TGx-DDI Qualification of a Preclinical Biomarker C-Path - PSTC - - PowerPoint PPT Presentation
TGx-DDI Qualification of a Preclinical Biomarker C-Path - PSTC RIKEN Meeting Yokohama 2019 The HESI TGx-DDI Biomarker Qualification Working Group HESI: International non-profit building science for a safer, more sustainable world.
C-Path - PSTC – RIKEN Meeting – Yokohama 2019 The HESI TGx-DDI Biomarker Qualification Working Group
HESI: International non-profit building science for a safer, more sustainable world.
Universities, Research Institutes, and Scientific Foundations
Government Agencies & Institutes
Corporate Sponsors
Distinct Projects
Scientific Committees
18 Countries 18 Months >1000 Scientists
at HESI events in 2018
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PROVIDE decision- makers with sound science for better, more informed decisions. CONVENE collaborations across academic, government, NGO, clinical, and industry scientists CREATE and TEST technology and scientific frameworks that can be used to protect humans & the environment.
(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
Assessment of relevance to human provides a challenge to sponsors and regulatory agencies
DNA damage
Chromosome damage Point mutations
Cancer in animals
Carcinogenicity testing
Cancer risk in humans
carci data
Genetics and health status
Environment
Genotoxicity testing
Non-genotoxic mechanisms
specificity
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)
TGx-DDI Results (Negative/Positive) Relevant WoE Assessment
Consider TGx-DDI results and other data/assays relevant for assessment of genotoxic potential.
Irrelevant
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
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cytotoxicity (MTT) 4 and 24 h, 6 -10 concentrations
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Expression of three stress response genes
, Gadd45a, p21 (qRT-PCR 6 concentrations) Phase 1
Test system validation
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Comparability with previous studies (testside- validation)
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Cell culture (TK6) and microarray (human whole genome array, Agilent)
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Cisplatin, 4 experiments, 4h treatment
Main study training set - 28 compounds DDI/ non-DDI
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calculation of biomarker (classifier panel) with training set – optimization with external test set (caffeine, 3-NP and iPMS ) using different bioinformatic tools LoO, NS C, S VM Phase 2
Main study validation set
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Established statistical analysis pipeline
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44 compounds, 5 distinct mechanistic classes
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Expression profile of all test substances, 4h treatment Phase 3
Validation studies
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Cross-laboratory/ cross-platform
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Case studies
Prediction of substance class
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Use of the biomarker (Classifier) on expression profiles and prediction of DNA-damaging potential
Study Design
Fold change Fold change
A B C D E
Stress gene expression measured by qPCR
The biomarker was developed using a training set of DNA-damaging and non- DNA-damaging model compounds.
Li et al. Env.Mol.Mut. 2015
Robert Tibshirani et al. PNAS 2002;99:10:6567-6572
after standardizing by the within-class standard deviation for each gene.
stable within the same class.
shrunken centroid is calculated and the class probability is determined.
Identifying the biomarker: Nearest Shrunken Centroids Probability Analysis
Entrez ID Gene Symbol Response ❖ p53 regulated Entrez ID Gene Symbol Response ❖ p53 regulated 59 ACTA2 yes 139285 FAM123B V
AEN yes 283464 GXYLT1 V
BTG2 yes 3008 HIST1H1E
C12orf5 yes 3018 HIST1H2B B
CDKN1A yes 8347 HIST1H2B C
DDB2 yes 8339 HIST1H2B G
DUSP14 yes 8346 HIST1H2B I
E2F7 yes 8342 HIST1H2B M
EI24 V yes 8341 HIST1H2B N
FBXO22 yes 8351 HIST1H3D
GADD45 A yes 3398 ID2 V
IKBIP yes 80271 ITPKC
MDM2 yes 3708 ITPR1 V
PHLDA3 yes 353135 LCE1E
PPM1D yes 9209 LRRFIP2 V
RPS27L yes 84206 MEX3B
RRM2B yes 79671 NLRX1 V
TP53I3 yes 5100 PCDH8
TRIAP1 yes 1263 PLK3
TRIM22 yes 5564 PRKAB1
ARRDC4
PRKAB2
B3GNT2
PTGER4 V
BLOC1S2
RAPGEF2
BRMS1L
RBM12B V
CBLB V
SEL1L V
CCP110
SEMG2
CEBPD V
SERTAD1
CENPE
SMAD5
COIL V
TM7SF3
DAAM1 V
TNFRSF17
DCP1B
TOPORS V
E2F8
USP41
the TGx-DDI biomarker
Principle Component Analysis Two- Dimensional Hierarchical Clustering Probability Analysis
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Cas Case e study: Accu ccurate predict ction of DDI DDI capacity of 3-Np Np, caffe feine and IPMS MS using TG TGx-DDI
From Li et al. Env.Mol.Mut. 2015
Application in the pr pres esen ence of S9 9 metab abolic act ctivation sys yste tem: Accu ccurate predict ction of B(a (a)P )P, A AFB1 1 and Dexam amethas asone
16 From Buick et al. Env Mol Mut 2015 Yauk et al. Env Mol Mut 2016
presence of S9 metabolic activation systems and confirmed to yield accurate predictions
Summary Ph Phase 1 1 TG TGx-DDI Biomarker to Predict ct D DNA D Damage-Inducing ( (DDI) C Chemical als
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TGx-DDI Publications for Methods Development, Validation, Application: TGx-28.65 biomarker development and validation
Development of method for use of biomarker with metabolic activation system
TGx-DDI Software development
Case study
The in vitro transcriptomic biomarker predicts the probability that an agent is DDI or non-DDI.
DDI Non-DDI
Agents Genes
Summa mmary TG TGx-DDI b biomarker a accu ccurately identifies D DDI and n non-DDI a 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
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.
analysis
frequency
Completed Ongoing
Develop methods Validation/ Proof of concept Context of use & Case Studies
FDA Biomarker Qualification Review Including:
biomarker software tool
technologies/cell models
NanoString (also TempO-Seq, RNA-seq)
aligned to fixed Context of Use.
completed, one underway.
questions with biomarker development team
steps to final qualification Final Qualification steps
qualification data
qualification We are here
Finalizing qualification process with FDA Exploring potential submission to other regulatory agencies (PMDA, EMA) Training on biomarker use Publication Promote access and use