Synthesis, antimycobacterial activity evaluation, and QSAR studies of chalcone derivatives

• Published in: Bioorganic & medicinal chemistry letters 2007-03, Vol.17 (6), p.1695-1700
• Main Authors: Sivakumar, P.M., Seenivasan, S. Prabu, Kumar, Vanaja, Doble, Mukesh
• Format: Article
• Language: English
• Subjects: Aldehydes - chemistry; Anti-Bacterial Agents - chemical synthesis; Anti-Bacterial Agents - pharmacology; Antibacterial agents; Antibiotics. Antiinfectious agents. Antiparasitic agents; Antimycobacterial activity; Artificial Intelligence; Bacteriophages - genetics; Biological and medical sciences; Chalcones; Chalcones - chemical synthesis; Chalcones - pharmacology; Chemical Phenomena; Chemistry, Physical; Computer Simulation; Genes, Reporter - genetics; Indicators and Reagents; Luciferases - genetics; Medical sciences; Microbial Sensitivity Tests; Mycobacterium - drug effects; Pharmacology. Drug treatments; QSAR; Quantitative Structure-Activity Relationship
• ISSN: 0960-894X; 1464-3405
• DOI: 10.1016/j.bmcl.2006.12.112

Twenty-five chalcones were synthesized, and their activity was evaluated by luciferase reporter phage (LRP) assay, and quantitative structure–activity relationship (QSAR) was developed. In order to develop relatively small molecules as antimycobacterial agents, twenty-five chalcones were synthesized, their activity was evaluated, and quantitative structure–activity relationship (QSAR) was developed. The synthesis was based on the Claisen-Schimdt scheme and the resultant compounds were tested for antitubercular activity by luciferase reporter phage (LRP) assay. Compound C 24 was found to be the most active (∼99%) in this series based on the percentage reduction in Relative Light Units at both 50 and 100 μg/ml levels, followed by compound C 21 . Four compounds at the 50 μg/ml and eight compounds at the 100 μg/ml showed activity above 90% level. QSAR model was developed between activity and spatial, topological, and ADME descriptors for the 50 μg/ml data. The statistical measures such as r, r 2, q 2, and F values obtained for the training set were in acceptable range and hence this relationship was used for the test set. The predictive ability of the model is satisfactory ( q 2 = 0.56) and it can be used for designing similar group of compounds.