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