Advances in Key Drug Target Identification and New Drug Development for Tuberculosis

Prevalence and Patterns of Drug-Resistant Mycobacterium tuberculosis in Newly Diagnosed Patients in China: A Systematic Review and Meta-Analysis

BACKGROUND

Tuberculosis (TB), one of the deadliest infectious diseases globally, is increasingly exacerbated in China by the emergence of resistant Mycobacterium tuberculosis (MTB) strains. Drug-resistant TB, including mono-drug resistant TB, multidrug-resistant TB (MDR-TB), and extensively drug-resistant TB (XDR-TB), presents significant public health challenges.

METHODS

We conducted a systematic literature review from January 2010 to February 2024 using databases such as PubMed, Embase, Web of Science, and Google Scholar. Our focus was on empirical data related to drug resistance patterns in newly diagnosed TB cases. Non-empirical studies were excluded through meticulous filtering. For meta-analysis, we used Review Manager (RevMan) 5.2 and assessed evidence quality using the Newcastle-Ottawa Scale (NOS).

RESULTS

Our search strategy identified 40 studies that met the inclusion criteria, encompassing a total sample size of 87,667 participants. Among new TB cases, the estimated prevalence of MDR-TB in China was 6.9% (95% CI: 5.6-8.1%). Prevalence rates for mono-drug resistance to first-line anti-TB medications were as follows: isoniazid at 18.2% (95% CI: 16.4-20.6%), rifampicin at 10.5% (95% CI: 8.6-12.8%), and ethambutol at 5.7% (95% CI: 4.1-7.3%). The prevalence of streptomycin resistance, a former first-line anti-TB drug, was 17.1% (95% CI: 14.6-19.1%). The prevalence of other types of mono-drug resistance was 15.2% (95% CI: 13.9-17.3%), and for XDR-TB, it was 0.9% (95% CI: 0.6-1.4%).

CONCLUSIONS

The high prevalence of drug-resistant TB in China poses a significant public health challenge. There is an urgent need for targeted interventions and continued surveillance to combat the spread of drug-resistant TB.

Development of New Drugs to Treat Tuberculosis Based on the Dinitrobenzamide Scaffold

Tuberculosis (TB) continues to be a major global health challenge and a leading cause of death from infectious diseases. Inspired by the results from a previous work by our group on antimycobacterial N-alkylnitrobenzamides, which are structurally related to the nitrobenzamide family of decaprenylphosphoryl-β-d-ribose oxidase (DprE1) inhibitors, the present study explored a broad array of substituted benzamides. We particularly focused on previously unexplored 3,5-dinitrobenzamide derivatives. Starting with 3,5-dinitrobenzoic acid, we synthesized a diverse library of amides, incorporating both linear and cyclic amine moieties and also assessed the impact of terminal aromatic groups connected through ether, ester, or amide bonds on the bioactivity of the compounds. The synthesis primarily utilized nucleophilic addition/elimination, SN2, and Mitsunobu reactions. The activity was impacted mainly by two structural features, the addition of an aromatic moiety as a terminal group and the type of linker. The most interesting compounds (c2, d1, and d2, MIC = 0.031 μg/mL) exhibited activities against Mycobacterium Tuberculosis (Mtb) H37Rv comparable to isoniazid. Complementary computational studies helped elucidate potential interactions with DprE1, enhancing our understanding of the molecular basis of their action. Our findings suggest that the most active compounds provide a promising foundation for the continued development of new antimycobacterial agents.

Antitubercular drugs: possible role of natural products acting as antituberculosis medication in overcoming drug resistance and drug-induced hepatotoxicity

Mycobacterium tuberculosis (Mtb) is a pathogenic bacterium which causes tuberculosis (TB). TB control programmes are facing threats from drug resistance. Multidrug-resistant (MDR) and extensively drug-resistant (XDR) Mtb strains need longer and more expensive treatment with many medications resulting in more adverse effects and decreased chances of treatment outcomes. The World Health Organization (WHO) has emphasised the development of not just new individual anti-TB drugs, but also novel medication regimens as an alternative treatment option for the drug-resistant Mtb strains. Many plants, as well as marine creatures (sponge; Haliclona sp.) and fungi, have been continuously used to treat TB in various traditional treatment systems around the world, providing an almost limitless supply of active components. Natural products, in addition to their anti-mycobacterial action, can be used as adjuvant therapy to increase the efficacy of conventional anti-mycobacterial medications, reduce their side effects, and reverse MDR Mtb strain due to Mycobacterium's genetic flexibility and environmental adaptation. Several natural compounds such as quercetin, ursolic acid, berberine, thymoquinone, curcumin, phloretin, and propolis have shown potential anti-mycobacterial efficacy and are still being explored in preclinical and clinical investigations for confirmation of their efficacy and safety as anti-TB medication. However, more high-level randomized clinical trials are desperately required. The current review provides an overview of drug-resistant TB along with the latest anti-TB medications, drug-induced hepatotoxicity and oxidative stress. Further, the role and mechanisms of action of first and second-line anti-TB drugs and new drugs have been highlighted. Finally, the role of natural compounds as anti-TB medication and hepatoprotectants have been described and their mechanisms discussed.

