Office of Research and Development



# Building Knowledgebases and Decision Support Tools Presentation to the FIFRA Scientific Advisory Panel

May 24, 2011 Pat Schmieder EPA, ORD, NHEERL, 

## Mid - Continent Ecology Division,

Duluth, MNOffice of Research and Development



# Decision Support Systems A decision support system ( DSS) is a computerbased information system that supports organizational decision - making activities. DSSs 

# include knowledge - based systems and expert systems .Office of Research and Development 

# Knowledge-base A knowledge-base is a special kind of database for knowledge management , providing the means for the computerized collection , organization , and 

# retrieval of knowledge. Knowledge - bases are more than databases because they are designed to help analyze information.Office of Research and Development 

# Expert System An expert system uses a knowledge - base of human expertise for problem solving & supporting decision making, or to clarify uncertainties where 

# normally one or more human experts would need to be consulted. Rules developed by experts can be codified into logic trees that display the 

# decision points in the expert system.Office of Research and Development 

# Benefits of Decision Support Systems

(DSS)
• Improves personal efficiency 

# • Speed up the process of decision making • Increases organizational control/consistency • Encourages exploration and discovery on the part 

# of the decision maker • Speeds up problem solving in an organization • Facilitates interpersonal communication 

# • Promotes learning or training • Generates new evidence in support of a decision • Reveals new approaches to thinking about the 

# problem space • Helps automate managerial processesOffice of Research and Development 

# KB/DSS components:

• Inputs: Factors, data and characteristics, to analyze • User Knowledge and Expertise: Inputs requiring manual analysis by the user • Outputs: Transformed/analyzed data from 

# which DSS "decisions" are generated • Decisions: Results generated by the DSS 

# based on user criteriaOffice of Research and Development 

# Knowledge-bases, Expert Systems

ECOTOX ACToR ToxRefDB 

# ToxCastDB DSSTox 

# ExpoCast Effectopedia ER Expert System 

# MetaPath

# ECOTOX
• Is a comprehensive, publicly- available on - line database, which provides EPA with a single source of open literature information for ecological effects data for aquatic and terrestrial organisms. 

## • Includes test results published in the open literature, and from other government data sources from: 35,723 publications; 7 electronic data files (EPA, USGS, OECD, Russia) 

## • All pertinent information on the species, chemical, test methods, and results are abstracted and encoded into the database 9,562 chemicals; 9,731 species; 765,032 individual test results 

## • Maintained with a high level of quality assurance and extensive 

## documentation. Continually improved in accessibility and usefulness and regularly updated. • Publicly available on Internet (www.epa.gov/ecotox) 

## • Developed and maintained by EPA/ORD/NHEERL/MED • ECOTOX is internationally recognized as the source for ecotoxicological data

# ECOTOX Uses within OPP

• Used to reduce ESA vulnerability • Complies with an agreement between EPA and the US Fish and Wildlife Service and the National Marine Fisheries Service that EPA’s use of ECOTOX complies with the Endangered Species Act requirement that Federal agencies use best available information in conducting analyses for threatened and endangered species 

## • Assists scientists in screening open - literature in an efficient/consistent manner.

– Serves as the source for searching open literature data by providing an extensive source of open literature data. Includes literature available to the public as well as the literature in the queue to be coded for the public.

– Applies the acceptance criteria described in OPP’s guidance for study selection.

– Provides standardized summary of the effects data that pass the acceptance criteria screen.

– Includes bibliographic file for rejected studies and a file for all studies. 

## • May supplement the standard effects studies required under FIFRA regulations (i.e., expanding the number of species tested). 

## • May reduce the uncertainties associated with the registrant-submitted toxicity data. • Use of ECOTOX ensures consistency and transparency in assessment process.

