Quantitative structure‐activity relationships for predicting mutagenicity and carcinogenicity

Abstract

Abstract-Quantitative structure-activity relationships (QSARs) for predicting mutagenicity and carcinogenicity were reviewed.

A roadmap for the development of alternative (non-animal) methods for systemic toxicity testing

“…Benigni and Bossa summarized (Benigni and Bossa, 2006) Benigni, 2004): "Study of the structure of the chemicals generates predictions with limited reliability for the individual chemicals" but Benigni sees enormous value for priority setting for testing. A key prerequisite for improving the available models will be to generate larger homogenous datasets for modeling (Patlewicz et al, 2003). An impressive discrepancy currently exists between studies employing external evaluations, such as the Predictive Toxicology Challenge (PTC), and internal validation results.…”

The human cytochrome P450 sub-family: Transcriptional regulation, inter-individual variation and interaction networks

“…Prediction of toxicity as a result of the bio-activation of chemicals that occurs during metabolism is relatively straightforward, and can be achieved to a good degree of accuracy using in silico programs such as DERECK, TOPCAT or HazardExpert [5][6][7]. However and area of increasing concern are toxic reactions caused due to interactions between coadministered chemicals.…”

Computer-Assisted Methods in Chemical Toxicity Prediction

“…(ii) The QSARs of acute toxicity end point have been reviewed by Lessigiarska et al (2005) [38]. (iv) Variety of models exist in mutagenicity end point prediction which are more specific such as QSARs for amines and aldehydes [41,42]. Some neural network studies have undergone in more complex non-linear model data.…”