Combining structure-based pharmacophore modeling and machine learning for the identification of novel BTK inhibitors

• Published in: International journal of biological macromolecules Vol. 222; pp. 239 - 250
• Main Authors: Sharma, Tanuj, Saralamma, Venu Venkatarame Gowda, Lee, Duk Chul, Imran, Mohammad Azhar, Choi, Jaehyuk, Baig, Mohammad Hassan, Dong, Jae-June
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2022
• Subjects: Bruton's tyrosine kinase; Machine learning; Pharmacophore; Virtual screening; CADD; FEC; MLC; LR; ROC; Virtual screening; PTKs; KCB; SVM; Bruton's tyrosine kinase; AUC; BCR; RF; Machine learning; MD; BTK; Pharmacophore; ML
• ISSN: 0141-8130; 1879-0003
• DOI: 10.1016/j.ijbiomac.2022.09.151

Bruton's tyrosine kinase (BTK) is a critical enzyme which is involved in multiple signaling pathways that regulate cellular survival, activation, and proliferation, making it a major cancer therapeutic target. We applied the novel integrated structure-based pharmacophore modeling, machine learning, and other in silico studies to screen the Korean chemical database (KCB) to identify the potential BTK inhibitors (BTKi). Further evaluation of these inhibitors on three different human cancer cell lines showed significant cell growth inhibitory activity. Among the 13 compounds shortlisted, four demonstrated consistent cell inhibition activity among breast, gastric, and lung cancer cells (IC50 below 3 μM). The selected compounds also showed significant kinase inhibition activity (IC50 below 5 μM). The current study suggests the potential of these inhibitors for targeting BTK malignant tumors.