Comprehensive population pharmacokinetic modelling of sugemalimab, an anti-programmed death-ligand 1 (PD-L1) human...

The aim of this study was to develop a population pharmacokinetics model for sugemalimab, a monoclonal antibody that targets programmed death-ligand 1 (PD-L1), using data from Phase I-III trials and to assess clinical factors affecting sugemalimab exposure.

A nonlinear mixed-effect modelling approach was employed to analyse pooled data from nine studies involving 1628 subjects to characterize the PopPK of sugemalimab. This investigation examined the influence of various covariates on sugemalimab pharmacokinetics (PK), encompassing demographics, baseline hepatic and renal function-related covariates, and others (including anti-drug antibody [ADA], combination treatment, Eastern Cooperative Oncology Group [ECOG] performance score, tumour burden and tumour type). Estimation accuracy and predictive ability of the final model were evaluated using various methods. The influence of covariates on sugemalimab exposure was assessed by simulation from the final model.

A two-compartment model with first-order elimination and time-varying clearance effectively described the PK of sugemalimab. Covariate analyses revealed significant relationships between sugemalimab clearance and body weight, albumin, gender, ADA, tumour burden and tumour type. The statistically significant covariates on central volume were body weight, albumin, gender and tumour type. No significant relationships were found in the final model for age, race, alanine aminotransferase, aspartate aminotransferase, creatinine, total bilirubin, alkaline phosphatase, combination treatment, creatinine clearance, ECOG, renal function or hepatic function. All significant covariates demonstrated less than a 20% effect on sugemalimab exposure.

The PopPK model adequately described the pharmacokinetic profile of sugemalimab with no clinically meaningful impact observed on its exposure across all covariates. Dose adjustment does not appear to be necessary.