Distinguishing between expert and statistical systems for application under ICH M7

Matrine and Oxymatrine: evaluating the gene mutation potential using in silico tools and the bacterial reverse mutation assay (Ames test)

The quinolizidine alkaloids matrine and its N-oxide oxymatrine occur in plants of the genus Sophora. Recently, matrine was sporadically detected in liquorice products. Morphological similarity of the liquorice plant Glycyrrhiza glabra with Sophora species and resulting confusion during harvesting may explain this contamination, but use of matrine as pesticide has also been reported. The detection of matrine in liquorice products raised concern as some studies suggested a genotoxic activity of matrine and oxymatrine. However, these studies are fraught with uncertainties, putting the reliability and robustness into question. Another issue was that Sophora root extracts were usually tested instead of pure matrine and oxymatrine. The aim of this work was therefore to determine whether matrine and oxymatrine have potential for causing gene mutations. In a first step and to support a weight-of-evidence analysis, in silico predictions were performed to improve the database using expert and statistical systems by VEGA, Leadscope (Instem®), and Nexus (Lhasa Limited). Unfortunately, the confidence levels of the predictions were insufficient to either identify or exclude a mutagenic potential. Thus, in order to obtain reliable results, the bacterial reverse mutation assay (Ames test) was carried out in accordance with OECD Test Guideline 471. The test set included the plate incorporation and the preincubation assay. It was performed with five different bacterial strains in the presence or absence of metabolic activation. Neither matrine nor oxymatrine induced a significant increase in the number of revertants under any of the selected experimental conditions. Overall, it can be concluded that matrine and oxymatrine are unlikely to have a gene mutation potential. Any positive findings with Sophora extracts in the Ames test may be related to other components. Notably, the results also indicated a need to extend the application domain of respective (Q)SAR tools to secondary plant metabolites.

Assessing the utility of common arguments used in expert review of in silico predictions as part of ICH M7 assessments

Expert review of two predictions, made by complementary (quantitative) structure-activity relationship models, to an overall conclusion is a key component of using in silico tools to assess the mutagenic potential of impurities as part of the ICH M7 guideline. In lieu of a specified protocol, numerous publications have presented best practise guides, often indicating the occurrence of common prediction scenarios and the evidence required to resolve them. A semi-automated expert review tool has been implemented in Lhasa Limited's Nexus platform following collation of these common arguments and assignment to the associated prediction scenarios made by Derek Nexus and Sarah Nexus. Using datasets primarily donated by pharmaceutical companies, an automated analysis of the frequency these prediction scenarios occur, and the likelihood of the associated arguments assigning the correct resolution, could then be conducted. This article highlights that a relatively small number of common arguments may be used to accurately resolve many prediction scenarios to a single conclusion. The use of a standardised method of argumentation and assessment of evidence for a given impurity is proposed to improve the efficiency and consistency of expert review as part of an ICH M7 submission.

Enhancing global and local decision making for chemical safety assessments through increasing the availability of data

Toxicity safety assessments are a fundamental part of the lifecycle of products and aim to protect human health and the environment from harmful exposures to chemical substances. To make decisions regarding the suitability of testing strategies, the applicability of individual tests or concluding an assessment for an individual chemical requires data. This review outlines how different forms of data sharing, from enhancing publicly-available data to extracting knowledge from commercially-sensitive data, leads to increased quantity and quality of evidence being available for safety assessors to review. This can result in more confident decisions for different use cases in the context of chemical safety assessments. Although a number of challenges remain with progressing the evolution of toxicity safety assessments, data sharing should be considered as a key approach to accelerating the development and uptake of new best practices.

Nonclinical safety evaluation of a novel ionizable lipid for mRNA delivery

mRNA vaccines hold tremendous potential in disease control and prevention for their flexibility with respect to production, application, and design. Recent breakthroughs in mRNA vaccination would have not been possible without major advances in lipid nanoparticles (LNPs) technologies. We developed an LNP containing a novel ionizable cationic lipid, Lipid-1, and three well known excipients. An in silico toxicity hazard assessment for genotoxicity, a genotoxicity assessment, and a dose range finding toxicity study were performed to characterize the safety profile of Lipid-1. The in silico toxicity hazard assessment, utilizing two prediction systems DEREK and Leadscope, did not find any structural alert for mutagenicity and clastogenicity, and prediction in the statistical models were all negative. In addition, applying a read-across approach a structurally very similar compound was tested negative in two in vitro assays confirming the low genotoxicity potential of Lipid-1. A dose range finding toxicity study in rabbits, receiving a single intramuscular injection of either different doses of an mRNA encoding Influenza Hemagglutinin H3 antigen encapsulated in the LNP containing Lipid-1 or the empty LNP, evaluated local tolerance and systemic toxicity during a 2-week observation period. Only rabbits exposed to the vaccine were able to develop a specific IgG response, indicating an appropriate vaccine take. The vaccine was well tolerated up to 250 μg mRNA/injection, which was defined as the No Observed Adverse Effect Level (NOAEL). These results support the use of the LNP containing Lipid-1 as an mRNA delivery system for different vaccine formulations and its deployment into clinical trials.

