Exposure–response relationship of sintilimab in advanced gastric cancer: insights from time-dependent pharmacokinetics and biomarker integration for precision immunotherapy

Background Immune checkpoint inhibitors (ICIs), particularly programmed cell death protein-1 (PD-1) antibodies, exhibit substantial interpatient variability in both efficacy and immune-related adverse events (irAEs). Conventional exposure-response (E-R) paradigms are often insufficient to capture these dynamics, limiting their utility for individualized immunotherapy. Objective To critically re-evaluate the E-R relationship of Sintilimab in advanced gastric cancer (AGC) by integrating pharmacokinetics (PK), host- and tumor-related determinants, and multidimensional biomarkers. Methods This review examines and critically discusses published PK and E-R data on Sintilimab and other PD-1/PD-L1 inhibitors, with a focus on time-dependent clearance, target-mediated drug disposition (TMDD), and inter-individual variability. Clinical and mechanistic studies linking exposure metrics, clearance (CL), efficacy, and irAEs were critically analyzed, and key covariates influencing drug exposure were identified. Results Time-dependent CL confounds traditional exposure metrics. CL may serve as a surrogate marker reflecting host physiology, tumor burden, and systemic inflammation. Multi-level biomarkers modulate the translation of drug exposure into therapeutic efficacy and toxicity. Conceptually, the exposure–efficacy relationship of PD-1 inhibitors is more likely to be saturable or plateau-like after sufficient target occupancy, whereas higher exposure levels may increase the risk of irAEs without substantially enhancing efficacy beyond target saturation. Thus, the concept of an optimal benefit-risk range, rather than a simple “higher-exposure-is-better” strategy, is more appropriate for describing the E-R relationship of PD-1 inhibitors. Conclusion We conceptualize a dynamic exposure–response framework for Sintilimab in AGC by integrating time-dependent clearance, disease-state confounding, biomarker modulation, therapeutic drug monitoring (TDM), and model-informed precision dosing (MIPD). This framework provides a conceptual basis for optimizing individualized therapy and improving the benefit-risk balance in patients with AGC.