Pharmacognosy researchers and medicinal chemists use their expertise and advanced instrumentation to discover or design effective antimycobacterial agents against Mycobacterium tuberculosis, M. leaper, M. bovis, M. phlei, etc. In terms of prevalence and mortality rate, tuberculosis caused by M. tuberculosis is more difficult to control as most of the first-line drugs are slowly becoming ineffective against emerging drug-resistant strains. Thus, it is urgent to explore and develop more active candidates: this is a challenging task for any researcher, drug developer, or pharmaceutical company.
During the mid-twentieth century, antituberculosis drugs namely, streptomycin (1943) and rifampicin (1965) were discovered from natural resources and revolutionized anti-mycobacterial drug discovery. At present, a huge number of natural products and synthetic candidates were reported to be effective against mycobacterium and some of them were effective at extremely low Minimum Inhibitory Concentrations (MIC), > 1 µg/mL. However, these potential candidates have not entered clinical trials or have failed due to toxicity, insufficient solubility, bioavailability, pharmacokinetics and/or drug-likeness profiles.
To overcome the inadequate drug-ability profiles of potential antimycobacterial candidates, we advocate adopting innovative strategies like chemical conjugation, structural hybridization, nano-formulations and structural analysis of prospective antimycobacterial candidates. Additionally, computer-aided drug design, artificial intelligence, machine learning, computational chemistry, and networking pharmacology are also helpful in drug discovery to guide the selection of the most promising candidates before investing in expensive experiments. Although the isolation of natural products and the synthesis of novel compounds will remain the primary source of novel therapeutic candidates, we need to improve our drug development and selection strategy.
The aim of this Research Topic is to gather a collection of original research articles and reviews, that are focused on (but not limited to):
- Isolated or newly synthesized potential antimycobacterial candidates.
- Medicinal and click chemistry to develop potential antimycobacterial candidates.
- Advanced nanotechnology to improve the drug-ability profiles.
- Chemical hybridization, surface modification for druggable antimycobacterial.
- The use of high-throughput bioinformatics and machine-learning approaches for the selection of the most promising antimycobacterial candidates.
- Larger scale structural activity relationships and critical drug prospective analyses towards the utilization of antimycobacterial candidates.
- Alternative strategies to overcome the solubility, bioavailability, pharmacokinetics, and drug-ability profiles of potential candidates.
- Development of databases and tools for antimycobacterial drug discovery.
Pharmacognosy researchers and medicinal chemists use their expertise and advanced instrumentation to discover or design effective antimycobacterial agents against Mycobacterium tuberculosis, M. leaper, M. bovis, M. phlei, etc. In terms of prevalence and mortality rate, tuberculosis caused by M. tuberculosis is more difficult to control as most of the first-line drugs are slowly becoming ineffective against emerging drug-resistant strains. Thus, it is urgent to explore and develop more active candidates: this is a challenging task for any researcher, drug developer, or pharmaceutical company.
During the mid-twentieth century, antituberculosis drugs namely, streptomycin (1943) and rifampicin (1965) were discovered from natural resources and revolutionized anti-mycobacterial drug discovery. At present, a huge number of natural products and synthetic candidates were reported to be effective against mycobacterium and some of them were effective at extremely low Minimum Inhibitory Concentrations (MIC), > 1 µg/mL. However, these potential candidates have not entered clinical trials or have failed due to toxicity, insufficient solubility, bioavailability, pharmacokinetics and/or drug-likeness profiles.
To overcome the inadequate drug-ability profiles of potential antimycobacterial candidates, we advocate adopting innovative strategies like chemical conjugation, structural hybridization, nano-formulations and structural analysis of prospective antimycobacterial candidates. Additionally, computer-aided drug design, artificial intelligence, machine learning, computational chemistry, and networking pharmacology are also helpful in drug discovery to guide the selection of the most promising candidates before investing in expensive experiments. Although the isolation of natural products and the synthesis of novel compounds will remain the primary source of novel therapeutic candidates, we need to improve our drug development and selection strategy.
The aim of this Research Topic is to gather a collection of original research articles and reviews, that are focused on (but not limited to):
- Isolated or newly synthesized potential antimycobacterial candidates.
- Medicinal and click chemistry to develop potential antimycobacterial candidates.
- Advanced nanotechnology to improve the drug-ability profiles.
- Chemical hybridization, surface modification for druggable antimycobacterial.
- The use of high-throughput bioinformatics and machine-learning approaches for the selection of the most promising antimycobacterial candidates.
- Larger scale structural activity relationships and critical drug prospective analyses towards the utilization of antimycobacterial candidates.
- Alternative strategies to overcome the solubility, bioavailability, pharmacokinetics, and drug-ability profiles of potential candidates.
- Development of databases and tools for antimycobacterial drug discovery.