Mycobacterium tuberculosis: Efflux-Pump Modulation and Next-Generation Therapeutics

Drug-resistant tuberculosis (TB) demands new approaches that address both novel antibacterial targets and the resistance and tolerance mechanisms that erode drug efficacy. Beyond target mutation, Mycobacterium tuberculosis can adapt under antimicrobial pressure through efflux, stress responses, and metabolic remodeling, enabling survival at drug exposures that should be lethal. These adaptive states complicate treatment, support persistence, and increase the likelihood that resistance will emerge and be selected during therapy.

This Research Topic will bring together research on efflux pump inhibitors (EPIs) and next-generation antitubercular agents, alongside mechanistic studies that define tolerance and adaptive resistance during drug exposure. We aim to link compound design and binding hypotheses (including docking and simulations) to whole-cell activity, target engagement and resistance profiling, and synergy with frontline drugs. In parallel, we welcome work that uses mechanistic omics to map transcriptional, proteomic, and metabolic responses to drug pressure and to identify vulnerabilities that can be exploited in combination regimens.

We particularly welcome submissions that integrate medicinal chemistry, chemical biology, microbiology, and translational pharmacology to support a clear path toward clinical evaluation. Studies addressing PK/PD, tissue/lesion penetration, and regimen optimization—especially combinations designed to minimise resistance emergence—are encouraged. Collectively, the goal is to advance antitubercular strategies that improve durable bacterial clearance by simultaneously expanding the drug pipeline and mitigating resistance and tolerance mechanisms.

Priority areas include (but are not limited to):

EPIs for M. tuberculosis (design/SAR, whole-cell validation, synergy with frontline drugs)
Next-generation antituberculars (novel chemotypes, dual-target strategies, innovative binding modes)
Mechanistic omics of tolerance and adaptive resistance under drug pressure
PK/PD, penetration, and combination regimens that minimise resistance emergence
Artificial intelligence in protein–ligand interactions and machine learning (ML) approaches for predicting AMR and target validation