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Effort 1

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Effort 1 Voluntary Genomics Data Submission (VGDS) – PowerPoint PPT presentation

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Title: Effort 1


1
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
2
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

3
ArrayTrack An Integrated Solution for omics
research
Clinical and non-clinical data
Chemical data
ArrayTrack
4
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5
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6
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

7
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
8
ArrayTrack/SysTox- From VGDS to VXDS
GeneTools
Microarray DB
ProteinLib
PathwayLib
GeneLib
9
Storing Protein and Metabolite Lists
Examining common pathways and functions shard by
expression data from genomics, proteomics and
metabolomics
10
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
11
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12
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

13
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.
14
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.
15
An Array of FDA Endeavors
16
Not One-Trick-Pony
Regulation-Oriented Projects
Bioinformatics
Chemoinformatics
statistics
17
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
18
Not One Trick Pony
Bioinformatics
Chemoinformatics
statistics
19
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

20
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

21
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
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