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

Journal of Pharmaceutical Sciences, Journal Year: 2022, Volume and Issue: 112(5), P. 1287 - 1304

Published: Nov. 17, 2022

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

Language: Английский

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Risk assessment of N‐nitrosamines in food

EFSA Journal, Journal Year: 2023, Volume and Issue: 21(3)

Published: March 1, 2023

EFSA was asked for a scientific opinion on the risks to public health related presence of

Language: Английский

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Mechanisms of Nitrosamine Mutagenicity and Their Relationship to Rodent Carcinogenic Potency

Chemical Research in Toxicology, Journal Year: 2024, Volume and Issue: 37(2), P. 181 - 198

Published: Feb. 5, 2024

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

Language: Английский

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

Regulatory Toxicology and Pharmacology, Journal Year: 2024, Volume and Issue: 150, P. 105640 - 105640

Published: May 14, 2024

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

Language: Английский

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Quantum Chemical Evaluation and QSAR Modeling of N-Nitrosamine Carcinogenicity

Chemical Research in Toxicology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 6, 2025

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

Language: Английский

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

Chemical Research in Toxicology, Journal Year: 2022, Volume and Issue: 35(3), P. 475 - 489

Published: Feb. 25, 2022

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

Language: Английский

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

Chemical Research in Toxicology, Journal Year: 2022, Volume and Issue: 35(11), P. 1997 - 2013

Published: Oct. 27, 2022

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

Language: Английский

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The Nitrosamine “Saga”: Lessons Learned from Five Years of Scrutiny

Organic Process Research & Development, Journal Year: 2023, Volume and Issue: 27(10), P. 1719 - 1735

Published: July 26, 2023

The onset of the N-nitrosamine (NA) saga in 2018 was chiefly related to certain small dialkyl N-nitrosamines originating from synthesis active pharmaceutical ingredient (API). However, subsequent comprehensive assessments performed on APIs, formulated drug products, and packaging put a different type NAs limelight: diverse range complex so-called nitrosamine drug-substance-related impurities (NDSRIs). They may form due presence potentially nitrosatable secondary or tertiary amine moieties APIs API nitrosating agents formed low levels nitrite present as impurities. unique properties functional group make it irreplaceable APIs. While be reduced, formation products cannot completely prevented, class default acceptable intake (AI) 18 ng/day currently poses significant challenges terms both viable control analysis at such levels. Even so, NA exposure through pharmaceuticals is expected orders magnitude lower than via food endogenous formation. robust carcinogenicity data are available for many small, simple NAs, there distinct absence most NDSRIs. Many working groups have therefore been established share rapidly improve knowledge (whether toxicity data, structure–activity relationships, analytical techniques), define best practices assess genotoxic potential NDSRIs, advance methods calculate AIs based solid scientific rationales. Ultimately, protect patients true cancer risk secure access important medicines, crucial manufacturers health authorities pursue efforts implement strategies that equally effective realistic. As patient safety paramount, industry committed ensuring medicines supplies safe effective. Where legitimate concerns exist, undisputed appropriate actions must taken, which could include withdrawal market.

Language: Английский

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

Regulatory Toxicology and Pharmacology, Journal Year: 2023, Volume and Issue: 142, P. 105415 - 105415

Published: May 29, 2023

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

Language: Английский

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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

Critical Reviews in Toxicology, Journal Year: 2024, Volume and Issue: 54(9), P. 659 - 684

Published: Sept. 3, 2024

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

Language: Английский

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