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.

Acceptable intakes (AIs) for 11 small molecule N-nitrosamines (NAs).

Low levels of N-nitrosamines (NAs) were detected in pharmaceuticals and, as a result, health authorities (HAs) have published acceptable intakes (AIs) in pharmaceuticals to limit potential carcinogenic risk. The rationales behind the AIs have not been provided to understand the process for selecting a TD50 or read-across analog. In this manuscript we evaluated the toxicity data for eleven common NAs in a comprehensive and transparent process consistent with ICH M7. This evaluation included substances which had datasets that were robust, limited but sufficient, and substances with insufficient experimental animal carcinogenicity data. In the case of robust or limited but sufficient carcinogenicity information, AIs were calculated based on published or derived TD50s from the most sensitive organ site. In the case of insufficient carcinogenicity information, available carcinogenicity data and structure activity relationships (SARs) were applied to categorical-based AIs of 1500 ng/day, 150 ng/day or 18 ng/day; however additional data (such as biological or additional computational modelling) could inform an alternative AI. This approach advances the methodology used to derive AIs for NAs.

Genotoxicity evaluation of a valsartan-related complex N-nitroso-impurity.

Recently, the formation of genotoxic and carcinogenic N-nitrosamines impurities during drug manufacturing of tetrazole-containing angiotensin-II blockers has been described. However, drug-related (complex) nitrosamines may also be generated under certain conditions, i.e., through nitrosation of vulnerable amines in drug substances in the presence of nitrite. An investigation of valsartan drug substance showed that a complex API-related N-nitrosamine chemically designated as (S)-2-(((2'-(1H-tetrazol-5-yl)-[1,1'-biphenyl]-4-yl)methyl)(nitroso)amino)-3-methylbutanoic acid (named 181-14) may be generated. 181-14 was shown to be devoid of a mutagenic potential in the Non-GLP Ames test. According to ICH M7 (R1) (2018), impurities that are not mutagenic in the Ames test would be considered Class 5 impurities and limited according to ICH Q3A (R2) and B (R2) (2006) guidelines. However, certain regulatory authorities raised the concern that the Ames test may not be sufficiently sensitive to detect a mutagenic potential of nitrosamines and requested a confirmatory in vivo study using a transgenic animal genotoxicity model. Our data show that 181-14 was not mutagenic in the transgenic gene mutation assay in MutaTMMice. The data support the conclusion that the Ames test is an adequate and sensitive test system to assess a mutagenic potential of nitrosamines.

Use of the bacterial reverse mutation assay to predict carcinogenicity of N-nitrosamines.

Under ICH M7, impurities are assessed using the bacterial reverse mutation assay (i.e., Ames test) when predicted positive using in silico methodologies followed by expert review. N-Nitrosamines (NAs) have been of recent concern as impurities in pharmaceuticals, mainly because of their potential to be highly potent mutagenic carcinogens in rodent bioassays. The purpose of this analysis was to determine the sensitivity of the Ames assay to predict the carcinogenic outcome with curated proprietary Vitic (n = 131) and Leadscope (n = 70) databases. NAs were selected if they had corresponding rodent carcinogenicity assays. Overall, the sensitivity/specificity of the Ames assay was 93-97% and 55-86%, respectively. The sensitivity of the Ames assay was not significantly impacted by plate incorporation (84-89%) versus preincubation (82-89%). Sensitivity was not significantly different between use of rat and hamster liver induced S9 (80-93% versus 77-96%). The sensitivity of the Ames is high when using DMSO as a solvent (87-88%). Based on the analysis of these databases, the Ames assay conducted under OECD 471 guidelines is highly sensitive for detecting the carcinogenic hazards of NAs.

Use of less-than-lifetime (LTL) durational limits for nitrosamines: Case study of N-Nitrosodiethylamine (NDEA).

