The current work aims to design and provide a preliminary IND-enabling study of selective BMX inhibitors for cancer therapeutics development. BMX is an emerging target, more notably in oncology. In this work, we have employed a predictive AI-based platform to design the selective inhibitors. Furthermore, selected top candidates were synthesized and chemically characterized. Employing a panel of biochemical and cancer cell lines, the selected molecules were tested. In addition, we used AI to predict and evaluate several critical IND-focused physicochemical and pharmacokinetics values of the selected molecules. More than 50 molecules were designed, and two molecules were nominated for further IND-focused studies. Compound II showed promising in-vitro activity against BMX in both enzymatic and cancer cell lines. Interestingly, compound II showed very favorable physicochemical and pharmacokinetics properties as predicted by used platforms. The current work provides promising data on a selective BMX inhibitor as a potential lead for therapeutics development.
Learning Objectives:
Upon completion, participants will be able to discover how to develop an AI's mindset to facilitate drug development from an idea to patient.
Upon completion, participants will be able to understand how to implement such a mindset to navigate through discovery & development of therapeutics for an emerging target.
Upon completion, participants will be able to understand the importance of Continues maintenance of such a mindset to further move the process forward.