Predicting DNA-Reactivity of N-Nitrosamines: A Quantum Chemical Approach

Tale of Three N-Nitrosamines and the Variables Needed to Assess Their Carcinogenicity In Silico Incorporated into a Single Workflow

N-Nitrosamine impurities in pharmaceuticals present a considerable challenge for regulators and industry alike, where the absence of carcinogenic-potency studies has left a gap that must be adequately filled to protect public health. In the interim, this means balancing risk assessment with the necessity to continue research, development, and supply of pharmaceuticals. In the long term, we need a cost-effective solution that optimizes both. As if beholden to Newton's Third Law, every crisis breeds an opportunity of equal magnitude. Consequently, cross-industry consortia have been racing to find a solution by advancing our current science. Recent spotlight has been on in silico tools, as a fast and increasingly reliable alternative to in vivo and in vitro testing. Because N-nitrosamine bioactivation lends itself uniquely to quantum mechanics (QM) approaches, the integration of electronic-structure considerations has emerged as the dominant in silico approach. This signifies a considerable leap in predictive toxicology, which has, for much of its existence, relied on atomistic (quantitative) structure-activity relationships, i.e., (Q)SARs. Here we present a validation of an integrated docking-QM approach within the CADRE program and demonstrate its utility on three different impurities, N-nitroso-7-monomethylamino-6-deoxytetracycline, N-nitroso-dabigatran etexilate, and 1-methyl-4-nitrosopiperazine. We show that a combined in silico strategy, which considers bioavailability, transport, cytochrome P450 binding, and reactivity, can be leveraged to supplement the overly conservative Carcinogenic Potency Categorization Approach (CPCA) in setting the daily acceptable intake (AI) using defensible, highly mechanistic, and quantitative drivers of N-nitrosamine metabolism. To that end, we argue that while N-nitroso-7-monomethylamino-6-deoxytetracycline and 1-methyl-4-nitrosopiperazine are cohort-of-concern impurities, N-nitroso-dabigatran etexilate is not a potent carcinogen (TD50 > 1.5 mg/kg/day), contrasting the CPCA-derived AI. Lastly, we discuss how the CADRE tool can be integrated with the broader landscape of QM methods and the CPCA into a single harmonized in silico strategy for carcinogenicity assessment of N-nitrosamine impurities.

HESI GTTC ring trial: Concordance between Ames and rodent carcinogenicity outcomes for N-nitrosamines (NAs) with rat and hamster metabolic conditions

A multi-sector study (i.e., Ring Trial) was designed to improve the in vitro detection of N-nitrosamine (NA)-associated mutagenicity by optimizing the bacterial reverse mutation (i.e., Ames) assay protocol and testing various conditions on the sensitivity and specificity for the prediction of rodent carcinogenicity. A total of 29 NAs and 3 N-nitroso drug-like compounds from different structural classes and carcinogenicity outcomes were tested (two independent laboratories per compound) across 5 bacterial strains using a 30-min pre-incubation protocol. To evaluate the impact of different metabolic activating systems (MASs), testing conditions included the use of 10 or 30 % liver S9 fractions prepared from rats or hamsters pretreated with inducers of enzymatic activity. Results indicate that E. coli and Salmonella typhimurium strains detecting single base pair mutations, coupled with MASs containing 30 % hamster S9s were the most sensitive (90 %) for identifying NAs that are rodent carcinogens. Regarding MAS combinations, the highest sensitivity was 30 % rat and 30 % hamster (93 %), but has low specificity (45 %), with good laboratory agreement for the Ames calls (91 %). DMSO and water were considered suitable solvents, except for small-molecular weight alkyl NAs. These results will support harmonized Ames testing of NAs, giving high confidence for a negative result.

