Establishing best practise in the application of expert review of mutagenicity under ICH M7

Safety assessment of drug impurities for patient safety: A comprehensive review

Drug impurities are undesirable but unavoidable chemicals which can occur throughout the drug life cycle. The safety implications of drug impurities can be significant given that they can impact safety, quality, and efficacy of drug products and that certain drug impurities are mutagenic, carcinogenic, or teratogenic. The characteristics of drug impurities could be specific to drug modalities (e.g., small molecules vs. biologics). The commonly encountered drug impurities include elemental impurity, residual solvent, organic impurity, host cell protein and DNA, residual viral vector, extractable and leachable, and particle. They can cause various adverse effects such as immunogenicity, infection, genotoxicity, and carcinogenicity upon significant exposure. Therefore, the effective control of these drug impurities is central for patient safety. Regulations and guidelines are available for drug developers to manage them. Their qualification is obtained based on authoritative qualification thresholds or safety assessment following the classic toxicological risk assessment. The current review focuses on the safety assessment science and methodology used for diverse types of drug impurities. Due to the different nature of diverse drug impurities, their safety assessment represents a significant challenge for drug developers.

Advances in Activity/Property Prediction from Chemical Structures

Recent technological advancement in AI modeling of molecular property databases has significantly expanded the opportunities for drug design and development. Quantitative structure-activity relationships (QSARs) are shown to provide more accurate predictions with regards to biological activity as well as toxicological assessment. By using a combination of in-silico models or by combining disparate structure-activity databases, researchers have been able to improve accuracy for a variety of drug discovery and analysis methods, generating viable compounds, which in certain cases, can be synthesized and further studied in vitro to find candidates for potential development. Additionally, the development of compounds of determined toxicology can be discontinued earlier, allowing alternative routes to be evaluated, preventing wasted time and resources. Although the progress that has been made is tremendous, expert review is still necessary for most in-silico generated predictions. Regardless, the scientific community continues to move ever closer to completely automated drug discovery and evaluation.

Genotoxic Impurities in Critical Analysis of Product Development: Recent Advancements, Patents, and Current Challenges

The current review intends to regulate and accurately evaluate genotoxic contaminants in drug substance and drug product method and formulation process development, validation, and degradation pathways. The Quality by Design (QbD) principles can be applied to the systematic evaluation and control of impurities enabled by the development of modern analytical techniques, including the performance of risk assessment, the screening of Critical Process Parameters (CPPs), and the identification of the most influential variables in the optimization of the evaluation and control methods. Current difficulties in removing genotoxic contaminants and the procedures for doing so have been outlined in this review, along with the steps necessary to acquire optimum techniques and the most acceptable formulations. In addition to this, division, characterization, assessment, quantification, and formation of genotoxic impurities sources and control strategy for genotoxic impurities, handling of nitrosamine assay content of drug products in different industrial methodologies and their chemometric prospects and associated recent patents are also explored.

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.

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new technologies with regulatory applications through the annual conference Global Summit on Regulatory Science (GSRS). The 11th annual GSRS conference (GSRS21) focused on "Regulatory Sciences for Food/Drug Safety with Real-World Data (RWD) and Artificial Intelligence (AI)." The conference discussed current advancements in both AI and RWD approaches with a specific emphasis on how they impact regulatory sciences and how regulatory agencies across the globe are pursuing the adaptation and oversight of these technologies. There were presentations from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the United Kingdom, and the United States. These presentations highlighted how various agencies are moving forward with these technologies by either improving the agencies' operation and/or preparing regulatory mechanisms to approve the products containing these innovations. To increase the content and discussion, the GSRS21 hosted two debate sessions on the question of "Is Regulatory Science Ready for AI?" and a workshop to showcase the analytical data tools that global regulatory agencies have been using and/or plan to apply to regulatory science. Several key topics were highlighted and discussed during the conference, such as the capabilities of AI and RWD to assist regulatory science policies for drug and food safety, the readiness of AI and data science to provide solutions for regulatory science. Discussions highlighted the need for a constant effort to evaluate emerging technologies for fit-for-purpose regulatory applications. The annual GSRS conferences offer a unique platform to facilitate discussion and collaboration across regulatory agencies, modernizing regulatory approaches, and harmonizing efforts.

