What is it about?
After some bioinformatics to identify potential targets, we used pharmacophores for over 60 metabolites and substrates in Mtb to search a vendor library, these compounds were scored with Bayesian models, ~100 were tested and a quinoxaline di N-oxide was used as a starting point for medicinal chemistry optimization. We generated some ADME data and one compound was tested in vivo and had poor PK data.
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Why is it important?
We used a combined bioinformatics and cheminformatics approach alongside medicinal chemistry and biology screening. One compound tested was also active against drug resistant strains of Mtb. This provides additional data for pursuit of quinoxaline di N-oxide as a novel class of Mtb inhibitors. The key challenge is resolving ADME/PK.
Perspectives
This was a collaborative project which merged several different groups and techniques for Mtb drug discovery. It further shows how computational data can be useful for identifying compounds of interest. This work represents a phase II STTR and lead to the development of an API for the CDD Vault software as well as additional data in the public domain on compounds and targets for Mtb.
Dr Sean Ekins
Collaborations in Chemistry
Read the Original
This page is a summary of: Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery, PLoS ONE, October 2015, PLOS,
DOI: 10.1371/journal.pone.0141076.
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