developed. It majorly involves QSAR or pharmacophore-based modeling. In QSAR
modeling, the relationship between physicochemical properties called molecular
descriptors of the known ligands with the biological activity will be expressed as a
regression equation. Further, using this QSAR equation the activities of new
compounds will be predicted. QSAR is based on the assumption that the biological
activity of a compound depends upon the molecular features present in the structure. With the advancement of powerful drug designing algorithms and software,
the process of finding novel drugs has been improved a lot. Several successful drugs
are already being in a market which is developed by computational chemistry and
drug designing strategies. Some of the reported computer-aided drug designing
studies toward the development of novel anti-tubercular agents are already being
discussed in the above sections. With the use of high-end computational techniques,
many research works are carried out by different research groups, and some of the
recent advances in the in silico-based designing of anti-tubercular agents are discussed here.
Many anti-tubercular drug designing strategies are reported in past few years
involving development of structural analogues to existing TB drugs using ligandbased strategies. Aragwal et al. performed VS using a ligand-based pharmacophore
model and identified 95 compounds from small-molecule database. The identified
hits were evaluated further using molecular docking calculations, and 15 short listed
compounds were biological assays [139]. Anquetin et al. carried out QSAR studies
on the quinolone series of compounds. Based on the QSAR results, they synthesized six fluoroquinolones, and four of them were found to be active against Mtb
[142]. In another study, structure- and ligand-based computational models were
developed to identify potential inhibitors for Mtb GlmU. The ligand-based method
involved QSAR analysis of known GlmU inhibitors, and they identified lead
compounds with potential anti-mycobacterial activity [143].
Recently, an in silico-guided polypharmacological approach for drug screening
was reported. This approach considered three Mtb molecular targets, InhA, GlmU,
and DapB with the aim of simultaneous inhibition of several potential targets for the
treatment of drug-resistant TB. So, a combination of pharmacophore and
QSAR-based VS strategy considering these three targets resulted in initial 784 hits
from Asinex database small-molecule library. These hits were further subjected to
molecular docking calculations with other 33 Mtb druggable targets. Finally, 110
potential polypharmacological hits identified; however, they have not tested the
in vitro activity of those hits [144]. Choudhary et al. developed dynamics-based
pharmacophore model for mycobacterial cyclopropane synthase (CmaA1) based on
known inhibitors for the screening molecule library [145].
One of the most efficient computational methods was combinatorial design of
small molecules and screening of these compounds by a ligand-based pharmacophore models. Nandi et al. adopted such a method and generated a combinatorial
library of a series of 3850 fluoroquinolone and isothiazoloquinolone compounds,
which was further VS based on QSAR model against mycobacterial DNA gyrase.
The interactions of hits obtained were compared with the known ligands and 68
compounds, including 34 fluoroquinolones and 34 isothiazoloquinolones which
Impact of Target-Based Drug Design in Anti-bacterial …
331
modeling, the relationship between physicochemical properties called molecular
descriptors of the known ligands with the biological activity will be expressed as a
regression equation. Further, using this QSAR equation the activities of new
compounds will be predicted. QSAR is based on the assumption that the biological
activity of a compound depends upon the molecular features present in the structure. With the advancement of powerful drug designing algorithms and software,
the process of finding novel drugs has been improved a lot. Several successful drugs
are already being in a market which is developed by computational chemistry and
drug designing strategies. Some of the reported computer-aided drug designing
studies toward the development of novel anti-tubercular agents are already being
discussed in the above sections. With the use of high-end computational techniques,
many research works are carried out by different research groups, and some of the
recent advances in the in silico-based designing of anti-tubercular agents are discussed here.
Many anti-tubercular drug designing strategies are reported in past few years
involving development of structural analogues to existing TB drugs using ligandbased strategies. Aragwal et al. performed VS using a ligand-based pharmacophore
model and identified 95 compounds from small-molecule database. The identified
hits were evaluated further using molecular docking calculations, and 15 short listed
compounds were biological assays [139]. Anquetin et al. carried out QSAR studies
on the quinolone series of compounds. Based on the QSAR results, they synthesized six fluoroquinolones, and four of them were found to be active against Mtb
[142]. In another study, structure- and ligand-based computational models were
developed to identify potential inhibitors for Mtb GlmU. The ligand-based method
involved QSAR analysis of known GlmU inhibitors, and they identified lead
compounds with potential anti-mycobacterial activity [143].
Recently, an in silico-guided polypharmacological approach for drug screening
was reported. This approach considered three Mtb molecular targets, InhA, GlmU,
and DapB with the aim of simultaneous inhibition of several potential targets for the
treatment of drug-resistant TB. So, a combination of pharmacophore and
QSAR-based VS strategy considering these three targets resulted in initial 784 hits
from Asinex database small-molecule library. These hits were further subjected to
molecular docking calculations with other 33 Mtb druggable targets. Finally, 110
potential polypharmacological hits identified; however, they have not tested the
in vitro activity of those hits [144]. Choudhary et al. developed dynamics-based
pharmacophore model for mycobacterial cyclopropane synthase (CmaA1) based on
known inhibitors for the screening molecule library [145].
One of the most efficient computational methods was combinatorial design of
small molecules and screening of these compounds by a ligand-based pharmacophore models. Nandi et al. adopted such a method and generated a combinatorial
library of a series of 3850 fluoroquinolone and isothiazoloquinolone compounds,
which was further VS based on QSAR model against mycobacterial DNA gyrase.
The interactions of hits obtained were compared with the known ligands and 68
compounds, including 34 fluoroquinolones and 34 isothiazoloquinolones which
Impact of Target-Based Drug Design in Anti-bacterial …
331