In silico toxicology protocols

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

Read-across to rank skin sensitization potential: subcategories for the Michael acceptor domain

BACKGROUND: Eliminating animal testing for skin sensitization is a significant challenge in consumer safety risk assessment. To be able to perform resilient risk assessments in the future, one will need alternative approaches to fill the data gaps. OBJECTIVES: To this end, we propose a subcategory-based read-across approach to estimate and rank skin sensitization potential of chemicals. The example described here is for the mechanism of Michael-type nucleophilic addition with subcategories being limited to carbonyl-containing compounds. PATIENTS/METHODS: In this approach, in silico tools based on structural alerts were used to determine both the mechanism of protein binding and the relative subcategories within that mechanism. RESULTS: Fifty compounds previously evaluated in the in vivo mouse local lymph node assay (LLNA) were placed in 10 subcategories defined by their polarized alpha,beta-unsaturated substructure. To offset the limitations and skewness of the published in vivo data, in chemico glutathione (GSH) depletion data also were included. CONCLUSIONS: It was shown that the read-across approach can be successfully used to rank qualitatively skin sensitization potential of an untested carbonyl-containing Michael acceptor chemical by using subcategories. Moreover, the use of the more resilient in chemico GSH depletion data added further support to the read-across result

Description of the Electronic Structure of Organic Chemicals Using Semi-Empirical and Ab initio Methods for Development of Toxicological QSARs

The quality of quantitative structure-activity relationship (QSAR) models depends on the quality of their constitutive elements including the biological activity, statistical procedure applied, and the physicochemical and structural descriptors. The aim of this study was to assess the comparative use of ab initio and semiempirical quantum chemical calculations for the development of toxicological QSARs applied to a large and chemically diverse data set. A heterogeneous collection of 568 organic compounds with 96 h acute toxicity measured to the fish fathead minnow (Pimephales promelas) was utilized. A total of 162 descriptors were calculated using the semiempirical AM1 Hamiltonian, and 121 descriptors were compiled using an ab initio (B3LYP/6-31G**) method. The QSARs were derived using multiple linear regression (MLR) and partial least squares (PLS) analyses. Statistically similar models were obtained using AM1 and B3LYP calculated descriptors supported by the use of the logarithm of the octanol-water partition coefficient (log Kow). The main difference between the models derived by both MLR and PLS with the two sets of quantum chemical descriptors was concentrated on the type of descriptors selected. It was concluded that for large-scale predictions, irrespective of the mechanism of toxic action, the use of precise but time-consuming ab initio methods does not offer considerable advantage compared to the semiempirical calculations and could be avoided