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.

A deep dive into historical Ames study data for N-nitrosamine compounds.

Mutagenicity data is a core component of the safety assessment data required by regulatory agencies for acceptance of new drug compounds, with the OECD-471 bacterial reverse mutation (Ames) assay most widely used as a primary screen to assess drug impurities for potential mutagenic risk. N-Nitrosamines are highly potent mutagenic carcinogens in rodent bioassays and their recent detection as impurities in pharmaceutical products has sparked increased interest in their safety assessment. Previous literature reports indicated that the Ames test might not be sensitive enough to detect the mutagenic potential of N-nitrosamines in order to accurately predict a risk of carcinogenicity. To explore this hypothesis, public Ames and rodent carcinogenicity data pertaining to the N-nitrosamine class of compounds was collated for analysis. Here we present how variations to the OECD 471-compliant Ames test, including strain, metabolic activation, solvent type and pre-incubation/plate incorporation methods, may impact the predictive performance for carcinogenicity. An understanding of optimal conditions for testing of N-nitrosamines may improve both the accuracy and confidence in the ability of the Ames test to identify potential carcinogens.

What Makes a Potent Nitrosamine? Statistical Validation of Expert-Derived Structure-Activity Relationships.

The discovery of carcinogenic nitrosamine impurities above the safe limits in pharmaceuticals has led to an urgent need to develop methods for extending structure-activity relationship (SAR) analyses from relatively limited datasets, while the level of confidence required in that SAR indicates that there is significant value in investigating the effect of individual substructural features in a statistically robust manner. This is a challenging exercise to perform on a small dataset, since in practice, compounds contain a mixture of different features, which may confound both expert SAR and statistical quantitative structure-activity relationship (QSAR) methods. Isolating the effects of a single structural feature is made difficult due to the confounding effects of other functionality as well as issues relating to determining statistical significance in cases of concurrent statistical tests of a large number of potential variables with a small dataset; a naïve QSAR model does not predict any features to be significant after correction for multiple testing. We propose a variation on Bayesian multiple linear regression to estimate the effects of each feature simultaneously yet independently, taking into account the combinations of features present in the dataset and reducing the impact of multiple testing, showing that some features have a statistically significant impact. This method can be used to provide statistically robust validation of expert SAR approaches to the differences in potency between different structural groupings of nitrosamines. Structural features that lead to the highest and lowest carcinogenic potency can be isolated using this method, and novel nitrosamine compounds can be assigned into potency categories with high accuracy.

Updating the Dermal Sensitisation Thresholds using an expanded dataset and an in silico expert system.

The Dermal Sensitisation Thresholds (DST) are Thresholds of Toxicological Concern, which can be used to justify exposure-based waiving when conducting a skin sensitisation risk assessment. This study aimed to update the published DST values by expanding the size of the Local Lymph Node Assay dataset upon which they are based, whilst assigning chemical reactivity using an in silico expert system (Derek Nexus). The potency values within the expanded dataset fitted a similar gamma distribution to that observed for the original dataset. Derek Nexus was used to classify the sensitisation activity of the 1152 chemicals in the expanded dataset and to predict which chemicals belonged to a High Potency Category (HPC). This two-step classification led to three updated thresholds: a non-reactive DST of 710 µg/cm2 (based on 79 sensitisers), a reactive (non-HPC) DST of 73 µg/cm2 (based on 331 sensitisers) and an HPC DST of 1.0 µg/cm2 (based on 146 sensitisers). Despite the dataset containing twice as many sensitisers, these values are similar to the previously published thresholds, highlighting their robustness and increasing confidence in their use. By classifying reactivity in silico the updated DSTs can be applied within a skin sensitisation risk assessment in a reproducible, scalable and accessible manner.

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.

Are all nitrosamines concerning? A review of mutagenicity and carcinogenicity data.

The control of potentially mutagenic impurities in pharmaceutical products is of key importance in assessing carcinogenic risk to humans. The recent discovery of nitrosamine impurities in several marketed pharmaceuticals has increased interest in their mutagenic and carcinogenic potential. This chemical class is considered part of a 'cohort of concern', indicating that standard control protocols, such as the use of a threshold of toxicological concern (TTC), cannot be applied. Whilst some nitrosamines are known to be exceptionally potent carcinogens, it's not clear whether this is a property of all members of the class. To investigate the mutagenic and carcinogenic potential of nitrosamines, data was extracted from published literature to augment that already present in the Vitic and Lhasa Carcinogenicity Databases. This data was analysed to assess the application of the ICH M7 guideline to nitrosamine impurities, with respect to the predictivity of the Ames test for carcinogenic potential and the distribution of carcinogenic potency. It was found that 18% of nitrosamines were considered non-carcinogenic. Nitrosamines showed a greater correlation between mutagenicity and carcinogenicity compared to non-nitrosamine compounds. Whilst nitrosamines, in general, are more potent carcinogens than non-nitrosamines, there is a significant overlap between the two distributions of TD50s for each class.