New science, drug regulation, and emergent public health issues: The work of FDA's division of applied regulatory science

The U.S. Food and Drug Administration (FDA) Division of Applied Regulatory Science (DARS) moves new science into the drug review process and addresses emergent regulatory and public health questions for the Agency. By forming interdisciplinary teams, DARS conducts mission-critical research to provide answers to scientific questions and solutions to regulatory challenges. Staffed by experts across the translational research spectrum, DARS forms synergies by pulling together scientists and experts from diverse backgrounds to collaborate in tackling some of the most complex challenges facing FDA. This includes (but is not limited to) assessing the systemic absorption of sunscreens, evaluating whether certain drugs can convert to carcinogens in people, studying drug interactions with opioids, optimizing opioid antagonist dosing in community settings, removing barriers to biosimilar and generic drug development, and advancing therapeutic development for rare diseases. FDA tasks DARS with wide ranging issues that encompass regulatory science; DARS, in turn, helps the Agency solve these challenges. The impact of DARS research is felt by patients, the pharmaceutical industry, and fellow regulators. This article reviews applied research projects and initiatives led by DARS and conducts a deeper dive into select examples illustrating the impactful work of the Division.

Strategies for Assessing Acceptable Intakes for Novel N-Nitrosamines Derived from Active Pharmaceutical Ingredients

The detection of N-nitrosamines, derived from solvents and reagents and, on occasion, the active pharmaceutical ingredient (API) at higher than acceptable levels in drug products, has led regulators to request a detailed review for their presence in all medicinal products. In the absence of rodent carcinogenicity data for novel N-nitrosamines derived from amine-containing APIs, a conservative class limit of 18 ng/day (based on the most carcinogenic N-nitrosamines) or the derivation of acceptable intakes (AIs) using structurally related surrogates with robust rodent carcinogenicity data is recommended. The guidance has implications for the pharmaceutical industry given the vast number of marketed amine-containing drugs. In this perspective, the rate-limiting step in N-nitrosamine carcinogenicity, involving cytochrome P450-mediated α-carbon hydroxylation to yield DNA-reactive diazonium or carbonium ion intermediates, is discussed with reference to the selection of read-across analogs to derive AIs. Risk-mitigation strategies for managing putative N-nitrosamines in the preclinical discovery setting are also presented.

QSAR-Guided Proposition of N-(4-methanesulfonyl)Benzoyl-N'-(Pyrimidin-2-yl)Thioureas as Effective and Safer Herbicides

Chlorinated agrochemicals play a major role in toxicity due especially to the labile C - Cl bond and high lipophilicity of organochlorines. In turn, urea and thiourea herbicides are widely used for weed control. A series of substituted N-benzoyl-N'-pyrimidin-2-yl thioureas has been recently synthesized and tested against Brassica napus L., demonstrating promising herbicidal activities, particularly for chlorinated derivatives. We have therefore modeled these activities using multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) to find out a significant and reliable correlation between measured and predicted inhibition of B. napus L. root growth (%) and, ultimately, to propose effective, non-chlorinated and/or less lipophilic N-(4-methanesulfonyl)benzoyl-N'-(pyrimidin-2-yl)thiourea candidates. The model was found to be predictive, giving an average r²_pred in the external validation of 0.833. The predicted data for the proposed herbicides, interpreted in terms of MIA-plots of the chemical moieties responsible for bioactivity and supported by docking studies towards the photosystem II enzyme, suggest that substituents at both R¹ and R² positions modulate the agrochemical (R¹ = Cl increases and R² = OR decreases bioactivity) and environmental friendship (particularly with R² = OH) performances of this class of compounds.

Usability of the login authentication process: passphrases and passwords

Purpose

The average employee spends a total of 18.6 h every two months on password-related activities, including password retries and resets. The problem is caused by the user forgetting or mistyping the password (usually because of character switching). The source of this issue is that while a password containing combinations of lowercase characters, uppercase characters, digits and special characters (LUDS) offers a reasonable level of security, it is complex to type and/or memorise, which prolongs the user authentication process. This results in much time being spent for no benefit (as perceived by users), as the user authentication process is merely a prerequisite for whatever a user intends to accomplish. This study aims to address this issue, passphrases that exclude the LUDS guidelines are proposed.