# ACToR

Aggregated Computational Toxicology Resource

• 

### Collaboration across EPA (ORD, OPP, OPPT, OW, GLNPO, EDSP)
• 

### Compile all publicly available information on environmental chemicals

– One - stop shop – over 600 individual data sources, 500,000 chemicals

– Make previously hard -to - access data transparent (e.g. from OPP)
• 

### Make data available for downloading, data mining

– Available through data.gov



### – Entire DB can be downloaded and installed locally
• 

### Make it easy to see data gaps

– Provides resource for EPA testing programs
• 

### Make it widely used



### – over 2000 regular users

http://actor.epa.gov

# DSSTox

Distributed Structure-Searchable Toxicity Database Network

ToxRefDB ACToR

ToxCast ExpoCast



# Chemical Structure Annotation Chemical Management Chemical QC

DSSTox

http://www.epa.gov/ncct/dsstox

# ToxRefDB

Capturing OPP Guideline Animal Study Data DER Input Pesticide Active Ingredients Non - Pesticide Chemicals 

# Output Structure 

# SOURCE DATA 

# DATABASE APPLICATION ToxCast Retro Status: 572 chemicals, 3184 studies http://actor.epa.gov/toxrefdb/faces/Home.jsp

# ToxCastDB



# High Throughput Screening (HTS) Data

• 

# ToxCastDB provides access to all ToxCast data

• 

# Screening 1,000 chemicals in over 500 rapid tests (high throughput screening assays

• 

# Users can search and download data for all ToxCast chemicals, assays, genes, pathways and endpoints

• 

# Database allows for statistical associations and biologically driven data mining

• 

# Provide links to available animal data through ToxRefDB http://actor.epa.gov/actor/faces/ToxCastDB/Home.jsp

## Data from NERL studies 1) American Health Home Survey 2) HUD Child Care Center Survey (“CCC” ) 3) CTEPP – NC 4) CTEPP – OH 

## Browse data capability > By study name, chemical list, media list Descriptive statistics capability 

## > Detection frequency = # of samples above MDL/total # of samples > N, max, MDL (if result concentration is < MDL, then result = 2/3 * MDL) > Units > Mean, standard deviation, geometric mean, geometric standard 

## deviation (GSD), 25 th 

## and 75 th 

## percentile

- Only show these if detection frequency is >= 25 %



# ExpoCastDB 

# Exposure data sets

http://actor.epa.gov/actor/faces/ExpoCastDB/Home.jsp

# ACToR

Aggregated Computational Toxicology Resource

http://actor.epa.gov

# A knowledgebase that defines AOPs in multiple dimensions Life stage Taxonomy Gender Time to effect Level of biological organization

Adverse Outcome Assessment Endpoint Molecular Initiating Events

# DEFINING AN ADVERSE OUTCOME PATHWAY

Chemical Adverse Outcome MIE 1 

### MIE 2
MIE n Effect 1 

### Effect 2 Effect m Effect 1 Effect 2 Effect k1
Effect 1 Effect 2 Effect k2
Effect 1 Effect 2 Effect kn ... 

### Adverse Outcome Assessment Endpoint Molecular Initiating Events 

# BREAKING DOWN THE COMPLEXITY

# Functionalities Visual editor Search Engine 

# History tracking module Suggestion engine and filters Chemical database connectivity Reference repository 

# Advanced pathway visualization / analysis tools: Show pathways in different relevant pathway projections Sorting and filtering data. Vulnerability slider

# Facilitates hazard assessment

- catalog of AOPs explaining linkages of biological effects at different levels of observation

- library of molecular initiating events (MIEs) for important adverse outcome pathways.

- extend QSAR methods by integrating existing models of hundreds of in vitro endpoints with relevant assessment endpoints and outcomes

- extend the OECD Toolbox with chemical profilers for identifying chemicals with similar receptor binding profiles (MIEs) — ”what other chemicals in the inventory might bind to this receptor”

- extend the AOPs to all metabolites derived from QSAR - driven simulators for specific organ/tissue metabolism 

# Facilitates forming chemical categories

- organizes expert data and knowledge of AOPs and facilitates the grouping of chemicals into categories based on:

- QSAR allows extrapolating adverse effects determined for one chemical to similar chemicals if the chemicals have the same mechanism through the same pathway (AOP)

