Dataset Information

Late-Stage Isotopic Carbon Labeling of Pharmaceutically Relevant Cyclic Ureas Directly from CO2.

ABSTRACT: A robust, click-chemistry-inspired procedure for radiolabeling of cyclic ureas was developed. This protocol, suitable for all carbon isotopes (11 C, 13 C, 14 C), is based on the direct functionalization of carbon dioxide: the universal building block for carbon radiolabeling. The strategy is operationally simple and reproducible in different radiochemistry centers, exhibits remarkably wide substrate scope with short reaction times, and demonstrates superior reactivity as compared to previously reported systems. With this procedure, a variety of pharmaceuticals and an unprotected peptide were labeled with high radiochemical efficiency.

SUBMITTER: Del Vecchio A

PROVIDER: S-EPMC6099343 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Late-Stage Isotopic Carbon Labeling of Pharmaceutically Relevant Cyclic Ureas Directly from CO2.

Del Vecchio Antonio A Caillé Fabien F Chevalier Arnaud A Loreau Olivier O Horkka Kaisa K Halldin Christer C Schou Magnus M Camus Nathalie N Kessler Pascal P Kuhnast Bertrand B Taran Frédéric F Audisio Davide D

Angewandte Chemie (International ed. in English) 20180703 31

A robust, click-chemistry-inspired procedure for radiolabeling of cyclic ureas was developed. This protocol, suitable for all carbon isotopes (11 C, 13 C, 14 C), is based on the direct functionalization of carbon dioxide: the universal building block for carbon radiolabeling. The strategy is operationally simple and reproducible in different radiochemistry centers, exhibits remarkably wide substrate scope with short reaction times, and demonstrates superior react ...[more]

PMID: 29862657

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Project description:BackgroundIn drug discovery, a positive Ames test for bacterial mutation presents a significant hurdle to advancing a drug to clinical trials. In a previous paper, we discussed success in predicting the genotoxicity of reagent-sized aryl-amines (ArNH2), a structure frequently found in marketed drugs and in drug discovery, using quantum mechanics calculations of the energy required to generate the DNA-reactive nitrenium intermediate (ArNH:+). In this paper we approach the question of what molecular descriptors could improve these predictions and whether external data sets are appropriate for further training.ResultsIn trying to extend and improve this model beyond this quantum mechanical reaction energy, we faced considerable difficulty, which was surprising considering the long history and success of QSAR model development for this test. Other quantum mechanics descriptors were compared to this reaction energy including AM1 semi-empirical orbital energies, nitrenium formation with alternative leaving groups, nitrenium charge, and aryl-amine anion formation energy. Nitrenium formation energy, regardless of the starting species, was found to be the most useful single descriptor. External sets used in other QSAR investigations did not present the same difficulty using the same methods and descriptors. When considering all substructures rather than just aryl-amines, we also noted a significantly lower performance for the Novartis set. The performance gap between Novartis and external sets persists across different descriptors and learning methods. The profiles of the Novartis and external data are significantly different both in aryl-amines and considering all substructures. The Novartis and external data sets are easily separated in an unsupervised clustering using chemical fingerprints. The chemical differences are discussed and visualized using Kohonen Self-Organizing Maps trained on chemical fingerprints, mutagenic substructure prevalence, and molecular weight.ConclusionsDespite extensive work in the area of predicting this particular toxicity, work in designing and publishing more relevant test sets for compounds relevant to drug discovery is still necessary. This work also shows that great care must be taken in using QSAR models to replace experimental evidence. When considering all substructures, a random forest model, which can inherently cover distinct neighborhoods, built on Novartis data and previously reported external data provided a suitable model.

Dataset Information

Late-Stage Isotopic Carbon Labeling of Pharmaceutically Relevant Cyclic Ureas Directly from CO2.

Publications

Late-Stage Isotopic Carbon Labeling of Pharmaceutically Relevant Cyclic Ureas Directly from CO<sub>2</sub>.

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