Design/methodology/approach

To discover constructs that create security and to investigate usability concerns relating to the memory and typing issues concerning passphrases, this study was guided by three theories as follows: Shannon’s entropy theory was used to assess security, chunking theory to analyse memory issues and the keystroke level model to assess typing issues. These three constructs were then evaluated against passwords and passphrases to determine whether passphrases better address the security and usability issues related to text-based user authentication. A content analysis was performed to identify common password compositions currently used. A login assessment experiment was used to collect data on user authentication and user – system interaction with passwords and passphrases in line with the constructs that have an impact on user authentication issues related to security, memory and typing. User–system interaction data was collected from a purposeful sample size of 112 participants, logging in at least once a day for 10 days. An expert review, which comprised usability and security experts with specific years of industry and/or academic experience, was also used to validate results and conclusions. All the experts were given questions and content to ensure sufficient context was provided and relevant feedback was obtained. A pilot study involving 10 participants (experts in security and/or usability) was performed on the login assessment website and the content was given to the experts beforehand. Both the website and the expert review content was refined after feedback was received from the pilot study.

Findings

It was concluded that, overall, passphrases better support the user during the user authentication process in terms of security, memory issues and typing issues.

Originality/value

This research aims at promoting the use of a specific type of passphrase instead of complex passwords. Three core aspects need to be assessed in conjunction with each other (security, memorisation and typing) to determine whether user-friendly passphrases can support user authentication better than passwords.

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.

Implementation of in silico toxicology protocols within a visual and interactive hazard assessment platform

Mechanistically-driven alternative approaches to hazard assessment invariably require a battery of tests, including both in silico models and experimental data. The decision-making process, from selection of the methods to combining the information based on the weight-of-evidence, is ideally described in published guidelines or protocols. This ensures that the application of such approaches is defendable to reviewers within regulatory agencies and across the industry. Examples include the ICH M7 pharmaceutical impurities guideline and the published in silico toxicology protocols. To support an efficient, transparent, consistent and fully documented implementation of these protocols, a new and novel interactive software solution is described to perform such an integrated hazard assessment based on public and proprietary information.

RespiraTox - Development of a QSAR model to predict human respiratory irritants

Respiratory irritation is an important human health endpoint in chemical risk assessment. There are two established modes of action of respiratory irritation, 1) sensory irritation mediated by the interaction with sensory neurons, potentially stimulating trigeminal nerve, and 2) direct tissue irritation. The aim of our research was to, develop a QSAR method to predict human respiratory irritants, and to potentially reduce the reliance on animal testing for the identification of respiratory irritants. Compounds are classified as irritating based on combined evidence from different types of toxicological data, including inhalation studies with acute and repeated exposure. The curated project database comprised 1997 organic substances, 1553 being classified as irritating and 444 as non-irritating. A comparison of machine learning approaches, including Logistic Regression (LR), Random Forests (RFs), and Gradient Boosted Decision Trees (GBTs), showed, the best classification was obtained by GBTs. The LR model resulted in an area under the curve (AUC) of 0.65, while the optimal performance for both RFs and GBTs gives an AUC of 0.71. In addition to the classification and the information on the applicability domain, the web-based tool provides a list of structurally similar analogues together with their experimental data to facilitate expert review for read-across purposes.

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.

Appraisal of the human health related toxicological information available on dicyclopentadiene (DCPD) in view of assessing the substance's potential to cause endocrine disruption

Dicyclopentadiene (DCPD) is an olefinic hydrocarbon which is manufactured and imported into the European Union (EU) at greater than 1000 tons per year. Concerns related to fetotoxic effects observed in reproductive toxicity studies at high doses led the REACH registrants to self-classify DCPD as a Category 2 reproductive toxicant under the EU CLP Regulation. DCPD was also reviewed in the European Union in the frame of an ongoing European Chemical Agency (ECHA) Community Rolling Action Plan (CoRAP) procedure and under the French National Strategy on Endocrine Disruptors (SNPE). To elucidate whether the developmental effects may be triggered by an endocrine mode of action, the Lower Olefins Sector Group (LOSG) of the European Chemical Industry Council (CEFIC) formed an ad hoc expert team to review the available scientific information pertaining to the potential endocrine activity and adversity of DCPD. Existing experimental data was complemented with structure activity modelling using ECHA-recommended (Q)SAR tools. Overall, considering the available information from (Q)SAR, mechanistic in vitro and in vivo studies, no indication of endocrine-mediated adversity was found. Hence, the available evidence supports the conclusion that DCPD does not cause developmental toxicity via an endocrine mode of action. Further work is ongoing to support this conclusion.

