Impact of Drug–Drug Interactions on Clinical Outcomes in Metastatic Melanoma Patients Treated With Combined BRAF/MEK Inhibitors: A Real-World Study

Abstract

The unique pharmacokinetics of BRAF and MEK inhibitors make patients vulnerable to drug–drug interactions (DDIs), which may compromise treatment efficacy in metastatic melanoma. This study evaluates the impact of DDIs on clinical outcomes in patients with metastatic melanoma treated with BRAF/MEK inhibitors. This multicenter, observational, retrospective study assessed DDIs using the Drug-PIN software. Associations between the Drug-PIN continuous score, Drug-PIN light, and treatment outscomes were analyzed along with the specific drugs involved in the DDIs. A total of 177 patients with BRAF-mutant metastatic melanoma undergoing BRAF/MEK inhibitor therapy were included. Of these, 94 patients (55.9%) were exposed to complex drug regimens related to comorbidities, supportive care, and symptom management. A significant change in Drug-PIN scores was observed before and after therapy initiation. Patients with low-grade DDIs demonstrated significantly longer median overall survival (OS) and progression-free survival (PFS) compared to those with high-grade DDIs (log-rank p = 0.0045 and p = 0.012, respectively); this observation was further validated by multiple regression analysis. By combining clinical and DDI data, we identified four patient subgroups with distinct prognoses, showing statistically significant differences in OS and PFS (log-rank p < 0.0001). The subgroup with the highest clinical risk and high DDI had markedly poorer outcomes (HR 2.87, 95% CI [1.7–4.8], p < 0.001). The drugs involved in high-level pharmacological interactions were analyzed. DDIs significantly contribute to poorer OS and PFS outcomes, independent of other clinical risk factors. Optimizing pharmacological regimens to minimize DDIs should be prioritized to enhance treatment efficacy in oncology. Prospective clinical trials are warranted to further validate the advantages of individualized, preemptive therapy optimization.