Characterizing Extractables and Leachables Chemical Space to Support In Silico Toxicological Hazard Assessments.

This article describes the development of a representative dataset of extractables and leachables (E&L) from the combined Extractables and Leachables Safety Information Exchange (ELSIE) Consortium and the Product Quality Research Institute (PQRI) published datasets, representing a total of 783 chemicals. A chemical structure-based clustering of the combined dataset identified 142 distinct chemical classes with two or more chemicals across the combined dataset. The majority of these classes (105 chemical classes out of 142) contained chemicals from both datasets, whereas 8 classes contained only chemicals from the ELSIE dataset and 29 classes contain only chemicals from the PQRI dataset. This evaluation also identified classes containing chemicals that were flagged as potentially mutagenic as well as potent (strong or extreme) dermal sensitizers by in silico tools. The prevalence of alerting structures in the E&L datasets was approximately 9% (69 examples) for mutagens and 3% (25 examples) for potent sensitizers. This analysis showed that most (80%; 20 of 25) E&L predicted to be strong or extreme dermal sensitizers were also flagged as potential mutagens. Only two chemical classes, each containing three chemicals (alkyl bromides and isothiocyanates), were uniquely identified in the PQRI dataset and contained chemicals predicted to be potential mutagens and/or potent dermal sensitizers.

In Silico Assessment of Biomolecule Reactivity with Leachables.

Leachables in pharmaceutical products may react with biomolecule active pharmaceutical ingredients (APIs), for example, monoclonal antibodies (mAb), peptides, and ribonucleic acids (RNA), potentially compromising product safety and efficacy or impacting quality attributes. This investigation explored a series of in silico models to screen extractables and leachables to assess their possible reactivity with biomolecules. These in silico models were applied to collections of known leachables to identify functional and structural chemical classes likely to be flagged by these in silico approaches. Flagged leachable functional classes included antimicrobials, colorants, and film-forming agents, whereas specific chemical classes included epoxides, acrylates, and quinones. In addition, a dataset of 22 leachables with experimental data indicating their interaction with insulin glargine was used to evaluate whether one or more in silico methods are fit-for-purpose as a preliminary screen for assessing this biomolecule reactivity. Analysis of the data showed that the sensitivity of an in silico screen using multiple methodologies was 80%-90% and the specificity was 58%-92%. A workflow supporting the use of in silico methods in this field is proposed based on both the results from this assessment and best practices in the field of computational modeling and quality risk management.

Mutagenic potential and structural alerts of phytotoxins.

Toxic plant-produced chemicals, so-called phytotoxins, constitute a category of natural compounds belonging to a diversity of chemical classes. Some of them (e.g., alkaloids, terpenes, saponins) are associated with high toxic potency, while for many of others no toxicological data is available. In this study, the mutagenic potential of 1586 phytotoxins, as obtained from a publicly available database, was investigated applying different in silico approaches. (Q)SAR models (including statistical-based and rule-based systems) were used for the prediction of bacterial in vitro mutagenicity (Ames test) and the results from multiple tools were combined to assign consensus predicted values (i.e., positive, negative, inconclusive). The overall consensus outcome was then employed to investigate relationships between structural features of classes of phytotoxins and potential mutagenicity, allowing the identification of structural alerts raising a specific concern. The results highlighted that about 10% of the screened compounds were predicted to have mutagenic potential and the critical classes of concern, such as alkaloids, were further investigated in terms of subclasses (e.g., indole alkaloids, isoquinoline alkaloids), getting a deeper insight into the mutagenic potential of possible naturally occurring chemicals in plant materials and their structural alerts.

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.

Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches.

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.

In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities.

The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.

In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity.

Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.

In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs.

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

In silico models for genotoxicity and drug regulation.