Assessing the impact of expert knowledge on ICH M7 (Q)SAR predictions. Is expert review still needed?

The ICH M7 (R1) guideline recommends the use of complementary (Q)SAR models to assess the mutagenic potential of drug impurities as a state-of-the-art, high-throughput alternative to empirical testing. Additionally, it includes a provision for the application of expert knowledge to increase prediction confidence and resolve conflicting calls. Expert knowledge, which considers structural analogs and mechanisms of activity, has been valuable when models return an indeterminate (equivocal) result or no prediction (out-of-domain). A retrospective analysis of 1002 impurities evaluated in drug regulatory applications between April 2017 and March 2019 assessed the impact of expert review on (Q)SAR predictions. Expert knowledge overturned the default predictions for 26% of the impurities and resolved 91% of equivocal predictions and 75% of out-of-domain calls. Of the 261 overturned default predictions, 15% were upgraded to equivocal or positive and 79% were downgraded to equivocal or negative. Chemical classes with the most overturns were primary aromatic amines (46%), aldehydes (45%), Michael-reactive acceptors (37%), and non-primary alkyl halides (33%). Additionally, low confidence predictions were the most often overturned. Collectively, the results suggest that expert knowledge continues to play an important role in an ICH M7 (Q)SAR prediction workflow and triaging predictions based on chemical class and probability can improve (Q)SAR review efficiency.

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.

A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling

This study assesses whether currently available acute oral toxicity (AOT) in silico models, provided by the widely employed Leadscope software, are fit-for-purpose for categorization and labelling of chemicals. As part of this study, a large data set of proprietary and marketed compounds from multiple companies (pharmaceutical, plant protection products, and other chemical industries) was assembled to assess the models' performance. The absolute percentage of correct or more conservative predictions, based on a comparison of experimental and predicted GHS categories, was approximately 95%, after excluding a small percentage of inconclusive (indeterminate or out of domain) predictions. Since the frequency distribution across the experimental categories is skewed towards low toxicity chemicals, a balanced assessment was also performed. Across all compounds which could be assigned to a well-defined experimental category, the average percentage of correct or more conservative predictions was around 80%. These results indicate the potential for reliable and broad application of these models across different industrial sectors. This manuscript describes the evaluation of these models, highlights the importance of an expert review, and provides guidance on the use of AOT models to fulfill testing requirements, GHS classification/labelling, and transportation needs.

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.

Revisiting the bacterial mutagenicity assays: Report by a workgroup of the International Workshops on Genotoxicity Testing (IWGT)

The International Workshop on Genotoxicity Testing (IWGT) meets every four years to obtain consensus on unresolved issues associated with genotoxicity testing. At the 2017 IWGT meeting in Tokyo, four sub-groups addressed issues associated with the Organization for Economic Cooperation and Development (OECD) Test Guideline TG471, which describes the use of bacterial reverse-mutation tests. The strains sub-group analyzed test data from >10,000 chemicals, tested additional chemicals, and concluded that some strains listed in TG471 are unnecessary because they detected fewer mutagens than other strains that the guideline describes as equivalent. Thus, they concluded that a smaller panel of strains would suffice to detect most mutagens. The laboratory proficiency sub-group recommended (a) establishing strain cell banks, (b) developing bacterial growth protocols that optimize assay sensitivity, and (c) testing "proficiency compounds" to gain assay experience and establish historical positive and control databases. The sub-group on criteria for assay evaluation recommended that laboratories (a) track positive and negative control data; (b) develop acceptability criteria for positive and negative controls; (c) optimize dose-spacing and the number of analyzable doses when there is evidence of toxicity; (d) use a combination of three criteria to evaluate results: a dose-related increase in revertants, a clear increase in revertants in at least one dose relative to the concurrent negative control, and at least one dose that produced an increase in revertants above control limits established by the laboratory from historical negative controls; and (e) establish experimental designs to resolve unclear results. The in silico sub-group summarized in silico utility as a tool in genotoxicity assessment but made no specific recommendations for TG471. Thus, the workgroup identified issues that could be addressed if TG471 is revised. The companion papers (a) provide evidence-based approaches, (b) recommend priorities, and (c) give examples of clearly defined terms to support revision of TG471.

