Points to consider regarding the use and implementation of virtual controls in nonclinical general toxicology studies.

The replacement of a proportion of concurrent controls by virtual controls in nonclinical safety studies has gained traction over the last few years. This is supported by foundational work, encouraged by regulators, and aligned with societal expectations regarding the use of animals in research. This paper provides an overview of the points to consider for any institution on the verge of implementing this concept, with emphasis given on database creation, risks, and discipline-specific perspectives.

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.

The application of acute oral toxicity computational models in dangerous goods classification.

Acute oral toxicity (AOT) data inform the acute toxicity potential of a compound and guides occupational safety and transportation practices. AOT data enable the categorization of a chemical into the appropriate AOT Globally Harmonized System (GHS) category based on the severity of the hazard. AOT data are also utilized to identify compounds that are Dangerous Goods (DGs) and subsequent transportation guidance for shipping of these hazardous materials. Proper identification of DGs is challenging for novel compounds that lack data. It is not feasible to err on the side of caution for all compounds lacking AOT data and to designate them as DGs, as shipping a compound as a DG has cost, resource, and time implications. With the wealth of available historical AOT data, AOT testing approaches are evolving, and in silico AOT models are emerging as tools that can be utilized with confidence to assess the acute toxicity potential of de novo molecules. Such approaches align with the 3R principles, offering a reduction or even replacement of traditional in vivo testing methods and can also be leveraged for product stewardship purposes. Utilizing proprietary historical in vivo AOT data for 210 pharmaceutical compounds (PCs), we evaluated the performance of two established in silico AOT programs: the Leadscope AOT Model Suite and the Collaborative Acute Toxicity Modeling Suite. These models accurately identified 94% and 97% compounds that were not DGs (GHS categories 4, 5, and not classified (NC)) suggesting that the models are fit-for-purpose in identifying PCs with low acute oral toxicity potential (LD50 >300 mg/kg). Utilization of these models to identify compounds that are not DGs can enable them to be de-prioritized for in vivo testing. This manuscript provides a detailed evaluation and assessment of the two models and recommends the most suitable applications of such models.

Genotoxicity Assessment of Drug Metabolites in the Context of MIST and Beyond.

While there are dedicated guidelines for industry regarding the assessment of the genotoxic potential of new pharmaceuticals and impurities, and the general safety assessment of major drug metabolites, only limited guidance exists on the assessment of potential genotoxic minor drug metabolites. In this Perspective, we discuss challenges associated with assessing the genotoxic potential of human metabolites and share five case studies within the context of an "aware-avoid-assess" paradigm. A special focus is on a class of potentially genotoxic carcinogens, aromatic amines (arylamines and anilines). This compound class is frequently used as building blocks and may show up as impurities, metabolites, or degradants in pharmaceuticals. We propose several recommendations that should help project teams at different stages of pharmaceutical development. In most cases, proactive interactions with the relevant health authority should be considered to endorse the proposed genotoxicity assessment strategy for minor drug metabolites.

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.

Qualification of impurities based on metabolite data.

Regulatory Guidance documents ICH Q3A (R2) and ICH Q3B (R2) state that "impurities that are also significant metabolites present in animal and/or human studies are generally considered qualified". However, no guidance is provided regarding data requirements for qualification, nor is a definition of the term "significant metabolite" provided. An opportunity is provided to define those categories and potentially avoid separate toxicity studies to qualify impurities. This can reduce cost, animal use and time, and avoid delays in drug development progression. If the concentration or amount of a metabolite, in animals or human, is similar to that of the known, structurally identical impurity (arising from the administered test material), the qualification of the impurity on the grounds of it also being a metabolite is justified. We propose two complementary approaches to support conclusions to this effect: 1) demonstrate that the impurity is formed by metabolism in animals and/or man, based preferably on plasma exposures or, alternatively, amounts excreted in urine, and, where appropriate, 2) show that animal exposure to (or amount of) the impurity/metabolite is equal or greater in animals than in humans. An important factor of both assessments is the maximum theoretical concentration (or amount) (MTC or MTA) of the impurity/metabolite achievable from the administered dose and recommendations on the estimation of the MTC and MTA are presented.

Skin sensitization in silico protocol.

