Consideration of predicted small-molecule metabolites in computational toxicology

Lomana, Marina Garcia de; Svensson, Fredrik; Volkamer, Andrea; Mathea, Miriam; Kirchmair, Johannes

doi:10.1039/d1dd00018g

Cited by 11 publications

(9 citation statements)

References 35 publications

(54 reference statements)

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“…It is reasonable to propose that sufficient electronic 'signature' is present in the parent molecules to elucidate differential electrophilic reactivity of their metabolites. 53 To that end, calculated maxima in electrophilic susceptibility on the α-C, 𝑓 + (𝐶 𝛼 ) = [𝜌 𝑁+1 (𝐶 𝛼 ) − 𝜌 𝑁 (𝐶 𝛼 )], showed that both NDEA and NDMA are more susceptible to a nucleophilic attack (Table 1, Column 7) than the remaining nitrosamines. This finding is consistent with our analysis above, indicating lower reactivity (and carcinogenicity) of the selected 5 nitrosamines than the well-characterized NDEA and NDMA compounds.…”

Section: Esr Case Studymentioning

confidence: 99%

A Quantum-Mechanical Approach to Predicting Carcinogenic Potency of N-nitrosamine Impurities in Pharmaceuticals

Kostal

Voutchkova‐Kostal

2022

Preprint

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N-nitrosamine contaminants in medicinal products are of concern due to their high carcinogenic potency; however, not all nitrosamines are created equal, and some are relatively benign chemicals. Understanding the structure-activity relationships (SARs) that drive hazard 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, used broadly by the pharmaceutical and personal care industries to assess environmental and human health endpoints, to predict carcinogenic potency of N-nitrosamines. The model distinguishes compounds in three potency categories with 78% accuracy in external testing, which surpasses reproducibility of rodent cancer bioassays and constraints imposed by limited (quality) data. Robustness of predictions for more complex pharmaceutical nitrosamines is maximized by capturing key SARs using quantum mechanics., i.e., by hinging the model on the underlying chemistry vs. 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 comparison between well-studied analogs and unknown contaminants.

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Section: Esr Case Studymentioning

confidence: 99%

A Quantum-Mechanical Approach to Predicting Carcinogenic Potency of N-nitrosamine Impurities in Pharmaceuticals

Kostal

Voutchkova‐Kostal

2022

Preprint

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“…It is reasonable to propose that sufficient electronic "signature" is present in the parent molecules to elucidate the differential electrophilic reactivity of their metabolites. 59 To that end, calculated maxima in electrophilic susceptibility on the α-C,…”

Section: ■ Introductionmentioning

confidence: 99%

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

Kostal

Voutchkova‐Kostal

2023

Chem. Res. Toxicol.

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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.

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“…However, this small size means they can often interact with other proteins, and many exhibit off-target effects and toxicity. Their non-endogenous metabolites can also induce undesired physiological responses [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study

Ahmad

Mirza

Trant

2023

Journal of Pharmaceutical Analysis

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Consideration of predicted small-molecule metabolites in computational toxicology

Abstract: Exploration of computational approaches for including metabolism information in machine learning models for toxicity prediction.

Cited by 11 publications

References 35 publications

A Quantum-Mechanical Approach to Predicting Carcinogenic Potency of N-nitrosamine Impurities in Pharmaceuticals

A Quantum-Mechanical Approach to Predicting Carcinogenic Potency of N-nitrosamine Impurities in Pharmaceuticals

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

Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study

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