Review of QSAR Models and Software Tools for predicting Biokinetic Properties

In the assessment of industrial chemicals, cosmetic ingredients, and active substances in pesticides and biocides, metabolites and degradates are rarely tested for their toxicologcal effects in mammals. In the interests of animal welfare and cost-effectiveness, alternatives to animal testing are needed in the evaluation of these types of chemicals. In this report we review the current status of various types of in silico estimation methods for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their metabolites/degradation products. The review was performed in a broad sense, with emphasis on QSARs and rule-based approaches and their applicability to estimation of oral bioavailability, human intestinal absorption, blood-brain barrier penetration, plasma protein binding, metabolism and. This revealed a vast and rapidly growing literature and a range of software tools. While it is difficult to give firm conclusions on the applicability of such tools, it is clear that many have been developed with pharmaceutical applications in mind, and as such may not be applicable to other types of chemicals (this would require further research investigation). On the other hand, a range of predictive methodologies have been explored and found promising, so there is merit in pursuing their applicability in the assessment of other types of chemicals and products. Many of the software tools are not transparent in terms of their predictive algorithms or underlying datasets. However, the literature identifies a set of commonly used descriptors that have been found useful in ADME prediction, so further research and model development activities could be based on such studies.JRC.DG.I.6-Systems toxicolog

Investigating the influence of data splitting on the predictive ability of QSAR/QSPR models

The study was aimed at investigating how the method of splitting data into a training set and a test set influences the external predictivity of quantitative structure-activity and/or structure-property relationships (QSAR/QSPR) models. Six models of good quality were collected from the literature and then redeveloped and validated on the basis of five alternative splitting algorithms, namely: (i) a commonly used algorithm ('Z:1'), in which every zth (e.g. third) from the compounds sorted ascending (according to the response values, y) is selected into the test set; (ii-iv) three variations of the Kennard-Stone algorithm; and (v) the duplex algorithm. The external validation statistics reported for each model served as a basis for the final comparison. We demonstrated that the splitting techniques utilizing the values of molecular descriptors alone (X) or in combination with the model response (y) always lead to the development of the models yielding better external predictivity in comparison with the models designed with methodologies based on the y-values only. Moreover, we showed that the external validation coefficient (Q2EXT) is more sensitive to the splitting technique than the root mean square error of prediction (RMSEP). This difference becomes especially important when the test set is relatively small (between 5-10 compounds). In the case of the models trained/validated with a small number of compounds, it is strongly recommended that both statistics (Q2EXT and RMSEP) are taken into account for the external predictivity evaluation.JRC.I.6-Systems toxicolog

A Framework for assessing in silico Toxicity Predictions: Case Studies with selected Pesticides

In the regulatory assessment of chemicals, the use of in silico prediction methods such as (quantitative) structure-activity relationship models ([Q]SARs), is increasingly required or encouraged, in order to increase the efficiency and effectiveness of the risk assessment process, and to minimise the reliance on animal testing. The main question for the assessor concerns the usefulness of the prediction approach, which can be broken down into the practical applicability of the method and the adequacy of the predictions. A framework for assessing and documenting (Q)SAR models and their predictions has been established at the European and international levels. Exactly how the framework is applied in practice will depend on the provisions of the specific legislation and the context in which the non-testing data are being used. This report describes the current framework for documenting (Q)SAR models and their predictions, and discuses how it might be built upon to provide more detailed guidance on the use of (Q)SAR predictions in regulatory decision making. The proposed framework is illustrated by using selected pesticide active compounds as examples.JRC.DG.I.6-Systems toxicolog

The Applicability of Software Tools for Genotoxicity and Carcinogenicity Prediction: Case Studies relevant to the Assessment of Pesticides

This report presents research results obtained in the framework of a project on the Applicability of Quantitative Structure-Activity Relationship (QSAR) analysis in the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances. During this project, which was funded by the European Food Safety Authority (EFSA), the Joint Research Centre (JRC) performed several investigations to evaluate the comparative performance of selected software tools for genotoxicity and carcinogenicity prediction, and to develop a number of case studies to illustrate the opportunities and difficulties arising in the computational assessment of pesticides. This exercise also included an investigation of the chemical space of several pesticides datasets. The results indicate that different software tools have different advantages and disadvantages, depending on the specific requirements of the user / risk assessor. It is concluded that further work is needed to develop acceptance criteria for specific regulatory applications (e.g. evaluation of pesticide metabolites) and to develop batteries of models fulfilling such criteria.JRC.DG.I.6-Systems toxicolog

