1 / 35

Division of Biometry and Risk Assessment

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

Ava
Download Presentation

Division of Biometry and Risk Assessment

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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

  3. 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

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

  5. 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)

  6. Hypothesis • Fumonisin B1 affects the incidence ofliver tumor formation in mice byincreasing the death rate of cellswhich leads tocompensatory proliferation.

  7. 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

  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

  9. 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

  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 Probability ppm

  11. 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

  12. 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?

  13. 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

  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)

  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

  16. 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

  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

  18. 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

  19. 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.

  20. 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

  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

  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

  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

  24. 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

  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

  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 Dunson’s method • Status: Ongoing.

  27. 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

  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

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

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

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

  32. 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

  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

  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)

  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

More Related