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In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol. The protocol outlines a hazard assessment framework including key effects/mechanisms and their relationships to endpoints such as gene mutation and clastogenicity. IST models and data are reviewed that support the assessment of these effects/mechanisms along with defined approaches for combining the information and evaluating the confidence in the assessment. This protocol has been developed through a consortium of toxicologists, computational scientists, and regulatory scientists across several industries to support the implementation and acceptance of in silico approaches., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

An artist-led exploration of portrait accuracy and likeness involved 12 Artists producing 12 portraits referencing a life-size 3D print of the same Sitter. The works were assessed during a public exhibition, and the resulting likeness assessments were compared to portrait accuracy as measured using geometric morphometrics (statistical shape analysis). Our results are that, independently of the assessors' prior familiarity with the Sitter's face, the likeness judgements tended to be higher for less morphologically accurate portraits. The two highest rated were the portrait that most exaggerated the Sitter's distinctive features, and a portrait that was a more accurate (but not the most accurate) depiction. In keeping with research showing photograph likeness assessments involve recognition, we found familiar assessors rated the two highest ranked portraits even higher than those with some or no familiarity. In contrast, those lacking prior familiarity with the Sitter's face showed greater favour for the portrait with the highest morphological accuracy, and therefore most likely engaged in face-matching with the exhibited 3D print. Furthermore, our research indicates that abstraction in portraiture may not enhance likeness, and we found that when our 12 highly diverse portraits were statistically averaged, this resulted in a portrait that is more morphologically accurate than any of the individual artworks comprising the average.

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., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

A continuing education (CE) course at the 2014 American College of Toxicology annual meeting covered the topic of (Quantitative) Structure-Activity Relationships [(Q)SAR]. The (Q)SAR methodologies use predictive computer modeling based on predefined rules to describe the relationship between chemical structure and a chemical's associated biological activity or statistical tools to find correlations between biologic activity and the molecular structure or properties of a compound. The (Q)SAR has applications in risk assessment, drug discovery, and regulatory decision making. Pressure within industry to reduce the cost of drug development and societal pressure for government regulatory agencies to produce more accurate and timely risk assessment of drugs and chemicals have necessitated the use of (Q)SAR. Producing a high-quality (Q)SAR model depends on many factors including the choice of statistical methods and descriptors, but first and foremost the quality of the data input into the model. Understanding how a (Q)SAR model is developed and applied is critical to the successful use of such a tool. The CE session covered the basic principles of (Q)SAR, practical applications of these computational models in toxicology, how regulatory agencies use and interpret (Q)SAR models, and potential pitfalls of using them., (© The Author(s) 2015.)

Patients receiving haemodialysis are at an increased risk of hepatitis B infection; regular screening for incident infection and vaccination of susceptible individuals is recommended. Haemodialysis patients often require repeated high-dose hepatitis B vaccination boosters because of impaired response. Since the hepatitis B surface antigen is used as an immunogenic agent for vaccination and as a marker of hepatitis B infection, it has occasionally been detected in the blood shortly after vaccine administration and can be mistaken for a new infection. These transient results, however, are unlikely to persist for longer than 14 days after vaccination. We report the case of a haemodialysis patient who tested weakly positive for hepatitis B surface antigen 52 days after a vaccine booster. This is the longest vaccine-induced antigenaemia described in the literature and indicates that vaccination can cause weakly positive hepatitis B surface antigen results for longer than previously reported., (2014 BMJ Publishing Group Ltd.)