Quantitative Determination of Four Potential Genotoxic Impurities in the Active Pharmaceutical Ingredients in TSD-1 Using UPLC-MS/MS

A novel method of ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) was developed for the identification and quantification of four potential genotoxic impurities (PGIs) in the active pharmaceutical ingredients of TSD-1, a novel P2Y12 receptor antagonist. Four PGIs were named, 4-nitrobenzenesulfonic acid, methyl 4-nitrobenzenesulfonate, ethyl 4-nitrobenzenesulfonate, and isopropyl 4-nitrobenzenesulfonate. Following the International Conference of Harmonization (ICH) guidelines, this methodology is capable of quantifying four PGIs at 15.0 ppm in samples of 0.5 mg/mL concentration. This validated approach presented very low limits (0.1512−0.3897 ng/mL), excellent linearity (coefficients > 0.9900), and a satisfactory recovery range (94.9−115.5%). The method was sufficient in terms of sensitivity, linearity, precision, accuracy, selectivity, and robustness and, thus, has high practicality in the pharmaceutical quality control of TSD-1.

Use of transcriptomics in hazard identification and next generation risk assessment: A case study with clothianidin

Toxicological risk assessment is essential in the evaluation and authorization of different classes of chemical substances. Genotoxicity and mutagenicity testing are of highest priority and rely on established in vitro systems with bacterial and mammalian cells, sometimes followed by in vivo testing using rodent animal models. Transcriptomic approaches have recently also shown their value to determine transcript signatures specific for genotoxicity. Here, we studied how transcriptomic data, in combination with in vitro tests with human cells, can be used for the identification of genotoxic properties of test compounds. To this end, we used liver samples from a 28-day oral toxicity study in rats with the pesticidal active substances imazalil, thiacloprid, and clothianidin, a neonicotinoid-type insecticide with, amongst others, known hepatotoxic properties. Transcriptomic results were bioinformatically evaluated and pointed towards a genotoxic potential of clothianidin. In vitro Comet and γH2AX assays in human HepaRG hepatoma cells, complemented by in silico analyses of mutagenicity, were conducted as follow-up experiments to check if the genotoxicity alert from the transcriptomic study is in line with results from a battery of guideline genotoxicity studies. Our results illustrate the combined use of toxicogenomics, classic toxicological data and new approach methods in risk assessment. By means of a weight-of-evidence decision, we conclude that clothianidin does most likely not pose genotoxic risks to humans.

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Analysis of clothianidin genotoxicity in silico, in vitro and in vivo.

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Application of a toxicogenomics approach to analyze genotoxicity.

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Weight-of-evidence decision supports classification as “non-genotoxic”.

Use of Lhasa Limited Products for the In Silico Prediction of Drug Toxicity

Lhasa Limited have had a role in the in silico prediction of drug and other chemical toxicity for over 30 years. This role has always been multifaceted, both as a provider of predictive software such as Derek Nexus, and as an honest broker for the sharing of proprietary chemical and toxicity data. A changing regulatory environment and the drive for the Replacement, Reduction and Refinement (the 3Rs) of animal testing have led both to increased acceptance of in silico predictions and a desire for the sharing of data to reduce duplicate testing. The combination of these factors has led to Lhasa Limited providing a suite of products and coordinating numerous data-sharing consortia that do indeed facilitate a significant reduction in the testing burden that companies would otherwise be laboring under. Many of these products and consortia can be organized into workflows for specific regulatory use cases, and it is these that will be used to frame the narrative in this chapter.

Discovery of new acetylcholinesterase inhibitors for Alzheimer's disease: virtual screening and in vitro characterisation

For more than two decades, the development of potent acetylcholinesterase (AChE) inhibitors has been an ongoing task to treat dementia associated with Alzheimer's disease and improve the pharmacokinetic properties of existing drugs. In the present study, we used three docking-based virtual screening approaches to screen both ZINC15 and MolPort databases for synthetic analogs of physostigmine and donepezil, two highly potent AChE inhibitors. We characterised the in vitro inhibitory concentration of 11 compounds, ranging from 14 to 985 μM. The most potent of these compounds, S-I 26, showed a fivefold improved inhibitory concentration in comparison to rivastigmine. Moderate inhibitors carrying novel scaffolds were identified and could be improved for the development of new classes of AChE inhibitors.

