SAR

/Tag:SAR

The Development and Characterisation of a Structure-activity Relationship Model of the Draize Eye Irritation Test

Herbert S. Rosenkranz, Ying Ping Zhang and Gilles Klopman2

A structure-activity relationship (SAR) model based on the results of 297 chemicals tested in the Draize eye irritation assay was developed. The SAR model displayed a predictivity of 74% for chemicals not included in the model. The SAR analysis indicated that chemical reactivity was not a requirement for eye irritation. The major structural determinants included hydrophilicity, alkalinity (i.e. primary, secondary and tertiary amines), acidity (for example, the carboxylic acid moiety), and putative lipophobic 4.5–5.4Å receptor-binding ligands. The analysis revealed that, while there were significant structural overlaps between the SAR models of ocular irritation, allergic contact dermatitis and respiratory hypersensitivity, there was much less overlap between ocular irritation and cell toxicity. This decreased overlap must be considered in developing strategies to replace the Draize test with in vitro cellular toxicity assays.
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Lack of Predictivity of the Rat Lethality (LD50) Test for Ecological and Human Health Effects

Herbert S. Rosenkranz and Albert R. Cunningham

The relationship between acute toxicity in rats (LD50 values) and indicators of potential health hazards in humans was investigated, based on a chemical population-based paradigm (i.e. the “chemical diversity approach”). These structure–activity relationship-based analyses indicate that high toxicity in rats (i.e. a low LD50 value) is not a good predictor of health effects in humans. In fact, it was found that high acute toxicity to minnows, as well as toxicity to cultured cells, showed significantly greater associations with the potential for health effects than rat LD50 values.
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2017-01-09T06:34:11+00:00 Tags: , , , |

Computational Toxicology Approaches at the US Food and Drug Administrationa

Chihae Yang, Luis G. Valerio, Jr and Kirk B. Arvidson

For over a decade, the United States Food and Drug Administration (US FDA) has been engaged in the applied research, development, and evaluation of computational toxicology methods used to support the safety evaluation of a diverse set of regulated products. The basis for evaluating computational toxicology methods is multi-factorial, including the potential for increased efficiency, reduction in the numbers of animals used, lower costs, and the need to explore emerging technologies that support the goals of the US FDA’s Critical Path Initiative (e.g. to make decision support information available early in the drug review process). The US FDA’s efforts have been facilitated by agency-approved data-sharing agreements between government and commercial software developers. This commentary review describes former and current scientific initiatives at the agency, in the area of computational toxicology methods. In particular, toxicology-based QSAR models, ToxML databases and knowledgebases will be addressed. Notably, many of the computational toxicology tools available are commercial products — however, several are emerging as non-commercial products, which are freely-available to the public, and which will facilitate the understanding of how these programs work and avoid the “black box” paradigm. Through productive collaborations, the US FDA Center for Drug Evaluation and Research, and the Center for Food Safety and Applied Nutrition, have worked together to evaluate, develop and apply these methods to chemical toxicity endpoints of regulatory interest.
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