The assessment of skin sensitization has evolved over the past few years to include in vitro assessments of key events along the adverse outcome pathway and opportunistically capitalize on the strengths of in silico methods to support a weight of evidence assessment without conducting a test in animals. While in silico methods vary greatly in their purpose and format; there is a need to standardize the underlying principles on which such models are developed and to make transparent the implications for the uncertainty in the overall assessment. In this contribution, the relationship between skin sensitization relevant effects, mechanisms, and endpoints are built into a hazard assessment framework. Based on the relevance of the mechanisms and effects as well as the strengths and limitations of the experimental systems used to identify them, rules and principles are defined for deriving skin sensitization in silico assessments. Further, the assignments of reliability and confidence scores that reflect the overall strength of the assessment are discussed. This skin sensitization protocol supports the implementation and acceptance of in silico approaches for the prediction of skin sensitization.

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.

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.

Principles and procedures for implementation of ICH M7 recommended (Q)SAR analyses.

The ICH M7 guideline describes a consistent approach to identify, categorize, and control DNA reactive, mutagenic, impurities in pharmaceutical products to limit the potential carcinogenic risk related to such impurities. This paper outlines a series of principles and procedures to consider when generating (Q)SAR assessments aligned with the ICH M7 guideline to be included in a regulatory submission. In the absence of adequate experimental data, the results from two complementary (Q)SAR methodologies may be combined to support an initial hazard classification. This may be followed by an assessment of additional information that serves as the basis for an expert review to support or refute the predictions. This paper elucidates scenarios where additional expert knowledge may be beneficial, what such an expert review may contain, and how the results and accompanying considerations may be documented. Furthermore, the use of these principles and procedures to yield a consistent and robust (Q)SAR-based argument to support impurity qualification for regulatory purposes is described in this manuscript.

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

The ICH M7 guidelines for the assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals allows for the consideration of in silico predictions in place of in vitro studies. This represents a significant advance in the acceptance of (Q)SAR models and has resulted from positive interactions between modellers, regulatory agencies and industry with a shared purpose of developing effective processes to minimise risk. This paper discusses key scientific principles that should be applied when evaluating in silico predictions with a focus on accuracy and scientific rigour that will support a consistent and practical route to regulatory submission.

Standardized UV-vis spectra as the foundation for a threshold-based, integrated photosafety evaluation.

Phototoxicity is a relatively common phenomenon and is an adverse effect of some systemic drugs. The fundamental initial step of photochemical reactivity is absorption of a photon; however, little guidance has been provided thus far regarding how ultraviolet-visible (UV-vis) light absorption spectra may be used to inform testing strategies for investigational drugs. Here we report the results of an inter-laboratory study comparing the data from harmonized UV-vis light absorption spectra obtained in methanol with data from the in vitro 3T3 Neutral Red Uptake Phototoxicity Test. Six pharmaceutical companies submitted data according to predefined quality criteria for 76 compounds covering a wide range of chemical classes showing a diverse but "positive"-enhanced distribution of photo irritation factors (22%: PIF<2, 12%: PIF 2-5, 66%: PIF>5). For compounds being formally positive (PIF value above 5) the lowest reported molar extinction coefficient (MEC) was 1700 L mol⁻¹ cm⁻¹ in methanol. However, the majority of these formally positive compounds showed MEC values being significantly higher (up to almost 40,000 L mol⁻¹ cm⁻¹). In conclusion, an MEC value of 1000 L mol⁻¹ cm⁻¹ may represent a reasonable and pragmatic threshold warranting further experimental photosafety evaluation.

Toward implementing virtual control groups in nonclinical safety studies.

Historical data from control groups in animal toxicity studies is currently mainly used for comparative purposes to assess validity and robustness of study results. Due to the highly controlled environment in which the studies are performed and the homogeneity of the animal collectives it has been proposed to use the historical data for building so-called virtual control groups, which could replace partly or entirely the concurrent control. This would constitute a substantial contribution to the reduction of animal use in safety studies. Before the concept can be implemented, the prerequisites regarding data collection, curation and statistical evaluation together with a validation strategy need to be identified to avoid any impairment of the study outcome and subsequent consequences for human risk assessment. To further assess and develop the concept of virtual control groups the transatlantic think tank for toxicology (t4) sponsored a workshop with stakeholders from the pharmaceutical and chemical industry, academia, FDA, pharmaceutical, contract research organizations (CROs), and non-governmental organizations in Washington, which took place in March 2023. This report summarizes the current efforts of a European initiative to share, collect and curate animal control data in a centralized database and the first approaches to identify optimal matching criteria between virtual controls and the treatment arms of a study as well as first reflections about strategies for a qualification procedure and potential pitfalls of the concept.

