were selected as potential leads [146]. Similarly, Singh et al. identified substituted
hydrazine carbothioamide as potent anti-tubercular agents by using QSAR studies
[147]. In other studies, Mtb DprE1 inhibitors are identified by employing molecular
docking calculations [148, 149].
In order to find novel drug candidates, the understanding of pathogenesis of the
underlying disease needs to be done. Kandasami et al. used bioinformatics techniques involving pharmacophore modeling to identify the catalytic residues of PknI,
and those residues were experimentally validated by site-directed mutagenesis
technique. This active site residue information was used in identifying an inhibitor
specific to PknI, which was further validated by laboratory experiments [150].
Several other computer-aided drug designing studies are reported involving various
in silico techniques comprising of homology modeling, QSAR, pharmacophore
modeling, molecular docking-based VS, etc., against various Mtb drug targets
[151–154].
Apart from the in silico drug screening techniques, computational approaches
are also used to build databases of compounds for the ease of VS them toward
various druggable targets. Prakash et al. developed an anti-tubercular compound
database and a data mining procedure in the search for novel anti-tubercular agents
and targets. They computed a minimum common bioactive substructure (MCBS)
responsible for the activity of anti-tubercular agents by employing QSAR and
pharmacophore modeling techniques [155]. This database of compounds will help
to identify potential compounds with the structural feature similar to the known
anti-tubercular drugs. Dalecki et al. developed an easy-to-use software solution for
streamlining, processing, and analysis of biological screening data for Mtb. This
software also offers a scaffold of compounds from screening data, which will further
expand the scope of finding new compounds with improved activity [156].
A database, BioPhytMol, was designed to store and analyze the anti-mycobacterial
phytomolecules and plant extracts, which would in future have immense potential
as a drug discovery resource [157].
5 Novel TB Drugs in the Clinical Pipeline
Several new compounds as well as repurposed drugs for TB treatment are now in
the clinical pipeline and are listed in Table 5. Auranofin is enrolled in the Phase 2
clinical trials for TB, which is an anti-rheumatic agent. Auranofin targets
mycobacterial thioredoxin reductase, and in a cell-based screen, the drug exhibited
anti-tubercular activity against non-replicating Mtb [158]. Nitazoxanide is another
repurposed drug for TB, now in Phase 2 clinical trials. It is an FDA-approved
anti-protozoal agent [159]. GSK-286 is a new chemical class having a novel
mechanism of action against Mtb. It targets mycobacterial cholesterol catabolism,
and the compound was found to penetrate into the necrotic lesions and kill intracellular Mtb with an MIC of > 10 µM. GSK070 belongs to the oxaborole chemical
class, shown to inhibit mycobacterial LeuRS enzyme. It has been shown to be
332
A. C. Pushkaran et al.
hydrazine carbothioamide as potent anti-tubercular agents by using QSAR studies
[147]. In other studies, Mtb DprE1 inhibitors are identified by employing molecular
docking calculations [148, 149].
In order to find novel drug candidates, the understanding of pathogenesis of the
underlying disease needs to be done. Kandasami et al. used bioinformatics techniques involving pharmacophore modeling to identify the catalytic residues of PknI,
and those residues were experimentally validated by site-directed mutagenesis
technique. This active site residue information was used in identifying an inhibitor
specific to PknI, which was further validated by laboratory experiments [150].
Several other computer-aided drug designing studies are reported involving various
in silico techniques comprising of homology modeling, QSAR, pharmacophore
modeling, molecular docking-based VS, etc., against various Mtb drug targets
[151–154].
Apart from the in silico drug screening techniques, computational approaches
are also used to build databases of compounds for the ease of VS them toward
various druggable targets. Prakash et al. developed an anti-tubercular compound
database and a data mining procedure in the search for novel anti-tubercular agents
and targets. They computed a minimum common bioactive substructure (MCBS)
responsible for the activity of anti-tubercular agents by employing QSAR and
pharmacophore modeling techniques [155]. This database of compounds will help
to identify potential compounds with the structural feature similar to the known
anti-tubercular drugs. Dalecki et al. developed an easy-to-use software solution for
streamlining, processing, and analysis of biological screening data for Mtb. This
software also offers a scaffold of compounds from screening data, which will further
expand the scope of finding new compounds with improved activity [156].
A database, BioPhytMol, was designed to store and analyze the anti-mycobacterial
phytomolecules and plant extracts, which would in future have immense potential
as a drug discovery resource [157].
5 Novel TB Drugs in the Clinical Pipeline
Several new compounds as well as repurposed drugs for TB treatment are now in
the clinical pipeline and are listed in Table 5. Auranofin is enrolled in the Phase 2
clinical trials for TB, which is an anti-rheumatic agent. Auranofin targets
mycobacterial thioredoxin reductase, and in a cell-based screen, the drug exhibited
anti-tubercular activity against non-replicating Mtb [158]. Nitazoxanide is another
repurposed drug for TB, now in Phase 2 clinical trials. It is an FDA-approved
anti-protozoal agent [159]. GSK-286 is a new chemical class having a novel
mechanism of action against Mtb. It targets mycobacterial cholesterol catabolism,
and the compound was found to penetrate into the necrotic lesions and kill intracellular Mtb with an MIC of > 10 µM. GSK070 belongs to the oxaborole chemical
class, shown to inhibit mycobacterial LeuRS enzyme. It has been shown to be
332
A. C. Pushkaran et al.