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.

Isolated Co-phthalocyanine (CoPc) molecules were moved on a monolayer of CoPc on Cu(111) using an STM tip. If placed almost on top of another, the CoPc molecule in the second layer locks in place and the STM image at negative bias changes substantially. Density functional theory calculations explain the nature of the bonding mode and the change in STM.

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Our study was performed in the context of an in vitro primary hepatic cell culture as an alternative for the in vivo cancerogenic bioassay. The 29 substances which are to be used in the in vitro primary hepatic cell culture have been tested in 2-year bioassays and a 14-day short term study. The aim of this modelling study was to simulate the concentration--time profile of the compounds when given by the oral route at the doses tested in the previous studies taking into account the percentage of the dose absorbed. The model contained seven tissue compartments with uptake from the gastrointestinal tract into the portal vein. Because the primary hepatic cell culture is metabolically competent and the primary interest was to model the concentration in the portal vein, the hepatic vein and the systemic circulation (blood) in the beginning we did not include elimination. Partitioning between blood and tissues was calculated according to a published biologically based algorithm. The substances' kinetic profile differed according to their blood: tissue partitioning. Maximal concentrations in portal vein, hepatic vein and the blood depended mainly on the dose and the fraction absorbed which were the most critical parameters in this respect. Our study demonstrates an application of BPTK modelling for the purpose to simulate concentrations for planning the doses for an in vitro study. BPTK modelling seems to be a better approach than using data from in vitro studies on cytotoxicity.

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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.