Development of predictive classification models for whole cell antimycobacterial activity of benzothiazinones.

Schieferdecker S, Bernal FA, Wojtas KP, Keiff F, Li Y, Dahse HM, Kloss F (2022) Development of predictive classification models for whole cell antimycobacterial activity of benzothiazinones. J Med Chem 65(9), 6748-6763.

Abstract

Nitrobenzothiazinones (BTZs) are a very potent class of antibiotics against Mycobacterium tuberculosis. However, relationships between their structural properties and whole cell activity remain poorly predictable. Herein, we present the synthesis and antimycobacterial evaluation of a diverse set of BTZs. High potency was predominantly achieved by piperidine and piperazine substitutions, whereupon three compounds were identified as promising candidates, showing preferable metabolic stability. Lack of correlation between potency and calculated binding energies suggested that target inhibition is not the only requirement to obtain suitable antimycobacterial agents. In contrast, prediction of whole cell activity class was successfully accomplished by extensively validated machine learning models. The performance of the superior model was further verified by >70% correct class predictions for a large set of reported BTZs. Our generated model is thus a key prerequisite to streamline lead optimization endeavors, particularly regarding the improvement of overall hit rates in whole cell antimycobacterial assays.

Leibniz-HKI-Autor*innen

Freddy Alexander Bernal
Hans-Martin Dahse
François Keiff
Florian Kloß
Yan Li
Sebastian Schieferdecker
Philip Wojtas

Identifier

doi: 10.1021/acs.jmedchem.2c00098

PMID: 35502994