Effort 1

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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)1105-1107, 2006
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Effort 2 - ArrayTrack

• Need a bioinformatics tool to accomplish
• Objective 1 Data repository
• Objective 2 Reproduce the sponsors 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

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ArrayTrack An Integrated Solution for omics
research
Clinical and non-clinical data
Chemical data
ArrayTrack
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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

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Gene Expression vs Clinical Pathology
Gene
Gene name is hidden
Clinical pathology data
CLinChem name is hidden
R0.72
Each cell represents a gene-ClinChem correlation
The color represents the degree of correlation
Gene
Clinical pathology
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ArrayTrack/SysTox- From VGDS to VXDS
GeneTools
Microarray DB
ProteinLib
PathwayLib
GeneLib
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Storing Protein and Metabolite Lists
Examining common pathways and functions shard by
expression data from genomics, proteomics and
metabolomics
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ArrayTrack-Freely Available to Public
Web-access
Local installation
of unique users access the locally installed
version of ArrayTrack
of unique users access the web version of
ArrayTrack
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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) (http//www.fda.gov/cde
  r/genomics/conceptpaper_20061107.pdf)
• ArrayTrack translates Best Practice into real
  practice

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Effort 4 - MicroArray Quality Control (MAQC)
Project

• 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

of microarray-related publications indexed in
PubMed has been increasing exponentially.
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Results from the MAQC Study Published in Nature
Biotechnology on Sept and Oct 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

Nat. Biotechnol. 24(9) and 24(10s), 2006
Plus Editorial Nature Biotechnology Forewo
rd Casciano DA and Woodcock J Stanford
Commentary Ji H and Davis RW FDA Commentary
Frueh FW EPA Commentary Dix DJ et al.
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An Array of FDA Endeavors
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Not One-Trick-Pony
Regulation-Oriented Projects
Bioinformatics
Chemoinformatics
statistics
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Decision Forest A robust consensus approach

• Key points
• Combining several identical models produce no
  gain
• Combining several highly correct models that
  disagree as much as possible

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
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Not One Trick Pony
Bioinformatics
Chemoinformatics
statistics
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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

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Overview of NCTRs 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 gt200 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

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Priority Setting of 58,000 Chemicals
Prioritized Groups
No. of Chemicals
124 317 3,183 6,186 30,012

• 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