Animal safety studies are usually performed with three groups of animals where increasing amounts of the test chemical are given to the animals and one control group where the animals do not receive the test chemical. The design of such studies, the characteristics of the animals, and the measured parameters are often very similar from study to study. Therefore, it has been suggested that measurement data from the control groups could be reused from study to study to lower the total number of animals per study. This could reduce animal use by up to 25% for such standardized studies. A workshop was held to discuss the pros and cons of such a concept and what would have to be done to implement it without threatening the reliability of the study outcome or the resulting human risk assessment.

Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities.

Genotoxicity hazard identification is part of the impurity qualification process for drug substances and products, the first step of which being the prediction of their potential DNA reactivity using in silico (quantitative) structure-activity relationship (Q)SAR models/systems. This white paper provides information relevant to the development of the draft harmonized tripartite guideline ICH M7 on potentially DNA-reactive/mutagenic impurities in pharmaceuticals and their application in practice. It explains relevant (Q)SAR methodologies as well as the added value of expert knowledge. Moreover, the predictive value of the different methodologies analyzed in two surveys conveyed in the US and European pharmaceutical industry is compared: most pharmaceutical companies used a rule-based expert system as their primary methodology, yielding negative predictivity values of ⩾78% in all participating companies. A further increase (>90%) was often achieved by an additional expert review and/or a second QSAR methodology. Also in the latter case, an expert review was mandatory, especially when conflicting results were obtained. Based on the available data, we concluded that a rule-based expert system complemented by either expert knowledge or a second (Q)SAR model is appropriate. A maximal transparency of the assessment process (e.g. methods, results, arguments of weight-of-evidence approach) achieved by e.g. data sharing initiatives and the use of standards for reporting will enable regulators to fully understand the results of the analysis. Overall, the procedures presented here for structure-based assessment are considered appropriate for regulatory submissions in the scope of ICH M7.

In silico methods combined with expert knowledge rule out mutagenic potential of pharmaceutical impurities: an industry survey.

With the increasing emphasis on identification and low level control of potentially genotoxic impurities (GTIs), there has been increased use of structure-based assessments including application of computerized models. To date many publications have focused on the ability of computational models, either individually or in combination, to accurately predict the mutagenic effects of a chemical in the Ames assay. Typically, these investigations take large numbers of compounds and use in silico tools to predict their activity with no human interpretation being made. However, this does not reflect how these assessments are conducted in practice across the pharmaceutical industry. Current guidelines indicate that a structural assessment is sufficient to conclude that an impurity is non-mutagenic. To assess how confident we can be in identifying non-mutagenic structures, eight companies were surveyed for their success rate. The Negative Predictive Value (NPV) of the in silico approaches was 94%. When human interpretation of in silico model predictions was conducted, the NPV increased substantially to 99%. The survey illustrates the importance of expert interpretation of in silico predictions. The survey also suggests the use of multiple computational models is not a significant factor in the success of these approaches with respect to NPV.

The 3T3 neutral red uptake phototoxicity test: practical experience and implications for phototoxicity testing--the report of an ECVAM-EFPIA workshop.

This is the report from the "ECVAM-EFPIA workshop on 3T3 NRU Phototoxicity Test: Practical Experience and Implications for Phototoxicity Testing", jointly organized by ECVAM and EFPIA and held on the 25-27 October 2010 in Somma Lombardo, Italy. The European Centre for the Validation of Alternative Methods (ECVAM) was established in 1991 within the European Commission Joint Research, based on a Communication from the European Commission (1991). The main objective of ECVAM is to promote the scientific and regulatory acceptance of alternative methods which are of importance to the biosciences and which reduce, refine and replace the use of laboratory animals. The European Federation of Pharmaceuticals Industries and Association (EFPIA) represent the pharmaceutical industry operating in Europe. Through its direct membership of 31 national associations and 40 leading pharmaceutical companies, EFPIA is the voice on the EU scene of 2200 companies committed to researching, developing and bringing to patients new medicines that improve health and the quality of life around the world. The workshop, co-chaired by Joachim Kreysa (ECVAM) and Phil Wilcox (GSK, EFPIA) involved thirty-five experts from academia, regulatory authorities and industry, invited to contribute with their experiences in the field of phototoxicology. The main objectives of the workshop were: -to present 'in use' experience of the pharmaceutical industry with the 3T3 Neutral Red Uptake Phototoxicity Test (3T3 NRU-PT), -to discuss why it differs from the results in the original validation exercise, -to discuss technical issues and consider ways to improve the usability of the 3T3 NRU-PT for (non-topical) pharmaceuticals, e.g., by modifying the threshold of chemical light absorption to trigger photo-toxicological testing, and by modifying technical aspects of the assay, or adjusting the criteria used to classify a positive response. During the workshop, the assay methodology was reviewed by comparing the OECD Test Guideline (TG 432) with the protocols used in testing laboratories, data from EFPIA and JPMA 'surveys' were presented and possible reasons for the outcomes were discussed. Experts from cosmetics and pharmaceutical industries reported on their experience with the 3T3 NRU-PT and evidence was presented for phototoxic clinical symptoms that could be linked to certain relevant molecules. Brainstorming sessions discussed if the 3T3 NRU-PT needed to be improved and whether alternatives to the 3T3 NRU-PT exist. Finally, the viewpoint from EU and US regulators was presented. In the final session, the conclusions of the meeting were summarized, with action points. It was concluded that the 3T3 NRU-PT identifies phototoxicological hazards with a 100% sensitivity, and thus is accepted as the tier one test that correctly identifies the absence of phototoxic potential. Consequently, positive results in the 3T3 NRU-PT often do not translate into a clinical phototoxicity risk. Possible ways to improve the practical use of this assay include: (i) adaptation of changed UV/vis-absorption criteria as a means to reduce the number of materials tested, (ii) reduction of the highest concentration to be tested, and (iii) consideration of modifying the threshold criteria for the prediction of a positive call in the test.

