Richard V. Williams

Latest advances in computational genotoxicity prediction

Introduction: Computational approaches for genotoxicity prediction have existed for over two decades. Numerous methodologies have been utilized and the results of various evaluations have published. Areas covered: In silico methods are considered mature enough to be part of draft FDA regulatory guidelines for the assessment of genotoxic impurities. However, aspects of how best to use predictive systems remain unresolved: i) methodologies to measure how similar two compounds need to be in order to assume they have the same biological outcome; and ii) defining whether a compound is close enough to the model training set such that a model prediction can be considered reliable. Expert opinion: In silico prediction of genotoxicity is a fundamental part of screening strategies for the assessment genotoxic impurities in drug products. However, the concept of using chemical similarity to infer mutagenic potential from one of known activity to another whose activity is unknown remains a scientific challenge. Similarly, defining when an in silico model prediction can be considered to be reliable is also difficult. Reaction mechanisms and the functional group building blocks of chemistry are pretty much constant, and so when data-gaps appear, it tends to be for compounds that have been regularly used but rarely tested.

It's difficult, but important, to make negative predictions.

At the confluence of predictive and regulatory toxicologies, negative predictions may be the thin green line that prevents populations from being exposed to harm. Here, two novel approaches to making confident and robust negative in silico predictions for mutagenicity (as defined by the Ames test) have been evaluated. Analyses of 12 data sets containing >13,000 compounds, showed that negative predictivity is high (∼90%) for the best approach and features that either reduce the accuracy or certainty of negative predictions are identified as misclassified or unclassified respectively. However, negative predictivity remains high (and in excess of the prevalence of non-mutagens) even in the presence of these features, indicating that they are not flags for mutagenicity.

Distinguishing between expert and statistical systems for application under ICH M7

Graphical abstract Figure. No Caption available. HighlightsDefining characteristics of expert and statistical in silico systems are presented.Mechanisms to ensure both systems complement each other are described.Risks of in silico models that may be inappropriate for ICH M7 are highlighted.

Using in vitro structural alerts for chromosome damage to predict in vivo activity and direct future testing

While the in vivo genotoxicity of a compound may not always correlate well with its activity in in vitro test systems, for certain compound classes a good overlap may exist between the two endpoints. The difficulty, however, lies in establishing the cases where this relationship holds true and selecting the most appropriate protocol to highlight any potential in vivo hazard. With this in mind, a project was initiated in which existing structural alerts for in vitro chromosome damage in the expert system Derek Nexus were assessed for their relevance to in vivo activity by assessing their predictivity against an in vivo chromosome damage data set. An expert assessment was then made of selected alerts. Information regarding the findings from specific in vivo tests was added to the alert along with any significant correlations between activity and test protocol or mechanism. A total of 32 in vitro alerts were updated using this method resulting in a significant improvement in the coverage of in vivo chromosome damage in Derek Nexus against a data set compiled by the mammalian mutagenicity study group of Japan. The detailed information relating to in vivo activity and protocol added to the alerts in combination with the mechanistic information provided will prove useful in directing the further testing of compounds of interest.

Utility of published DNA reactivity alerts

ABSTRACT The identification of impurities with mutagenic potential is required for any potential pharmaceutical. The ICH M7 guidelines state that two complementary in silico toxicity prediction tools may be used to predict the mutagenic potential of pharmaceutical impurities. An expert review of the resulting in silico predictions is required, and numerous publications have been released to guide the expert review process. One such publication suggests that literature‐based structural alerts (LBSAs) may provide a suitable aid in the expert review process. This publication provides a study of the effect of using one such set of LBSAs for the expert review of mutagenicity predictions from two complementary in silico tools. The analysis was performed using an Ames test dataset of 2619 compounds, and required interpretation of the LBSAs which proved to be a subjective process. Globally the LBSAs produced many more false positives than the in silico systems; whilst some exhibited a predictive performance comparable to the in silico systems, the majority were overly sensitive at the cost of accuracy. Use of LBSAs as part of an expert review process, without considering mitigating factors, could result in many more false positives and potentially the need to carry out additional and unnecessary Ames tests. HIGHLIGHTSAmes test dataset of unseen chemicals was assembled.Predictions were generated as per ICH M7.Literature‐based structural alerts were applied in lieu of an expert review.Interpretation of literature‐based structural alerts was subjective.Application of the literature‐based alerts increased false positive rate.

