Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project.

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

Insights into possibilities for grouping and read-across for nanomaterials in EU chemicals legislation.

This paper presents a comprehensive review of European Union (EU) legislation addressing the safety of chemical substances, and possibilities within each piece of legislation for applying grouping and read-across approaches for the assessment of nanomaterials (NMs). Hence, this review considers both the overarching regulation of chemical substances under REACH (Regulation (EC) No 1907/2006 on registration, evaluation, authorization, and restriction of chemicals) and CLP (Regulation (EC) No 1272/2008 on classification, labeling and packaging of substances and mixtures) and the sector-specific pieces of legislation for cosmetic, plant protection and biocidal products, and legislation addressing food, novel food, and food contact materials. The relevant supporting documents (e.g. guidance documents) regarding each piece of legislation were identified and reviewed, considering the relevant technical and scientific literature. Prospective regulatory needs for implementing grouping in the assessment of NMs were identified, and the question whether each particular piece of legislation permits the use of grouping and read-across to address information gaps was answered.

Applicability Domain Dependent Predictive Uncertainty in QSAR Regressions.

Predictive models used in decision making, such as QSARs in chemical regulation or drug discovery, call for evaluated approaches to quantitatively assess associated uncertainty in predictions. Uncertainty in less reliable predictions may be captured by locally varying predictive errors. In the current study, model-based bootstrapping was combined with analogy reasoning to generate predictive distributions varying in magnitude over a model's domain of applicability. A resampling experiment based on PLS regressions on four QSAR data sets demonstrated that predictive errors assessed by k nearest neighbour or weighted PRedicted Error Sum of Squares (PRESS) on samples of external test data or by internal cross-validation improved the performance of the uncertainty assessment. Analogy using similarity defined by Euclidean distances, or differences in standard deviation in perturbed predictions, resulted in better performances than similarity defined by distance to, or density of, the training data. Locally assessed predictive distributions had on average at least as good coverage as Gaussian distribution with variance assessed from the PRESS. An R-code is provided that evaluates performances of the suggested algorithms to assess predictive error based on log likelihood scores and empirical coverage graphs, and which applies these to derive confidence intervals or samples from the predictive distributions of query compounds.

An evaluation of the implementation of the Cramer classification scheme in the Toxtree software.

Risk assessment for most human health effects is based on the threshold of a toxicological effect, usually derived from animal experiments. The Threshold of Toxicological Concern (TTC) is a concept that refers to the establishment of a level of exposure for all chemicals below which there would be no appreciable risk to human health. When carefully applied, the TTC concept can provide a means of waiving testing based on knowledge of exposure limits. Two main approaches exist; the first of these is a General Threshold of Toxicological Concern; the second approach is a TTC in relation to structural information and/or toxicological data of chemicals. The structural scheme most routinely used is that of Cramer and co-workers from 1978. Recently this scheme was encoded into a software program called Toxtree, specifically commissioned by the European Chemicals Bureau (ECB). Here we evaluate two published datasets using Toxtree to demonstrate its concordance and highlight potential software modifications. The results were promising with an overall good concordance between the reported classifications and those generated by Toxtree. Further evaluation of these results highlighted a number of inconsistencies which were examined in turn and rationalised as far as possible. Improvements for Toxtree were proposed where appropriate. Notable of these is a necessity to update the lists of common food components and normal body constituents as these accounted for the majority of false classifications observed. Overall Toxtree was found to be a useful tool in facilitating the systematic evaluation of compounds through the Cramer scheme.

Toxmatch-a new software tool to aid in the development and evaluation of chemically similar groups.

Chemical similarity is a widely used concept in toxicology, and is based on the hypothesis that similar compounds should have similar biological activities. This forms the underlying basis for performing read-across, forming chemical groups and developing (Quantitative) Structure-Activity Relationships ((Q)SARs). Chemical similarity is often perceived as structural similarity but in fact there are a number of other approaches that can be used to assess similarity. A systematic similarity analysis usually comprises two main steps. Firstly the chemical structures to be compared need to be characterised in terms of relevant descriptors which encode their physicochemical, topological, geometrical and/or surface properties. A second step involves a quantitative comparison of those descriptors using similarity (or dissimilarity) indices. This work outlines the use of chemical similarity principles in the formation of endpoint specific chemical groupings. Examples are provided to illustrate the development and evaluation of chemical groupings using a new software application called Toxmatch that was recently commissioned by the European Chemicals Bureau (ECB), of the European Commission's Joint Research Centre. Insights from using this software are highlighted with specific focus on the prospective application of chemical groupings under the new chemicals legislation, REACH.

How can structural similarity analysis help in category formation?

Chemical category is a regulatory concept facilitating filling safety data gaps. Practically, all chemical management programs like the OECD HPV Program, EU REACH, or the Canadian DSL Categorization are planning to use or are already using categorization approaches to reduce resources including animal testing. The aim of the study was to discuss the feasibility to apply computational structural similarity methods to augment formation of a category. The article discusses also how this understanding can be translated into computer readable format, an ultimate need for practical, broad scope applications. We conclude that for the skin sensitization endpoint, used as a working example, mechanistic understanding expressed as chemical reactivity can be exploited by computational structural similarity methods to augment category formation process. We propose a novel method, atom environments ranking (AER), to assess similarity to a reference training set representing a common mechanism of action, as a potential method for grouping chemicals into reactivity domains.