The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

The ICH M7 guideline describes a consistent approach to identify, categorize, and control DNA reactive, mutagenic, impurities in pharmaceutical products to limit the potential carcinogenic risk related to such impurities. This paper outlines a series of principles and procedures to consider when generating (Q)SAR assessments aligned with the ICH M7 guideline to be included in a regulatory submission. In the absence of adequate experimental data, the results from two complementary (Q)SAR methodologies may be combined to support an initial hazard classification. This may be followed by an assessment of additional information that serves as the basis for an expert review to support or refute the predictions. This paper elucidates scenarios where additional expert knowledge may be beneficial, what such an expert review may contain, and how the results and accompanying considerations may be documented. Furthermore, the use of these principles and procedures to yield a consistent and robust (Q)SAR-based argument to support impurity qualification for regulatory purposes is described in this manuscript.

The aim of the SEURAT‐1 (Safety Evaluation Ultimately Replacing Animal Testing‐1) research cluster, comprised of seven EU FP7 Health projects co‐financed by Cosmetics Europe, is to generate a proof‐of‐concept to show how the latest technologies, systems toxicology and toxicogenomics can be combined to deliver a test replacement for repeated dose systemic toxicity testing on animals. The SEURAT‐1 strategy is to adopt a mode‐of‐action framework to describe repeated dose toxicity, combining in vitro and in silico methods to derive predictions of in vivo toxicity responses. ToxBank is the cross‐cluster infrastructure project whose activities include the development of a data warehouse to provide a web‐accessible shared repository of research data and protocols, a physical compounds repository, reference or “gold compounds” for use across the cluster (available via wiki.toxbank.net), and a reference resource for biomaterials. Core technologies used in the data warehouse include the ISA‐Tab universal data exchange format, REpresentational State Transfer (REST) web services, the W3C Resource Description Framework (RDF) and the OpenTox standards. We describe the design of the data warehouse based on cluster requirements, the implementation based on open standards, and finally the underlying concepts and initial results of a data analysis utilizing public data related to the gold compounds.

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There is an urgent need for the development of alternative methods to replace animal testing for the prediction of repeat dose chemical toxicity. To address this need, the European Commission and Cosmetics Europe have jointly funded a research program for 'Safety Evaluation Ultimately Replacing Animal Testing.' The goal of this program was the development of in vitro cellular systems and associated computational capabilities for the prediction of hepatic, cardiac, renal, neuronal, muscle, and skin toxicities. An essential component of this effort is the choice of appropriate reference compounds that can be used in the development and validation of assays. In this review, we focus on the selection of reference compounds for liver pathologies in the broad categories of cytotoxicity and lipid disorders. Mitochondrial impairment, oxidative stress, and apoptosis are considered under the category of cytotoxicity, while steatosis, cholestasis, and phospholipidosis are considered under the category of lipid dysregulation. We focused on four compound classes capable of initiating such events, i.e., chemically reactive compounds, compounds with specific cellular targets, compounds that modulate lipid regulatory networks, and compounds that disrupt the plasma membrane. We describe the molecular mechanisms of these compounds and the cellular response networks which they elicit. This information will be helpful to both improve our understanding of mode of action and help in the selection of appropriate mechanistic biomarkers, allowing us to progress the development of animal-free models with improved predictivity to the human situation.

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The International Council for Harmonization (ICH) M7 guideline describes a hazard assessment process for impurities that have the potential to be present in a drug substance or drug product. In the absence of adequate experimental bacterial mutagenicity data, (Q)SAR analysis may be used as a test to predict impurities’ DNA reactive (mutagenic) potential. However, in certain situations, (Q)SAR software is unable to generate a positive or negative prediction either because of conflicting information or because the impurity is outside the applicability domain of the model. Such results present challenges in generating an overall mutagenicity prediction and highlight the importance of performing a thorough expert review. The following paper reviews pharmaceutical and regulatory experiences handling such situations. The paper also presents an analysis of proprietary data to help understand the likelihood of misclassifying a mutagenic impurity as non-mutagenic based on different combinations of (Q)SAR results. This information may be taken into consideration when supporting the (Q)SAR results with an expert review, especially when out-of-domain results are generated during a (Q)SAR evaluation.