The ICH M7(R1) guideline describes a framework to assess the carcinogenic risk of mutagenic and carcinogenic pharmaceutical impurities following less-than-lifetime (LTL) exposures. This LTL framework is important as many pharmaceuticals are not administered for a patient's lifetime and as clinical trials typically involve LTL exposures. While there has been regulatory caution about applying LTL concepts to cohort of concern (COC) impurities such as N-nitrosamines, ICH M7 does not preclude this and indeed literature data suggests that the LTL framework will be protective of patient safety for N-nitrosamines. The goal was to investigate if applying the LTL framework in ICH M7 would control exposure to an acceptable excess cancer risk in humans. Using N-nitrosodiethylamine as a case study, empirical data correlating exposure duration (as a percentage of lifespan) and cancer incidence in rodent bioassays indicate that the LTL acceptable intake (AI) as derived using the ICH M7 framework would not exceed a negligible additional risk of cancer. Therefore, controlling N-nitrosamines to an LTL AI based on the ICH M7 framework is thus demonstrated to be protective for potential carcinogenic risk to patients over the exposure durations typical of clinical trials and many prescribed medicines.

Calculating qualified non-mutagenic impurity levels: Harmonization of approaches.

The presence of impurities in drugs is unavoidable. As impurities offer no direct benefit to the patient, it is critical that impurities do not compromise patient safety. Current guidelines on the derivation of acceptable impurity levels leave aspects of calculations open for interpretation, resulting in inconsistencies across industry and regulators. To understand current impurity qualification practices from a safety standpoint, regulatory expectations and the safety risk that impurities pose, the IQ DruSafe Impurities Working Group (WG) conducted a pharmaceutical industry-wide survey. Survey results highlighted areas that could benefit from harmonization, including nonclinical species/sex selection and the application of adjustment factors (i.e., body surface area). Recommendations for alignment on these topics is included in this publication. Additionally, the WG collated repeat-dose toxicity information for 181 starting materials and intermediates, reflective of pharmaceutical impurities, to understand the toxicological risks they generally pose in relation to the drug substance (DS) and the assumptions surrounding the calculation of qualified impurity levels. An evaluation of this dataset and the survey were used to harmonize how to calculate a safe limit for an impurity based on toxicology testing of the impurity when present within the DS.

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.

Considerations when deriving compound-specific limits for extractables and leachables from pharmaceutical products: Four case studies.

Leachables from pharmaceutical container closure systems are a subset of impurities that present in drug products and may pose a risk to patients or compromise product quality. Extractable studies can identify potential leachables, and extractables and leachables (E&Ls) should be evaluated during development of the impurity control strategy. Currently, there is a lack of specific regulatory guidance on how to risk assess E&Ls; this may lead to inconsistency across the industry. This manuscript is a cross-industry Extractables and Leachables Safety Information Exchange (ELSIE) consortium collaboration and follow-up to Broschard et al. (2016), which aims to provide further clarity and detail on the conduct of E&L risk assessments. Where sufficient data are available, a health-based exposure limit termed Permitted Daily Exposure (PDE) may be calculated and to exemplify this, case studies of four common E&Ls are described herein, namely bisphenol-A, butylated hydroxytoluene, Irgafos® 168, and Irganox® 1010. Relevant discussion points are further explored, including the value of extractable data, how to perform route-to-route extrapolations and considerations around degradation products. By presenting PDEs for common E&L substances, the aim is to encourage consistency and harmony in approaches for deriving compound-specific limits.

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.

Making reliable negative predictions of human skin sensitisation using an in silico fragmentation approach.

A previously published fragmentation method for making reliable negative in silico predictions has been applied to the problem of predicting skin sensitisation in humans, making use of a dataset of over 2750 chemicals with publicly available skin sensitisation data from 18 in vivo assays. An assay hierarchy was designed to enable the classification of chemicals within this dataset as either sensitisers or non-sensitisers where data from more than one in vivo test was available. The negative prediction approach was validated internally, using a 5-fold cross-validation, and externally, against a proprietary dataset of approximately 1000 chemicals with in vivo reference data shared by members of the pharmaceutical, nutritional, and personal care industries. The negative predictivity for this proprietary dataset was high in all cases (>75%), and the model was also able to identify structural features that resulted in a lower accuracy or a higher uncertainty in the negative prediction, termed misclassified and unclassified features respectively. These features could serve as an aid for further expert assessment of the negative in silico prediction.

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.

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

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

Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained.

The relative wealth of bacterial mutagenicity data available in the public literature means that in silico quantitative/qualitative structure activity relationship (QSAR) systems can readily be built for this endpoint. A good means of evaluating the performance of such systems is to use private unpublished data sets, which generally represent a more distinct chemical space than publicly available test sets and, as a result, provide a greater challenge to the model. However, raw performance metrics should not be the only factor considered when judging this type of software since expert interpretation of the results obtained may allow for further improvements in predictivity. Enough information should be provided by a QSAR to allow the user to make general, scientifically-based arguments in order to assess and overrule predictions when necessary. With all this in mind, we sought to validate the performance of the statistics-based in vitro bacterial mutagenicity prediction system Sarah Nexus (version 1.1) against private test data sets supplied by nine different pharmaceutical companies. The results of these evaluations were then analysed in order to identify findings presented by the model which would be useful for the user to take into consideration when interpreting the results and making their final decision about the mutagenic potential of a given compound.

Assessing safety of extractables from materials and leachables in pharmaceuticals and biologics - Current challenges and approaches.

Leachables from pharmaceutical container closure systems can present potential safety risks to patients. Extractables studies may be performed as a risk mitigation activity to identify potential leachables for dosage forms with a high degree of concern associated with the route of administration. To address safety concerns, approaches to toxicological safety evaluation of extractables and leachables have been developed and applied by pharmaceutical and biologics manufacturers. Details of these approaches may differ depending on the nature of the final drug product. These may include application, the formulation, route of administration and length of use. Current regulatory guidelines and industry standards provide general guidance on compound specific safety assessments but do not provide a comprehensive approach to safety evaluations of leachables and/or extractables. This paper provides a perspective on approaches to safety evaluations by reviewing and applying general concepts and integrating key steps in the toxicological evaluation of individual extractables or leachables. These include application of structure activity relationship studies, development of permitted daily exposure (PDE) values, and use of safety threshold concepts. Case studies are provided. The concepts presented seek to encourage discussion in the scientific community, and are not intended to represent a final opinion or "guidelines."

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.

Mutagenicity assessment strategy for pharmaceutical intermediates to aid limit setting for occupational exposure.

Pharmaceutical intermediates (IM) are used in the synthesis of active pharmaceutical ingredients. They are not intended for human administration, yet employees may be exposed to IM during the manufacturing process. In the context of occupational health, hazard assessment of IM is needed to identify potential intrinsic hazards which could cause unwanted adverse effects. In particular, a carcinogenic potential influences the protection strategy in the workplace. DNA reactive substances may, even if present at very low levels, lead to mutations and therefore, potentially cause cancer. The use of in silico methods to predict mutagenicity is increasingly acknowledged and implemented in the recently released ICH M7 guideline for the limitation of DNA reactive impurities. In this study we investigate the possibility to apply (quantitative) structure-activity-relationships ((Q)SARs) during hazard identification to reduce the number of Ames tests needed for a hazard assessment of IM while maintaining high standards of protection of employees. Ames test outcomes for 188 substances used in the pharmaceutical production were compared with their in silico predictions using two different (Q)SAR methodologies (knowledge based and statistical) complemented by expert knowledge. The results of the analysis showed that a negative prediction for mutagenicity provides a high confidence that the IM is not mutagenic in the Ames test with the negative predictive value of 97%. On the other hand the positive predictive value was only 57% and therefore considered too low to reliably consider positive predicted IM to be mutagenic. In order to avoid any unnecessary burden for occupational health purposes caused by falsely positive predicted IM, all positive predicted IM and those with insufficient coverage by the in silico systems are submitted to an Ames test to verify or reject the prediction. It is shown that the described in silico prediction approach ensures appropriate protection strategy of the employees. Resources for performing Ames tests which do not add additional or new information for the purpose of hazard assessment could be reduced.

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.