Computational drug repurposing reveals Alectinib as a potential lead targeting Cathepsin S for therapeutic developments against cancer and chronic pain - Alrouji, Mohammed; Alshammari, Mohammed S.; Alhajlah, Sharif; Tasqeeruddin, Syed; Dinislam, Khuzin; Shamsi, Anas; Anwar, Saleha (Frontiers in Bioinformatics, 2025)
Cathepsin S (CathS) is a cysteine protease known to play a role in extracellular matrix (ECM) re-modelling, antigen presentation, immune cells polarisation, and cancer progression and chronic pain pathophysiology. CathS also causes an immunosuppressive environment in solid tumors and is involved in nociceptive signaling. Although several small-molecule inhibitors with favorable in vivo properties have been developed, their clinical utility is limited due to resistance, off-target effects, and suboptimal efficacy. Therefore, alternative therapeutic strategies are urgently needed. In the present study, we utilized an integrated virtual screening protocol to screen 3,500 commercially available FDA-approved drug molecules from DrugBank against the CathS crystal structure, based on which drug-likeness profile and interaction studies were performed to filter putative candidates. Alectinib was found to be a top hit and had significant interactions with the important active-site residues His278 and Cys139. PASS predictions suggested relevant anticancer and anti-pain activities for Alectinib in reference to the control inhibitor Q1N. Later, 500-ns molecular dynamics simulations under the CHARMM36 condition revealed that the CathS-Alectinib complex maintained its structural stability, as indicated by conformational parameters, hydrogen-bond persistence, and essential dynamics analyses. Further MM-PBSA calculations also confirmed a favorable binding free energy (ΔG –20.16 ± 2.59 kcal/mol) dominated by the van der Waals and electrostatic contributions. These computational findings suggest that Alectinib may have potential as a repurposed CathS inhibitor, warranting further experimental testing in relevant cancer and chronic pain models. Notably, these results are based solely on computational analysis and require empirical validation.