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Targeting Dormant Tuberculosis Bacilli: Results for Molecules with a Novel Pyrimidone Scaffold
Author(s) -
Joshi Rohit R.,
Barchha Avinash,
Khedkar Vijay M.,
Pissurlenkar Raghuvir R. S.,
Sarkar Sampa,
Sarkar Dhiman,
Joshi Rohini R.,
Joshi Ramesh A.,
Shah Anamik K.,
Coutinho Evans C.
Publication year - 2015
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.12373
Subject(s) - bacilli , tuberculosis , virulence , computational biology , drug discovery , quantitative structure–activity relationship , chemistry , drug , strain (injury) , combinatorial chemistry , biology , stereochemistry , bacteria , microbiology and biotechnology , biochemistry , medicine , pharmacology , genetics , gene , pathology , anatomy
Our inability to completely control TB has been due in part to the presence of dormant mycobacteria. This also renders drug regimens ineffective and is the prime cause of the appearance of drug‐resistant strains. In continuation of our efforts to develop novel antitubercular agents that especially target dormant mycobacteria, a set of 55 new compounds belonging to the pyrimidone class were designed on the basis of Co MFA and Co MSIA studies, and these were synthesized and subsequently tested against both the dormant and virulent BCG strain of M. tuberculosis . Some novel compounds have been identified which selectively inhibit the dormant tuberculosis bacilli with significantly low IC 50 values. This study reports the second molecule after TMC ‐207, having the ability to inhibit tuberculosis bacilli exclusively in its dormant phase. The synthesis was accomplished by a modified multicomponent Biginelli reaction. A classification model was generated using the binary QSAR approach – recursive partitioning ( RP ) to identify structural characteristics related to the activity. Physicochemical, structural, topological, connectivity indices, and E‐state key descriptors were used for generation of the decision tree. The decision tree could provide insights into structure–activity relationships that will guide the design of more potent inhibitors.