The Use of Computational Methods in the Toxicological Assessment of Chemicals in Food: Current Status and Future Prospects

A wide range of chemicals are intentionally added to, or unintentially found in, food products, often in very small amounts. Depending on the situation, the experimental data needed to complete a dietary risk assessment, which is the scientific basis for protecting human health, may not be available or obtainable, for reasons of cost, time and animal welfare. For example, toxicity data are often lacking for the metabolites and degradation products of pesticide active ingredients. There is therefore an interest in the development and application of efficient and effective non-animal methods for assessing chemical toxicity, including Quantitative Structure-Activity Relationship (QSAR) models and related computational methods. This report gives an overview of how computational methods are currently used in the field of food safety by national regulatory bodies, international advisory organisations and the food industry. On the basis of an international survey, a comprehensive literature review and a detailed QSAR analysis, a range of recommendations are made with the long-term aim of promoting the judicious use of suitable QSAR methods. The current status of QSAR methods is reviewed not only for toxicological endpoints relevant to dietary risk assessment, but also for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their reaction products. By referring to the concept of the Threshold of Toxicological Concern (TTC), the risk assessment context in which QSAR methods can be expected to be used is also discussed. This Joint Research Centre (JRC) Reference Report provides a summary and update of the findings obtained in a study carried out by the JRC under the terms of a contract awarded by the European Food Safety Authority (EFSA).JRC.DG.I.6-Systems toxicolog

Quantitative Structure - Skin permeability Relationships

This paper reviews in silico models currently available for the prediction of skin permeability with the main focus on the quantitative structure-permeability relationship (QSPR) models. A comprehensive analysis of the main achievements in the field in the last decade is provided. In addition, the mechanistic models are discussed and comparative studies that analyse different models are discussed

Computational Toxicology at the European Commission's Joint Research Centre

Importance of the field: The methods and tools of computational toxicology an essential and integrating pillar in the new paradigm of predictive toxicology which seeks to develop more efficient and effective means of assessing chemical toxicity, while also reducing animal testing. Areas covered in this review: The increasingly prominent role of computational toxicology in the implementation of European chemicals legislation is described, along with initiatives by the European Commission¿s Joint Research Centre to promote the acceptance and use of computational methods. Outstanding needs and scientific challenges are also outlined. What the reader will gain: an awareness of the current situation regarding the application of computational methods in regulatory toxicology. Take home message: In recent years, there has been impressive scientific and technological advances in computational toxicology. However, considerable progress is still needed to increase the acceptance of computational methods, and in particular to develop a deeper and common understanding of how to apply computational toxicology in regulatory decision making.JRC.I.5-Systems Toxicolog

Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities

The toxicological assessment of genotoxic impurities is an important consideration in the regulatory framework for pharmaceuticals. In this context, the application of promising computational methods (e.g. Quantitative Structure-Activity Relationships (QSARs), Structure-Activity Relationships (SARs) and/or expert systems) for the evaluation of genotoxicity is needed, especially when very limited information on impurities is available, both for practical reasons and to respect the principle of the 3Rs (Replacement, Reduction and Refinement) of animal use. To gain an overview of how computational methods are used internationally in the regulatory assessment of pharmaceutical impurities, the current regulatory documents were reviewed. The software recommended in the guidelines (e.g. MCASE, MC4PC, Derek for Windows) or, practically used by various regulatory agencies (e.g. U.S. Food and Drug Administration, U.S. and Danish Environmental Protection Agencies), as well as the other existing programs were analysed, highlighting their benefits and limitations. Both statistically-based and knowledge-based (expert system) tools were analysed. Information on the models’ training sets as well as their applicability domains was retrieved. The overall conclusions on the available in silico tools for genotoxicity and carcinogenicity prediction are quite optimistic and the regulatory application of QSAR methods is constantly growing. For regulatory purposes, it is recommended that the predictions of genotoxicity/carcinogenicity should be based on a battery of models, combining high sensitivity models (low rate of false negatives) with high specificity ones (low rate of false positives), and in vitro assays in an integrated manner.JRC.I.5-Systems Toxicolog

Quantitative structure-skin permeability relationships

This paper reviews in silico models currently available for the prediction of skin permeability. A comprehensive discussion on the developed methods is presented, focusing on quantitative structure-permeability relationships. In addition, the mechanistic models and comparative studies that analyse different models are discussed. Limitations and strengths of the different approaches are highlighted together with the emergent issues and perspectives.JRC.F.3-Chemicals Safety and Alternative Method