Considerations for the Use of Mutation as a Regulatory Endpoint in Risk Assessment

Assessment of a chemical's potential to cause permanent changes in the genetic code has been a common practice in the industry and regulatory settings for decades. Furthermore, the genetic toxicity battery of tests has typically been employed during the earliest stages of the research and development programs of new product development. A positive outcome from such battery has a major impact on the chemical's utility, industrial hygiene, product stewardship practices, and product life cycle analysis, among many other decisions that need to be taken by the industry, even before the registration of a chemical is undertaken. Under the prevailing regulatory paradigm, the dichotomous (yes/no) evaluation of the chemical's genotoxic potential leads to a conservative, linear no-threshold (LNT) risk assessment, unless compelling and undeniable data to the contrary can be provided to satisfy regulators, typically in a number of different global jurisdictions. With the current advent of predictive methods, new testing paradigms, mode-of-action/adverse outcome pathways, and quantitative risk assessment approaches, various stakeholders are starting to employ these state-of-the-science methodologies to further the conversation on decision making and advance the regulatory paradigm beyond the dominant LNT status quo. This commentary describes these novel methodologies, relevant biological responses, and how these can affect internal and regulatory risk assessment approaches. Environ. Mol. Mutagen. 61:84-93, 2020. © 2019 Wiley Periodicals, Inc.

In silico prediction of toxicity and its applications for chemicals at work

OBJECTIVE AND METHODS

This study reviewed the concept of in silico prediction of chemical toxicity for prevention of occupational cancer and future prospects in workers' health. In this review, a new approach to determine the credibility of in silico predictions with raw data is explored, and the method of determining the confidence level of evaluation based on the credibility of data is discussed. I searched various papers and books related to the in silico prediction of chemical toxicity and carcinogenicity. The intention was to utilize the most recent reports after 2015 regarding in silico prediction.

RESULTS AND CONCLUSION

The application of in silico methods is increasing with the prediction of toxic risks to human and the environment. The various toxic effects of industrial chemicals have triggered the recognition of the importance of using a combination of in silico models in the risk assessments. In silico occupational exposure models, industrial accidents, and occupational cancers are effectively managed and chemicals evaluated. It is important to identify and manage hazardous substances proactively through the rigorous evaluation of chemicals.

Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses

The International Council on Harmonisation (ICH) M7(R1) guideline describes the use of complementary (quantitative) structure-activity relationship ((Q)SAR) models to assess the mutagenic potential of drug impurities in new and generic drugs. Historically, the CASE Ultra and Leadscope software platforms used two different statistical-based models to predict mutations at G-C (guanine-cytosine) and A-T (adenine-thymine) sites, to comprehensively assess bacterial mutagenesis. In the present study, composite bacterial mutagenicity models covering multiple mutation types were developed. These new models contain more than double the number of chemicals (n = 9,254 and n = 13,514) than the corresponding non-composite models and show better toxicophore coverage. Additionally, the use of a single composite bacterial mutagenicity model simplifies impurity analysis in an ICH M7 (Q)SAR workflow by reducing the number of model outputs requiring review. An external validation set of 388 drug impurities representing proprietary pharmaceutical chemical space showed performance statistics ranging from of 66-82% in sensitivity, 91-95% in negative predictivity and 96% in coverage. This effort represents a major enhancement to these (Q)SAR models and their use under ICH M7(R1), leading to improved patient safety through greater predictive accuracy, applicability, and efficiency when assessing the bacterial mutagenic potential of drug impurities.

Genetic toxicology in silico protocol

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.

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

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.