Extrapolation of in vitro structural alerts for mutagenicity to the in vivo endpoint.

As part of the hazard and risk assessment of chemicals in man, it is important to assess the ability of a chemical to induce mutations in vivo. Because of the commonalities in the molecular initiating event, mutagenicity in vitro can correlate well to the in vivo endpoint for certain compound classes; however, the difficulty lies in identifying when this correlation holds true. In silico alerts for in vitro mutagenicity may therefore be used as the basis for alerts for mutagenicity in vivo where an expert assessment is carried out to establish the relevance of the correlation. Taking this into account, a data set of publicly available transgenic rodent gene mutation assay data, provided by the National Institute of Health Sciences of Japan, was processed in the expert system Derek Nexus against the in vitro mutagenicity endpoint. The resulting predictivity was expertly reviewed to assess the validity of the observed correlations in activity and mechanism of action between the two endpoints to identify suitable in vitro alerts for extension to the in vivo endpoint. In total, 20 alerts were extended to predict in vivo mutagenicity, which has significantly improved the coverage of this endpoint in Derek Nexus against the data set provided. Updating the Derek Nexus knowledge base in this way led to an increase in sensitivity for this data set against this endpoint from 9% to 66% while maintaining a good specificity of 89%.

A quantitative in silico model for predicting skin sensitization using a nearest neighbours approach within expert-derived structure-activity alert spaces.

Dermal contact with chemicals may lead to an inflammatory reaction known as allergic contact dermatitis. Consequently, it is important to assess new and existing chemicals for their skin sensitizing potential and to mitigate exposure accordingly. There is an urgent need to develop quantitative non-animal methods to better predict the potency of potential sensitizers, driven largely by European Union (EU) Regulation 1223/2009, which forbids the use of animal tests for cosmetic ingredients sold in the EU. A Nearest Neighbours in silico model was developed using an in-house dataset of 1096 murine local lymph node (LLNA) studies. The EC3 value (the effective concentration of the test substance producing a threefold increase in the stimulation index compared to controls) of a given chemical was predicted using the weighted average of EC3 values of up to 10 most similar compounds within the same mechanistic space (as defined by activating the same Derek skin sensitization alert). The model was validated using previously unseen internal (n = 45) and external (n = 103) data and accuracy of predictions assessed using a threefold error, fivefold error, European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) and Globally Harmonized System of Classification and Labelling of Chemicals (GHS) classifications. In particular, the model predicts the GHS skin sensitization category of compounds well, predicting 64% of chemicals in an external test set within the correct category. Of the remaining chemicals in the previously unseen dataset, 25% were over-predicted (GHS 1A predicted: GHS 1B experimentally) and 11% were under-predicted (GHS 1B predicted: GHS 1A experimentally). Copyright © 2017 John Wiley & Sons, Ltd.

Use of Lhasa Limited Products for the In Silico Prediction of Drug Toxicity.

Lhasa Limited have had a role in the in silico prediction of drug and other chemical toxicity for over 30 years. This role has always been multifaceted, both as a provider of predictive software such as Derek Nexus, and as an honest broker for the sharing of proprietary chemical and toxicity data. A changing regulatory environment and the drive for the Replacement, Reduction and Refinement (the 3Rs) of animal testing have led both to increased acceptance of in silico predictions and a desire for the sharing of data to reduce duplicate testing. The combination of these factors has led to Lhasa Limited providing a suite of products and coordinating numerous data-sharing consortia that do indeed facilitate a significant reduction in the testing burden that companies would otherwise be laboring under. Many of these products and consortia can be organized into workflows for specific regulatory use cases, and it is these that will be used to frame the narrative in this chapter.

The importance of expert review to clarify ambiguous situations for (Q)SAR predictions under ICH M7.

The use of in silico predictions for the assessment of bacterial mutagenicity under the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 guideline is recommended when two complementary (quantitative) structure-activity relationship (Q)SAR models are used. Using two systems may increase the sensitivity and accuracy of predictions but also increases the need to review predictions, particularly in situations where results disagree. During the 4th ICH M7/QSAR Workshop held during the Joint Meeting of the 6th Asian Congress on Environmental Mutagens (ACEM) and the 48th Annual Meeting of the Japanese Environmental Mutagen Society (JEMS) 2019, speakers demonstrated their approaches to expert review using 20 compounds provided ahead of the workshop that were expected to yield ambiguous (Q)SAR results. Dr. Chris Barber presented a selection of the reviews carried out using Derek Nexus and Sarah Nexus provided by Lhasa Limited. On review of these compounds, common situations were recognised and are discussed in this paper along with standardised arguments that may be used for such scenarios in future.