overview of selected epa activities related to...

Office of Research and Development

Elaine Francis, Ph.D.National Program Director for Pesticides and Toxics Research

David Dix, Ph.D.Acting Deputy Director, National Center for Computational Toxicology

Overview of Selected EPA Activities Related to Biotechnology 19th Meeting of the US-EC Task Force on

Biotechnology Research

June 25, 2009

1Office of Research and Development

• Evaluating the potential ecological effects of biotechnology products, specifically plant incorporated protectants (PIPs), on non-target species

• Characterizing the impact resulting from the escape of altered plants to the natural environment and the likelihood and effects of gene transfer

• Characterizing the development of pesticide resistance in the target insect species

• Developing risk management approaches

• Developing methods to assess for the potential allergenicity of genetically engineered plants

EPA’s Biotechnology Research Program

epa.gov/nheerl/publications/files/biotechnology_research_program_4_8_05.pdf


Biotechnology - Assessing Potential Food Allergy of Pesticide Proteins

Incorporated into Plants

GOAL: Develop reliable and accurate methods to predict allergenicity of novel proteins

What makes a protein an allergen?

Jiang Long/Illustrator “The Science Creative Quarterly” (www.scq.ubc.ca)

Regulatory ResponsibilityPlant-Incorporated Pesticides

EPA’s Office of Pesticide Programs is responsible for evaluating the safety of pesticides, including those genetically engineered

Concern that GE crops may introduce novel proteins into the food supply and introduce a food allergen

Currently unable to adequately evaluate potential allergenicitybecause valid animals models and other methods have not been adequately developed


Developing Approaches to Assess Potential Allergenicity of GE Foods

Research Areas of InterestHazard Assessment for Dietary Allergenicity

• Development and evaluation of animal models

• Development of targeted or specific serological assays

• Determination of structure-activity relationships of allergen proteins

Basis for Human Sensitization to Dietary Allergies

• Genetic, developmental, or other determinants

• Mechanisms underlying food allergies

• Influence of route, duration, and timing of dietary exposure

Mechanisms• Intramural research

• Extramural research through STAR program (www.epa.gov/ncer)

– EPA only (2005, 2009)– EPA and NIAID (2007)

• Bringing scientists/decisionmakerstogether

– Co-organized several workshops (e.g., Selgrade et. Al., Toxicol Sci. 2009 Jul;110(1):31-9. Epub 2009 Apr 10)

– Organized special sessions at professional society meetings (e.g., 2009 SOT)


David Dix, Acting Deputy Director, NCCT

Computational Toxicology @ EPA19th Meeting of the US-EC Task Force onBiotechnology Research

June 25, 2009


Key Points

• ORD’s mission is to lead the translation of scientific advances to address problems of national and international importance relative to protecting human health and the environment

• Multiple program offices within EPA recognize that the current methods for assessing chemical hazard and risk are insufficient for their tasks

– Legislation such as the new “Kid’s Safe Chemicals Act” and FQPA in the U.S. and REACH in the EU highlight the problem

• Recent advances in biology and computer sciences are enabling research that could not have been anticipated even 10 years ago.

• The transformation in toxicology necessitates an active researchprogram within ORD and strategic staffing in the Program Offices

• ORD foresaw the emergence of computational toxicology, and its investment is now recognized internationally as the leading edge of change

– Requires integrated, multidisciplinary effort over prolonged period


Current Approach for Toxicity Testing

Cancer

ReproTox

DevTox

NeuroTox

PulmonaryTox

ImmunoTox

in vivo testing

$Millions

For a food use pesticide: up to $10m in toxicology, $1m to interpret, years to complete


Too Many Chemicals Too Little Data (%)

EPA’s Need for Prioritization

0

10

20

30

40

50

60

Acute Cancer Gentox

Dev Tox Repro Tox

Judson, et al EHP (2009)

1

10

100

1000

10000

IRIS TRI Pesticides

Inerts CCL 1 & 2 HPV

MPV

9912


Transforming Toxicology

Strategic Goals•Toxicity Pathway ID and Screening•Pathway Based Risk Assessment•Institutional Transition


