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Division of Biometry and Risk Assessment. John Appleget Computer Specialist James Chen, Ph.D. Mathematical Statistician Yi-Ju Chen Post Doc Robert Delongchamp, Ph.D. Mathematical Statistician Ralph Kodell, Ph.D. Director Daniel Molefe, Ph.D. Post Doc

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division of biometry and risk assessment
Division ofBiometry and Risk Assessment
  • John Appleget Computer Specialist
  • James Chen, Ph.D. Mathematical Statistician
  • Yi-Ju Chen Post Doc
  • Robert Delongchamp, Ph.D. Mathematical Statistician
  • Ralph Kodell, Ph.D. Director
  • Daniel Molefe, Ph.D. Post Doc
  • Bruce Pearce Computer Specialist
  • Susan Taylor Program Support Specialist
  • Angelo Turturro, Ph.D. Research Biologist
  • Cruz Velasco, Ph.D. Post Doc
  • John Young, Ph.D. Research Biologist
  • Qi Zheng, Ph.D. Staff Fellow
research highlights
Research Highlights
  • Fumonisin B1 Risk Modeling
  • Cryptosporidium parvum Study
  • Cumulative Risk for Chemical Mixtures
  • Computational Toxicology
  • Photocarcinogenicity Theory & Methods
  • Analysis of cDNA Microarray Data
  • Staff Enrichment
fumonisin b 1 risk modeling
Fumonisin B1 Risk Modeling

Qi Zheng et al.

  • NTP IAG Study in rats and mice (P. Howard)
  • Liver tumors in female mice…kidney tumors in male rats
  • Directed/encouraged by Bern Schwetz
    • CFSAN, CVM
  • Two recommendations of SAB SVT
    • Project related to Food Safety Initiative
    • Project for intra-division collaboration
slide4

Female Mouse Liver Tumors

  • Adjusted tumor rates at 104 weeks
  • Hepatocellular adenoma or carcinoma

Probability

ppm

slide5

Mathematical Model

  • Use MVK two-stage, cell-proliferation model to predict probability of tumor at 104 weeks

(t)

1

2

Normal

N(t)

Malignant

Preneoplastic

(t)

hypothesis
Hypothesis
  • Fumonisin B1 affects the incidence ofliver tumor formation in mice byincreasing the death rate of cellswhich leads tocompensatory proliferation.
implementing the model
Implementing the Model
  • Use allometric relationship between liver weight and body weight, LW(t)=a[BW(t)]b, to estimate theliver weight
  • Estimate the number of cells in the liver by N(t)=LW(t)/CW
  • Estimate the net growth rate of the liver using d[logLW(t)]/dt
implementing the model1
Implementing the Model
  • Use PCNA data to estimate the cell birth rate,(t)
  • Estimate the cell death rate by(t)=(t)-d[logLW(t)]/dt
implementing the model2
Implementing the Model
  • Relate differential effect of FB1on (t), and, consequently, (t) by level of sphinganine in liver
  • Infer mutation rates, 1 and 2, (constant w.r.t. FB1 and time) from tumor data
female mouse liver tumors
Female Mouse Liver Tumors
  • Tumor incidence at 104 weeks
  • Hepatocellular adenoma or carcinoma
  • Observed: .117,.065,.021,.427, .883
  • Predicted: .091,.084,.105,.284, .992

Probability

ppm

slide11

Male and Female Mouse Liver Tumors

Male

Observed: .268, .211, .190, .213, .213

Predicted: .199, .201, .198, .233, .237

Observed: .117, .065, .021, .427, .883

Predicted: .091, .084, .105, .284, .992

Female

Probability

ppm

fumonisin b 1 summary
Fumonisin B1 Summary
  • Data and model are consistent with hypothesis
  • FDA Workshop on Fumonisins Risk Assessment: February, 2000
  • Food Additives and Contaminants, 2001
  • FAO/WHO JECFA (Feb., 2001) used extensively in draft report on fumonisins …CFSAN (Mike Bolger)
  • Model kidney tumor risk in male rats?
cryptosporidium parvum study
Cryptosporidium parvum Study

Angelo Turturro et al.E07082.01

  • IAG with EPA-NCEA, Cincinnati - B. Boutin
  • Much input from CFSAN (R. Buchanan, G. Jackson, M. Miliotis)
  • New challenge for NCTR
  • Cryptosporidium parvum is a protozoan
    • Common contaminant of drinking water
    • Can also contaminate the food supply
objectives
Objectives
  • To develop a model for transmission dynamics of Cryptosporidium parvum in human outbreaks
  • To standardize the dose of Cp strains in the neonatal mouse (three isolates)
  • To establish an appropriate animal model
    • Brown Norway rat
    • Chemically supressed C57Bl/6 mouse (Dex)
objectives cont
Objectives (cont.)
  • To investigate subpopulations with varying degrees of immunocompetence
    • Three age groups - young, adult, elderly
    • Pregnant
    • Immunosuppressed similar to AIDS
    • Physiologically stressed - diet, exercise
  • Status: Protocol reviewed, revised, re-submitted
cumulative risk for chemical mixtures
Cumulative Risk for Chemical Mixtures