Off-targetP ML: an open source machine learning framework for off-target panel safety assessment of small molecules.

Unpredicted drug safety issues constitute the majority of failures in the pharmaceutical industry according to several studies. Some of these preclinical safety issues could be attributed to the non-selective binding of compounds to targets other than their intended therapeutic target, causing undesired adverse events. Consequently, pharmaceutical companies routinely run in-vitro safety screens to detect off-target activities prior to preclinical and clinical studies. Hereby we present an open source machine learning framework aiming at the prediction of our in-house 50 off-target panel activities for ~ 4000 compounds, directly from their structure. This framework is intended to guide chemists in the drug design process prior to synthesis and to accelerate drug discovery. We also present a set of ML approaches that require minimum programming experience for deployment. The workflow incorporates different ML approaches such as deep learning and automated machine learning. It also accommodates popular issues faced in bioactivity predictions, as data imbalance, inter-target duplicated measurements and duplicated public compound identifiers. Throughout the workflow development, we explore and compare the capability of Neural Networks and AutoML in constructing prediction models for fifty off-targets of different protein classes, different dataset sizes, and high-class imbalance. Outcomes from different methods are compared in terms of efficiency and efficacy. The most important challenges and factors impacting model construction and performance in addition to suggestions on how to overcome such challenges are also discussed.

Increasing the Value of Data Within a Large Pharmaceutical Company Through In Silico Models.

The present contribution describes how in silico models and methods are applied at different stages of the drug discovery process in the pharmaceutical industry. A description of the most relevant computational methods and tools is given along with an evaluation of their performance in the assessment of potential genotoxic impurities and the prediction of off-target in vitro pharmacology. The challenges of predicting the outcome of highly complex in vivo studies are discussed followed by considerations on how novel ways to manage, store, exchange, and analyze data may advance knowledge and facilitate modeling efforts. In this context, the current status of broad data sharing initiatives, namely, eTOX and eTransafe, will be described along with related projects that could significantly reduce the use of animals in drug discovery in the future.

Development of Duration-Based Non-Mutagenic Thresholds of Toxicological Concern (TTCs) Relevant to Parenteral Extractables and Leachables (E&Ls).

The threshold of toxicological concern (TTC), i.e., the dose of a compound lacking sufficient experimental toxicity data that is unlikely to result in an adverse health effect in humans, is important for evaluating extractables and leachables (E&Ls) as it guides analytical testing and minimizes the use of animal studies. The Extractables and Leachables Safety Information Exchange (ELSIE) consortium, which consists of member companies that span biotechnology, pharmaceutical, and medical device industries, brought together subject matter expert toxicologists to derive TTC values for organic, non-mutagenic E&L substances when administered parenterally. A total of 488 E&L compounds from the ELSIE database were analyzed and parenteral point of departure (PPOD) estimates were derived for 252 compounds. The PPOD estimates were adjusted to extrapolate to subacute, subchronic, and chronic durations of nonclinical exposure and the lower fifth percentiles were calculated. An additional 100-fold adjustment factor to account for nonclinical species and human variability was subsequently applied to derive the parenteral TTC values for E&Ls. The resulting parenteral TTC values are 35, 110, and 180 µg/day for human exposures of >10 years to lifetime, >1-10 years, and ≤1 year, respectively. These parenteral TTCs are expected to be conservative for E&Ls that are considered non-mutagenic per ICH M7(R1) guidelines.

Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods.

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.