Effort 1 – Voluntary Genomics Data Submission (VGDS) FDA Guidance to Industry: Pharmacogenomics data submission (Draft 2003, final publication 2005) –Invite industry to submit microarray data at the voluntary basis – A VGDS mechanism –Facilitate scientific progress in the area of pharmacogenomics. Felix Frueh Nat. Biotechnol. 24(9): , 2006

Effort 2 - ArrayTrack Need a bioinformatics tool to accomplish: –Objective 1: Data repository –Objective 2: Reproduce the sponsor’s results –Objective 3: Conduct alternative analysis ArrayTrack – A FDA genomic tool –AT version 1 (2001): Filter array; data management tool –AT version 2 (2002): in-house microarray core facility –AT version 2.2 (late 2003): Open to public –AT version 3.1 (2004): VGDS –AT version 3.2 (2005): MAQC –AT version 4 (2006 – present): VGDS  VXDS

Microarray data Proteomics data Metabolomics data Chemical data Clinical and non- clinical data Public data ArrayTrack ArrayTrack: An Integrated Solution for omics research

Protein Gene Metabolite

Study DB TOOL Study domain Microarray DB TOOL Array domain LIB

Study Data Management and Analysis FDA eSubmission efforts –Clinical data: Clinical Data Interchanges Standards Consortium (CDISC) –Non-clinical data: Standard for Exchange of Nonclinical Data (SEND) Subject, treatment, Clinical pathology, histopathology, … Conforming to SDTM used for CDISC/SEND Microarray data management and analysis are processed in Array Domain and the findings are available to correlate with data in Study Domain

Gene Expression vs Clinical Pathology Clinical pathology data R=0.72 Gene Clinical pathology R Each cell represents a gene-ClinChem correlation The color represents the degree of correlation CLinChem name is hidden Gene name is hidden Gene

ProteinLibPathwayLib ProteinTools Proteomics DB MetaboliteTools Metabonomics DB ToxicantLib ArrayTrack/SysTox - From VGDS to VXDS Microarray DB GeneLib GeneTools

Storing Protein and Metabolite Lists Examining common pathways and functions shard by expression data from genomics, proteomics and metabolomics

ArrayTrack-Freely Available to Public # of unique users access the web version of ArrayTrack # of unique users access the locally installed version of ArrayTrack Web-access Local installation

Knowledge Base 1.ToxicantLib 2.Liver Tox Knowledge Base (LTKB) 3.Sex Determined Toxicity in Gene Expression 4.…

Effort 3 - Best Practice Document One of the VGDS objectives is to communicate with the private industry and gain experience on –How to exchange genomic data (data submission) –How to analyze genomic data –How to interpret genomic data Lessons Learned from VGDS has led to development of Best Practice Document (Led by Federico Goodsaid) –Recommendations for the Generation and Submission of Genomic Data (Nov 2006) ( ArrayTrack translates “Best Practice” into real practice

QC issue – How good is good enough? –Assessing the best achievable technical performance of microarray platforms (QC metrics and thresholds) Analysis issue – Can we reach a consensus on analysis methods? –Assessing the advantages and disadvantages of various data analysis methods Cross-platform issue – Do different platforms generate different results? –Assessing cross-platform consistency Effort 4 - MicroArray Quality Control (MAQC) Project # of microarray- related publications indexed in PubMed has been increasing exponentially.

Results from the MAQC Study Published in Nature Biotechnology on Sept and Oct 2006 Nat. Biotechnol. 24(9) and 24(10s), 2006 Six research papers: MAQC Main Paper Validation of Microarray Results RNA Sample Titrations One-color vs. Two-color Microarrays External RNA Controls Rat Toxicogenomics Validation Plus: Editorial Nature Biotechnology Foreword Casciano DA and Woodcock J Stanford Commentary Ji H and Davis RW FDA Commentary Frueh FW EPA Commentary Dix DJ et al.

An Array of FDA Endeavors ArrayTrack MAQC VGDS

Not One-Trick-Pony Computational Toxicology statistics Bioinformatics Chemoinformatics Regulation-Oriented Projects Bioinformatics

Decision Forest – A robust consensus approach DF-Array: Classification using gene expression data DF-SELDI: Classification using proteomics data DF-SNPs: Classification using SNPs profiles DF-Seq: Sequence-based classification of protein function DF-SAR: Predictive tox using chemical structure Tree 1 Tree 4 Tree 3 Tree 2 Input Combining Results Key points Combining several identical models produce no gain Combining several highly correct models that disagree as much as possible

Not One Trick Pony Computational Toxicology statistics Bioinformatics Chemoinformatics Bioinformatics Predictive Toxicology

Endocrine Disruptors An international issue Two laws passed by US congress require evaluation of chemicals found in foods and water for endocrine disruption. Similar regulation is also implemented in Europe and Asia ~ 90,000 commercial chemicals needs to be screened EPA has identified ~58,000 eligible chemicals A minimum of 8,000 of the 58,000 chemicals are FDA- regulated, including cosmetic ingredients, drug products …

Overview of NCTR’s Endocrine Disruptor Knowledge Base (EDKB) Begun 1996, prior to endocrine disruptor (ED) issues ED issues emerge - ACC and EPA collaboration & support results Program expands: –Separately assayed over >200 chemicals for estrogen (ER), androgen (AR), serum protein (AFP and SHBG) receptor binding –Web-based relational database with in vitro and in vivo assay data, bibliography and chemical structure search –Exhaustive SAR/QSAR model development for both ER and AR binding, guided by data and crystal structures

,183 6,186 30,012 Prioritized Groups No. of Chemicals Priority Setting of 58,000 Chemicals Only ~3600 chemicals need to be tested ~6200 chemicals might be active with activity below 100,000-fold less than estradiol 30,000 chemicals are predicted to be inactive