Important considerations for the validation of QSAR models for in vitro mutagenicity

While high-level performance metrics generated from the validation of quantitative structure-activity relationship (QSAR) systems can provide valuable information on how well these models perform and where they need to be improved, they require appropriate interpretation. There is no universal performance metric which will answer all of the questions a user might ask relating to a model, and therefore, a combination of metrics should usually be considered. Furthermore, results may vary according to the chemical space being used to validate a model, and, in some cases, it may be the validation data which is lacking or ambiguous rather than the prediction being made. Finally, users also need to consider the interpretability of the predictions being made, alongside the accuracy of the predictions. In this paper, we will discuss these important considerations in more detail within the context of the results obtained at Lhasa Limited as part of the National Institute of Health Sciences (NIHS) QSAR challenge project.

Extrapolation of in vitro structural alerts for mutagenicity to the in vivo endpoint

As part of the hazard and risk assessment of chemicals in man, it is important to assess the ability of a chemical to induce mutations in vivo. Because of the commonalities in the molecular initiating event, mutagenicity in vitro can correlate well to the in vivo endpoint for certain compound classes; however, the difficulty lies in identifying when this correlation holds true. In silico alerts for in vitro mutagenicity may therefore be used as the basis for alerts for mutagenicity in vivo where an expert assessment is carried out to establish the relevance of the correlation. Taking this into account, a data set of publicly available transgenic rodent gene mutation assay data, provided by the National Institute of Health Sciences of Japan, was processed in the expert system Derek Nexus against the in vitro mutagenicity endpoint. The resulting predictivity was expertly reviewed to assess the validity of the observed correlations in activity and mechanism of action between the two endpoints to identify suitable in vitro alerts for extension to the in vivo endpoint. In total, 20 alerts were extended to predict in vivo mutagenicity, which has significantly improved the coverage of this endpoint in Derek Nexus against the data set provided. Updating the Derek Nexus knowledge base in this way led to an increase in sensitivity for this data set against this endpoint from 9% to 66% while maintaining a good specificity of 89%.

Improvement of quantitative structure–activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project

Abstract The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure–activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

Carbamates and ICH M7 classification: Making use of expert knowledge

&NA; Carbamates are widely used in the chemical industry so understanding their toxicity is important to safety assessment. Carbamates have been associated with certain toxicities resulting in publication of structural alerts, including alerts for mutagenicity. Structural alerts for bacterial mutagenicity can be used in combination with statistical systems to enable ICH M7 classification, which allows assessment of the genotoxic risk posed by pharmaceutical impurities. This study tested a hypothetical bacterial mutagenicity alert for carbamates and examined the impact it would have on ICH M7 classifications using (Q)SAR predictions from the expert rule‐based system Derek Nexus and the statistical‐based system Sarah Nexus. Public datasets have a low prevalence of mutagenic carbamates, which highlighted that systems containing an alert for carbamates perform poorly for achieving correct ICH M7 classifications. Carbamates are commonly used as protecting groups and proprietary datasets containing such compounds were also found to have a low prevalence of mutagenic compounds. Expert review of the mutagenic compounds established that mutagenicity was often only observed under certain (non‐standard) conditions and more generally that the Ames test may be a poor predictor for the risk of carcinogenicity posed by chemicals in this class. Overall a structural alert for the in vitro bacterial mutagenesis of carbamates does not benefit workflows for assigning ICH M7 classification to impurities. Graphical abstract Figure. No caption available. HighlightsCarbamates are associated with certain toxicities including in vitro mutagenicity.A structural alert for carbamates would perform poorly for ICH M7 classification.Expert review found no common mechanism for in vitro mutagenic carbamates.Datasets for carbamate protecting groups have a low prevalence of mutagens.

CHAPTER 3:Tools for Green Molecular Design to Reduce Toxicological Risk

Making “greener” chemicals involves maximizing resource efficiency and eliminating or reducing hazards to human health and environmental systems. This chapter presents a review of various tools used in chemical design and predictive toxicology as a practical guide for chemists early in the molecular design process to deal with potential concerns on the front end of synthesis or development. A summary of concepts that can be applied in engineering chemicals that are less likely to be absorbed or biologically active is discussed along with the current milieu of software tools used for toxicity prediction. In this context, the challenges that remain in greener molecular design related to computational toxicology are highlighted. Based on these analyses, a proposed ideal green molecular design tool is outlined.