Ligand- and Structure-Based Drug Repurposing by Morgan Fingerprint: Reveals Azelastine as a Candidate Therapy for G1269A Mutant ALK in Metastatic NSCLC

A severe type of lung cancer that spreads to other body areas and gets worse over time is called metastatic non-small cell lung cancer NSCLC). Anaplastic lymphoma kinase (ALK) gene mutations are a significant cause of NSCLC. When ALK is altered, it becomes overactive and leads to unchecked cancer cell division. Normally, ALK aids in controlling cell development. One such mutation is G1269A, which modifies the structure of the ALK protein and decreases the efficacy of some medications that target ALK, resulting in drug resistance and the advancement of the disease. It is difficult to find novel medications that can treat the ALK G1269A mutation. Ligand-based drug repurposing, a computer-based method that searches for current medications that might be effective against novel targets, is one helpful tactic. Morgan fingerprint similarity was used in this investigation to choose potential medications. The FDA-approved medications were the focus of this investigation. Structure-based docking is performed using CB-Dock. This study explores the possibility that the antihistaminic drug azelastine may bind to the ALK G1269A mutant protein more effectively than brigatinib. Brigatinib is a targeted cancer medication that inhibits the growth of tumour cells and stops dangerous cancer cells from spreading by inhibiting the activity of the ALK enzyme. Similar to brigatinib, azelastine can bind steadily within the mutant ALK protein's active region, according to computer simulations. Strong contacts, such as hydrogen bonds and hydrophobic interactions, are formed by azelastine with important amino acids surrounding the G1269A mutation site, which aid in stabilising the drug–protein complex. Azelastine may be a promising repurposed medication for treating metastatic non-small cell lung cancer with the ALK G1269A mutation, according to this study. In order to find new therapeutic applications for already available medications, this work presents a novel computational method that combines ligand-based similarity analysis and structure-based docking.