A Systems Biology and Artificial Intelligence Approach to Unveil Brigatinib’s Pharmacological Mechanism in Brain Metastases in ALK+ Non-Small Cell Lung Cancer

Background/Objectives: Brain metastases (BM) are a major challenge in the treatment of non-small cell lung cancer (NSCLC), particularly among patients with anaplastic lymphoma kinase rearrangements (ALK+ NSCLC), where incidence can reach up to 60% during the course of the disease. This study used in silico systems biology and artificial intelligence-based modeling to investigate the mechanistic effects of brigatinib, a second-generation ALK inhibitor, on metastatic processes in both primary tumors (PT) and established BM. Methods: We applied the Therapeutic Performance Mapping System (TPMS) technology, which integrates systems biology and artificial intelligence, to simulate the impact of brigatinib on metastasis-associated pathways in PT and BM of ALK+ NSCLC patients. Results: In these simulations, brigatinib was predicted to modulate a broad set of proteins implicated in metastasis in both PT and BM, acting mainly through IGF1R, EGFR, FLT3, and ROS1, in addition to its known target ALK. Conclusions: These results suggest brigatinib’s potential to impact key pathways involved in metastatic progression and intracranial disease control. Overall, this study provides insights into brigatinib’s multifaceted role in targeting metastatic processes in ALK+ NSCLC, underscoring its potential benefits in both PT and BM. Nonetheless, further experimental and clinical studies would confirm our results and the potential of in silico models reported here.