Mark T. D. Cronin

Read-across approaches--misconceptions, promises and challenges ahead.

Read-across is a data gap filling technique used within category and analogue approaches. It has been utilized as an alternative approach to address information requirements under various past and present regulatory programs such as the OECD High Production Volume Programme as well as the EUs Registration, Evaluation, Authorisation and restriction of CHemicals (REACH) regulation. Although read-across raises a number of expectations, many misconceptions still remain around what it truly represents; how to address its associated justification in a robust and scientifically credible manner; what challenges/issues exist in terms of its application and acceptance; and what future efforts are needed to resolve them. In terms of future enhancements, read-across is likely to embrace more biologically-orientated approaches consistent with the Toxicity in the 21st Century vision (Tox-21c). This Food for Thought article, which is notably not a consensus report, aims to discuss a number of these aspects and, in doing so, to raise awareness of the ongoing efforts and activities to enhance read-across. It also intends to set the agenda for a CAAT read-across initiative in 2014-2015 to facilitate the proper use of this technique.

Toward good read-across practice (GRAP) guidance

Summary Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislation such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document presents the state of the art, summarizes insights learned from reviewing ECHA published decisions regarding the relative successes/pitfalls surrounding read-across under REACH, and compiles the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHAs published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data, and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015.

QSAR Modeling: Where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Pitfalls in QSAR

Abstract There are no formal guidelines for the development of quantitative structure–activity relationships (QSARs). However, there are a number of practices that should be avoided. This paper describes the pitfalls in QSAR, and problems that can arise if they occur. The emphasis of this paper is particularly for the development of QSARs for toxicity for environmental endpoints and drugs, but is equally applicable to pharmacological endpoints. Problems may arise from all three areas of the QSAR, namely the biological activity, physico-chemical and/or structural descriptors, and the use of a statistical technique. Biological data for use in a QSAR should be of a known (and preferably high) quality. Physico-chemical descriptors and statistical processes should be appropriate for the endpoint being modelled. They should allow for the development of a clear, transparent and mechanistically interpretable QSAR. To have any practical utility, QSARs should be validated by means of an external testing set.

Quantitative structure-permeability relationships (QSPRs) for percutaneous absorption.

Quantitative structure-permeability relationships (QSPRs) have been derived by many researchers to model the passive, diffusion-controlled, percutaneous penetration of exogenous chemicals. Most of these relationships are based on experimental data from the published literature. They indicate that molecular size (as molecular weight) and hydrophobicity (as the logarithm of the octanol-water partition coefficient; log k(ow)) are the main determinants of transdermal penetration. This article reviews the current state of the art in QSPRs for absorption of chemicals through the skin, and where this technology can be exploited in future research. The main shortfalls in QSPR models result from inconsistency and error of the experimental values used to derive them. This is probably caused by the manner in which they employ data from a variety of sources and, in some cases, slightly different experimental protocols. Further, most current models are based on data generated from either aqueous or ethanolic solution, where each penetrant is present at its saturated solubility or a fraction of its saturated solubility. No models currently account for the influences of formulation upon percutaneous penetration. Current QSPR models provide a significant tool for assessing the percutaneous penetration of chemicals. They may be important in determining the bioavailability of a range of topically applied exogenous chemicals, and in issues of dermal toxicology and risk assessment. However, their current use may be limited by their lack of applicability across different formulation types. As a consequence, their true value may be to make predictions within specific formulation types, as opposed to a general model based on a range of formulation types. In addition, the endpoint of models may be inappropriate for specific applications other than the systemic delivery of topically applied chemicals.

How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)

Although thousands of quantitative structure–activity and structure–property relationships (QSARs/QSPRs) have been published, as well as numerous papers on the correct procedures for QSAR/QSPR analysis, many analyses are still carried out incorrectly, or in a less than satisfactory manner. We have identified 21 types of error that continue to be perpetrated in the QSAR/QSPR literature, and each of these is discussed, with examples (including some of our own). Where appropriate, we make recommendations for avoiding errors and for improving and enhancing QSAR/QSPR analyses.