Insight into the molecular initiating event of mutagenic N-nitrosamines: a computational study on DNA alkylation by their diazonium ions

N-Nitrosamines are a class of compounds that includes the potent mutagenicity and carcinogenicity of many of its members and is distributed widely throughout the human environment. DNA alkylation by their diazonium ions formed metabolically acts as a molecular initiating event (MIE) that links molecular chemistry to mutagenicity. However, the regiochemistry for diazonium ions reacting with DNA bases is still under debate. Hence, density functional theory calculations involving SN2 alkylation of guanine (Gua) by 14 diverse diazonium ions are presented, the results of which showed the mutagenicity-related shift from GuaN7- to GuaO6-alkylation proceeds by increasing complexity of the alkylating agents, along with a greater proportion of SN1 characteristic in SN2 transition states. Hence, "high oxyphilic" and "low oxyphilic" alkylating agents may instead be "SN1" and "SN2" species, respectively. As the degree of MIE selectivity for hard-hard interactions can be quantified by hard and soft acids and bases theory, quantitative relationships were modeled between the nucleophilic index (ω-) and hydrophobicity (log P) of diazonium ions and their carcinogenic potency. Therefore, the mechanistic link from MIE to target toxicity can be bridged by computational chemistry.

Quantum Chemical Evaluation and QSAR Modeling of N-Nitrosamine Carcinogenicity

N-Nitrosamine compounds in pharmaceuticals are a major concern due to their carcinogenic potential. However, not all nitrosamines are strong carcinogens, and understanding the structure-activity relationships of this compound group is a major challenge. The determination of the acceptable intake limits for this compound group is determined by applying either a simple carcinogenic potency categorization approach (CPCA) or read-across analysis from simple nitrosamines where experimental data exist. However, the emergence of structurally complex nitrosamines makes quantitative models desirable. Here, we present a two-step modeling approach based on a linear discriminant analysis of a set of quantum mechanical and classical descriptors followed by a 3D-QSAR PLS regression model to predict the logTD50 of nitrosamine compounds.

N-Nitrosamine drug substance related impurities (NDSRIs) - A proposal for the addition of subcategories to carcinogenic potency categorization approach categories 1 and 2 for NDSRIs with a molecular weight > 200 Da

The carcinogenicity potency categorization approach (CPCA) derived and harmonized by Health Authorities was a significant milestone, as it defined molecular properties that allowed for the rapid evaluation of the chemical structures of N-nitrosamine drug substance related impurities (NDSRIs) and the assignment of associated lifetime Acceptable Intake (AI) limits to inform on appropriate impurity control strategies in certain drug products. Nonetheless, it is important to continue to refine and improve on the CPCA based upon data-derived evidence. Herein, we focus on the default CPCA AI for NDSRIs, which is largely based on the small molecule N-nitrosamines (NAs). Considering the carcinogenic potency of NAs with a molecular weight >200 Da (NDSRIs molecular weight is typically 200-600 Da), we propose that in the absence of any compound specific data, the lowest lifetime Acceptable Intake for NAs, such as NDSRIs, should be 10x less (i.e., 150 ng/day) than the ICH M7 Threshold of Toxicological Concern of 1500 ng/day, (even for NDSRIs that are considered CPCA Category 1 and 2) which would conservatively result in a theoretical cancer risk of <1 in 100,000.

Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning

This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure-activity relationships (RASAR). Molecular similarity-based computational tools, such as quantitative structure-activity relationships (QSARs) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties, and biological aspects are being characterized. More recent developments of RA's integration with QSAR have resulted in the emergence of novel models such as ToxRead, generalized read-across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context.

Analytical Methodologies to Detect N-Nitrosamine Impurities in Active Pharmaceutical Ingredients, Drug Products and Other Matrices