In silico prediction of chromosome damage: comparison of three (Q)SAR models

Two major endpoints for genotoxicity tests are gene mutation and chromosome damage (CD), which includes clastogenicity and aneugenicity detected by chromosomal aberration (CA) test or micronucleus (MN) test. Many in silico prediction systems for bacterial mutagenicity (i.e. Ames test results) have been developed and marketed. They show good performance for prediction of Ames mutagenicity. On the other hand, it seems that in silico prediction of CD does not progress as much as Ames prediction. Reasons for this include different mechanisms and detection methods, many false positives and conflicting test results. However, some (quantitative) structure-activity relationship ((Q)SAR) models (e.g. Derek Nexus [Derek], ADMEWorks [AWorks] and CASE Ultra [MCase]) can predict CA test results. Therefore, performances of the three (Q)SAR models were compared using the expanded Carcinogenicity Genotoxicity eXperience (CGX) dataset for understanding current situations and future development. The constructed dataset contained 440 chemicals (325 carcinogens and 115 non-carcinogens). Sensitivity, specificity, accuracy or applicability of each model were 56.0, 86.9, 68.6 or 89.1% in Derek, 67.7, 61.5, 65.2 or 99.3% in AWorks, and 91.0, 64.9, 80.5 or 97.7% in MCase, respectively. The performances (sensitivity and accuracy) of MCase were higher than those of Derek or AWorks. Analysis of predictivity of (Q)SAR models of certain chemical classes revealed no remarkable differences among the models. The tendency of positive prediction by (Q)SAR models was observed in alkylating agents, aromatic amines or amides, aromatic nitro compounds, epoxides, halides and N-nitro or N-nitroso compounds. In an additional investigation, high sensitivity but low specificity was noted in in vivo MN prediction by MCase. Refinement of test data to be used for in silico system (e.g. consideration of cytotoxicity or re-evaluation of conflicting test results) will be needed to improve performance of CD prediction.

A practice of expert review by read-across using QSAR Toolbox

The International Council for Harmonisation of Technical Requirement for Pharmaceuticals for Human Use (ICH) M7 guideline on 'Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk' provides the application of two types of quantitative structure-activity relationship (QSAR) systems (rule- and statistics-based) as an alternative to the Ames test for evaluating the mutagenicity of impurities in pharmaceuticals. M7 guideline also states that the expert reviews can be applied when the outcomes of the two QSAR analyses show any conflicting or inconclusive prediction. However, the guideline does not provide any information of how to conduct expert reviews. Therefore, a conservative approach was chosen in this study, which is based on the intention to capture any mutagenic chemical substances. The 36 chemical substances, which are the model chemical substances in which positive mutagenicity was not observed according to the two types of QSAR analyses (i.e. the results are either conflicting or both negative), were selected from the list of chemical substances with strong mutagenicity known as the reported chemicals under the Industrial Safety and Health Act in Japan. The QSAR Toolbox was used in this study to rationally determine the positive mutagenicity of the 36 model chemical substances by applying a read-across method, a technique to evaluate the endpoint of the model chemical substances using the endpoint information of chemicals that are structurally similar to the model chemical substances. Resulting from the expert review by the read-across method, the 23 model chemical substances (63.8%) were rationally concluded as positive. In addition, 9 out of 11 model chemical substances that were assessed as negative for mutagenicity by both of the QSAR systems had positive analogues, supporting their mutagenicity. These results suggested that the read-across is a useful method, when conducting a conservative approach intended to capture any mutagenic chemical substances.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: is aromatic N-oxide a structural alert for predicting DNA-reactive mutagenicity?

