Association of probiotic use with nivolumab efficacy in gastric cancer patients on gastric acid suppressants
Abstract
This study aimed to evaluate the association between concomitant probiotic use and the effectiveness of nivolumab in patients with advanced gastric cancer (GC) receiving gastric acid suppressants (GASs). In this multicenter, retrospective, observational study, conducted in Japan, medical records of consecutive patients with advanced GC treated with nivolumab between September 2017 and March 2024 were reviewed. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using the Cox proportional hazards model. Overall, 249 of the total 416 patients surveyed received GASs. After 1:2 matching, 162 patients were included in this study. The probiotic group showed significantly longer PFS and OS compared to the non-probiotic group (HR, 0.61; 95% CI, 0.43-0.87 and HR, 0.69; 95% CI, 0.48-0.99). These hypothesis-generating findings suggest that probiotics may improve the effectiveness of nivolumab in patients with GC taking GASs.
Read the paper
Related studies
- The Prediction of the In Vitro Release Curves for PLGA-Based Drug Delivery Systems with Neural Networks
- Promising Ursolic Acid as a Novel Antituberculosis Agent: Current Progress and Challenges
- Proof of principle concept for the analysis and functional prediction of rare genetic variants in the CYP2C19 and CYP2D6 genes
- Fabrication, characterization, and docking studies of furosemide-loaded nanosponges using the emulsion solvent diffusion method
- The Effect of Concomitant Administration of Proton Pump Inhibitors on the Pharmacokinetics of CDK4/6 Inhibitors in Rats: Implications for the Evaluation of Hepatic and Transporter-Mediated Drug–Drug Interactions
- Mining the Metabolic Capacity of Clostridium sporogenes Aided by Machine Learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.