INTRODUCTION: Whereas in the past, (Q)SAR methods have been largely used to support the design of new drugs, in the last few decades, there has been a new interest in its applications for the assessment of drug safety. In particular, the ICH M7 guideline has introduced the concept that (Q)SAR predictions for the Ames mutagenicity of drug impurities can be used for regulatory purposes. AREAS COVERED: This review introduces the ICH M7 conceptual framework and illustrates the most updated evaluations of the in silico approaches for the prediction of genotoxicity. The strengths and weaknesses of the state-of-the-art are presented and future perspectives are discussed. EXPERT OPINION: Given the growing recognition of (Q)SAR approaches, more investment will be devoted to its improvement. The major areas of research should be the expansion and curation of the experimental training sets, with particular attention to the portions of chemical space which are poorly represented. New modeling methodologies (e.g. machine-learning methods) may support this effort, particularly for treating proprietary data without disclosure. Research on new integrative approaches for regulatory decisions will also be important.

Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology.

Read-across (RAx) translates available information from well-characterized chemicals to a substance for which there is a toxicological data gap. The OECD is working on case studies to probe general applicability of RAx, and several regulations (e.g., EU-REACH) already allow this procedure to be used to waive new in vivo tests. The decision to prepare a review on the state of the art of RAx as a tool for risk assessment for regulatory purposes was taken during a workshop with international experts in Ranco, Italy in July 2018. Three major issues were identified that need optimization to allow a higher regulatory acceptance rate of the RAx procedure: (i) the definition of similarity of source and target, (ii) the translation of biological/toxicological activity of source to target in the RAx procedure, and (iii) how to deal with issues of ADME that may differ between source and target. The use of new approach methodologies (NAM) was discussed as one of the most important innovations to improve the acceptability of RAx. At present, NAM data may be used to confirm chemical and toxicological similarity. In the future, the use of NAM may be broadened to fully characterize the hazard and toxicokinetic properties of RAx compounds. Concerning available guidance, documents on Good Read-Across Practice (GRAP) and on best practices to perform and evaluate the RAx process were identified. Here, in particular, the RAx guidance, being worked out by the European Commission's H2020 project EU-ToxRisk together with many external partners with regulatory experience, is given.

Filling Data Gaps by Read-across: A Mini Review on its Application, Developments and Challenges.

Read-across is a non-testing data gap filling technique which provides information for toxicological assessments by inferring from known toxicity data of compound(s) with a "similar" property or chemical profile. The increased usage of read-across was driven by monetary, timing and ethical costs associated with in vivo testing, as well as promoted by regulatory frameworks to minimize new animal testing (e. g., EU-REACH). Several guidance documents have been published by ECHA and OECD providing guidelines on how to perform, assess and document a read-across study. In parallel, much effort was invested by the scientific community to provide good read-across practices and structured frameworks to enhance validity of read-across justifications. Nevertheless, read-across is an evolving method with several open issues and opportunities. A brief review is here provided on key developments on the use of read-across, regulatory and scientific expectations, practical hurdles and open challenges.

Data mining by total ranking methods: a case study on optimisation of the "pulp and bleaching" process in the paper industry.

Total order ranking methods are multicriteria decision making techniques used for the ranking of various alternatives on the basis of more than one criterion. The criteria, which are the standards by which the elements of the system are judged are not always in agreement, they can be conflicting, motivating the need to find an overall optimum that can deviate from the optima of one or more of the single criteria. Total order ranking methods are based on an aggregation of the criteria in a scalar function, i.e. an order or ranking index, which allow to sort elements according to its numerical value. Several evaluation methods which define a ranking parameter generating a total order ranking have been proposed in the literature. Four total order ranking methods are here described: Desirability functions, Utility functions, Dominance functions and Absolute Reference method. These methods have been compared to each other by applying them to a decision making problem in the paper industry. Various bleaching processes have been analysed and compared on the basis of multiple criteria, the aim being to find out best bleaching process among the ones proposed in the last years as alternative to chlorine bleaching process which is of high environmental impact due to the potential for chlorinated dioxin production.

The Consultancy Activity on In Silico Models for Genotoxic Prediction of Pharmaceutical Impurities.

The toxicological assessment of DNA-reactive/mutagenic or clastogenic impurities plays an important role in the regulatory process for pharmaceuticals; in this context, in silico structure-based approaches are applied as primary tools for the evaluation of the mutagenic potential of the drug impurities. The general recommendations regarding such use of in silico methods are provided in the recent ICH M7 guideline stating that computational (in silico) toxicology assessment should be performed using two (Q)SAR prediction methodologies complementing each other: a statistical-based method and an expert rule-based method.Based on our consultant experience, we describe here a framework for in silico assessment of mutagenic potential of drug impurities. Two main applications of in silico methods are presented: (1) support and optimization of drug synthesis processes by providing early indication of potential genotoxic impurities and (2) regulatory evaluation of genotoxic potential of impurities in compliance with the ICH M7 guideline. Some critical case studies are also discussed.