Selection of Multi-Drug Targets against Drug-Resistant Mycobacterium tuberculosis XDR1219 Using the Hyperbolic Mapping of the Protein Interaction Network

Tuberculosis remains the leading cause of death from a single pathogen. On the other hand, antimicrobial resistance (AMR) makes it increasingly difficult to deal with this disease. We present the hyperbolic embedding of the Mycobacterium tuberculosis protein interaction network (mtbPIN) of resistant strain (MTB XDR1219) to determine the biological relevance of its latent geometry. In this hypermap, proteins with similar interacting partners occupy close positions. An analysis of the hypermap of available drug targets (DTs) and their direct and intermediate interactors was used to identify potentially useful drug combinations and drug targets. We identify rpsA and rpsL as close DTs targeted by different drugs (pyrazinamide and aminoglycosides, respectively) and propose that the combination of these drugs could have a synergistic effect. We also used the hypermap to explain the effects of drugs that affect multiple DTs, for example, forcing the bacteria to deal with multiple stresses like ethambutol, which affects the synthesis of both arabinogalactan and lipoarabinomannan. Our strategy uncovers novel potential DTs, such as dprE1 and dnaK proteins, which interact with two close DT pairs: arabinosyltransferases (embC and embB), Ser/Thr protein kinase (pknB) and RNA polymerase (rpoB), respectively. Our approach provides mechanistic explanations for existing drugs and suggests new DTs. This strategy can also be applied to the study of other resistant strains.

Advances in computational frameworks in the fight against TB: The way forward

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Recent Advances in Anti-Tuberculosis Drug Discovery Based on Hydrazide-Hydrazone and Thiadiazole Derivatives Targeting InhA

Tuberculosis is an extremely serious problem of global public health. Its incidence is worsened by the presence of multidrug-resistant (MDR) strains of Mycobacterium tuberculosis. More serious forms of drug resistance have been observed in recent years. Therefore, the discovery and/or synthesis of new potent and less toxic anti-tubercular compounds is very critical, especially having in mind the consequences and the delays in treatment caused by the COVID-19 pandemic. Enoyl-acyl carrier protein reductase (InhA) is an important enzyme involved in the biosynthesis of mycolic acid, a major component of the M. tuberculosis cell wall. At the same time, it is a key enzyme in the development of drug resistance, making it an important target for the discovery of new antimycobacterial agents. Many different chemical scaffolds, including hydrazide hydrazones and thiadiazoles, have been evaluated for their InhA inhibitory activity. The aim of this review is to evaluate recently described hydrazide-hydrazone- and thiadiazole-containing derivatives that inhibit InhA activity, resulting in antimycobacterial effects. In addition, a brief review of the mechanisms of action of currently available anti-tuberculosis drugs is provided, including recently approved agents and molecules in clinical trials.

Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds

The cell wall of Mycobacterium tuberculosis and related organisms has a very complex and unusual organization that makes it much less permeable to nutrients and antibiotics, leading to the low activity of many potential antimycobacterial drugs against whole-cell mycobacteria compared to their isolated molecular biotargets. The ability to predict and optimize the cell wall permeability could greatly enhance the development of novel antitubercular agents. Using an extensive structure-permeability dataset for organic compounds derived from published experimental big data (5371 compounds including 2671 penetrating and 2700 non-penetrating compounds), we have created a predictive classification model based on fragmental descriptors and an artificial neural network of a novel architecture that provides better accuracy (cross-validated balanced accuracy 0.768, sensitivity 0.768, specificity 0.769, area under ROC curve 0.911) and applicability domain compared with the previously published results.

Antibiotic Resistance to Mycobacterium tuberculosis and Potential Use of Natural and Biological Products as Alternative Anti-Mycobacterial Agents

Background: Tuberculosis (TB) is an infectious disease caused by the bacillus Mycobacterium tuberculosis (Mtb). TB treatment is based on the administration of three major antibiotics: isoniazid, rifampicin, and pyrazinamide. However, multi-drug resistant (MDR) Mtb strains are increasing around the world, thus, allowing TB to spread around the world. The stringent response is demonstrated by Mtb strains in order to survive under hostile circumstances, even including exposure to antibiotics. The stringent response is mediated by alarmones, which regulate bacterial replication, transcription and translation. Moreover, the Mtb cell wall contributes to the mechanism of antibiotic resistance along with efflux pump activation and biofilm formation. Immunity over the course of TB is managed by M1-macrophages and M2-macrophages, which regulate the immune response against Mtb infection, with the former exerting inflammatory reactions and the latter promoting an anti-inflammatory profile. T helper 1 cells via secretion of interferon (IFN)-gamma, play a protective role in the course of TB, while T regulatory cells secreting interleukin 10, are anti-inflammatory. Alternative therapeutic options against TB require further discussion. In view of the increasing number of MDR Mtb strains, attempts to replace antibiotics with natural and biological products have been object of intensive investigation. Therefore, in this review the anti-Mtb effects exerted by probiotics, polyphenols, antimicrobial peptides and IFN-gamma will be discussed. All the above cited compounds are endowed either with direct antibacterial activity or with anti-inflammatory and immunomodulating characteristics.