- OECD category approach groups chemicals that have similar metabolism and hazardous effect profilesOffice of Research and Development 

# Expert System An expert system uses a knowledge - base of human expertise for problem solving & supporting decision making, or to clarify uncertainties where 

# normally one or more human experts would need to be consulted. Rules developed by experts can be codified into logic trees that display the 

# decision points in the expert system.Office of Research and Development 

# Expert System Development 1) the creation of a knowledge base which uses some knowledge representation structure to capture the knowledge of the Subject Matter Expert (SME); 2) a process of gathering that knowledge from the SME and codifying it according to the structure, 

# which is called knowledge engineering ; 3) once the system is developed, it is placed in the same real world problem solving situation as the 

# human SME, i.e., to aid decision making.In vitro Assay focus area 

# ER-mediated Reproductive Impairment AOP QSAR focus area Inerts; Antimicrobial Chemicals Receptor

Binding ER Binding Liver Cell

Protein Expression Vitellogenin (egg protein transported to ovary) Liver

Altered proteins (Vtg) Sex

reversal; Altered behavior; Repro. MOLECULAR Target CELLULAR Response TISSUE/ORGAN INDIVIDUAL Skewed Sex Ratios; Yr Class POPULATION

Greater Toxicological Understanding Greater Risk Relevance

# 1) the creation of a knowledge base which uses some knowledge representation structure to capture the knowledge of the Subject Matter 

# Expert (SME);

Log Kow = 3.05 Log Kow = 5.12 Log Kow = 3.39 Log Kow = 4.06 Log Kow = 0.90

Log Kow = 1.96 Log Kow = 2.40 Log Kow = 1.39 

## ER Knowledge base C0

C1 C2 C8 C3 C4 C5 C6

# 2) a process of gathering that knowledge from the SME and codifying it according to the structure, which is called knowledge engineering;

Contains a Charge
#2 ... Contains sulfonic acid, carboxylic acid, phosphoric acid or quaternary ammonium functional group. Has AND Contains 3 Oxygen Atoms AND Does NOT Contain N, P, F, Cl, Br or I Atoms Alkylaromatic Sulfonic Acids
Has AND Number of Cycles >= 3 Sulfonic Acid Dyes #2.1

#2.2 Structure has c1(S(=O)(=O)O)ccccc1 AND structure has O; number of fragments=3 AND NOT structure has Rx:N,P,F,Cl,Br,I Rx

Structure has c1(S(=O)(=O)O)ccccc1 AND N_Cycles >=3 

### Current Formal Rules (Refined with each inventory) Decision Tree Node26 Contains a Cycle Yes

Log KOW <1.3 “Special Rule” Applies

Site “A” Contains Phenol Fragment

Log Kow Range 4-Alkylanilines ( 1.4 - 5.1) 4-Alkoxy Anilines ( 2.6 - 3.7) Phthalates ( 2.5 - 6.8) Phenones - (branched) ( 2.7 - 4.8) 4-Alkyl cyclohexanols ( 2.4 - 3.8) 4-Alkyl cyclohexanones ( 2.4 - 3.8) * 2 - ; 4- ; or 2,4,6 - Benzoates (substitution - dependent Log Kows) Possible Low Affinity Site A-Type, Site B -Type

Site “B” Contains “Specified” Fragment Log Kow Range Alkylphenols (1.5 - 8.0) Alkoxy phenols (2.3 - 4.3) Parabens (1.9 - 5.4) Salicylates (2.5 - 5.1) Belongs to known Active Sub - class RBA > 0.00001% Yes

No VI

Unknown Binding Potential No

Yes

Yes
No Yes

Belongs to known Active Sub - class Belong to untested class Unknown Binding Potential Mixed Phenols

