Slide 1

1 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 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

Slide 2

2 Research Highlights Fumonisin B 1 Risk Modeling Cryptosporidium parvum Study Cumulative Risk for Chemical Mixtures Computational Toxicology Photocarcinogenicity Theory & Methods Analysis of cDNA Microarray Data Staff Enrichment

Slide 3

3 Fumonisin B 1 Risk Modeling NTP IAG Study in rats and mice (P. Howard) Liver tumors in female micekidney 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 Qi Zheng et al.

Slide 4

4 Female Mouse Liver Tumors Adjusted tumor rates at 104 weeks Hepatocellular adenoma or carcinoma ppm Probability

Slide 5

5 Mathematical Model Use MVK two-stage, cell-proliferation model to predict probability of tumor at 104 weeks Normal N(t) Preneoplastic Malignant 11 22 (t) (t)

Slide 6

6 Hypothesis Fumonisin B 1 affects the incidence of liver tumor formation in mice by increasing the death rate of cells which leads to compensatory proliferation.

Slide 7

7 Implementing the Model Use allometric relationship between liver weight and body weight, LW(t)=a[BW(t)] b, to estimate the liver 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

Slide 8

8 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

Slide 9

9 Implementing the Model Relate differential effect of FB 1 on (t), and, consequently, (t) by level of sphinganine in liver Infer mutation rates, 1 and 2, (constant w.r.t. FB 1 and time) from tumor data

Slide 10

10 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 ppm Probability

Slide 11

11 Observed:.268,.211,.190,.213,.213 Predicted:.199,.201,.198,.233,.237 Observed:.117,.065,.021,.427,.883 Predicted:.091,.084,.105,.284,.992 ppm Probability Male and Female Mouse Liver Tumors Male Female

Slide 12

12 Fumonisin B 1 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?

Slide 13

13 Cryptosporidium parvum Study 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 Angelo Turturro et al. E07082.01

Slide 14

14 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)

Slide 15

15 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

Slide 16

16 Cumulative Risk for Chemical Mixtures IAG with EPA-NCEA, Cincinnati - G. Rice, L. Teuschler Objective: To develop and apply a Relative Potency Factor (RPF) methodology for estimating the cumulative risk from exposure to a mixture of chemicals having a common mode of action (e.g., organophosphates: cholinesterase inhibition) FQPA, 1996 James Chen, Yi-Ju Chen et al. E07087.01

Slide 17

17 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

Slide 18

18 Advantages Uses actual dose-response functions of mixture components, not just ED 10 s, say (like TEF, HI, etc.) If the RPF is constant 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

Slide 19

19 Computational Toxicology 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 ( 13 C-NMR) spectroscopy. John Young et al.E07083.01

Slide 20

20 Motivation FDAs 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 systems prediction

Slide 21

21 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

Slide 22

22 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

Slide 23

23 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

Slide 24

24 Photocarcinogenicity Theory & Methods FDA CFSANCosmetics CDERDrugs (K. Lin) NCTRs Phototoxicity Program (P. Howard) CRADA w/ ARGUS Laboratory: S00213 Post Doc funding through NTP: E02037.01 Ralph Kodell, Daniel Molefe et al.E07061.01

Slide 25

25 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

Slide 26

26 Accomplishments/Plans Model developed for repeated-exposure case Computational optimization procedure developed Data on first of eight Argus studies analyzed Compare to logrank and Dunsons method Status: Ongoing.

Slide 27

27 Analysis of cDNA Microarray Data cDNA Microarrays popular new biotech tool vast amounts of data on gene expression quickly Statistical issues Experimental design Analysis and interpretation Bob Delongchamp, Cruz Velasco et al. E07096.01

Slide 28

28 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

Slide 29

29 Figure 1. Intensities observed in rat hepatocytes. Upper Right - Untreated Array Lower Left - MP Treated Array Lower Right - PM Treated Array

Slide 30

30 Figure 2. Array maps of log(I ga /I g ). Upper Right - Untreated Array Lower Left - MP Treated Array Lower Right - PM Treated Array

Slide 31

31 Figure 3. Intensities adjusted within 6x6 blocks. Upper Right - Untreated Array Lower Left - MP Treated Array Lower Right - PM Treated Array

Slide 32

32 Figure 4. Intensities adjusted for splotches (K a ) and saturation (K* a ). Upper Right - Untreated Array Lower Left - MP Treated Array Lower Right - PM Treated Array

Slide 33

33 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

Slide 34

34 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)

Slide 35

35 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