Since 2018, N-nitrosamine impurities have become a widespread concern in the global regulatory landscape of pharmaceutical products. This concern arises due to their potential for contamination, toxicity, carcinogenicity, and mutagenicity and their presence in many active pharmaceutical ingredients, drug products, and other matrices. N-Nitrosamine impurities in humans can lead to severe chemical toxicity effects. These include carcinogenic effects, metabolic disruptions, reproductive harm, liver diseases, obesity, DNA damage, cell death, chromosomal alterations, birth defects, and pregnancy loss. They are particularly known to cause cancer (tumors) in various organs and tissues such as the liver, lungs, nasal cavity, esophagus, pancreas, stomach, urinary bladder, colon, kidneys, and central nervous system. Additionally, N-nitrosamine impurities may contribute to the development of Alzheimer's and Parkinson's diseases and type-2 diabetes. Therefore, it is very important to control or avoid them by enhancing effective analytical methodologies using cutting-edge analytical techniques such as LC-MS, GC-MS, CE-MS, SFC, etc. Moreover, these analytical methods need to be sensitive and selective with suitable precision and accuracy, so that the actual amounts of N-nitrosamine impurities can be detected and quantified appropriately in drugs. Regulatory agencies such as the US FDA, EMA, ICH, WHO, etc. need to focus more on the hazards of N-nitrosamine impurities by providing guidance and regular updates to drug manufacturers and applicants. Similarly, drug manufacturers should be more vigilant to avoid nitrosating agents and secondary amines during the manufacturing processes. Numerous review articles have been published recently by various researchers, focusing on N-nitrosamine impurities found in previously notified products, including sartans, metformin, and ranitidine. These impurities have also been detected in a wide range of other products. Consequently, this review aims to concentrate on products recently reported to contain N-nitrosamine impurities. These products include rifampicin, champix, famotidine, nizatidine, atorvastatin, bumetanide, itraconazole, diovan, enalapril, propranolol, lisinopril, duloxetine, rivaroxaban, pioglitazones, glifizones, cilostazol, and sunitinib.

Estimation of acceptable daily intake values based on modeling and in vivo mutagenicity of NDSRIs of fluoxetine, duloxetine and atomoxetine

Nitrosamine drug substance related impurities or NDSRIs can be formed if an active pharmaceutical ingredient (API) has an intrinsic secondary amine that can undergo nitrosation. This is a concern as 1) nitrosamines are potentially highly potent carcinogens, 2) secondary amines in API are common, and 3) NDSRIs that might form from such secondary amines will be of unknown carcinogenic potency. Approaches for evaluating NDSRIs include read across, quantum mechanical modeling of reactivity, in vitro mutation data, and transgenic in vivo mutation data. These approaches were used here to assess NDSRIs that could potentially form from the drugs fluoxetine, duloxetine and atomoxetine. Based on a read across informed by modeling of physicochemical properties and mechanistic activation from quantum mechanical modeling, NDSRIs of fluoxetine, duloxetine, and atomoxetine were 10-100-fold less potent compared with highly potent nitrosamines such as NDMA or NDEA. While the NDSRIs were all confirmed to be mutagenic in vitro (Ames assay) and in vivo (TGR) studies, the latter data indicated that the potency of the mutation response was ≥4400 ng/day for all compounds-an order of magnitude higher than published regulatory limits for these NDSRIs. The approaches described herein can be used qualitatively to better categorize NDSRIs with respect to potency and inform whether they are in the ICH M7 (R2) designated Cohort of Concern.

A Kinetic Model for Assessing Potential Nitrosamine Carcinogenicity

Understanding the potential carcinogenic potency of nitrosamines is necessary to setting acceptable intake limits. Nitrosamines and the components that can form them are commonly present in food, water, cosmetics, and tobacco. The recent observation of nitrosamines in pharmaceuticals highlighted the need for effective methods to determine acceptable intake limits. Herein, we describe two computational models that utilize properties based upon quantum mechanical calculations in conjunction with mechanistic insights and established data to determine the carcinogenic potency of a variety of common nitrosamines. These models can be applied to experimentally untested nitrosamines to aid in the establishment of acceptable intake limits.

Quantum chemical calculations of nitrosamine activation and deactivation pathways for carcinogenicity risk assessment