James Chen, Yi-Ju Chen et al.E07087.01

  • IAG with EPA-NCEA, Cincinnati- G. Rice, L. Teuschler
  • Objective: To develop and apply a Relative Potency Factor (RPF) methodology for estimating the cumulative riskfrom exposure to a mixture of chemicals having a common mode of action (e.g., organophosphates: cholinesterase inhibition) FQPA, 1996
specific aims
Specific Aims
  • To use an expanded definition of dose addition to develop a risk estimation method that does not depend strictly on parallelism of log-dose-response curves
  • To develop a classification algorithm for clustering chemicals into several constant relative potency subsets
advantages
Advantages
  • Uses actual dose-response functions of mixture components, not just ED10s, say (like TEF, HI, etc.)
  • If the RPF isconstant across all chemicals, then invariant to choice of index chemical
  • Can be used even when the RPF differs for different subsets of chemicals in the mixture
  • Status: Protocol in review
computational toxicology
Computational Toxicology

John Young et al. E07083.01

  • Objective: To develop an expert computational system for prediction of organ-specific rodent carcinogenicity by applying structure activity relationships (SAR) in conjunction with data on short-term toxicity tests (STT) and nuclear magnetic resonance (13C-NMR) spectroscopy.
motivation
Motivation
  • FDA’s need to
    • bring safe products to market more quickly
    • screen out unsafe products reliably
  • CFSAN (M. Cheeseman)
    • streamline toxicity testing, e.g., require sponsor to conduct target-specific toxicity based on system’s prediction
database
Database
  • 1298 chemicals in Carcinogenic Potency Database
    • Group 1: carcinogenicity in liver
    • Group 2: carcinogenicity, but not in liver
    • Group 3: no carcinogenicity in any organ
  • Add data on SAR, STT and NMR
database cont
Database (cont.)
  • 392 NTP chemicals in CPDB
    • 342 positive in liver for  1 species-sex combo.
  • For good mix of positive/negative, might need to do
    • species-specific prediction
    • sex-specific prediction
strategy
Strategy
  • Training set
    • Use 392 NTP chemicals in CPDB
  • Testing set
    • Use 288 literature chemicals in CPDB
    • Use 282 pharmaceuticals in CDER database
      • 33 positive in liver for  1 species-sex combo.
  • Status: Protocol recently approved and implemented
photocarcinogenicity theory methods
Photocarcinogenicity Theory & Methods

Ralph Kodell, Daniel Molefe et al. E07061.01

  • FDA
    • CFSAN Cosmetics
    • CDER Drugs (K. Lin)
  • NCTR’s Phototoxicity Program (P. Howard)
    • CRADA w/ ARGUS Laboratory: S00213
    • Post Doc funding through NTP: E02037.01
statistical approaches
Statistical Approaches
  • Standard Testing Method
    • Logrank test for differences in distributions of time to first observed tumor
  • New Testing Method
    • Test for difference in number of induced tumors
    • Test for difference in distributions of time to observation of tumors
accomplishments plans
Accomplishments/Plans
  • Model developed for repeated-exposure case
    • Computational optimization procedure developed
    • Data on first of eight Argus studies analyzed
  • Compare to logrank and Dunson’s method
  • Status: Ongoing.
analysis of cdna microarray data
Analysis of cDNA Microarray Data

Bob Delongchamp, Cruz Velasco et al.E07096.01

  • cDNA Microarrays
    • popular new biotech tool
    • vast amounts of data on gene expression quickly
  • Statistical issues
    • Experimental design
    • Analysis and interpretation
statistical issues
Statistical Issues
  • Experimental design
    • Replication: arrays and genes
  • Data analysis
    • Adjustment for nuisance sources of variation
    • Appropriate methods for assessing differences
    • Adjustment for multiple comparisons
    • Identification of genetic profiles
slide29

Figure 1. Intensities observed in rat hepatocytes.

Upper Right - Untreated Array

Lower Left - MP Treated Array

Lower Right - PM Treated Array

slide30

Figure 2. Array maps of

log(Iga/Ig).

Upper Right - Untreated Array

Lower Left - MP Treated Array

Lower Right - PM Treated Array

slide31

Figure 3. Intensities adjusted

within 6x6 blocks.

Upper Right - Untreated Array

Lower Left - MP Treated Array

Lower Right - PM Treated Array

slide32

Figure 4. Intensities adjusted

for splotches (Ka)

and saturation (K*a).

Upper Right - Untreated Array

Lower Left - MP Treated Array

Lower Right - PM Treated Array

objectives1
Objectives
  • Data analysis
    • Appropriate methods for assessing differences
      • Individual genes
      • Clusters of genes (profiles)
    • Adjustment for multiple comparisons
      • PCER, FWER, FDR
  • Status: Protocol in development
staff enrichment
Staff Enrichment
  • Short courses and conferences
    • UCLA Functional Genomics (Chen)
    • IBS/ENAR Conference (Chen, Delongchamp, Kodell)
    • Gordon Conference on Bioinformatics (Zheng)
    • Genetic and Evolutionary Computation Conference (Pearce)
  • IAG with UAMS (R. Evans)
staff enrichment1
Staff Enrichment
  • Lab visits
    • Academia Sinica, Taiwan (Chen, 2 weeks)
      • Visualization, classification (C-H Chen)
    • Jackson Lab. (Delongchamp, 1 month)
      • Differential gene expression (G Churchill)
  • Visits to other FDA Centers
    • CDRH (Greg Campbell): Delongchamp, Velasco, Harris
  • Visiting scientists
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