1,3-Butadiene (BD) is a confirmed rodent carcinogen and a suspect human carcinogen that forms mutagenic epoxide metabolites during biotransformation. Species differences in the roles of individual DNA reactive intermediates in BD mutagenicity and carcinogenicity are not completely understood. Evidence suggests that 1,2:3,4-diepoxybutane (DEB) is responsible for the mutagenic effect induced by exposures to low concentrations of BD in mice and that metabolites of 3-butene-1,2-diol (BD-diol) are involved in the mutagenicity at high exposures in both mice and rats. Two reactive metabolites, 3,4-epoxy-1,2-butanediol (EB-diol) and hydroxymethylvinyl ketone (HMVK), are formed during the biotransformation of BD-diol and could potentially be involved in BD-diol associated mutagenicity. To examine the role of EB-diol in BD-diol mutagenicity we have evaluated the dosimetry of N7-(2,3,4-trihydroxybutyl)guanine (THB-Gua) and N-(2,3,4-trihydroxybutyl)valine (THB-Val) in female B6C3F1 mice and female F344 rats exposed by inhalation to 0, 6, 18 and 36 p.p.m. BD-diol for 4 weeks (6 h/day x 5 days/week). Results showed higher levels of both THB-Gua and THB-Val in mice than in rats. An evaluation of THB-Gua adducts showed virtually no differences between liver and lung for either species, suggesting that EB-diol is stable and is freely circulated. The data also indicated that THB adduct formation began to plateau around 18 p.p.m. in both species. Most importantly, the shape of the dose-response curve for THB adduct formation mimicked the one observed for hypoxanthine-guanine phosphoribosyltransferase (Hprt) mutation frequency. This showed that THB adducts, which are not thought to be responsible for causing the mutations, are good quantitative indicators of mutagenicity in rodents exposed to BD-diol. Although the potential contribution of HMVK still needs to be evaluated, the data suggest that EB-diol is responsible, at least in part, for BD-diol associated mutagenicity in rodents.

In mice, styrene is pneumotoxic, and there is some evidence of tumorigenicity. This toxicity is thought to be related to its bioactivation to styrene oxide in lung. To determine if human tissues have this capacity, the metabolism of styrene to styrene oxide was measured in human liver and lung microsomal preparations. Hepatic microsomes metabolized styrene to styrene oxide, but lung microsomes had essentially no activity. However, microsomes from both tissues metabolized benzene to phenol. The data suggest that human lung has low styrene metabolizing activity and may be much less of a target organ than in mouse.

Benzene is an occupational hazard and environmental toxicant found in cigarette smoke, gasoline, and the chemical industry. The major health concern associated with benzene exposure is leukemia. The toxic effects of benzene are dependent on its metabolism by the cytochrome P450 enzyme system. Previous research has identified CYP2E1 as the primary P450 isozyme responsible for benzene metabolism at low concentrations, whereas CYP2B1 is involved at higher concentrations. Our studies using microsomal preparations from human, mouse, and rat indicate that CYP2E1 is the P450 isozyme primarily responsible for benzene metabolism in lung and in liver. CYP2B isozymes have little involvement in benzene metabolism by either lung or liver. Our results also indicate that isozymes of the CYP2F subfamily may play a role in benzene metabolism by lung.

Benzene is an occupational hazard and environmental toxicant found in cigarette smoke, gasoline, and the chemical industry. The major health concern associated with benzene exposure is leukemia. Studies using microsomal preparations from human, mouse, rabbit, and rat to determine species differences in the metabolism of benzene to phenol, hydroquinone and catechol, indicate that the rat is most similar, both quantitatively and qualitatively, to the human in pulmonary microsomal metabolism of benzene. With hepatic microsomes, rat is most similar to human in metabolite formation at the two lower concentrations examined (24 and 200 microM), while at the two higher concentrations (700 and 1000 microM) mouse is most similar in phenol formation. In all species, the enzyme system responsible for benzene metabolism approached saturation in hepatic microsomes but not in pulmonary microsomes. In pulmonary microsomes from mouse, rat, and human, phenol appeared to competitively inhibit benzene metabolism resulting in a greater proportion of phenol being converted to hydroquinone when the benzene concentration increased. The opposite effect was seen in hepatic microsomes. These findings support the hypothesis that the lung plays an important role in benzene metabolism, and therefore, toxicity.