Literature DB >> 8564854

U.S. EPA regulatory perspectives on the use of QSAR for new and existing chemical evaluations.

M Zeeman1, C M Auer, R G Clements, J V Nabholz, R S Boethling.   

Abstract

As testing is not required, ecotoxicity or fate data are available for approximately 5% of the approximately 2,300 new chemicals/year (26,000 + total) submitted to the US-EPA. The EPA's Office of Pollution Prevention and Toxics (OPPT) regulatory program was forced to develop and rely upon QSARs to estimate the ecotoxicity and fate of most of the new chemicals evaluated for hazard and risk assessment. QSAR methods routinely result in ecotoxicity estimations of acute and chronic toxicity to fish, aquatic invertebrates, and algae, and in fate estimations of physical/chemical properties, degradation, and bioconcentration. The EPA's Toxic Substances Control Act (TSCA) Inventory of existing chemicals currently lists over 72,000 chemicals. Most existing chemicals also appear to have little or no ecotoxicity or fate data available and the OPPT new chemical QSAR methods now provide predictions and cross-checks of test data for the regulation of existing chemicals. Examples include the Toxics Release Inventory (TRI), the Design for the Environment (DfE), and the OECD/SIDS/HPV Programs. QSAR screening of the TSCA Inventory has prioritized thousands of existing chemicals for possible regulatory testing of: 1) persistent bioaccumulative chemicals, and 2) the high ecotoxicity of specific discrete organic chemicals.

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Year:  1995        PMID: 8564854     DOI: 10.1080/10629369508234003

Source DB:  PubMed          Journal:  SAR QSAR Environ Res        ISSN: 1026-776X            Impact factor:   3.000


  8 in total

1.  SCRAM: A scoring and ranking system for persistent, bioaccumulative, and toxic substances for the North American Great Lakes. Part I: Structure of the scoring and ranking system.

Authors:  E M Snyder; S A Snyder; J P Giesy; S A Blonde; G K Hurlburt; C L Summer; R R Mitchell; D M Bush
Journal:  Environ Sci Pollut Res Int       Date:  2000-03       Impact factor: 4.223

2.  Application of a genetic algorithm and an artificial neural network for global prediction of the toxicity of phenols to Tetrahymena pyriformis.

Authors:  Aziz Habibi-Yangjeh; Mohammad Danandeh-Jenagharad
Journal:  Monatsh Chem       Date:  2009-10-13       Impact factor: 1.451

3.  Structure-activity relationship models for rat carcinogenesis and assessing the role mutagens play in model predictivity.

Authors:  C A Carrasquer; K Batey; S Qamar; A R Cunningham; S L Cunningham
Journal:  SAR QSAR Environ Res       Date:  2014-04-04       Impact factor: 3.000

4.  Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors.

Authors:  Daniel E Dawson; Brandall L Ingle; Katherine A Phillips; John W Nichols; John F Wambaugh; Rogelio Tornero-Velez
Journal:  Environ Sci Technol       Date:  2021-04-15       Impact factor: 9.028

Review 5.  Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances.

Authors:  Mark T D Cronin; John D Walker; Joanna S Jaworska; Michael H I Comber; Christopher D Watts; Andrew P Worth
Journal:  Environ Health Perspect       Date:  2003-08       Impact factor: 9.031

Review 6.  Healthy environments for healthy people: bioremediation today and tomorrow.

Authors:  C Bonaventura; F M Johnson
Journal:  Environ Health Perspect       Date:  1997-02       Impact factor: 9.031

7.  An ensemble model of QSAR tools for regulatory risk assessment.

Authors:  Prachi Pradeep; Richard J Povinelli; Shannon White; Stephen J Merrill
Journal:  J Cheminform       Date:  2016-09-22       Impact factor: 5.514

8.  An automated framework for QSAR model building.

Authors:  Samina Kausar; Andre O Falcao
Journal:  J Cheminform       Date:  2018-01-16       Impact factor: 5.514

  8 in total

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