AI & experimental-based discovery and preclinical IND-enabling studies of selective BMX inhibitors for development of cancer therapeutics

Rwan Elsanhoury; Abdulaziz Alasmari; Prashanth Parupathi; Mouhannad Jumaa; Suliman Al-Fayoumi; Avinash Kumar; Raed Khashan; Sami Nazzal; Ahmed Abu Fayyad

doi:10.1016/j.ijpharm.2023.123384

AI & experimental-based discovery and preclinical IND-enabling studies of selective BMX inhibitors for development of cancer therapeutics

Int J Pharm. 2023 Oct 15:645:123384. doi: 10.1016/j.ijpharm.2023.123384. Epub 2023 Sep 8.

Authors

Rwan Elsanhoury¹, Abdulaziz Alasmari¹, Prashanth Parupathi¹, Mouhannad Jumaa², Suliman Al-Fayoumi², Avinash Kumar¹, Raed Khashan¹, Sami Nazzal³, Ahmed Abu Fayyad⁴

Affiliations

¹ Division of Pharmaceutical Sciences, Arnold and Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, NY, USA.
² Fekra Therapeutics Inc., Boston, MA, USA.
³ College of Pharmacy, Texas Tech University Health Sciences Center, Dallas, TX, USA.
⁴ Division of Pharmaceutical Sciences, Arnold and Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, NY, USA. Electronic address: Ahmed.AbuFayyad@liu.edu.

PMID: 37678472
DOI: 10.1016/j.ijpharm.2023.123384

Abstract

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 oncological and immunological diseases. In this work, we have employed a predictive AI-based platform to design the selective inhibitors considering the novelty, IP prior protection, and drug-likeness properties. Furthermore, selected top candidates from the initial iteration of the design were synthesized and chemically characterized utilizing ¹H NMR and LC-MS. Employing a panel of biochemical (enzymatic) and cancer cell lines, the selected molecules were tested against these assays. In addition, we used artificial intelligence to predict and evaluate several critical IND-focused physicochemical and pharmacokinetics values of the selected molecules. A secondary objective of the current work was also to validate the sole role of BMX in animal models known to be mediated by BMX. More than 50 molecules were designed in the present study employing five novel discovered scaffolds. Two molecules were nominated for further IND-focused studies. Compound II showed promising in-vitro activity against BMX in both enzymatic assays compared to other kinases and in cancer cell lines with known BMX overexpression. Interestingly, compound II showed very favorable physicochemical and pharmacokinetics properties as predicted by the used platforms. The animal study further confirmed the sole role of BMX in the disease model. The current work provides promising data on a selective BMX inhibitor as a potential lead for therapeutics development, and the asset is currently in the optimization stage. Notably, the current study shows a framework for a combined approach employing both AI and experimentation that can be used by academic labs in their research programs to more streamline programs into IND-focused to be bridged easily for further clinical development with industrial partners.

Keywords: Academia-industry bridging; Cancer therapeutics development; Discovery pharmaceutics; IND-enabling; Novel targets; Predictive AI-based platform; Translational pharmaceutics.