“ …to integrate modern computing and information techn ology with molecular biology to improve Agency prioritiza tion of data requirements and risk assessment of chemicals”

www.epa.gov/ncct

Decision Support Tools for High-Throughput Risk Assessment

National Center for Computational ToxicologyMission Statement


CompToxCompTox Program DevelopmentProgram Development

FY02

FY03

FY04

FY05

Congressional redirection

EDC Proof of Concepts

Design Team

Framework document

SAB and BOSC reviews

RTP Workshop

STAR HTPS RFA

CTISC

Proof of Concepts +

STAR Systems Biology RFA

NCCT formed

Prioritization Initiative

sBOSC I

ToxCast Concept

DSSTox v1

FY06

1st STAR Centers

1st Implementation Plan

ToxCast Design

sBOSC II

FY07

ToxCast Launch

1st Title 42s

CP CoP

v-Tissues

Intl Science Forum

FY08

Staffing Complete

NAS Vision

ToxCast Phase 1

3rd STAR Center sBOSC III

Tox21 MOU

DSSTox v2

FY09

ACToR

ToxRefDB

ToxCast I Done

ExpoCast

1st TDAS

vTissues 09

4th STAR Center

2nd Gen Imp Plan

sBOSC IV

ToxCast II Launch


Attributes of EPA’s CompTox Program

• Tackling problem of National and International importance �Staff has unique expertise in both biological science, computational

models and information technology

• Only Agency with such a staff dedicated to risk assessment�Relatively small staff requires collaboration both within and outside of

the agency

�Developing partnerships, and sharing data and analytical tools is critical to success

• Operating under tight timelines� Initial 5 years to prove approach works

�Limited resources require novel approaches to science and IM

• Commitment to transparency and public release of all data


Implications for Success

•Hazard Identification•Prioritizing Chemicals•Closing Data Gaps•Efficient Animal Usage•Better Resource Utilization

•Risk Assessment•Focusing on highest priority chemicals•Providing Mode(s) of Action•Targeted/Intelligent Testing •Identifying Susceptible Populations

•Ancillary Applications•Mixtures•Nanomaterials•Green Chemistry


Applying Computational Toxicology Along Applying Computational Toxicology Along the Source to Outcome Continuumthe Source to Outcome Continuum

Source/Stressor Formation

Environmental Conc.

External Dose Target Dose

Biological Event

Effect/Outcome

ToxCast

Reverse Toxicokinetics

ToxRef

v-Tissues

ExpoCast


What’s Needed

• Digitization of legacy data

• Applicable to large number of chemicals• Ability to probe a wide variety of key biological pathways

• Obtaining quantitative information• Data mining and management

• Providing efficient tools for the Program Offices


Digitizing Legacy in Vivo Data in ToxRefDB

Chronic/CancerMultigenationDevelopmental

Che

mic

als

30 years and more than $2B worth of data

Martin et al 2009a,bKnudsen et al 2009


FY08 FY09 FY10 FY11 FY12

Proof of ConceptProof of ConceptVerification/ExtensionVerification/Extension

Reduce to PracticeReduce to Practice

ToxCast Prioritization Product Timeline

FY07

FY09 -10~$15 -20K>200PMN Nanomaterials>12IId

FY09~$20 -25k>400ExtrapolationKnown Human

Toxicants>100IIb

FY09$10K166PilotNanomaterials15Ib

FY09~$20 -25k>400ValidationData Rich Chemicals>300IIa

>300

>400

552

Number of Assays

Data poor

Expanded Structure and Use Diversity

Data Rich

(pesticides)

Chemical Criteria

FY11 -12

FY10

FY08

TargetDate

~$15 -20k

~$20 -25k

$20k

Cost per Chemical

Prediction and Prioritization

Extension

Signature Development

PurposeNumber of Chemicals

Phase

ThousandsIII

>300IIc

320I

FY09 -10~$15 -20K>200PMN Nanomaterials>12IId

FY09~$20 -25k>400ExtrapolationKnown Human

Toxicants>100IIb

FY09$10K166PilotNanomaterials15Ib

FY09~$20 -25k>400ValidationData Rich Chemicals>300IIa

>300

>400

552

Number of Assays

Data poor

Expanded Structure and Use Diversity

Data Rich

(pesticides)

