TGx-DDI – Qualification of a Preclinical Biomarker C-Path - PSTC – RIKEN Meeting – Yokohama 2019The HESI TGx-DDI Biomarker Qualification Working Group

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18 Months

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CREATE and TEST technology and scientific frameworks that can be used to protect humans

& the environment.

What HESI does...

HESI Biomarker

Qualification Consortium Leadership

(representing HESI e-STAR committee)

Jiri Aubrecht, PhD

Scientific Director, Takeda

Carole Yauk, PhD

Research Scientist, Health Canada

Al Fornace, MD

Professor, Georgetown University

Henghong Li, MD, PhD

Assistant Professor, Georgetown University

Roland Froetschl, PhD

Scientific Research Group Leader, BfArM Germany

Heidrun Ellinger-Ziegelbauer, PhD

Senior Scientist, Bayer Pharma

Andrew Williams, MSc

Biostatistician, Health Canada

Julie Buick,

Biostatistician, Health Canada

Syril D Pettit, DrPH

Executive Director, HESI

Lauren Peel, BS

Scientific Program Manager, HESI

Positive findings of in vitro chromosome damage assays Assessment of relevance to human provides a challenge to sponsors and regulatory agencies

DNA damageChromosome damagePoint mutations Cancer in animals

Carcinogenicity testing

• Required for NDA• 2-year bioassay• Cost:$3M/cmpd• Time: 3 years

Cancer risk in humans

• Evaluating genetox and carci data

• Mechanistic studies• Epidemilogical studies• IARC process• Cost: ???• Time: decades

Genetics and health statusGenetic SusceptibilityDisease state, stress

EnvironmentFoodPollutants

Genotoxicity testing

• Required for IND• Genetox battery• Cost: $60K/cmpd• Time: 1-3 month

Non-genotoxic mechanismsProliferationNuclear hormone receptor activationEpigenetics

• High sensitivity, but low specificity

• ~30% lead chemicals positive for in vitro chromosome damage assays

Drug Candidate

ICH S2(R1) Option 1

AMES (negative)

In vivo micronucleus (negative)

In vitro chromosome damage (positive)

• Chromosomal aberration • Micronucleus (CREST negative)

TGx-DDI Results(Negative/Positive)

Relevant

WoE AssessmentConsider TGx-DDI results and other data/assays relevant for

assessment of genotoxic potential.

Irrelevant

TGx-DDI Context of use

TGx-DDI Qualification:

A Long Path...

March 2004 - HESI TGx-biomarker development

project initiated

Dec 2009 - Letter of intent to FDA to submit a

biomarker qualification plan

May 2011 – HESI notifies FDA of plan to submit a genomic biomarker for

qualification

July 2011 - Qualification plan briefing meeting at

FDA

Dec 2016 - Biomarker qualification data package

submitted to FDA

June 2017 - FDA responds with questions on

submission

August 2017 - HESI responsed to FDA

questions

August 2017 – FDA moves from prior qualification

program to new program under 21st Century Cures

legislation.

October 2017 Letter of Support from FDA to HESI

June 2018 – HESI Submits new Biomarker

Qualification Status Report to FDA

Oct 2018 - TGx-DDI Qualification meeting at

FDA

Today – HESI team developing final reports

and completing additional cross-lab technical

validation.

Concentration and time point optimization

– cytotoxicity (MTT) 4 and 24 h, 6 -10 concentrations

– Expression of three stress response genes - ATF, Gadd45a, p21 (qRT-PCR 6 concentrations)

Phase 1

Test system validation

– Comparability with previous studies (testside-validation)

– Cell culture (TK6) and microarray (human whole genome array, Agilent)

– Cisplatin, 4 experiments, 4h treatment

Main study training set - 28 compounds DDI/non-DDI

– calculation of biomarker (classifier panel) with training set – optimization with external test set (caffeine, 3-NP and iPMS) using different bioinformatic tools LoO, NSC, SVM

Phase 2

Main study validation set

– Established statistical analysis pipeline

– 44 compounds, 5 distinct mechanistic classes

– Expression profile of all test substances, 4h treatment

Phase 3

Validation studies

– Cross-laboratory/cross-platform

– Case studies

Prediction of substance class

– Use of the biomarker (Classifier) on expression profiles and prediction of DNA-damaging potential

Study Design

Fold

cha

nge

Fold

cha

nge

A B C

D E

Stress response gene expression used for dose finding

Stress gene expressionmeasured by qPCR

Classifier training setThe biomarker was developed using a training set of DNA-damaging and non-DNA-damaging model compounds.

Li et al. Env.Mol.Mut. 2015

Phase 1. Statistical methods

Nearest shrunken centroids probability analysis

Principle Component Analysis

Two dimensional hierarchical clustering

Robert Tibshirani et al. PNAS 2002;99:10:6567-6572

The class centroids are shrunk toward the overall centroids after standardizing by the within-class standard deviation for each gene.

This gives higher weight to genes whose expression is stable within the same class.

