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.

Determination of the no observed adverse effect level (NOAEL) of a substance is an important step in safety and regulatory assessments. Application of conventional in silico strategies, for example, quantitative structure−activity relationship (QSAR) models, to predict NOAEL values is inherently problematic. Whereas QSAR models for well-defined toxicity endpoints such as Ames mutagenicity or skin sensitization can be developed from mechanistic knowledge of molecular initiating events and adverse outcome pathways, QSAR is not appropriate for predicting a NOAEL value, a concentration at which "no effect" is observed. This paper presents a chemoinformatics approach and explores how it can be further refined through the incorporation of toxicity endpoint-specific information to estimate confidence bounds for the NOAEL of a target substance, given experimentally determined NOAEL values for one or more suitable analogues. With a sufficiently large NOAEL database, we analyze how a difference in NOAEL values for pairs of structures depends on their pairwise similarity, where similarity takes both structural features and physicochemical properties into account. The width of the estimate NOAEL confidence interval is proportional to the uncertainty. Using the new threshold of toxicological concern (TTC) database enriched with antimicrobials, examples are presented to illustrate how uncertainty decreases with increasing analogue quality and also how NOAEL bounds estimation can be significantly improved by filtering the full database to include only substances that are in structure categories relevant to the target and analogue.

Knowledge about metabolism is very important to understand the health risks posed by chemicals. The biochemical process of metabolism causes activation, inactivation, toxification, detoxification as well as changes in the physicochemical properties of a chemical. The long time consumption and high costs associated with animal tests and the challenges faced by traditional quantitative structure-activity relationship (QSAR) models in dealing with situations wherein parent chemical structures are less relevant to the ultimate effects have led to the development of in silico techniques for the prediction of xenobiotic metabolism. The strengths and limitations of some of the most commonly used in silico expert systems, and their application in studying metabolism of xenobiotic chemicals, have been reviewed. The in silico metabolism simulators possessed several distinguishing features imparted in part by the nature of knowledge rules (algorithms) encoded within them and in part by the integration of QSAR libraries and computational engines.

No abstract

No abstract

Existing Quantitative Structure-Activity Relationship (QSAR) models have limited predictive capabilities for aromatic azo compounds. In this study, 2 new models were built to predict Ames mutagenicity of this class of compounds. The first one made use of descriptors based on simplified molecular input-line entry system (SMILES), calculated with the CORAL software. The second model was based on the k-nearest neighbors algorithm. The statistical quality of the predictions from single models was satisfactory. The performance further improved when the predictions from these models were combined. The prediction results from other QSAR models for mutagenicity were also evaluated. Most of the existing models were found to be good at finding toxic compounds but resulted in many false positive predictions. The 2 new models specific for this class of compounds avoid this problem thanks to a larger set of related compounds as training set and improved algorithms.

No abstract

Alterations in molecular structure are responsible for the differential biological response(s) of a chemical inside a biosystem. Structural and functional parameters that govern a chemical's metabolic course and determine its ultimate outcome in terms of mutagenic/carcinogenic potential are extensively reviewed here. A large number of environmentally-significant organic chemicals are addressed under one or more broadly classified groups each representing one or more characteristic structural feature. Numerous examples are cited to illustrate the influence of key structural and functional parameters on the metabolism and DNA adduction properties of different chemicals. It is hoped that, in the event of limited experimental data on a chemical's bioactivity, such knowledge of the likely roles played by key molecular features should provide preliminary information regarding its bioactivation, detoxification and/or mutagenic potential and aid the process of screening and prioritising chemicals for further testing.