Chemical Criteria

FY11 -12

FY10

FY08

TargetDate

~$15 -20k

~$20 -25k

$20k

Cost per Chemical

Prediction and Prioritization

Extension

Signature Development

PurposeNumber of Chemicals

Phase

ThousandsIII

>300IIc

320Ia


ToxCast in vitro data (467 assays)

Che

mic

als

Cell Free HTSMultiplexed TFHuman BioMapHCSqNPAsXMEsImpedanceGenotoxicity

>200,000 dose response experiments

Judson et al, submitted


111 “hits”


Multiple Assays per Endpointand Pathway


Identification and Characterization of Toxicity Pathways

Receptors / Enzymes / etc.Direct Molecular Interaction

Pathway Regulation / Genomics

Cellular Processes

Tissue / Organ / Organism Tox Endpoint

Chemical


Predictive Signature Derivation for Rat Liver Carcinogens


Endocrine Profiling of the EDSP Priority Chemicals

23Office of Research and Development 23

ACToR

• Aggregated Computational Toxicology Resource

• Internet portal of information of chemicals

• +200 public sources

• +500,000 chemicals• Searchable by

– Name, CASRN, substructure• Tool for identifying chemicals of

concern and their data gaps

• Public access to ToxCast data

• http://actor.epa.gov


ToxCast Data Analysis Summit 1Global Partners

May 14-15, 2009U.S. EPA, RTP, NC


Lessons Learned from ToxCast Phase I

• High quality HTS data is obtainable

• A number of expected observations were found, as we re a number of unexpected ones

• Multiple assays per biological pathway are importan t to include

• Many chemicals in the library interact with a numbe r of targets

• The in vitro and in vivo data sets are complicated and will require extensive data analysis to determine optimal approaches

• Prioritization scores based on hazard potential are feasible

• Metabolism remains a challenge to incorporate in ma ny assays

• Greater numbers of chemicals and assays are needed

26

Tox21: U.S. Government Partnership


ToxicityCellChanges

MolecularTargets

Tissues

CellularNetworks

Cellular Systems

TissueDose

MolecularPathways

Predicting Human Toxicity: The Grand Challenge in Toxicology

Biochemical HTS

Cell-Based HTS

Complex Cellular and

HCS HTS

Model Organism

MTS

ToxRefDB

Virtual Tissues

MeasurementsAssays

MeasurementsAssays

DataRepositories

DataRepositories

QuantitativeModels

QuantitativeModels

ACToR

HEDS

HTS

ToxRefDB

PrioritizationTools

PrioritizationTools

BBDRs

Molecular epidemiology (bioindicators)

In vitro Assays

Rodent models

PBPK (e.g. ERDEM)

ExpoCast ToxCast

Exposure (e.g. SHEDS)

ExperimentalSystems

ExperimentalSystems

Human studies

EnvironmentalRelease

EnvironmentalRelease Environmental

Concentration

EnvironmentalConcentration Individual

Exposure

IndividualExposure Internal

Dose

InternalDose Biological

Event

BiologicalEvent Effect

Effect

CHAD

QSAR ToxMiner

TTDB

VirtualSystems

VirtualSystems

rTK

EFT

MetaPath

IndividualsPopulations Tissues

Risk Assessment Tools

KnowledgebasesKnowledgebases Molecular/Cellular/Tissue KBEnv/Individual/Organ KB

Individuals

Ambient Monitoring

BiomonitoringPersonal

Monitoring

U.S. EPA’s Computational Toolbox

HERO

29Office of Research and Development 29

HEHE

HE

HEHE

HE

HE

HEHE

HE

HEHE

Low exposure potentialHigh exposure potential

HEHE

HE

ToxCast Hazard Prediction

Intelligent, Targeted Testing

Near-Term Future State: Using Hazard and Exposure Information and Predictions for

Prioritizing Testing and Monitoring

Human Biomonitoring

ToxCast LowHazard

Prediction Low Priority for Bioactivity Profiling

ToxCast targets

Lower Priority for Testing and Monitoring


v-LiverTM The Virtual Liver Project

Imran Shah, PhDNational Center for Computational Toxicology


Why the Liver?