No VII No Belongs to known Inactive Sub - class Log Kow Range 4- n -Alkylfluorobenzenes ( 2.3 - 4.9) Alkylphenol (2,6 -subst) ( 4.2) Benzamides ( 1.7 - 2.1) Borate Esters (- 0.5 - 1.0) Benzoates (non - ring subst) ( 3.6 - 4.2) Bis - anilines ( 1.6 - 6.2) Hydrofurans (alcohol & ketone) ( 0.0 - 1.2) Imidazolidines (- 0.9 - 0.6) Isothiazolines ( 0.6 - 2.5) Mono - cyclic Hydrocarbons ( 2.7 - 4.6) Oxazoles (<0.3) Phenones (n - alkyl) ( 1.6 - 4.1) Pyrrolidiones (- 0.4 - 3.3) Sorbitans ( 3.2 - 5.9) Triazines (- 0.4 - 3.4) Yes DDT- Like Tamoxifen - Like Multicyclic hydrocarbons Alkylchlorobenzenes Thiophosphate Esters

Yes No

No Yes

RBA > 0.00001% RBA < 0.00001% I

RBA < 0.00001%

Yes No V III Contains a Charge No Yes II

Exact match to Mixed Phenols Training Set Structure and measured RBA RBA > 0.00001% RBA < 0.00001% No Yes

Unknown Binding Potential Mixed Organics

No Exact match to Mixed Organics

Training Set Structure and measured RBA RBA > 0.00001% RBA < 0.00001% No Yes Unknown Binding Potential Exact match to Group Training Set Structure and measured RBA RBA > 0.00001% RBA < 0.00001% No Yes Alkylaromatic Sulfonic Acids Log Kow ( - 0.6 - 5.7) Sulfonic Acid Dyes Log Kow ( - 0.4 - 6.0) 4-Alkylbenzthiols Log Kow ( 2.9 - 4.2) Miscellaneous Functional Groups w/ Charge Contains a phenolic OH, and additional OH and/or =0 IV Meets specific distance criteria for: Sites “A- B”; Sites “A- C”; Sites “A- B - C”

Contains some attenuating feature, steric or other High Affinity “A- B”;“A- C” or “A- B - C” RBA > 0.1%

RBA < 0.00001%
Low Affinity “A- B”,“A- C” or “A- B - C” 0.00001% < RBA < 0.1% Strength of attenuation factors Yes

No

Strong Weak Start Here: Chemical List 393 Food Use Inerts 

# 3) once the system is developed, it is placed in the 

# same real world problem solving situation as the human SME, e.g., to aid decision making.Office of Research and Development 

# Metabolism Path ways Knowledgebase



# MetaPath

• Collaborative OPP & ORD Project
• National Health and Environmental Effects Research Laboratory (NHEERL), Mid - Continent Ecology Division (MED), Duluth, MN • National Exposure Research Laboratory (NERL), Ecosystems Research Division (ERD), Athens, GA • Cooperative Agreement with Laboratory of Mathematical Chemistry, Bourgas University


# • Database & Data Evaluation Tools 

# • Common Assessment Tool Box • International Partnerships (Global Acceptance) • Stakeholder EngagementOffice of Research and Development



# Metabolism Data Use in 

# Risk Assessment

• Pesticides risk assessment must not only 

# consider the active ingredient but also pesticide residues. • Exposure to and potential toxicity of the parent compound and any major metabolites or degradates is assessed.

# Building a scientific knowledgebase to use existing metabolism information in real time & in real situations Heavy Reliance On Animal Studies Less Reliance on Animals 

### Generate information For All possible Outcomes 

# The Paradigm Shift

Tailor data generation & obtain right information For chemical of interest Empirical approach



# Increasing Effectiveness & Efficiency

Mechanism Based ApproachOffice of Research and Development 

# MetaPath

• Knowledgebase that stores information on pesticide metabolism and residues in food organisms • Lab animals, livestock, plants, and environmental transformation products 

# • Allows for visualization of parent chemicals & metabolites displayed as a pathway or map • Enables efficient and systematic comparisons 

# of metabolites across chemicals, species, and environmental media to identify metabolites of potential risk concernOffice of Research and Development



# Key Benefits of MetaPath

• Provides structure - based searching and 

# identification of structurally similar or unique metabolites • Identifies common metabolites as residues in plants, and livestock. • Provides a platform for global joint reviews/work share efforts • Systematic compilation of metabolism 

