TIMES-SS – Recent refinements resulting from an industrial skin sensitisation consortium

Abstract

The TImes MEtabolism Simulator platform for predicting Skin Sensitisation (TIMES-SS) is a hybrid expert system, first developed at Bourgas University using funding and data from a consortium of industry and regulators. TIMES-SS encodes structure-toxicity and structure-skin metabolism relationships through a number of transformations, some of which are underpinned by mechanistic 3D QSARs. The model estimates semi-quantitative skin sensitisation potency classes and has been developed with the aim of minimising animal testing, and also to be scientifically valid in accordance with the OECD principles for (Q)SAR validation. In 2007 an external validation exercise was undertaken to fully address these principles. In 2010, a new industry consortium was established to coordinate research efforts in three specific areas: refinement of abiotic reactions in the skin (namely autoxidation) in the skin, refinement of the manner in which chemical reactivity was captured in terms of structure-toxicity rules (inclusion of alert reliability parameters) and defining the domain based on the underlying experimental data (study of discrepancies between local lymph node assay Local Lymph Node Assay (LLNA) and Guinea Pig Maximisation Test (GPMT)). The present paper summarises the progress of these activities and explains how the insights derived have been translated into refinements, resulting in increased confidence and transparency in the robustness of the TIMES-SS predictions.

Read-across approaches - misconceptions, promises and challenges ahead

“…Skin sensitization, for example: there is an abundance of mechanistic knowledge about the induction of skin sensitization as summarized in the adverse outcome pathway (AOP) recently published by the OeCD (2012). In turn, much of this information has been encoded into software tools such as tIMeS (Patlewicz et al, 2014a) or the OECD (Q)SAR Toolbox, as well as into QSARs published in the literature (Roberts et al, 2008;Patlewicz and Worth, 2008).…”

In silico prediction of prostaglandin D2 synthase inhibitors from herbal constituents for the treatment of hair loss

“…SSA uses a SMARTS pattern-based approach to predict the reaction mechanisms between the compounds and human skin protein (Patlewicz et al, 2014;Roberts et al, 2006). In general, skin sensitisation responses are triggered by bond formation with chemicals.…”

“…The common mechanisms include Michael acceptor (MA), Schiff base formation (SB), acyl transfer agents (AC), nucleophilic aromatic substitution (SNAr) and second-order nucleophilic aliphatic substitution (SN2). SSA can predict these mechanisms with high speed and accuracy (Patlewicz et al, 2014;Roberts et al, 2006).…”

Application of IATA – A case study in evaluating the global and local performance of a Bayesian network model for skin sensitization

“…TIMES-SS is a module within the TIssue MEtabolism Simulator platform (Laboratory Mathematical Chemistry (LMC), University "As Zlatarov", Bourgas, Bulgaria), which relies on structuremetabolism and structure-activity rules for the prediction of skin sensitization potency. The development and performance of the TIMES-SS expert system has been discussed in much more detail elsewhere [25,26].…”