In the test cases, the standardized distance to the shrunken centroid is calculated and the class probability is determined.

Identifying the biomarker:Nearest Shrunken Centroids Probability Analysis

Biomarker panel Entrez ID Gene Symbol Response❖ p53 regulated Entrez ID Gene Symbol Response ❖ p53 regulated 59 ACTA2 yes 139285 FAM123B V - 64782 AEN yes 283464 GXYLT1 V - 7832 BTG2 yes 3008 HIST1H1E - 57103 C12orf5 yes

3018 HIST1H2BB

-

1026 CDKN1A yes 8347

HIST1H2BC

-

1643 DDB2 yes 8339

HIST1H2BG

-

11072 DUSP14 yes 8346

HIST1H2BI

-

144455 E2F7 yes 8342

HIST1H2BM

-

9538 EI24 V yes 8341

HIST1H2BN

-

26263 FBXO22 yes 8351 HIST1H3D - 1647 GADD45

A yes

3398 ID2 V -

121457 IKBIP yes 80271 ITPKC - 4193 MDM2 yes 3708 ITPR1 V - 23612 PHLDA3 yes 353135 LCE1E - 8493 PPM1D yes 9209 LRRFIP2 V - 51065 RPS27L yes 84206 MEX3B - 50484 RRM2B yes 79671 NLRX1 V - 9540 TP53I3 yes 5100 PCDH8 - 51499 TRIAP1 yes 1263 PLK3 - 10346 TRIM22 yes 5564 PRKAB1 - 91947 ARRDC4 - 5565 PRKAB2 - 10678 B3GNT2 - 5734 PTGER4 V - 282991 BLOC1S2 - 9693 RAPGEF2 - 84312 BRMS1L - 389677 RBM12B V - 868 CBLB V - 6400 SEL1L V - 9738 CCP110 - 6407 SEMG2 - 1052 CEBPD V - 29950 SERTAD1 - 1062 CENPE - 4090 SMAD5 - 8161 COIL V - 51768 TM7SF3 - 23002 DAAM1 V - 608 TNFRSF17 - 196513 DCP1B - 10210 TOPORS V - 79733 E2F8 - 373856 USP41 -

Transcripts comprisingthe TGx-DDI biomarker

Entrez ID

Gene Symbol

Response❖

p53 regulated

Entrez ID

Gene Symbol

Response

❖

p53 regulated

59

ACTA2

yes

139285

FAM123B

V

-

64782

AEN

yes

283464

GXYLT1

V

-

7832

BTG2

yes

3008

HIST1H1E

-

57103

C12orf5

yes

3018

HIST1H2BB

-

1026

CDKN1A

yes

8347

HIST1H2BC

-

1643

DDB2

yes

8339

HIST1H2BG

-

11072

DUSP14

yes

8346

HIST1H2BI

-

144455

E2F7

yes

8342

HIST1H2BM

-

9538

EI24

V

yes

8341

HIST1H2BN

-

26263

FBXO22

yes

8351

HIST1H3D

-

1647

GADD45A

yes

3398

ID2

V

-

121457

IKBIP

yes

80271

ITPKC

-

4193

MDM2

yes

3708

ITPR1

V

-

23612

PHLDA3

yes

353135

LCE1E

-

8493

PPM1D

yes

9209

LRRFIP2

V

-

51065

RPS27L

yes

84206

MEX3B

-

50484

RRM2B

yes

79671

NLRX1

V

-

9540

TP53I3

yes

5100

PCDH8

-

51499

TRIAP1

yes

1263

PLK3

-

10346

TRIM22

yes

5564

PRKAB1

-

91947

ARRDC4

-

5565

PRKAB2

-

10678

B3GNT2

-

5734

PTGER4

V

-

282991

BLOC1S2

-

9693

RAPGEF2

-

84312

BRMS1L

-

389677

RBM12B

V

-

868

CBLB

V

-

6400

SEL1L

V

-

9738

CCP110

-

6407

SEMG2

-

1052

CEBPD

V

-

29950

SERTAD1

-

1062

CENPE

-

4090

SMAD5

-

8161

COIL

V

-

51768

TM7SF3

-

23002

DAAM1

V

-

608

TNFRSF17

-

196513

DCP1B

-

10210

TOPORS

V

-

79733

E2F8

-

373856

USP41

-

Principle Component Analysis

Two- Dimensional Hierarchical Clustering

Probability Analysis

Applying the biomarker

15

Case study: Accurate prediction of DDI capacity of 3-Np, caffeine andIPMS using TGx-DDI

From Li et al. Env.Mol.Mut. 2015

Application in the presence of S9 metabolic activation system: Accurate prediction of B(a)P, AFB1 and Dexamethasone

16

From Buick et al. Env Mol Mut 2015Yauk et al. Env Mol Mut 2016

- 16 chemicals tested in presence of S9 metabolic activation systems and confirmed to yield accurate predictions

Summary Phase 1TGx-DDI Biomarker to Predict DNA Damage-Inducing (DDI) Chemicals

17

TGx-DDI Publications for Methods Development, Validation, Application:

TGx-28.65 biomarker development and validation

• Li, HH et al. Environ Mol Mutagen (2015)• Li, HH et al. PNAS (2017)

Development of method for use of biomarker with metabolic activation system

• Buick, JK et al. Environ Mol Mutagen (2015)• Yauk CL et al. Environ Mol Mutagen (2016)

TGx-DDI Software development

• Jackson, MA et al. Environ Mol Mutagen (2017)Case study

• Buick, JK et al. Mutat Res (2017)

The in vitro transcriptomic biomarker predicts the probability that an agent is DDI or non-DDI.