# information will facilitate development of a metabolism simulatorOffice of Research and Development 

# MetaPath a metabolism pathways database and data evaluation tools 

# DER Composer a software template for efficient data entry to facilitate:



## - creation of draft Data Evaluation Records (DER) for risk assessor evaluations of OPP metabolism studies

- auto - population submitted data into MetaPath

- enhanced quality control through electronic datahandling 

# Metabolism Simulator uses observed metabolite occurrence data from MetaPath and knowledge of biotransformation reaction types 

## to simulate metabolism pathways 

# Computational Tools for 

# Metabolism Research and Risk Assessment2013



# MetaPath Metabolic Simulators Building a Scientific Database to Use Metabolism information in Real Time 

### Developing & Testing a Predictive Model that is based on MetaPath

Livestock & Plant Residues Environmental

Degradates Lab animal (mostly rat) 

### Faster more consistent document preparation Easy information retrieval & searching 

### >Are there similar metabolites from different parent AIs? >Are there sex & species differences? Reduce Animal Testing: Read Across Predict



### A B C X? Are there likely AI metabolites not found in submitted data? Assess metabolism for data poor chemicals > Are there toxic metab/

degradates from antimicrobials or inert ingredients? 

# Building a scientific knowledgebase 

# to assess existing metabolism/degradate information in real time & in real situationsOffice of Research and Development 

# MetaPath Two components: • Database

- pathways



# - metadata • Data evaluation tools Definitions: map = pathway = tree 

## experimental matrices = urine, bile, feces, plasma

### The main MetaPath display screen

Display of Metabolism Pathway (map): parent chemical + metabolites List of pesticides in the database: Search by Common name, ChemName, CAS, Agency Code, etc Clickable ‘Tabs’ to view supporting information, e.g., Results tables of summary metabolite quantities as compiled from Registrant studies and reported in DER/DARs.Bioassay information associated with the study for a given chemical/map is displayed as RESULTS tables to facilitate user interpretation, and can be selected for export and printing.

### Pharmacokinetic data associated with the study for a given chemical/map

# Analyzing Treatment Group Effects

Maps in the database are the combined pathways across all treatment groups (as reported in the study) (e.g., M +, F; low + high dose; all matrices, etc) User selects treatment group of interest; unique color shown with each treatment. For example, this metabolite was observed only in urine of male rats at low dose. Highlight Treatment Group: Indicates which metabolites were observed under what experimental conditionMap Comparisons – One selected map is compared to another (e.g., Species comparisons), or to all others within a database. Example: Metabolites common to both maps are shown in green boxes. All 3 metabolites found in chickens exposed to tebuconazole ( right screen ) are found in rats (left screen ), however, additional metabolites are found in the rat.

# Leverage Existing Knowledge

Search & Group Chemicals by Shared Features Chemical structure Physical chemical properties (Q)SAR models In vitro or HTP data In vivo data 

## Apply data from a group of tested chemicals for a 

## particular property or effect to a similar untested chemical for that endpoint Inherent Properties of the Chemical & Chemical Class

## Searches: Setting up a Query (Q)

The database can be searched by building Queries (Q) Looking for a specific chemical structure, sub -structure, species,



## type of dose administration, etc.

Simple (one parameter_Q1)

or complex (multiple parameter_Q1 to Qx) searches using "AND", "OR", "NOT“

logic Example: find all chemicals in the database with: Q1_Species = rat Q2_Administered Dose = oral Q3_Metabolite = 1,2,4-triazole

RESULTS – next slide

# SEARCH RESULTS

Within the entire database, 3 metabolites were found matching search criteria, triazole metabolite found in rats dosed by

oral gavage. Info is provided to trace back to the parent chemical and the entire metabolic

pathway.

