false positive rate

1% or 0.1% false positive rate

Change of % false positive rate

10

+ Does changing the screening cut-point from 5% false positives to 1% or 0.1% affect the conclusions?

5% 1% 0.1% 5% 1% 0.1

%0510152050100150200250300350

STUDY 4 (n= 338)

% positive false rate (during screening)

no. o

f sam

ples

8 8 8 6

SCREENING CONFIRMATORY

5% 1% 0.1% 5% 1% 0.1

%0

20

40

60

80

100

120

140

STUDY 5 (n= 128)

% positive false rate (during screening)

no. o

f sam

ples

56 51 49 50

SCREENING CONFIRMATORY

5% 1% 0.1% 5% 1% 0.1

%0

10

20

30

40

50

60

70

STUDY 10 (n= 64)

% positive false rate (during screening)

no. o

f sam

ples

35 33 30 24

SCREENING CONFIRMATORY

Kubiak et al. J Pharm Biomed Anal. 2013

*1% *1%

*0.1%

Pre-clinical ADA assay assessment

11

Optimization of study design to obtain cut-point

Change of % false positive rate whendetermining screening cut-point

Singlicate versus duplicate sampleanalysis

Simplifying ADA

assessment

Singlicate versus duplicate analysis

12

+ Does ADA sample analysis in duplicate or singlicate affect the conclusions ?

Study Type of study Method % false positive for SCP

Study 1 Pre-clinical MSD bridging 5%

Study 2 Pre-clinical MSD bridging 5%

Study 3 Pre-clinical MSD bridging 5%

Study 4 Pre-clinical ELISA bridging 5%

Study 5 Pre-clinical ELISA bridging 5%

Study 6 Pre-clinical MSD bridging 0.1%

Study 7 Clinical MSD bridging 5%

Singlicate versus duplicate analysis

13

DUPL

SINGL 1

SINGL 2

DUPL

SINGL 1

SINGL 2

0

2

4

510152025

STUDY 1 (n=24)

no. o

f sam

ples

3 3 31 1 1

SCREENING CONFIRMATORY

DUPL

SINGL 1

SINGL 2

DUPL

SINGL 1

SINGL 2

02468

10

15

20

25

STUDY 2 (n=24)

7 7 7 6 6 6

SCREENING CONFIRMATORY

DUPL

SINGL 1

SINGL 2

DUPL

SINGL 1

SINGL 2

02468

10121415

20

25

STUDY 3 (n=25)

12 12 12 9 9 9

SCREENING CONFIRMATORY

DUPL

SINGL 1

SINGL 2

DUPL

SINGL 1

SINGL 2

02468

1050

100150200250300350

STUDY 4 (n= 338)

no. o

f sam

ples

2 diff.

SCREENING CONFIRMATORY

8 7 106 6 7

DUPL

SINGL 1

SINGL 2

DUPL

SINGL 1

SINGL 2

0

20

40

606080

100120140

STUDY 5 (n=128)

SCREENING CONFIRMATORY

56 56 56 50 50 50

DUPL

SINGL 1

SINGL 2

DUPL

SINGL 1

SINGL 2

0

25

50

75

100

125

STUDY 6 (n=125)SCREENING CONFIRMATORY

72 72 73

*0.1%

Singlicate versus duplicate analysis

14

Singlicate analysis could also be used in clinical studies.

DUPL

SINGL 1

SINGL 2

DUPL

SINGL 1

SINGL 2

0

10

20

30100200300400

STUDY 7 (n=368)

no. o

f sam

ples

4 diff. 4 diff.

28 29 2818 18 18

SCREENING CONFIRMATORY*5 %

Pre-clinical ADA assay assessment

15

Optimization of study design to obtain cut-point

Change of % false positive rate whendetermining screening cut-point

Singlicate versus duplicate sampleanalysis

Simplifying ADA

assessment

Fit-for purpose optimization - Preclinical

16

Reagent labeling

Reagent optimization

• Positive control assessment and sensitivity calculation

• Negative controls for screening cut-point setting (5% false positive rate) (3 days, 2 analysts)

• Intra-/inter-assay precision• Confirmatory drug concentration • Other parameters (selectivity, prozone,

drug tolerance, etc.)

• SCP/CCP for Screening and Confirmatory cut-point determination

• Positive control assessment and sensitivity calculation

• Intra-/inter-assay precision• Other parameters (selectivity, prozone,

drug tolerance, stability, etc.)

Sample analysis - Duplicate Sample analysis - Duplicate

Dev

elop

men

tVa

lidat

ion

Reagent labeling

Reagent optimization(prozone and drug tolerance)

• Positive control assessment and sensitivity calculation

• Negative controls for screening cut-point setting (1% or 0.1% false positive rate ) (2 days, 2 analysts)

• Other parameters (selectivity)

• Intra-/inter-assay precision• Other parameters (selectivity, drug tolerance,

stability, etc.)

1

5

25

20

40

1

3

15

10

20

New approach

Sample analysis - Singlicate

Time and resourcereduction

Acknowledgments

Princeton, New JerseyXianghong Liu, PhD

Huntingdon, UKDeborah McManus, BScLaure Queyrel, BSc

James Lawrence, BScPaolo Repeto, BSc

17

Barcelona, SpainJofre Ferrer-Dalmau, PhDPaula Mendoza, MSc

Laura Rocamora, MScCarla Cendón, PhD

Together, we make the world a safer and healthier place to live.