Quantitative structure–activity relationships (QSARs) in toxicology: a historical perspective

Abstract The history of the use of quantitative structure–activity relationships (QSARs) in toxicology, both for environmental, and human health effects is described. A particular emphasis is made on the science in response to the United States Toxic Substance Control Act of 1976. Specifically, the basic concepts and objectives of QSARs for toxicity are reviewed. QSARs for environmental and human health effects are discussed separately. Environmental, and more specifically, ecotoxicity, QSARs have focused historically on modeling congeneric series and non-specific effects in aquatic organisms through the use of the logarithm of the 1-octanol/water partition coefficient to describe hydrophobicity, and hence uptake. Compounds that do not fit these QSARs (namely the outliers) have been explained by differences in mechanism of acute toxicity, especially as a result of electro(nucleo)philic interactions. In light of this, mechanisms of acute toxicity are discussed. QSAR approaches to receptor-mediated effects, such as those exhibited by environmental estrogens, and competitive binding to the estrogen receptor, are different from those typically applied to model acute toxic endpoints. Several of these approaches, including three-dimensional QSAR techniques, are reviewed. Human health effects include both local and systemic effects. Local effects (e.g. corrosivity and skin sensitization) are often modeled by multivariate QSAR methods such as linear regression and discriminant analysis. The prediction of systemic effects such as mutagenesis and carcinogenesis requires consideration of the endpoint and a more mechanistic basis for modeling. Approaches to predict these endpoints include the use of expert systems.

Predicting Chemical Toxicity and Fate

INTRODUCTION Predicting Chemical Toxicity and Fate in Humans and the Environment - An Introduction METHODOLOGY Toxicity Data Sources Calculation of Physicochemical Properties Good Practice in Physicochemical Property Prediction Whole Molecule and Atom Based Topological Descriptors Quantum Chemical Descriptors in Structure-Activity Relationships - Calculation, Interpretation and comparison of Methods Building QSAR Models - A Practical Guide QSARs FOR HUMAN HEALTH ENDPOINTS Prediction of Human Health Endpoints: Mutagenicity and Carcinogenicity The Use of Expert Systems for Toxicity Prediction - Illustrated with Reference to the DEREK Program Computer-Based Methods for the Prediction of Chemical Metabolism and Biotransformation within Biological Organisms Prediction of Pharmacokinetic Parameters in Drug Design and Toxicology QSARs FOR ENVIRONMENTAL TOXICITY AND FATE An Exercise in External Validation: The Benzene Response-Surface Model for Tetrahymena Toxicity Receptor-Mediated Toxicity: QSARs for Oestrogen Receptor Binding and Priority Setting of Potential Oestrogenic Endocrine Disruptors Prediction of Persistence QSAR Modelling of Bioaccumulation QSAR Modelling of Soil Sorption Application of Catabolic-Based Biosensors to Develop QSARs for Degradation APPLICATION The Tiered Approach to Toxicity Assessment Based on the Integrated Use of Alternative (Non-Animal) Tests The Use of Quantitative Structure-Activity Relationships and Expert Systems to Predict Toxicity by Governmental Regulatory Agencies A Framework for Promoting the Acceptance and Regulatory Use of (Quantitative) Structure-Activity Relationships.

Investigation of the mechanism of flux across human skin in vitro by quantitative structure–permeability relationships

Permeability coefficients for 114 compounds across excised human skin in vitro were taken from Kirchner et al. Forty-seven descriptors were calculated encompassing the relevant physicochemical parameters of the compounds. Quantitative structure-permeability relationships (QSPRs) were developed using least-squares regression analysis. A two-parameter QSPR, describing the permeability coefficients (Kp) across excised skin, was obtained: log Kp=0.772 log P -0.0103 Mr - 2.33 where log P is the logarithm of the octanol-water partition coefficient and Mr is molecular mass. This equation indicates that percutaneous absorption is mediated by the hydrophobicity and the molecular size of the penetrant. Comparison with a QSPR based on penetration across a synthetic (polydimethylsiloxane) membrane suggests that the mechanisms of drug flux across polydimethylsiloxane membranes and excised human skin are significantly different.

Structure-toxicity relationships for phenols to Tetrahymena pyriformis

Quantitative structure-activity relationships are developed for the toxicity of 166 varied phenol derivatives to the ciliate Tetrahymena pyriformis. A variety of physico-chemical descriptors were calculated but no significant relationship could be obtained for all 166 compounds. When certain chemical groups were omitted from the correlation however, notably the carboxyl-, amino-, nitro, nitroso and acetamide- substituted phenols, an excellent correlation was obtained between toxicity and two parameters. These two parameters (log P and energy of the lowest unoccupied molecular orbital) are explained mechanistically in that they model transport and electrophilicity. The resultant QSAR gave accurate prediction of the toxicity of alkyl, halogenated, alkoxy and aldehyde substituted phenols.