Strategies to Combat Multi-Drug Resistance in Tuberculosis

Abstract

"Drug resistance is an unavoidable consequence of the use of drugs; however, the emergence of multi-drug resistance can be managed by accurate diagnosis and tailor-made regimens."Antimicrobial resistance (AMR), is one of the most paramount health perils that has emerged in the 21st century. The global increase in drug-resistant strains of various bacterial pathogens prompted the World Health Organization (WHO) to develop a priority list of AMR pathogens. Mycobacterium tuberculosis (Mtb), an acid-fast bacillus that causes tuberculosis (TB), merits being one of the highest priority pathogens on this list since drug-resistant TB (DR-TB) accounts for ∼29% of deaths attributable to AMR. In recent years, funded collaborative efforts of researchers from academia, not-for-profit virtual R&D organizations and industry have resulted in the continuous growth of the TB drug discovery and development pipeline. This has so far led to the accelerated regulatory approval of bedaquiline and delamanid for the treatment of DR-TB. However, despite the availability of drug regimes, the current cure rate for multi-drug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) treatment regimens is 50% and 30%, respectively. It is to be noted that these regimens are administered over a long duration and have a serious side effect profile. Coupled with poor patient adherence, this has led to further acquisition of drug resistance and treatment failure. There is therefore an urgent need to develop new TB drugs with novel mechanism of actions (MoAs) and associated regimens.This Account recapitulates drug resistance in TB, existing challenges in addressing DR-TB, new drugs and regimens in development, and potential ways to treat DR-TB. We highlight our research aimed at identifying novel small molecule leads and associated targets against TB toward contributing to the global TB drug discovery and development pipeline. Our work mainly involves screening of various small molecule chemical libraries in phenotypic whole-cell based assays to identify hits for medicinal chemistry optimization, with attendant deconvolution of the MoA. We discuss the identification of small molecule chemotypes active against Mtb and subsequent structure-activity relationships (SAR) and MoA deconvolution studies. This is followed by a discussion on a chemical series identified by whole-cell cross-screening against Mtb, for which MoA deconvolution studies revealed a pathway that explained the lack of in vivo efficacy in a mouse model of TB and reiterated the importance of selecting an appropriate growth medium during phenotypic screening. We also discuss our efforts on drug repositioning toward addressing DR-TB. In the concluding section, we preview some promising future directions and the challenges inherent in advancing the drug pipeline to address DR-TB.

Figure 1

Timeline of TB drug discovery and emergence of resistance. Dates are associated with the publication/approval.

Figure 2

Current clinical development pipeline for anti-TB drugs and regimens, March 2021. *, new chemical class; OBR, optimized background regimen. (Adapted with permission from the Stop TB Partnership Working Group on New Drugs pipeline; for detailed information, please see: http://www.newtbdrugs.org.)

Figure 3

Kinetics of tuberculosis treatment. Here we consider a heterogeneous population of naturally occurring M. tuberculosis (Mtb) as an infectious dose. After the start of chemotherapy, there can be three possible outcomes: eradication of the disease (desired outcome), partially effective longer treatment due to the emergence of persister bacteria, and failure of the treatment due to the emergence of predominantly resistant Mtb. The presence of persister bacterial populations can be exemplified by a classic biphasic kill curve after the start of treatment: a brief period of rapid killing followed by a delayed killing.

Figure 4

Early drug discovery test-cascade used at the Drug Discovery and Development Centre (H3D). Mtb, M. tuberculosis H37Rv; R, replicating; MIC, minimum inhibitory concentration (7–14 days); cytotoxicity, against VERO (kidney epithelial cells extracted from an African green monkey) and/or CHO (Chinese hamster ovarian) and/or HepG2 (human liver cancer) mammalian cell-lines; solubility, in fasted-state intestinal fluid (FaSSIF) kinetic-solubility assay at pH 6.5 and in PBS at pH 7.4; MBC, minimum bactericidal concentration (7–28 days); NR, nonreplicating, nutrient starvation/hypoxia/4-stress model; ex vivo, using RAW264.7, J774, and/or THP.1 derived macrophages; MoA, mechanism of action; SRM, spontaneous resistant mutant; WGS, whole-genome sequencing; PK, pharmacokinetics; PPB, plasma protein binding; F, bioavailability; CYP, Cytochrome P450; B:P, blood:plasma ratio; MetID, metabolite identification; MoR, mechanism of resistance; SI, selectivity-index; CL, clearance; IVCL, in vivo clearance; genotoxicity, AMES unless the compounds are active against Salmonella, mouse lymphoma, and mouse micronucleation assays; off-target activities, secondary pharmaceutical panels of enzymes, GPCRs, ion channels, receptors, transporters, etc. Dotmatics is used in data management.

Figure 5

Structures of selected compounds explored for SAR and/or MoA.

Figure 6

SAR summary of compounds 1, 3, 4, 5, 6, and 7. Mtb, Mycobacterium tuberculosis; LHS, left-hand side; RHS, right-hand side.

Figure 7

Addressing drug resistance using drug repositioning approaches: (a) chlorpromazine, (b) integrated approach investigating fusidic acid, and (c) verapamil.