Identification of known drugs as potential SARS-CoV-2 Mpro inhibitors using ligand- and structure-based virtual screening

Background: The new coronavirus pandemic has had a significant impact worldwide, and therapeutic treatment for this viral infection is being strongly pursued. Efforts have been undertaken by medicinal chemists to discover molecules or known drugs that may be effective in COVID-19 treatment – in particular, targeting the main protease (Mpro) of the virus. Materials & methods: We have employed an innovative strategy – application of ligand- and structure-based virtual screening – using a special compilation of an approved and diverse set of SARS-CoV-2 crystallographic complexes that was recently published. Results and conclusion: We identified seven drugs with different original indications that might act as potential Mpro inhibitors and may be preferable to other drugs that have been repurposed. These drugs will be experimentally tested to confirm their potential Mpro inhibition and thus their effectiveness against COVID-19.

Toward regulatory acceptance and improving the prediction confidence of in silico approaches: a case study of genotoxicity

Introduction: Genotoxicity is an imperative component of the human health safety assessment of chemicals. Its secure forecast is of the utmost importance for all health prevention strategies and regulations.Areas covered: We surveyed several types of alternative, animal-free approaches ((quantitative) structure-activity relationship (Q)SAR, read-across, Adverse Outcome Pathway, Integrated Approaches to Testing and Assessment) for genotoxicity prediction within the needs of regulatory frameworks, putting special emphasis on data quality and uncertainties issues.Expert opinion: (Q)SAR models and read-across approaches for in vitro bacterial mutagenicity have sufficient reliability for use in prioritization processes, and as support in regulatory decisions in combination with other types of evidence. (Q)SARs and read-across methodologies for other genotoxicity endpoints need further improvements and should be applied with caution. It appears that there is still large room for improvement of genotoxicity prediction methods. Availability of well-curated high-quality databases, covering a broader chemical space, is one of the most important needs. Integration of in silico predictions with expert knowledge, weight-of-evidence-based assessment, and mechanistic understanding of genotoxicity pathways are other key points to be addressed for the generation of more accurate and trustable results.

Management of pharmaceutical ICH M7 (Q)SAR predictions - The impact of model updates

Pharmaceutical applicants conduct (Q)SAR assessments on identified and theoretical impurities to predict their mutagenic potential. Two complementary models—one rule-based and one statistical-based—are used, followed by expert review. (Q)SAR models are continuously updated to improve predictions, with new versions typically released on a yearly basis. Numerous releases of (Q)SAR models will occur during the typical 6–7 years of drug development until new drug registration. Therefore, it is important to understand the impact of model updates on impurity mutagenicity predictions over time. Compounds representative of pharmaceutical impurities were analyzed with three rule- and three statistical-based models covering a 4–8 year period, with the individual time frame being dependent on when the individual models were initially made available. The largest changes in the combined outcome of two complementary models were from positive or equivocal to negative and from negative to equivocal. Importantly, the cumulative change of negative to positive predictions was small in all models (<5%) and was further reduced when complementary models were combined in a consensus fashion. We conclude that model updates of the type evaluated in this manuscript would not necessarily require re-running a (Q)SAR prediction unless there is a specific need. However, original (Q)SAR predictions should be evaluated when finalizing the commercial route of synthesis for marketing authorization.

(Q)SAR tools for the prediction of mutagenic properties: Are they ready for application in pesticide regulation?

The assessment of human health risks resulting from the presence of metabolites in groundwater and food residues has become an important element in pesticide authorisation. In this context, the evaluation of mutagenicity is of particular interest and a paradigm shift from exposure-triggered testing to in silico-based screening has been recommended in the European Food Safety Authority (EFSA) Guidance on the establishment of the residue definition for dietary risk assessment. In addition, it is proposed to apply in silico predictions when experimental mutagenicity testing is not possible due to a lack of sufficient quantities of the pesticide metabolite. This, combined with animal welfare and economic considerations, has led to a situation where an increasing number of in silico studies are submitted to regulatory authorities. Whilst there is extensive experience with in silico predictions for mutagenicity in the chemical and pharmaceutical industry, their suitability in pesticide regulation is still insufficiently considered. Therefore, we herein discuss critical issues that need to be resolved to successfully implement (Quantitative) Structure-Activity Relationship ((Q)SAR) as an accepted tool in pesticide regulation. For illustration purposes, the results of a pilot study are included. The presented study highlights a need for further improvement regarding the predictivity and applicability domain of (Q)SAR systems for pesticides and their metabolites, but also raises other questions such as model selection, establishment of acceptance criteria, harmonised approaches to the combination of model outputs into overall conclusions, adequate reporting and data sharing. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

The importance of expert review to clarify ambiguous situations for (Q)SAR predictions under ICH M7

