Population Pharmacokinetic and Pharmacodynamic Modeling of Romiplostim Biosimilar GP40141 and Reference Product in Healthy Volunteers to Evaluate Biosimilarity.

GP40141 is a romiplostim biosimilar. A Phase 1 clinical trial was previously conducted in healthy volunteers to evaluate the pharmacodynamics (PD), pharmacokinetics (PK), and safety of GP40141 compared to the reference romiplostim (NCT05652595). Using noncompartmental analysis, the biosimilarity of PD end points was determined according to the classical criterion (0.8-1.25). PK end points were also in good agreement between GP40141 and the reference romiplostim; however, the confidence interval for the area under concentration-time curve from time 0 to the time of last measurement was slightly out of the bioequivalence range (0.91-1.29). Population PK/PD was used in the present study to characterize the individual PK and PD data of 56 healthy subjects in 2 cross-over periods of the Phase 1 clinical trial. Body weight and neutralizing antibodies to romiplostim were found to be important predictors of apparent volume of distribution and linear elimination constant, respectively. Within the framework of the conducted modeling, population estimates of PK/PD parameters were obtained, which were in agreement with literature data for the reference romiplostim. Additionally, values of intersubject variability, previously unreported for romiplostim in a healthy subject population, were derived. Covariate analysis, conducted during model development, as well as visual diagnostics and model-based simulations, demonstrated the absence of significant differences in PK and PD between GP40141 and romiplostim-ref.

A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics.

Clinical trials investigate treatment endpoints that usually include measurements of pharmacodynamic and efficacy biomarkers in early-phase studies and patient-reported outcomes as well as event risks or rates in late-phase studies. In recent years, a systematic trend in clinical trial data analytics and modeling has been observed, where retrospective data are integrated into a quantitative framework to prospectively support analyses of interim data and design of ongoing and future studies of novel therapeutics. Joint modeling is an advanced statistical methodology that allows for the investigation of clinical trial outcomes by quantifying the association between baseline and/or longitudinal biomarkers and event risk. Using an exemplar data set from non-small cell lung cancer studies, we propose and test a workflow for joint modeling. It allows a modeling scientist to comprehensively explore the data, build survival models, investigate goodness-of-fit, and subsequently perform outcome predictions using interim biomarker data from an ongoing study. The workflow illustrates a full process, from data exploration to predictive simulations, for selected multivariate linear and nonlinear mixed-effects models and software tools in an integrative and exhaustive manner.