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Design and synthesis of novel benzimidazole linked thiazole derivatives as promising inhibitors of drug-resistant tuberculosis

Authors
Veena, K.Raghu, Madihalli SrinivasYogesh, Kumar K.Pradeep, Kumar C.B.Alharti, Fahad A.Prashanth, Maralekere KrishnegowdaJeon, Byong-Hun
Issue Date
Dec-2022
Publisher
Elsevier B.V.
Keywords
Benzimidazoles; Thiazoles; Tuberculosis; Cytotoxicity; Molecular docking
Citation
Journal of Molecular Structure, v.1269, pp.1 - 9
Indexed
SCIE
SCOPUS
Journal Title
Journal of Molecular Structure
Volume
1269
Start Page
1
End Page
9
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185396
DOI
10.1016/j.molstruc.2022.133822
ISSN
0022-2860
Abstract
Despite combination therapy is effective in treating tuberculosis (TB) caused by susceptible Mycobacterium tuberculosis (Mtb) strains, overcoming multidrug resistance (MDR) remains a problem. As a result, it is critical to continue developing new chemotherapeutic drugs with unique mechanisms of action. A variety of benzimidazole-linked thiazole compounds was designed and developed for the treatment of Mtb in this work using a molecular hybridization approach. The newly synthesized compounds demonstrated potent anti-TB action against drug-resistant Mtb strains. Compounds 4g and 4h are more effective against MDR clinical isolates (CDCT-16 and CDCT-27) than isoniazid, with MIC ranges ranging from 3.68 to 8.15 µM. Other derivatives demonstrated moderate to good anti-TB efficacy against MDR clinical isolates. The cytotoxicity findings recommend that this class of compounds is rated as low. Further SAR analysis reveals that electron-withdrawing groups on the thiazole ring of the compounds are essential. The findings were also connected with molecular docking studies, which provided models for the inhibitors strong interactions with the mycobacterial membrane protein large 3 (MmpL3), which is a critical member in the formation of the Mtb stiff cell wall. Compounds potential ADMET descriptors, drug score and drug-likeness properties were validated by in-silico predictions.
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