N-nitrosamines and nitrosamine drug substance related impurities (NDSRIs) became a critical topic for the development and safety of small molecule medicines following the withdrawal of various pharmaceutical products from the market. To assess the mutagenic and carcinogenic potential of different N-nitrosamines lacking robust carcinogenicity data, several approaches are in use including the published carcinogenic potency categorization approach (CPCA), the Enhanced Ames Test (EAT), in vivo mutagenicity studies as well as read-across to analogue molecules with robust carcinogenicity data. We employ quantum chemical calculations as a pivotal tool providing insights into the likelihood of reactive ion formation and subsequent DNA alkylation for a selection of molecules including e.g., carcinogenic N-nitrosopiperazine (NPZ), N-nitrosopiperidine (NPIP), together with N-nitrosodimethylamine (NDMA) as well as non-carcinogenic N-nitrosomethyl-tert-butylamine (NTBA) and bis (butan-2-yl) (nitros)amine (BBNA). In addition, a series of nitroso-methylaminopyridines is compared side-by-side. We draw comparisons between calculated reaction profiles for structures representing motifs common to NDSRIs and those of confirmed carcinogenic and non-carcinogenic molecules with in vivo data from cancer bioassays. Furthermore, our approach enables insights into reactivity and relative stability of intermediate species that can be formed upon activation of several nitrosamines. Most notably, we reveal consistent differences between the free energy profiles of carcinogenic and non-carcinogenic molecules. For the former, the intermediate diazonium ions mostly react, kinetically controlled, to the more stable DNA adducts and less to the water adducts via transition-states of similar heights. Non-carcinogenic molecules yield stable carbocations as intermediates that, thermodynamically controlled, more likely form the statistically preferred water adducts. In conclusion, our data confirm that quantum chemical calculations can contribute to a weight of evidence approach for the risk assessment of nitrosamines.

Evaluation of the nitrosamine impurities of ACE inhibitors using computational, in vitro, and in vivo methods demonstrate no genotoxic potential

Evaluation and mitigation of the potential carcinogenic risks associated with nitrosamines in marketed pharmaceutical products are areas of interest for pharmaceutical companies and health authorities alike. Significant progress has been made to establish acceptable intake (AI) levels for N-nitrosamine drug substance-related impurities (NDSRIs) using SAR, however some compounds require experimental data to support derivation of a recommended AI. Many angiotensin-converting enzyme inhibitors, identified by the suffix "pril," have secondary amines that can potentially react to form nitrosamines. Here we consider a structural assessment and metabolism data, coupled with comprehensive in vitro and in vivo (mouse) genotoxicity testing to evaluate this particular class of nitrosamines. N-nitroso ramipril and N-nitroso quinapril, both of which are predicted to have inhibited nitrosamine bioactivation due to steric hinderance and branching at the α-position were non-genotoxic in the in vivo liver comet assay and non-mutagenic in the in vivo Big Blue® mutation and duplex sequencing assays. Predicted metabolism along with in vitro metabolism data and quantum chemical calculations related to DNA interactions offer a molecular basis for the negative results observed in both in vitro and in vivo testing. These nitrosamines are concluded to be non-mutagenic and non-carcinogenic; therefore, they should be controlled according to ICH Q3B guidance. Furthermore, these results for N-nitroso ramipril and N-nitroso quinapril should be considered when evaluating the appropriate AI and control strategy for other structurally similar "pril" NDSRIs.

Quantum Mechanical Assessment of Nitrosamine Potency

Nitrosamines are in the cohort of concern (CoC) as determined by regulatory guidance. CoC compounds are considered highly potent carcinogens that need to be limited below the threshold of toxicological concern, 1.5 μg/day. Nitrosamines like NDMA and NDEA require strict control, while novel nitrosamine drug substance-related impurities (NDSRIs) may or may not be characterized as potent carcinogens. A risk assessment based on the structural features of NDSRIs is important in order to predict potency because they lack substance-specific carcinogenicity. Herein, we present a quantum mechanical (QM)-based analysis on structurally diverse sets of nitrosamines to better understand how structure influences the reactivity that could result in carcinogenicity. We describe the potency trend through activation energies corresponding to α-hydroxylation, aldehyde formation, diazonium intermediate formation, reaction with DNA base, and hydrolysis reactions, and other probable metabolic pathways associated with the carcinogenicity of nitrosamines. We evaluated activation energies for selected cases such as N-nitroso pyrrolidines, N-nitroso piperidines, N-nitroso piperazines, N-nitroso morpholines, N-nitroso thiomorpholine, N-methyl nitroso aromatic, fluorine-substituted nitrosamines, and substituted aliphatic nitrosamines. We compare these results to the recent framework of the carcinogenic potency characterization approach (CPCA) proposed by health authorities which is meant to give guidance on acceptable intakes (AI) for NDSRIs lacking substance-specific carcinogenicity data. We show examples where QM modeling and CPCA are aligned and examples where CPCA both underestimates and overestimates the AI. In cases where CPCA predicts high potency for NDSRIs, QM modeling can help better estimate an AI. Our results suggest that a combined mechanistic understanding of α-hydroxylation, aldehyde formation, hydrolysis, and reaction with DNA bases could help identify the structural features that underpin the potency of nitrosamines. We anticipate this work will be a valuable addition to the CPCA and provide a more analytical way to estimate AI for novel NDSRIs.