Developed using human cells in culture (TK6 cells)

From exposure to 28 prototype DNA damage-inducing (DDI) and non-DDI chemicals

64 genes identified as being predictive of DDI potential

DDI Non-DDI

Agents

Gen

es

Phase 2. Main studyvalidation

44 Compounds

5 Mechanistic Classes

DNA microarrays

SummaryTGx-DDI biomarker accurately identifies DDI and non-DDI agents

Class 1 – Direct DDI agents

Class 2 – Indirect DDI agents

Class 4 – Non-DDI agents

Class 5 – IRRELEVANT in vitro positives

+ Metabolic activation

TGx-DDI effectively identifies Class 5 agents

Li et al., Development and validation of a high-throughput transcriptomic biomarker to address 21st century genetic toxicology needs. PNAS, 2017

Phase 3. Validation Studies

Cross-laboratory

Cross-platform

Case studies

Cross-platform comparison of performance of TGx-DDI

Li H-H et al. PNAS 2017; 114(51):E10881-E10889

High cross-platform reproducibility: nCounter and qPCR

Li H-H et al. PNAS 2017; 114(51):E10881-E10889

Microarray vs qPCR

Microarray vs nCounter

Agilent microarray

nCounter qPCR

Accuracy 93% 97% 79%

Sensitivity 100% 100% 75%

Specificity 90% 95% 81%

Case studies demonstrating application in dose-response assessment

Benzenetriol dose response assessment of cytotoxicity, micronucleus induction and TGx-DDI response

Buick et al., Mutation Research, 2017

Li et al., Development and validation of a high-throughput transcriptomic biomarker to address 21st century genetic toxicology needs. PNAS, 2017.

Two proposed contexts of use

Overall validation plan

• 28 reference compounds• 2 laboratories (A, B)• 3 platforms

• 42 test agents• 2 laboratories (A, B)• 3 platforms

• Metabolic activation• 2 laboratories (A, B)• 2 platforms

• Additional platforms/cell models• Affymetrix• HepaRG (14 chemicals)

• Dose-response• 16 chemicals

• 28 reference chemicals• 1 additional laboratory (C)• nCounter

• External laboratory dose-response analysis

• 1 additional laboratory (D)• 25 chemicals• Anchored against micronucleus

frequency• Affymetrix DNA microarrays

• Open data• Testing performance on open data sets

• Additional platforms• TempO-seq• RNA-seq

Completed Ongoing

Current Status of Qualification Procedure

Develop methods

Validation/Proof of concept

Context of use & Case

Studies

FDA Biomarker

Qualification Review

Including:• TGx-DDI

biomarker software tool

• New technologies/cell models

• ~100 chemicals tested

• Metabolic activation

• Microarray, qPCR, NanoString (also TempO-Seq, RNA-seq)

• SOPs finalized and aligned to fixed Context of Use.

• Three case studies completed, one underway.

• Discussion of FDA questions with biomarker development team

• Clarification of next steps to final qualification

Final Qualification

steps

• Compilation of cross qualification data

• Submission of final qualification

Weare

here

Next steps

Finalizing qualification process with FDA

Exploring potential submission to other regulatory agencies (PMDA, EMA)

Training on biomarker use

Publication

Promote access and use

For more information

www.hesiglobal.orglpeel@hesiglobal.org

http://www.hesiglobal.org/

TGx-DDI – Qualification of a Preclinical Biomarker Slide Number 2Slide Number 3HESI Biomarker Qualification Consortium Leadership�(representing HESI e-STAR committee)�Statement of needSlide Number 6TGx-DDI Qualification:��A Long Path...Study DesignSlide Number 9Slide Number 10Phase 1. Statistical methodsSlide Number 12Biomarker panelSlide Number 14Slide Number 15Application in the presence of S9 metabolic activation system: Accurate prediction of B(a)P, AFB1 and Dexamethasone Summary Phase 1�TGx-DDI Biomarker to Predict DNA Damage-Inducing (DDI) ChemicalsPhase 2. Main study validationSummary�TGx-DDI biomarker accurately identifies DDI and non-DDI agentsPhase 3. Validation StudiesSlide Number 21Slide Number 22Slide Number 23Two proposed contexts of useOverall validation planCurrent Status of Qualification ProcedureNext stepsFor more information��www.hesiglobal.org�lpeel@hesiglobal.org