# Excel Output from MetaPathOffice of Research and Development 

# Developed around Pharmacokinetic & Metabolism, and Nature of the Residue harmonized guidelines: 1) OPPTS 870.7485 (85- 1) Rat Metabolism 2) OPPTS 860.1300 Nature of Residues in Animals

• Data entered in DER Composers can output as:

– *.xml file for direct data import into MetaPath

– *.doc file draft Data Evaluation Record to record OPP decisions



# • Efficient standardized data entry

• Facilitates electronic data submission and e - population of 

# new data into MetaPath into the future



# DER Composer: 

# System for Auto-

population of MetaPath

## screen shots from ‘ DER Composer’ software
‘General Info’ screen corresponding to first page of an OPP rat metabolism Data Evaluation Record (DER); currently used for entry of archived DERs, but could be used to build new DER.

Note the OPP and OECD guideline numbers‘Materials and Methods’ tab, Part A: Materials. Chemical structure can be drawn with internal structure drawing package, or from imported SMILES string.‘Materials and Methods’ tab, Part B: Study Design and methods. This information is captured in table format similar to what is found in the DER.

Text boxes are included for addition of explanatory information as typically found in DERs. Analytical method details can be captured as text, or more systematically in a Table.Tables are built for Pharmacokinetic and Metabolite Characterization data.Text boxes are included for addition of explanatory information as typically found in DERs.Appendix Table 1: Systematic capture of treatment group

parameters for:

-Autopopulation of DER Table 1

- Specifying metabolite differences due to treatments, gender, matrix (Highlight Treatment Grp feature in

MetaPath)
Appendix Table 2: Chemical structures of parents and metabolites entered as SMILES strings; Connectivity between parent and each metabolite (i.e., the metabolism pathway).DER *.doc file DER Composer data entry screen

# MetaPath – ‘General Info’Appendix Table 1: Systematic capture of treatment group parameters for:

- autopopulation of DER Table 1

- Specifies treatments, i.e., M,F; dose level & duration; excretion route, etc., as ‘Highlight Treatment Group’ feature in MetaPath

Appendix Table 2:

-Chemical structures of parents and metabolites entered as SMILES strings;

-Connectivity between parent and each metabolite (i.e., the metabolism pathway) is specified here 

## DER Composer - Appendix Table 1 & Table 2Tables are built for Pharmacokinetic and Metabolite Characterization data.
Pre- populated with row & column headers as defined in Appendices; this info is used in MetaPath to associate which metabolites were found under which experimental conditions; e.g., if a cell in this table reports a number for a metabolite, then the metabolite will be shown as detected in that treatment (dose level; gender group) in MetaPath if selected in “Highlight Treatment

Group” 

## DER Composer - ‘Results’ – Metabolite Characterization

### MetaPath - Highlight Treatment Group FeatureOffice of Research and Development 

# Benefits of DER Composers

• improve efficiency in data summary and submission 

# • maximize electronic data transfer to minimize data errors • facilitate QA of data • autopopulate Agency knowledge - bases to improve PRIA workflow;

- getting data in the hands of the risk assessor more efficiently

- spend less time compiling data and more time assessingOffice of Research and Development 

# DER Composers

• OECD Working Group on Pesticides/MetaPath Users Group working with OECD Secretariat towards use of rat, livestock Data Summary Builders (DER Composers) for data capture and population of MetaPath



# • Using the same approach, EPA would develop DER Composers to facilitate electronic capture of registrant- submitted toxicity data
• DER Composers for EDSP T1S data submissionAssessment Team (Lead Toxicologist and Risk Assessors) review the Toxicology Profile using summary information in draft DER and refer to Study File as appropriate ROCKS Hazard Assessors pre - meet before full committee meeting to discuss team proposal and consider metabolites of potential hazard concern (using MetaPath and/or other (Q)SAR tools for predictions) summarized in draft