The use of in silico predictions for the assessment of bacterial mutagenicity under the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 guideline is recommended when two complementary (quantitative) structure-activity relationship (Q)SAR models are used. Using two systems may increase the sensitivity and accuracy of predictions but also increases the need to review predictions, particularly in situations where results disagree. During the 4th ICH M7/QSAR Workshop held during the Joint Meeting of the 6th Asian Congress on Environmental Mutagens (ACEM) and the 48th Annual Meeting of the Japanese Environmental Mutagen Society (JEMS) 2019, speakers demonstrated their approaches to expert review using 20 compounds provided ahead of the workshop that were expected to yield ambiguous (Q)SAR results. Dr. Chris Barber presented a selection of the reviews carried out using Derek Nexus and Sarah Nexus provided by Lhasa Limited. On review of these compounds, common situations were recognised and are discussed in this paper along with standardised arguments that may be used for such scenarios in future.

Benchmark Dose Analysis of DNA Damage Biomarker Responses Provides Compound Potency and Adverse Outcome Pathway Information for the Topoisomerase II Inhibitor Class of Compounds

Genetic toxicology data have traditionally been utilized for hazard identification to provide a binary call for a compound's risk. Recent advances in the scientific field, especially with the development of high-throughput methods to quantify DNA damage, have influenced a change of approach in genotoxicity assessment. The in vitro MultiFlow® DNA Damage Assay is one such method which multiplexes γH2AX, p53, phospho-histone H3 biomarkers into a single-flow cytometric analysis (Bryce et al., [2016]: Environ Mol Mutagen 57:546-558). This assay was used to study human TK6 cells exposed to each of eight topoisomerase II poisons for 4 and 24 hr. Using PROAST v65.5, the Benchmark Dose approach was applied to the resulting flow cytometric datasets. With "compound" serving as covariate, all eight compounds were combined into a single analysis, per time point and endpoint. The resulting 90% confidence intervals, plotted in Log scale, were considered as the potency rank for the eight compounds. The in vitro MultiFlow data showed a maximum confidence interval span of 1Log, which indicates data of good quality. Patterns observed in the compound potency rank were scrutinized by using the expert rule-based software program Derek Nexus, developed by Lhasa Limited. Compound sub-classification and structural alerts were considered contributory to the potencies observed for the topoisomerase II poisons studied herein. The Topo II poison Adverse Outcome Pathway was evaluated with MultiFlow endpoints serving as Key Events. The step-wise approach described herein can be considered as a foundation for risk assessment of compounds within a specific mode of action of interest. Environ. Mol. Mutagen. 2020. © 2020 Wiley Periodicals, Inc.

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights.

This article is categorized under:

Structure and Mechanism > Computational Biochemistry and Biophysics

Data Science > Chemoinformatics

Structural Alerts and Random Forest Models in a Consensus Approach for Receptor Binding Molecular Initiating Events

A molecular initiating event (MIE) is the gateway to an adverse outcome pathway (AOP), a sequence of events ending in an adverse effect. In silico predictions of MIEs are a vital tool in a modern, mechanism-focused approach to chemical risk assessment. For 90 biological targets representing important human MIEs, structural alert-based models have been constructed with an automated procedure that uses Bayesian statistics to iteratively select substructures. These models give impressive average performance statistics (an average of 92% correct predictions across targets), significantly improving on previous models. Random Forest models have been constructed from physicochemical features for the same targets, giving similarly impressive performance statistics (93% correct predictions). A key difference between the models is interpretation of predictions-the structural alert models are transparent and easy to interpret, while Random Forest models can only identify the most important physicochemical features for making predictions. The two complementary models have been combined in a consensus model, improving performance compared to each individual model (94% correct predictions) and increasing confidence in predictions. Variation in model performance has been explained by calculating a modelability index (MODI), using Tanimoto coefficient between Morgan fingerprints to identify nearest neighbor chemicals. This work is an important step toward building confidence in the use of in silico tools for assessment of toxicity.

(Q)SAR Methods for Predicting Genotoxicity and Carcinogenicity: Scientific Rationale and Regulatory Frameworks

Knowledge of the genotoxicity and carcinogenicity potential of chemical substances is one of the key scientific elements able to better protect human health. Genotoxicity assessment is also considered as prescreening of carcinogenicity. The assessment of both endpoints is a fundamental component of national and international legislations, for all types of substances, and has stimulated the development of alternative, nontesting methods. Over the recent decades, much attention has been given to the use and further development of structure-activity relationships-based approaches, to be used in isolation or in combination with in vitro assays for predictive purposes. In this chapter, we briefly introduce the rationale for the main (Q)SAR approaches, and detail the most important regulatory initiatives and frameworks. It appears that the existence and needs of regulatory frameworks stimulate the development of better predictive tools; in turn, this allows the regulators to fine-tune their requirements for an improved defense of human health.