Literature DB >> 7570650

Assessment of effect levels of chemicals from quantitative structure-activity relationship (QSAR) models. I. Chronic lowest-observed-adverse-effect level (LOAEL).

M M Mumtaz1, L A Knauf, D J Reisman, W B Peirano, C T DeRosa, V K Gombar, K Enslein, J R Carter, B W Blake, K I Huque.   

Abstract

With the multitude of new chemicals being synthesized and the paucity of long-term test data on chemicals that could be introduced into the environment, innovative approaches must be developed to determine the health and environmental effects of chemicals. Research was conducted to employ quantitative structure-activity relationship (QSAR) techniques to study the feasibility of developing models to estimate the noncarcinogenic toxicity of chemicals that are not addressed in the literature by relevant studies. A database of lowest-observed-adverse effect level (LOAEL) was assembled by extracting toxicity information from 104 U.S. EPA documents, 124 National Cancer Institute/National Toxicology Program (NCI/NTP) reports, and 6 current reports from the literature. A regression model, based on 234 chemicals of diverse structures and chemical classes including both alicyclic and aromatic compounds, was developed to assess the chronic oral LOAELs in rats. The model was incorporated into an automated computer package. Initial testing of this model indicates it has application to a wide range of chemicals. For about 55% of the compounds in the data set, the estimated LOAELs are within a factor of 2 of the observed LOAELs. For over 93%, they are within a factor of 5. Because of the paucity or absence of long-term toxicity data, the public health and risk assessment community could utilize such QSAR models to determine initial estimates of toxicity for the ever-increasing numbers of chemicals that lack complete pertinent data. However, this and other such models should be used only by expert toxicologists who must objectively look at the estimates thus generated in light of the overall weight of evidence of the available toxicologic information of the subject chemical(s).

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Year:  1995        PMID: 7570650     DOI: 10.1016/0378-4274(95)03365-r

Source DB:  PubMed          Journal:  Toxicol Lett        ISSN: 0378-4274            Impact factor:   4.372


  9 in total

1.  True prediction of lowest observed adverse effect levels.

Authors:  R García-Domenech; J V de Julián-Ortiz; E Besalú
Journal:  Mol Divers       Date:  2006-05-24       Impact factor: 2.943

2.  Variability in in vivo studies: Defining the upper limit of performance for predictions of systemic effect levels.

Authors:  Ly Ly Pham; Sean Watford; Prachi Pradeep; Matthew T Martin; Russell Thomas; Richard Judson; R Woodrow Setzer; Katie Paul Friedman
Journal:  Comput Toxicol       Date:  2020-08-01

3.  Exploring current read-across applications and needs among selected U.S. Federal Agencies.

Authors:  Grace Patlewicz; Lucina E Lizarraga; Diego Rua; David G Allen; Amber B Daniel; Suzanne C Fitzpatrick; Natàlia Garcia-Reyero; John Gordon; Pertti Hakkinen; Angela S Howard; Agnes Karmaus; Joanna Matheson; Moiz Mumtaz; Andrea-Nicole Richarz; Patricia Ruiz; Louis Scarano; Takashi Yamada; Nicole Kleinstreuer
Journal:  Regul Toxicol Pharmacol       Date:  2019-05-10       Impact factor: 3.271

4.  In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) for Drugs.

Authors:  Fabiola Pizzo; Domenico Gadaleta; Emilio Benfenati
Journal:  Methods Mol Biol       Date:  2022

Review 5.  In silico prediction of drug toxicity.

Authors:  John C Dearden
Journal:  J Comput Aided Mol Des       Date:  2003 Feb-Apr       Impact factor: 3.686

6.  Translational research to develop a human PBPK models tool kit-volatile organic compounds (VOCs).

Authors:  M Moiz Mumtaz; Meredith Ray; Susan R Crowell; Deborah Keys; Jeffrey Fisher; Patricia Ruiz
Journal:  J Toxicol Environ Health A       Date:  2012

7.  Application of physiologically based pharmacokinetic models in chemical risk assessment.

Authors:  Moiz Mumtaz; Jeffrey Fisher; Benjamin Blount; Patricia Ruiz
Journal:  J Toxicol       Date:  2012-03-19

8.  Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy.

Authors:  Swapnil Chavan; Ran Friedman; Ian A Nicholls
Journal:  Int J Mol Sci       Date:  2015-05-21       Impact factor: 5.923

9.  Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.

Authors:  Lisa Truong; Gladys Ouedraogo; LyLy Pham; Jacques Clouzeau; Sophie Loisel-Joubert; Delphine Blanchet; Hicham Noçairi; Woodrow Setzer; Richard Judson; Chris Grulke; Kamel Mansouri; Matthew Martin
Journal:  Arch Toxicol       Date:  2017-10-27       Impact factor: 5.153

  9 in total

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