DER 

### MetaPath/DER Composer use in OPP workflow Residues Of Concern Knowledgebase Subcommittee (ROCKS) Registrant produces draft DER (doc file) and XML for a ‘new’ chemical using ‘ DER Composer’ (rat metabolism, livestock residues, plant residues, environmental degradates) MetaPath tools used to assess common metabolites for Proposed Residue Definitions for inclusion in Risk Assessment and Tolerance Expression recorded

in draft DER ‘new’ chemical xml (after QA) is added to MetaPath database

ROCKS Committee Reviews the team’s proposal and renders an Expert opinion/ recommendation(s); draft DER updated and decision finalized and reported in final DER MetaPath

draft DEROffice of Research and Development 

# • A new sulfonyl urea “Pesticide B” is 

# being assessed. MetaPath could be used to ask:
• What other sulfonyl urea pesticides have we previously assessed? • What types of metabolic transformations 

# are likely to occur? • What are likely residues of concern? 

# MetaPath Example

### Ran search in MetaPath for structural analogs to Parent.

Pesticide B Structural sub -fragment for search RESULTS: Found Rat Metabolism maps within database for four sulfonyl ureas.

Pesticide W Pesticide X Pesticide Y Pesticide Z

### Map for Pesticide X shows primary metabolic pathway of: 

### ring hydroxylation, O - dealkylation, and cleavage to the sulfonamide

### Map for pesticide W also shows: ring hydroxylation, O - dealkylation, and cleavage to sulfonamide, potentially resulting in same residues of concern.

# KB/DSS components - MetaPath:

• Inputs : Factors, data and characteristics, to analyze: MetaPath - metabolic maps and associated metadata tables specified in the Data Evaluation Records as the study information important to the 

## evaluations (automated e-data entry via DER Composers) • User Knowledge and Expertise: Inputs requiring manual analysis by the user: MetaPath - User defines substructures for searching, map comparisons to make, etc 

## • Outputs: Transformed/analyzed data from which DSS "decisions" are generated: MetaPath – lists all AI containing substructure of concern; common metabolites across hundreds of maps in the KB are identified using the 

## software; residues of concern identified within the maps of the AIs listed ; etc • Decisions: Results generated by the DSS based on user criteria: MetaPath - User identifies residues of concern for current pesticide assessment (as a result of map comparisons; treatment group 

## comparisons; substructure searches; subsequent analysis metabolites/

degradates in MetaPath outputs in QSAR toolbox, expert systems, etc)

# Benefits of Decision Support Systems

• Improves personal efficiency ( desktop data availability) • Speed up the process of decision making ( automated loading of 

## new information into OPP proprietary database for comparison of new map information with legacy data) • Increases organizational control ( consistency ) • Encourages exploration and discovery on the part of the decision 

## maker ( allows new analyses/comparisons) • Speeds up problem solving in an organization • Facilitates interpersonal communication ( within OPP; with international partners in global joint reviews) • Promotes learning or training ( allows new analyses/comparisons) • Generates new evidence in support of a decision ( comparisons between chemicals; species - gender; dose -levels etc ) • Reveals new approaches to thinking about the problem space 

## • Helps automate managerial processes ( standardize data gathering; analysis; reporting )

# Collaborations

• Working toward a common international platform for storing & evaluating information

– International MetaPath User Group established 

# under OECD (Working Group on Pesticides)

– Collaborators: • Health Canada, Pesticide Management Regulatory Authority 

# • European Food Safety Authority and European member countries • Australia’s Pesticides & Veterinary Medicines Authority and Office of Chemical Safety & Environmental Health 

# • OECD SecretariatOffice of Research and Development Partnership between EPA/OPP and EPA/ORD/ NHEERL & NERL, and PMRA Health Canada: NHEERL, MED - Duluth, MN: P . Schmieder

, R. Kolanczyk NERL, ERD - Athens, GA: J . Jones Cooperative Agreement with: O. Mekenyan, Laboratory of Mathematical Chemistry (LMC) ,

Bourgas University, Bourgas , Bulgaria, OPP - Residues Of Concern Knowledge-bases Sub-committee (ROCKS): M. Manibusan, C. Olinger , R. Kent OPP, Environmental Fate and Effects Division (EFED): Fate Tech Team PMRA, Health Canada: L. Croteau , M. Gerrits 

# MetaPath Partners: