Mechanisms of Nitrosamine Mutagenicity and Their Relationship to Rodent Carcinogenic Potency.

A thorough literature review was undertaken to understand how the pathways of N-nitrosamine transformation relate to mutagenic potential and carcinogenic potency in rodents. Empirical and computational evidence indicates that a common radical intermediate is created by CYP-mediated hydrogen abstraction at the α-carbon; it is responsible for both activation, leading to the formation of DNA-reactive diazonium species, and deactivation by denitrosation. There are competing sites of CYP metabolism (e.g., ß-carbon), and other reactive species can form following initial bioactivation, although these alternative pathways tend to decrease rather than enhance carcinogenic potency. The activation pathway, oxidative dealkylation, is a common reaction in drug metabolism and evidence indicates that the carbonyl byproduct, e.g., formaldehyde, does not contribute to the toxic properties of N-nitrosamines. Nitric oxide (NO), a side product of denitrosation, can similarly be discounted as an enhancer of N-nitrosamine toxicity based on carcinogenicity data for substances that act as NO-donors. However, not all N-nitrosamines are potent rodent carcinogens. In a significant number of cases, there is a potency overlap with non-N-nitrosamine carcinogens that are not in the Cohort of Concern (CoC; high-potency rodent carcinogens comprising aflatoxin-like-, N-nitroso-, and alkyl-azoxy compounds), while other N-nitrosamines are devoid of carcinogenic potential. In this context, mutagenicity is a useful surrogate for carcinogenicity, as proposed in the ICH M7 (R2) (2023) guidance. Thus, in the safety assessment and control of N-nitrosamines in medicines, it is important to understand those complementary attributes of mechanisms of mutagenicity and structure-activity relationships that translate to elevated potency versus those which are associated with a reduction in, or absence of, carcinogenic potency.

Practical and Science-Based Strategy for Establishing Acceptable Intakes for Drug Product N-Nitrosamine Impurities.

The potential for N-nitrosamine impurities in pharmaceutical products presents a challenge for the quality management of medicinal products. N-Nitrosamines are considered cohort-of-concern compounds due to the potent carcinogenicity of many of the structurally simple chemicals within this structural class. In the past 2 years, a number of drug products containing certain active pharmaceutical ingredients have been withdrawn or recalled from the market due to the presence of carcinogenic low-molecular-weight N,N-dialkylnitrosamine impurities. Regulatory authorities have issued guidance to market authorization holders to review all commercial drug substances/products for the potential risk of N-nitrosamine impurities, and in cases where a significant risk of N-nitrosamine impurity is identified, analytical confirmatory testing is required. A key factor to consider prior to analytical testing is the estimation of the daily acceptable intake (AI) of the N-nitrosamine impurity. A significant proportion of N-nitrosamine drug product impurities are unique/complex structures for which the development of low-level analytical methods is challenging. Moreover, these unique/complex impurities may be less potent carcinogens compared to simple nitrosamines. In the present work, our objective was to derive AIs for a large number of complex N-nitrosamines without carcinogenicity data that were identified as potential low-level impurities. The impurities were first cataloged and grouped according to common structural features, with a total of 13 groups defined with distinct structural features. Subsequently, carcinogenicity data were reviewed for structurally related N-nitrosamines relevant to each of the 13 structural groups and group AIs were derived conservatively based on the most potent N-nitrosamine within each group. The 13 structural group AIs were used as the basis for assigning AIs to each of the structurally related complex N-nitrosamine impurities. The AIs of several N-nitrosamine groups were found to be considerably higher than those for the simple N,N-dialkylnitrosamines, which translates to commensurately higher analytical method detection limits.

Deriving Compound-Specific Exposure Limits for Chemicals Used in Pharmaceutical Synthesis: Challenges in Expert Decision-Making Exemplified Through a Case Study-Based Workshop.

A workshop entitled "Deriving Compound-Specific Exposure Limits for Chemicals Used in Pharmaceutical Synthesis" was held at the 2018 Genetic Toxicology Association annual meeting. The objectives of the workshop were to provide an educational forum and use case studies and live multiple-choice polling to establish the degree of similarity/diversity in approach/opinion of the industry experts and other delegates present for some of the more challenging decision points that need to be considered when developing a compound-specific exposure limit (ie, acceptable intake or permissible or permitted daily exposure). Herein we summarize the relevant background and case study information for each decision point topic presented as well as highlight significant polling responses and discussion points. A common observation throughout was the requirement for expert judgment to be applied at each of the decision points presented which often results in different reasoning being applied by the risk assessor when deriving a compound-specific exposure limit. This supports the value of precompetitive cross-industry collaborations to develop compound-specific limits and harmonize the methodology applied, thus reducing the associated uncertainty inherent in the application of isolated expert judgment in this context. An overview of relevant precompetitive cross-industry collaborations working to achieve this goal is described.

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.

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.

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.

Resolution of contradiction between in silico predictions and Ames test results for four pharmaceutically relevant impurities.

The ICH M7 Guideline requires low level control of mutagenic impurities in pharmaceutical products to minimize cancer risk in patients (ICHM7, 2014). Bacterial mutagenicity (Ames) data is generally used to determine mutagenic and possible carcinogenic potential of compounds. Recently, a publication on experiences of using two in silico systems to identify potentially mutagenic impurities highlighted the importance of performing a critical review of published Ames data utilized as part of a mutagenicity assessment of impurities (Greene et al., 2015). Four compounds (2-amino-5-hydroxybenzoic acid, 2-amino-3-chlorobenzoic acid, methyl 2-amino-4-chlorobenzoate and 4-morpholinopyridine) reported mutagenic were identified in a two system in silico assessment and expert review of the structuresas non-mutagenic. Likely reasons for mutagenicity could not be identified and the purity of the compounds tested was proposed. In the current investigation, the purest available sample of the four compounds was tested in an OECD-compliant Ames test. The compounds were all found to be non-mutagenic. Possible reasons for the discrepancy between previously reported and current results are discussed. Additionally, important points to consider when conducting an expert review of available Ames data are provided particularly in cases where reported Ames results are discrepant with a two system in silico assessment.

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.

Compensatory erythropoiesis has no impact on the outcome of the in vivo Pig-a mutation assay in rats following treatment with the haemolytic agent 2-butoxyethanol.

The Pig-a assay has rapidly gained international interest as a useful tool for assessing the mutagenic potential of compounds in vivo. Although a large number of compounds, including both mutagens and non-mutagens, have been tested in the rat Pig-a assay in haematopoietic cells, there is limited understanding of how perturbations in haematopoiesis affect assay performance. Of particular concern is the possibility that regenerative haematopoiesis alone, without exposure to a genotoxic agent, could result in elevated Pig-a mutant cell frequencies. To address this concern, Wistar-Han rats were dosed by oral gavage with a non-genotoxic haemolytic agent, 2-butoxyethanol (2-BE). Dose levels ranging from 0 to 450 mg/kg were tested using both single administration and 28-day treatment regimens. Haematology parameters were assessed at minimum within the first 24h of treatment and 8 days after the final administration. Pig-a mutant frequencies were assessed on Days 15 and ~30 for both treatment protocols and also on Days 43 and 57 for the 28-day protocol. Even at doses of 2-BE that induced marked intravascular lysis and strong compensatory erythropoiesis, the average Pig-a mutant phenotype red blood cell and reticulocyte frequencies were within the historical vehicle control distribution. 2-BE therefore showed no evidence of in vivo mutagenicity in these studies. The data suggest that perturbations in haematopoiesis alone do not lead to an observation of increased mutant frequency in the Pig-a assay.

A practical application of two in silico systems for identification of potentially mutagenic impurities.

The International Conference on Harmonization (ICH) M7 guidance for the assessment and control of DNA reactive impurities in pharmaceutical products includes the use of in silico prediction systems as part of the hazard identification and risk assessment strategy. This is the first internationally agreed guidance document to include the use of these types of approaches. The guideline requires the use of two complementary approaches, an expert rule-based method and a statistical algorithm. In addition, the guidance states that the output from these computer-based assessments can be reviewed using expert knowledge to provide additional support or resolve conflicting predictions. This approach is designed to maximize the sensitivity for correctly identifying DNA reactive compounds while providing a framework to reduce the number of compounds that need to be synthesized, purified and subsequently tested in an Ames assay. Using a data set of 801 chemicals and pharmaceutical intermediates, we have examined the relative predictive performances of some popular commercial in silico systems that are in common use across the pharmaceutical industry. The overall accuracy of each of these systems was fairly comparable ranging from 68% to 73%; however, the sensitivity of each system (i.e. how many Ames positive compounds are correctly identified) varied much more dramatically from 48% to 68%. We have explored how these systems can be combined under the ICH M7 guidance to enhance the detection of DNA reactive molecules. Finally, using four smaller sets of molecules, we have explored the value of expert knowledge in the review process, especially in cases where the two systems disagreed on their predictions, and the need for care when evaluating the predictions for large data sets.

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.

Determination of compound-specific acceptable daily intakes for 11 mutagenic carcinogens used in pharmaceutical synthesis.

The synthesis of pharmaceutical products often involves the use of reactive starting materials and intermediates. Low levels may be present in the final product as impurities and of particular concern are impurities that have mutagenic and carcinogenic potential. Regulatory guidance documents provide a general framework to minimise human exposure to these impurities; however, compound-specific recommendations are limited. Our practical experience with 11 pharmaceutical impurities is presented. The genotoxicity and carcinogenicity data are summarised and the approach used to derive an acceptable daily intake (ADI) is described for each chemical. We have highlighted the considerations and challenges associated with calculating ADIs based on available carcinogenicity data. This may provide a useful reference to others in the pharmaceutical industry regarding impurity control, where the weight of evidence indicates the chemical is a mutagenic carcinogen.

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.

Development and validation of an in vitro micronucleus assay platform in TK6 cells.

The Organization for Economic Co-operation and Development (OECD) has recently adopted Test Guideline 487 (TG487) for conducting the in vitro micronucleus (MNvit) assay. The purpose of this study is to evaluate and validate treatment conditions for the use of p53 competent TK6 human lymphoblastoid cells in a TG487 compliant MNvit assay. The ten reference compounds suggested in TG487 (mitomycin C, cytosine arabinoside, cyclophosphamide, benzo-a-pyrene, vinblastine sulphate, colchicine, sodium chloride, nalidixic acid and di(2-ethylhexyl)phthalate and pyrene) and noscapine hydrochloride were chosen for this study. In order to optimize the micronucleus response after treatment with some positive substances, we extended the recovery time after pulse treatment from 2 cell cycles recommended in TG487 to 3 cell cycles for untreated cells (40h). Each compound was tested in at least one of four exposure conditions: a 4h exposure followed by a 40h recovery, a 4h exposure followed by a 24h recovery, a 4h exposure in the presence of an exogenous metabolic activation system followed by a 40h recovery period, and a 27h continuous direct treatment. Results show that the direct acting clastogens, clastogens requiring metabolic activation and aneugens caused a robust increase in micronuclei in at least one test condition whereas the negative compounds did not induce micronuclei. The negative control cultures exhibited reproducibly low and consistent micronucleus frequencies ranging from 0.4 to 1.8% (0.8±0.3% average and standard deviation). Furthermore, extending the recovery period from 24h to 40h produced a 2-fold higher micronucleus frequency after a 4h pulse treatment with mitomycin C. In summary, the protocol described in this study in TK6 cells produced the expected result with model compounds and should be suitable for performing the MNvit assay in accordance with guideline TG487.

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.

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.

Defining EMS and ENU dose-response relationships using the Pig-a mutation assay in rats.

In recent years, experimental evidence has accumulated that supports the existence of sublinear dose-response relationships at low doses of DNA reactive mutagens. However, creating the in vivo data necessary to allow for a more detailed dose-response modeling with the currently available tools might not always be practical. The purpose of the current work was to evaluate the utility of the Pig-a gene mutation assay to rapidly identify dose-response relationships for direct acting genotoxicants. The induction of mutations in the peripheral blood of rats was evaluated following 28 days of exposure down to low doses of the direct acting alkylating agents ethyl methane sulfonate (EMS) and ethylnitrosourea (ENU). Using statistical modeling based on the 28-day studies, a threshold for mutation induction for EMS was estimated to be 21.9mg/kg, whereas for the more potent ENU, the threshold was estimated to be 0.88mg/kg. Comparing mutation frequencies from acute and sub-chronic dosing indicated less than additive dose-response relationships, further confirming the possibility of a threshold dose-response relationship for both compounds. In conclusion, the work presented provides evidence that the Pig-a assay might be a practical alternative to other in vivo mutation assays when assessing dose-response relationships for direct acting mutagens and that an experimental approach using fractionated dosing could be used to substantiate a biological mechanism responsible for the observation of a sublinear dose-response relationship.

Permitted daily exposure limits for noteworthy N-nitrosamines.

A genotoxic carcinogen, N-nitrosodimethylamine (NDMA), was detected as a synthesis impurity in some valsartan drugs in 2018, and other N-nitrosamines, such as N-nitrosodiethylamine (NDEA), were later detected in other sartan products. N-nitrosamines are pro-mutagens that can react with DNA following metabolism to produce DNA adducts, such as O6 -alkyl-guanine. The adducts can result in DNA replication miscoding errors leading to GC>AT mutations and increased risk of genomic instability and carcinogenesis. Both NDMA and NDEA are known rodent carcinogens in male and female rats. The DNA repair enzyme, methylguanine DNA-methyltransferase can restore DNA integrity via the removal of alkyl groups from guanine in an error-free fashion and this can result in nonlinear dose responses and a point of departure or "practical threshold" for mutation at low doses of exposure. Following International recommendations (ICHM7; ICHQ3C and ICHQ3D), we calculated permissible daily exposures (PDE) for NDMA and NDEA using published rodent cancer bioassay and in vivo mutagenicity data to determine benchmark dose values and define points of departure and adjusted with appropriate uncertainty factors (UFs). PDEs for NDMA were 6.2 and 0.6 µg/person/day for cancer and mutation, respectively, and for NDEA, 2.2 and 0.04 µg/person/day. Both PDEs are higher than the acceptable daily intake values (96 ng for NDMA and 26.5 ng for NDEA) calculated by regulatory authorities using simple linear extrapolation from carcinogenicity data. These PDE calculations using a bench-mark approach provide a more robust assessment of exposure limits compared with simple linear extrapolations and can better inform risk to patients exposed to the contaminated sartans.

A strategy for the risk assessment of human genotoxic metabolites.

The role of metabolism in genotoxicity and carcinogenicity of many chemicals is well established. Accordingly, both in vitro metabolic activation systems and in vivo assays are routinely utilized for genotoxic hazard identification of drug candidates prior to clinical investigations. This should, in most cases provide a high degree of confidence that the genotoxic potential of the parent and associated metabolites have been characterized. However, it is well known that significant differences can exist between human metabolism and that which occurs with in vitro and in vivo genotoxicity tests. This poses challenges when considering the adequacy of hazard identification and cancer risk assessment if a human metabolite of genotoxic concern is identified during the course of drug development. Since such challenges are particularly problematic when recognized in the later stages of drug development, a framework for conducting a carcinogenic risk assessment for human genotoxic metabolites is desirable. Here, we propose a risk assessment method that is dependent upon the availability of quantitative human and rodent ADME (absorption, distribution, metabolism, excretion) data, such that exposures to a metabolite of genotoxic concern can be estimated at the intended human efficacious dose and the maximum dose used in the 2-year rodent bioassay(s). The exposures are then applied to the risk assessment framework, based on known cancer potencies, that allows one to understand the probability of a known or suspect genotoxic metabolite posing a carcinogenic risk in excess of 1 in 100,000. Practical case examples are presented to illustrate the application of the risk assessment method within the context of drug development and to highlight its utility and limitations.