Prediction of dose-hepatotoxic response in humans based on toxicokinetic/toxicodynamic modeling with or without in vivo data: a case study with acetaminophen.

In the present legislations, the use of methods alternative to animal testing is explicitly encouraged, to use animal testing only 'as a last resort' or to ban it. The use of alternative methods to replace kinetics or repeated dose in vivo tests is a challenging issue. We propose here a strategy based on in vitro tests and QSAR (Quantitative Structure Activity Relationship) models to calibrate a dose-response model predicting hepatotoxicity. The dose response consists in calibrating and coupling a PBPK (physiologically-based pharmacokinetic) model with a toxicodynamic model for cell viability. We applied our strategy to acetaminophen and compared three different ways to calibrate the PBPK model: only with in vitro and in silico methods, using rat data or using all available data including data on humans. Some estimates of kinetic parameters differed substantially among the three calibration processes, but, at the end, the three models were quite comparable in terms of liver toxicity predictions and close to the usual range of human overdose. For the model based on alternative methods, the good adequation with the two other models resulted from an overestimated renal elimination rate which compensated for the underestimation of the metabolism rate. Our study points out that toxicokinetics/toxicodynamics approaches, based on alternative methods and modelling only, can predict in vivo liver toxicity with accuracy comparable to in vivo methods.

Thresholds of toxicological concern for endocrine active substances in the aquatic environment.

The threshold of toxicological concern (TTC) concept proposes that an exposure threshold value can be derived for chemicals, below which no significant risk to human health or the environment is expected. This concept goes further than setting acceptable exposure levels for individual chemicals, because it attempts to set a de minimis value for chemicals, including those of unknown toxicity, by taking the chemical's structure or mode of action (MOA) into consideration. This study examines the use of the TTC concern concept for endocrine active substances (EAS) with an estrogenic MOA. A case study formed the basis for a workshop of regulatory, industry and academic scientists held to discuss the use of the TTC in aquatic environmental risk assessment. The feasibility and acceptability, general advantages and disadvantages, and the specific issues that need to be considered when applying the TTC concept for EAS in risk assessment were addressed. Issues surrounding the statistical approaches used to derive TTCs were also discussed. This study presents discussion points and consensus findings of the workshop.

Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors.

Novel molecular descriptors based on a leverage matrix similar to that defined in statistics and usually used for regression diagnostics are presented. This leverage matrix, called Molecular Influence Matrix (MIM), is here proposed as a new molecular representation easily calculated from the spatial coordinates of the molecule atoms in a chosen conformation. The proposed molecular descriptors are called GETAWAY (GEometry, Topology, and Atom-Weights AssemblY) as they try to match 3D-molecular geometry provided by the molecular influence matrix and atom relatedness by molecular topology, with chemical information by using different atomic weightings (atomic mass, polarizability, van der Waals volume, and electronegativity, together with unit weights). A first set of molecular descriptors, called H-GETAWAY, is derived by using only the information provided by the molecular influence matrix, while a second set, called R-GETAWAY, combines this information with geometric interatomic distances in the molecule. The prediction ability in structure-property correlations of the new descriptors was tested by analyzing regressions of these descriptors for selected properties of octanes.

Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 2. Application of the novel 3D molecular descriptors to QSAR/QSPR studies.

In a previous paper the theory of the new molecular descriptors called GETAWAY (GEometry, Topology, and Atom-Weights AssemblY) was explained. These descriptors have been proposed with the aim of matching 3D-molecular geometry, atom relatedness, and chemical information. In this paper prediction ability in structure-property correlations of GETAWAY descriptors has been tested extensively by analyzing the regressions of these descriptors for selected properties of some reference compound classes. Moreover, the general performance of the new descriptors in QSAR/QSPR has been evaluated with respect to other well-known sets of molecular descriptors.