Determining recommended acceptable intake limits for N-nitrosamine impurities in pharmaceuticals: Development and application of the Carcinogenic Potency Categorization Approach (CPCA)

N-Nitrosamine impurities, including nitrosamine drug substance-related impurities (NDSRIs), have challenged pharmaceutical industry and regulators alike and affected the global drug supply over the past 5 years. Nitrosamines are a class of known carcinogens, but NDSRIs have posed additional challenges as many lack empirical data to establish acceptable intake (AI) limits. Read-across analysis from surrogates has been used to identify AI limits in some cases; however, this approach is limited by the availability of robustly-tested surrogates matching the structural features of NDSRIs, which usually contain a diverse array of functional groups. Furthermore, the absence of a surrogate has resulted in conservative AI limits in some cases, posing practical challenges for impurity control. Therefore, a new framework for determining recommended AI limits was urgently needed. Here, the Carcinogenic Potency Categorization Approach (CPCA) and its supporting scientific rationale are presented. The CPCA is a rapidly-applied structure-activity relationship-based method that assigns a nitrosamine to 1 of 5 categories, each with a corresponding AI limit, reflecting predicted carcinogenic potency. The CPCA considers the number and distribution of α-hydrogens at the N-nitroso center and other activating and deactivating structural features of a nitrosamine that affect the α-hydroxylation metabolic activation pathway of carcinogenesis. The CPCA has been adopted internationally by several drug regulatory authorities as a simplified approach and a starting point to determine recommended AI limits for nitrosamines without the need for compound-specific empirical data.

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., β-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.

Unlocking The Mysteries of DNA Adducts with Artificial Intelligence

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.

Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

The use of quantum mechanics (QM) has long been the norm to study covalent-binding phenomena in chemistry and biochemistry. The pharmaceutical industry leverages QM models explicitly in covalent drug discovery and implicitly to characterize short-range interactions in noncovalent binding. Predictive toxicology has resisted widespread adoption of QM, including in the pharmaceutical industry, despite its obvious relevance to the metabolic processes in the upstream of adverse outcome pathways and advances in both QM methods and computational resources, which support fit-for-purpose applications in reasonable timeframes. Here, we make the case for embracing QM as an indispensable part of a toxicologist's toolkit. We argue that QM provides the necessary orthogonality to alert-based expert systems and traditional QSARs, consistent with calls for animal-free integrated testing strategies for safety assessments of commercial chemicals. We outline existing roadblocks to this transition, including the need to train model developers in QM and the shift toward service-based toxicity models that utilize high-performance computing clusters. Lastly, we describe recent examples of successful implementations of QM in hazard assessments and propose how in silico toxicology can be further advanced by integrating QM with artificial intelligence.

Maximizing use of existing carcinogenicity data to support acceptable intake levels for mutagenic impurities in pharmaceuticals: Learnings from N-nitrosamine case studies

The unexpected finding of N-nitrosamine (NA) impurities in many pharmaceutical products raised significant challenges for industry and regulators. In addition to well-studied small molecular weight NAs, many of which are potent rodent carcinogens, novel NAs associated with active pharmaceutical ingredients have been found, many of which have limited or no safety data. A tiered approach to establishing Acceptable Intake (AI) limits for NA impurities has been established using chemical-specific data, read-across, or a class-specific TTC limit. There are ∼140 NAs with some rodent carcinogenicity data, but much of it is older and does not meet current guidelines for what constitutes a 'robust' bioassay. Nevertheless, these data are an important source of information to ensure the best science is used for assessing NA impurities and assuring consumer safety while minimizing impact that can lead to drug shortages. We present several strategies to maximize the use of imperfect data including using a lower confidence limit on a rodent TD50, and leveraging data from multiple NAs. Information on the chemical structure known to impact potency can also support development of an AI or potentially conclude that a particular NA does not fall in the cohort of concern for potent carcinogenicity.

