Utilisation of parametric methods to improve percentile-based estimates for the carcinogenic potency of nitrosamines

Synthesis and Trace-Level Quantification of Mutagenic and Cohort-of-Concern Ciprofloxacin Nitroso Drug Substance-Related Impurities (NDSRIs) and Other Nitroso Impurities Using UPLC-ESI-MS/MS-Method Optimization Using I-Optimal Mixture Design

Globally, the pharmaceutical industry has been facing challenges from nitroso drug substance-related impurities (NDSRIs). In the current study, we synthesized and developed a rapid new UPLC-MS/MS method for the trace-level quantification of ciprofloxacin NDSRIs and a couple of N-nitroso impurities simultaneously. (Q)-SAR methodology was employed to assess and categorize the genotoxicity of all ciprofloxacin N-nitroso impurities. The projected results were positive, and the cohort of concern (CoC) for all three N-nitroso impurities indicates potential genotoxicity. AQbD-driven I-optimal mixture design was used to optimize the mixture of solvents in the method. The chromatographic resolution was accomplished using an Agilent Poroshell 120 Aq-C18 column (150 mm × 4.6 mm, 2.7 μm) in isocratic elution mode with 0.1% formic acid in a mixture of water, acetonitrile, and methanol in the ratio of 475:500:25 v/v/v at a flow rate of 0.5 mL/min. Quantification was carried out using triple quadrupole mass detection with electrospray ionization (ESI) in a multiple reaction monitoring technique. The finalized method was validated successfully, affording ICH guidelines. All N-nitroso impurities revealed excellent linearity over the concentration range of 0.00125-0.0250 ppm. The Pearson correlation coefficient of each N-nitroso impurity was >0.999. The method accuracy recoveries ranged from 93.98 to 108.08% for the aforementioned N-nitrosamine impurities. Furthermore, the method was effectively applied to quantify N-nitrosamine impurities simultaneously in commercially available formulated samples, with its efficiency recurring at trace levels. Thus, the current method is capable of determining the trace levels of three N-nitroso ciprofloxacin impurities simultaneously from the marketed tablet dosage forms for commercial release and stability testing.

Inhibition of algal blooms by residual antibiotics in aquatic environments: Design, screening, and validation of antibiotic alternatives

Water blooms frequently appear in the aquatic environment with global warming. However, traditional methods for treating water bloom usually require the addition of algaecides, which may lead to secondary environmental pollution problems in the water environment. To solve this problem, researchers have initiated efforts to harness pre-existing chemical substances within aquatic environments to regulate algal blooms, thereby pioneering novel avenues for water body management. Therefore, an integrated approach involving molecular docking, molecular dynamics simulations, three-dimensional quantitative structure-activity relationship (3D-QSAR), and toxicokinetics methods were utilized for the molecular modification of fluoroquinolone antibiotics, to design and screen fluoroquinolone substitutes with improved toxicity of cyanobacteria and green algae, functionality, and environmental friendliness. A total of 143 fluoroquinolone alternatives were designed in this study, and lomefloxacin-6 (LOM6) was found as the optimum alternative to lomefloxacin (LOM), with increased toxicity to cyanobacteria and green algae by 31 % and 72 %. Molecular docking of LOM before and after modification with seven other cyanobacterial and green algal photosynthetic proteins revealed that LOM6 exhibited varying degrees of increased toxicity towards 6 of these photosynthetic proteins, of which 2J96 protein increased the most (136.25 %). It shows that the residual LOM6 in the water environment has a certain inhibitory effect on the algae bloom. In addition, results showed that LOM6 had synergistic toxic effects on cyanobacteria and green algae with other pollutants residual in the aqueous environment, such as trichloroethyl phosphate, triethyl phosphate, perfluorononanoic acid, perfluorooctanoic acid. This indicates that LOM6 has better algal removal effectiveness in aqueous environments where organophosphate flame retardants and perfluorinated compounds exist together. In this paper, a novel method was developed to remove cyanobacteria and green algae in water environment and reduce the secondary pollution through theoretical simulation, which provides theoretical support for the control of water blooms.

