Mtb pharmacophores and drug discovery
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.
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.
The following have contributed to this page: Dr Sean Ekins