Doortje Esther Lubberts1,2, Dr. Jeroen Koomen1,2, Dr. Douglas Eleveld1, Dr. Pieter Colin1
1Department of Anesthesiology, University Medical Center Groningen, University of Groningen, 2Medicines Evaluation Board (CBG-MEB)
Introduction Long-acting injectable (LAI) products are designed for prolonged drug release, offering benefits such as improved compliance and patient outcomes. For the approval of generic LAI, general EMA guidance requests both a single-dose (SD) and multiple-dose (MD) study, with the MD having a possibility of a waiver if a low risk of accumulation is shown [1,2]. Complex release characteristics hamper generic development including lengthy in vivo pharmacokinetic (PK) bioequivalence (BE) studies due to the need for long wash-out periods because of long apparent half-lives and feasibility issues accompanying safety concerns in healthy volunteers. As a consequence, a limited number of generics have been approved [3]. Allegedly model-based approaches might offer a novel direction in generic product development and regulatory approval [4,5]. We hypothesize that if accumulation can be adequately predicted from SD data, the degree of accumulation at steady-state is also predictable simulating MD studies and using the principle of superposition [6]. Objective This study examines the reliability of model-based extrapolation to LAI MD studies using population pharmacokinetic (popPK) models derived from SD LAI data. Methods SD and MD pharmacokinetic (PK) blood concentration data were collected from the electronic common technical documents (eCTD) submitted to support registration applications at the Medicines Evaluation Board (MEB) in the Netherlands for five different LAI products. To protect the confidentiality of the included LAI products they have been labeled A-E, and the data were pseudo-anonymized before modeling analysis. For the collected SD data, a range of models was tested for each LAI using non-linear mixed-effects modeling (NONMEM®) [7]. To handle samples reported as below the quantification limit (BLQ), the M3 method was applied, and the Laplacian approximation method was used for parameter estimation [7,8]. Several structural absorption models were tested with fixed (THETA) and random effects parameters (ETA) most reflective of the between-subject variability (BSV) [9]. The residual unexplained variability (RUV) was defined using a combined proportional and additive error model. The model where the model diagnostics [10] aligned best was selected as the final model applied for MD simulations. The clinical MD studies’ dosing and sampling time points were used to derive datasets for clinical trial simulation. MD simulations were performed in RxODE [11]. To account for uncertainty in the parameter estimates derived from the SD data, simulations were repeated 1,000 times [12]. For every simulation, a realization of all parameter estimates has been sampled using the NONMEM® covariance matrix [13]. The simulation results have been compared to the observed data via VPCs, and numerical predictive checks of AUC, Cmax, and Cthrough within the latest observed dosing interval, which is assumed to be a steady-state scenario. The acceptance criteria to conclude the simulation is not statistically different from the observed data, the 95% confidence interval (CI) for the geometric mean ratios must include 100%. Results The developed popPK models showed that LAI SD PK is best described using single or double first-order absorption structures, with or without lag time, including one or two distribution compartments, and a combined additive and proportional error model. All PK models included BSV on clearance and central compartment volume of distribution. One model also included BSV on the absorption rate (KA), while the parallel absorption models required BSV on the dose fraction (Fd) and one of the KAs. The 95% CI of Cthrough,ss for products A, B, C, D, and E were 79.8-105.8%, 101.1-133.3%, 46.7-102.3%, 52.3-99.7%, and 51.5-126.9% respectively; Cmax,ss for products A, D, and E were 80.6-98.2%, 104.1-142.5%, and 73.8-122.2%, respectively; and AUC0-t,ss for products A and E were 93.0-115.5% and 62.8-112.6%, respectively. Conclusion First-order absorption models accurately described the SD pharmacokinetics of the included LAIs. The NPCs indicate some significant differences in absorption between the SD and MD scenarios for currently approved LAIs. Nevertheless, VPCs suggest SD models might extrapolate well to MD data. With this work, we established an approach to guide the implementation of model-based extrapolation which might make performing both SD and MD studies to gain generic LAI approval redundant.
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Reference: PAGE 33 (2025) Abstr 11550 [www.page-meeting.org/?abstract=11550]
Poster: Drug/Disease Modelling - Absorption & PBPK