(Quantitative) structure-activity relationship or (Q)SAR predictions of DNA-reactive mutagenicity are important to support both the design of new chemicals and the assessment of impurities, degradants, metabolites, extractables and leachables, as well as existing chemicals. Aromatic N-oxides represent a class of compounds that are often considered alerting for mutagenicity yet the scientific rationale of this structural alert is not clear and has been questioned. Because aromatic N-oxide-containing compounds may be encountered as impurities, degradants and metabolites, it is important to accurately predict mutagenicity of this chemical class. This article analysed a series of publicly available aromatic N-oxide data in search of supporting information. The article also used a previously developed structure-activity relationship (SAR) fingerprint methodology where a series of aromatic N-oxide substructures was generated and matched against public and proprietary databases, including pharmaceutical data. An assessment of the number of mutagenic and non-mutagenic compounds matching each substructure across all sources was used to understand whether the general class or any specific subclasses appear to lead to mutagenicity. This analysis resulted in a downgrade of the general aromatic N-oxide alert. However, it was determined there were enough public and proprietary data to assign the quindioxin and related chemicals as well as benzo[c][1,2,5]oxadiazole 1-oxide subclasses as alerts. The overall results of this analysis were incorporated into Leadscope's expert-rule-based model to enhance its predictive accuracy.

Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses

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.

Construction and application of (Q)SAR models to predict chemical-induced in vitro chromosome aberrations

In drug development, genetic toxicology studies are conducted using in vitro and in vivo assays to identify potential mutagenic and clastogenic effects, as outlined in the International Council for Harmonisation (ICH) S2 regulatory guideline. (Quantitative) structure-activity relationship ((Q)SAR) models that predict assay outcomes can be used as an early screen to prioritize pharmaceutical candidates, or later during product development to evaluate safety when experimental data are unavailable or inconclusive. In the current study, two commercial QSAR platforms were used to build models for in vitro chromosomal aberrations in Chinese hamster lung (CHL) and Chinese hamster ovary (CHO) cells. Cross-validated CHL model predictive performance showed sensitivity of 80 and 82%, and negative predictivity of 75 and 76% based on 875 training set compounds. For CHO, sensitivity of 61 and 67% and negative predictivity of 68 and 74% was achieved based on 817 training set compounds. The predictive performance of structural alerts in a commercial expert rule-based SAR software was also investigated and showed positive predictivity of 48-100% for selected alerts. Case studies examining incorrectly-predicted compounds, non-DNA-reactive clastogens, and recently-approved pharmaceuticals are presented, exploring how an investigational approach using similarity searching and expert knowledge can improve upon individual (Q)SAR predictions of the clastogenicity of drugs.

Meeting report, ICH M7 relevant workshop: use of (Q)SAR systems and expert judgment

Use of Quantitative Structure-Activity Relationships ((Q)SAR) prediction tools has been increasing since the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 guideline was issued in June 2014. The Japanese Environmental Mutagen Society and the Bacterial Mutagenicity Study Group took the initiative of the workshop on (Q)SAR in 2016 to discuss using (Q)SAR to predict mutagenicity. The aim of the workshop was to form a common understanding on the current use of (Q)SAR tools in industry and for regulatory purposes and on the process of expert judgment. This report summarizes the general session that reviewed the use of (Q)SAR tools and the case study session that discussed expert judgment.

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.

Translating New Science Into the Drug Review Process: The US FDA's Division of Applied Regulatory Science

In 2011, the US Food and drug Administration (FDA) developed a strategic plan for regulatory science that focuses on developing new tools, standards, and approaches to assess the safety, efficacy, quality, and performance of FDA-regulated products. In line with this, the Division of Applied Regulatory Science was created to move new science into the Center for Drug Evaluation and Research (CDER) review process and close the gap between scientific innovation and drug review. The Division, located in the Office of Clinical Pharmacology, is unique in that it performs mission-critical applied research and review across the translational research spectrum including in vitro and in vivo laboratory research, in silico computational modeling and informatics, and integrated clinical research covering clinical pharmacology, experimental medicine, and postmarket analyses. The Division collaborates with Offices throughout CDER, across the FDA, other government agencies, academia, and industry. The Division is able to rapidly form interdisciplinary teams of pharmacologists, biologists, chemists, computational scientists, and clinicians to respond to challenging regulatory questions for specific review issues and for longer-range projects requiring the development of predictive models, tools, and biomarkers to speed the development and regulatory evaluation of safe and effective drugs. This article reviews the Division's recent work and future directions, highlighting development and validation of biomarkers; novel humanized animal models; translational predictive safety combining in vitro, in silico, and in vivo clinical biomarkers; chemical and biomedical informatics tools for safety predictions; novel approaches to speed the development of complex generic drugs, biosimilars, and antibiotics; and precision medicine.

(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.