Nitrosamine acceptable intakes should consider variation in molecular weight: The implication of stoichiometric DNA damage

N-nitrosamines (NAs) are a class of compounds of which many, especially of the small dialkyl type, are indirect acting DNA alkylating mutagens. Their presence in pharmaceuticals is subject to very strict acceptable daily intake (AI) limits, which are traditionally expressed on a mass basis. Here we demonstrate that AIs that are not experimentally derived for a specific compound, but via statistical extrapolation or read across to a suitable analog, should be expressed on a molar scale or corrected for the target substance's molecular weight. This would account for the mechanistic aspect that each nitroso group can, at maximum, account for a single DNA mutation and the number of molecules per mass unit is proportional to the molecular weight (MW). In this regard we have re-calculated the EMA 18 ng/day regulatory default AI for unknown nitrosamines on a molar scale and propose a revised default AI of 163 pmol/day. In addition, we provide MW-corrected AIs for those nitrosamine drug substance related impurities (NDSRIs) for which EMA has pre-assigned AIs by read-across. Regulatory acceptance of this fundamental scientific tenet would allow one to derive nitrosamine limits for NDSRIs that both meet the health-protection goals and are technically feasible.

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.

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.

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.

The Landscape of Potential Small and Drug Substance Related Nitrosamines in Pharmaceuticals

This article reports the outcome of an in silico analysis of more than 12,000 small molecule drugs and drug impurities, identifying the nitrosatable structures, assessing their potential to form nitrosamines under relevant conditions and the challenges to determine compound-specific AIs based on data available or read-across approaches for these nitrosamines and their acceptance by health authorities. Our data indicate that the presence of nitrosamines in pharmaceuticals is likely more prevalent than originally expected. In total, 40.4 % of the analyzed APIs and 29.6 % of the API impurities are potential nitrosamine precursors. Most structures identified through our workflow could form complex API-related nitrosamines, so-called nitrosamine drug substance related impurities (NDSRIs), although we also found structures that could release the well-known small and potent nitrosamines NDMA, NDEA, and others. Due to common structural motifs including secondary or tertiary amine moieties, whole essential drug classes such as beta blockers and ACE inhibitors are at risk. To avoid the risk of drug shortages or even the complete loss of therapeutic options, it will be essential that the well-established ICH M7 principles remain applicable for nitrosamines and that that the industry and regulatory authorities keep an open communication not only about the science but also to make sure there is a good balance between risk and benefit to patients.

Research progress of N-nitrosamine detection methods: a review

N-Nitrosamines (nitrosamines) are attracting increased attention because of their high toxicity and wide distribution. They have been strictly restricted by regulations in many fields. Researchers around the world have conducted substantial work on nitrosamine detection. This paper reviews the progress of research on nitrosamine detection methods with emphasis on biological-matrix samples. After introducing the category, toxicity, regulatory limit and source of nitrosamines, the paper discusses the most commonly used sample-preparation techniques and instrumental-detection techniques for nitrosamine detection, including some typical application cases.

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.

Developing Structure-Activity Relationships for N-Nitrosamine Activity

The detection of N-nitrosodimethylamine (NDMA) in several marketed drugs led regulatory agencies to require that N-nitrosamine risk assessments be performed on all marketed medical products [EMA/351053/2019 rev 1 (2019)]. Regulation of N-nitrosamine impurity levels in pharmaceutical drug substances and products is described in the ICH M7(R1) guideline where they are referred to as "cohort-of-concern" compounds as several are potent rodent carcinogens [Kroes et. al. 2004]. EMA, U.S. FDA and other regulatory agencies have set provisional acceptable daily intake limits for N-nitrosamines calculated from rodent carcinogenicity TD₅₀ values for experimentally measured N-nitrosamines or the measured TD₅₀ values of close analogs. The class-specific limit can be adjusted based upon a structure activity relationship analysis (SAR) and comparison with analogs having established carcinogenicity data [EMA/369136/2020, (2020)]. To investigate whether improvements in SARs can more accurately predict N-nitrosamine carcinogenic potency, an ad hoc workgroup of 23 companies and universities was established with the goals of addressing several scientific and regulatory issues including: reporting and review of N-nitrosamine mutagenicity and carcinogenicity reaction mechanisms, collection and review of available, public relevant experimental data, development of structure-activity relationships consistent with mechanisms for prediction of N-nitrosamine carcinogenic potency categories, and improved methods for calculating acceptable intake limits for N-nitrosamines based upon mechanistic analogs. Here we describe this collaboration and review our progress to date towards development of mechanistically based structure-activity relationships. We propose improving risk assessment of N-nitrosamines by first establishing the dominant reaction mechanism prior to retrieving an appropriate set of close analogs for use in read-across exercises.