Record-High Ultrasound-Sensitive NO Nanogenerators for Cascade Tumor Pyroptosis and Immunotherapy

Pyroptosis is a pro-inflammatory cell death that is associated with innate immunity promotion against tumors. Excess nitric oxide (NO)-triggered nitric stress has potential to induce pyroptosis, but the precise delivery of NO is challenging. Ultrasound (US)-responsive NO production has dominant priority due to its deep penetration, low side effects, noninvasion, and local activation manner. In this work, US-sensitive NO donor N-methyl-N-nitrosoaniline (NMA) with thermodynamically favorable structure is selected and loaded into hyaluronic acid (HA)-modified hollow manganese dioxide nanoparticles (hMnO2 NPs) to fabricate hMnO2 @HA@NMA (MHN) nanogenerators (NGs). The obtained NGs have a record-high NO generation efficiency under US irradiation and can release Mn2+ after targeting the tumor sites. Later on, cascade tumor pyroptosis and cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING)-based immunotherapy is achieved and tumor growth is effectively inhibited.

Computational Prediction of Metabolic α-Carbon Hydroxylation Potential of N-Nitrosamines: Overcoming Data Limitations for Carcinogenicity Assessment

Recent withdrawal of several drugs from the market due to elevated levels of N-nitrosamine impurities underscores the need for computational approaches to assess the carcinogenicity risk of nitrosamines. However, current approaches are limited because robust animal carcinogenicity data are only available for a few simple nitrosamines, which do not represent the structural diversity of the many possible nitrosamine drug substance related impurities (NDSRIs). In this paper, we present a novel method that uses data on CYP-mediated metabolic hydroxylation of CH₂ groups in non-nitrosamine xenobiotics to identify structural features that may also help in predicting the likelihood of metabolic α-carbon hydroxylation in N-nitrosamines. Our approach offers a new avenue for tapping into potentially large experimental data sets on xenobiotic metabolism to improve risk assessment of nitrosamines. As α-carbon hydroxylation is the crucial rate-limiting step in nitrosamine metabolic activation, identifying and quantifying the influence of various structural features on this step can provide valuable insights into their carcinogenic potential. This is especially important considering the scarce information available on factors that affect NDSRI metabolic activation. We have identified hundreds of structural features and calculated their impact on hydroxylation, a significant advancement compared to the limited findings from the small nitrosamine carcinogenicity data set. While relying solely on α-carbon hydroxylation prediction is insufficient for forecasting carcinogenic potency, the identified features can help in the selection of relevant structural analogues in read across studies and assist experts who, after considering other factors such as the reactivity of the resulting electrophilic diazonium species, can establish the acceptable intake (AI) limits for nitrosamine impurities.

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.

Quantum-Mechanical Approach to Predicting the Carcinogenic Potency of N-Nitroso Impurities in Pharmaceuticals

N-Nitroso contaminants in medicinal products are of concern due to their high carcinogenic potency; however, not all these compounds are created equal, and some are relatively benign chemicals. Understanding the structure-activity relationships (SARs) that drive hazards in one molecule versus another is key to both protecting human health and alleviating costly and sometimes inaccurate animal testing. Here, we report on an extension of the CADRE (computer-aided discovery and REdesign) platform, which is used broadly by the pharmaceutical and personal care industries to assess environmental and human health endpoints, to predict the carcinogenic potency of N-nitroso compounds. The model distinguishes compounds in three potency categories with 77% accuracy in external testing, which surpasses the reproducibility of rodent cancer bioassays and constraints imposed by limited (high-quality) data. The robustness of predictions for more complex pharmaceuticals is maximized by capturing key SARs using quantum mechanics, that is, by hinging the model on the underlying chemistry versus chemicals in the training set. To this end, the present approach can be leveraged in a quantitative hazard assessment and to offer qualitative guidance using electronic structure comparisons between well-studied analogues and unknown contaminants.