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Theses

Mise au point d’une méthode d’analyse déréplicative par RMN du carbone 13

Abstract : Extraction and isolation of natural products can be a tedious and time-consuming work and can unfortunately lead to molecules presenting little to no interest. That is why dereplication methods have been developed : they allow the identification of molecules within a mixture, without having to separate them, by comparing their signals to those of references, gathered in databases. In this work, we try to focus on polycyclic polyprenylated acylphloroglucinols (PPAPs), molecules that could be used a therapeutic tool to understand mechanisms involved in immune and inflammatory responses. We first were able to conclude that building databases using predicted values, instead of experimental ones, gave quality results for a dereplication work. Predicted databases were thus used for the rest of the experiments. After taking a look at the different kind of published dereplication methods, we decided to develop our own program based on 13C-NMR, in order to make it more discriminating than the current methods. To do so, in addition to 13C data, DEPT (135 and 90) information were added, allowing to narrow the search by carbon type. A graphic user interface was also implemented, making the program easier to use, but also providing the user with the possibility to interact with the results. This new method was first successfully tested on a diverse range of natural products mixtures, allowing the validation of the method. In the end, the method was used on Garcinia bancana extracts, and made possible the quick identification of the PPAPs we were interested in. Molecules were purified for further biological testing.
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https://tel.archives-ouvertes.fr/tel-03783739
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Submitted on : Thursday, September 22, 2022 - 12:23:09 PM
Last modification on : Friday, September 23, 2022 - 5:07:49 AM

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  • HAL Id : tel-03783739, version 1

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Antoine Bruguière. Mise au point d’une méthode d’analyse déréplicative par RMN du carbone 13. Médecine humaine et pathologie. Université d'Angers, 2019. Français. ⟨NNT : 2019ANGE0085⟩. ⟨tel-03783739⟩

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