VirtualToxLab ~ in silica prediction of the toxic potential of drugs and environmental chemicals: evaluation status and Internet access protocol

Abstract

SummaryWe present a receptor-modeling concept based on multidimensional QSAR (mQSAR)

Computational Methods in Drug Discovery

“…Another technique known as the pseudoreceptor technique (Tanrikulu and Schneider, 2008) uses pharmacophore mapping-like overlaying techniques for a collection of ligands that bind to the same binding site to establish a virtual representation of the binding site's structure, which is then used as a template for docking and other structure-based vHTS. This approach has been used by VirtualToxLab (Vedani et al, 2007) for the creation of nuclear receptors and cytochrome P450 binding site models in ADMET prediction tools and by Tanrikulu et al (2009). in the modeling of the H4 receptor binding site subsequently used to identify novel active scaffolds .…”

Assessment of hydroxylated metabolites of polychlorinated biphenyls as potential xenoestrogens: a QSAR comparative analysis∗

“…A full assessment of their possible endocrine-disrupting effects is critical for individual PCBs and their metabolites; unfortunately, traditional toxicological studies are costly and time consuming. To meet the needs of contemporary toxicology, a growing number of in silico tools and quantitative structure-activity relationship (QSAR) models are being developed for surrogate use in evaluation of chemicals for which data are lacking [33][34][35][36][37][38][39][40][41][42]. Through these efforts, meaningful chemical structural features can be identified that will lead to the development of robust predictive models, and specific selection criteria for use of such models [38,39,[41][42][43][44][45][46].…”

Food for thought … on in silico methods in toxicology

“…typical examples come from protein modelling, such as models of the estrogen receptor or various P-450 enzymes, where crystal structures are used to model the fit of the test compound into the reactive site of the receptor and to determine the likelihood of its triggering a response. these models are typically three-dimensional, but notably there are also 4D-models, which accommodate the induced fit of the substance on the receptor (Vedani et al, 2007) or pseudo-receptor models (Vedani et al, 2005). In case of the latter, in the absence of a real receptor, available data on a training set of ligands are used to emulate a hypothetic receptor to then be applied to new, untested substances.…”