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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


Female Mouse Liver Tumors

  • Adjusted tumor rates at 104 weeks

  • Hepatocellular adenoma or carcinoma

Probability

ppm


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


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


Figure 1. Intensities observed in rat hepatocytes.

Upper Right - Untreated Array

Lower Left - MP Treated Array

Lower Right - PM Treated Array


Figure 2. Array maps of

log(Iga/Ig).

Upper Right - Untreated Array

Lower Left - MP Treated Array

Lower Right - PM Treated Array


Figure 3. Intensities adjusted

within 6x6 blocks.

Upper Right - Untreated Array

Lower Left - MP Treated Array

Lower Right - PM Treated Array


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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