• Primary organ for environmental chemical detoxification

• Most frequent site of adverse effects (IRIS & ToxRefDB) in rodents – relevant to EPA

• Human relevance still uncertain

• Large amount of available molecular and tissue data


v-Liver TM PoC: Approach

v-Liver Knowledgebase (KB)�

Declarative description of •Molecular events•Cell events•Constraints

v-Liver Simulator (Sim)�

Dynamic Simulation of•Cellular & molecular system •Analyze collective response•Relate to patho/physiology

1.Focus: NR-mediated non-genotoxichepatocarcinogenicity

2.Select environmental chemicals from ToxCast Phase I

3.Gather knowledge on key physiologic events

4.Build v-Liver to simulate hepatic effects

5.Conduct studies to fill data gaps

6.Evaluate using PoC chemicals


v-Liver TM PoC: (A) Chemical Selection

Non-genotoxiccarcinogens

HTSMolecular Data

HCSCellular Data

ToxCast TM ToxRefDB(animal studies)�

Tissue Data(ex vivo) �

• Select ToxCast Phase I chemicals by

• Nuclear receptor activity

• Liver histopathology


v-Liver TM Architecture

Env.

Chems

Molecular

Events

Cell-Cell

Events

ToxCast

HTS, HCS

ex vivo

Cell Sys. &

Blood Flow

Assaysv-Liver

Knowledgebasev-Liver

Simulator

Cellular &

Tissue Effects

Outcomes


v-Liver TM: Milestones

• FY 09

– PoC chemicals: 20 NR + hepatocarcinogens +/- from ToxCast

– KB: nuclear receptor-mediated molecular circuits with ToxCast / public domain data

– Evaluate in vitro predictions for PoC chemicals• FY 10

– KB: Model nuclear receptor-mediated hyperplasia– Link with PBPK models for dosimetry

– Evaluate in vivo predictions for PoC chemicals • FY 11 & 12

– Expand information between genomic variation and MOA

– in vivo predictions for ToxCast Phase II chemicals / mixtures

Office of Research and DevelopmentOffice of Research and DevelopmentNational Center for Computational Toxicology

EPA’s Virtual Embryo

Thomas B. Knudsen, PhDNational Center for Computational Toxicology


Prenatal Developmental Toxicity

• adverse effects of chemicals on embryonic development captured for environmental chemicals by ToxRefDB

• phenotype spectrum: fetal weight reduction, malformations, prenatal death, (& functional deficits)

SOURCE: Knudsen et al. (2009) Reproductive Toxicology SOURCE: Knudsen et al. (2009) Reproductive Toxicology SOURCE: Knudsen et al. (2009) Reproductive Toxicology SOURCE: Knudsen et al. (2009) Reproductive Toxicology (in press) DOI 10.1016/j.reprotox.2009.03.016(in press) DOI 10.1016/j.reprotox.2009.03.016(in press) DOI 10.1016/j.reprotox.2009.03.016(in press) DOI 10.1016/j.reprotox.2009.03.016


EPA’s Virtual Embryo

• Motivation: computational (in silico) models to navigate complex relationships engaging developmental endpoints

• Goal: simulate embryos reacting to perturbation across chemical, system, stage, genetic makeup, dose and time

• Inputs: detailed knowledge of biochemical targets, molecular pathways, cellular networks and developmental phenotypes

• Outputs: working models of morphogenesis (short-term) and in silico reconstruction of the embryo (long-term)


Virtual Embryo Summary

• link pathway-level response with adverse outcome:

– know major components of ‘system’ (cells, molecules)– know relevant interactions among components (networks)– machine learning to mine inferred associations (predictions)

• virtual tissue can help by:

– framework to integrate from networks to higher-order systems– models to exercise system to conditions impractical experimentally – prioritize hypotheses for further experimentation


Points of Contact

• Biotechnology/Allergenicity – Elaine Francis –[email protected]

• Computational Toxicology – David Dix –[email protected]

• Virtual Liver – Imran Shah – [email protected]• Virtual Embryo – Tom Knudsen –[email protected]

overview of selected epa activities related to...

Documents