Using PBPK Modeling to support an adaptive “First-in-Pediatric” trial design of Sonlicromanol for the treatment of Primary Mitochondrial Disease
Stephan Schaller (1), Rob van Maanen (2), Gerritt Ruiterkamp (2), Jan Smeitink (2) , Herma Renkema (2)
(1) esqLABS GmbH, Saterland, Germany, (2) Khondrion BV, Transistorweg 5C, M Building, 6534, AT, Nijmegen, the Netherlands
Objectives: Primary mitochondrial diseases (PMD) are rare genetic disorders that affect cellular energy production, resulting in severe multisystem dysfunction. PMD has a significant impact on children, and effective therapies are urgently needed. Sonlicromanol (KH176) is a promising orally available small molecule drug developed to treat PMDs [1], which is a CYP3A4 and P-gp substrate. To support the design and conduct of a clinical trial for children aged from birth to 18 years, this study aimed to use a physiologically-based pharmacokinetic (PBPK) model-based approach to establish dosing regimens and sampling times for sonlicromanol in an adaptive “first-in-pediatric” randomized placebo-controlled, double-blind phase II study in children [2].
Methods: A PBPK model was developed for sonlicromanol and its metabolite KH176m based on single and multiple dose studies in adults using PK-Sim from the Open Systems Pharmacology Suite (www.open-systems-pharmacology.org [3]). The model used ontogeny and physiology information for children contained in the modeling platform to extrapolate PK to pediatric populations to establish the paediatric equivalent dose and optimal sampling times [4]. An adaptive PK study was conducted as a first phase of the Phase II trial to confirm the dose by a data safety and monitoring board (DSMB) before the 6-month phase of the trial. The PBPK model was used to analyze the data from the adaptive PK study to support the confirmation of dose by the DSMB. The PBPK-based PK predictions were re-evaluated with the PK data of each age group to evaluate and confirm the paediatric equivalent dose. Elder age groups were studied before their younger counterparts for continuous verification and evaluation of the PBPK model predictions to mitigate risk.
Results: The PBPK model well-characterized observed PK data of the adaptive study with the model simulations for sonlicromanol and its metabolite KH176m. Individual PK profiles could be matched by the model across all studied age ranges with reasonable adjustments of model input parameters in line with the range of population variation as observed in the adult population data used for the initial PBPK model development.
The PBPK model also provided a consistent characterization of the population-level PK of sonlicromanol and KH176m within the age groups. The PBPK-based analysis of the adaptive PK study thus did confirm the previously defined PEDs and concluded that no adjustment in the initial dose recommendations are warranted.
Conclusions: This study demonstrated the usefulness of PBPK modeling and simulation in establishing dosing regimens and sampling times for "first-in-pediatric" trials of sonlicromanol for the treatment of PMD in children. The PBPK-based approach also allowed for continuous verification and evaluation of the model predictions across age groups to mitigate risk, which was important in an adaptive trial design. Overall, this M&S study provides critical insights for the safe and successful development of new therapies for pediatric patients with PMD, which is currently an unmet need.
References:
[1] Beyrath J, Pellegrini M, Renkema H, Houben L, Pecheritsyna S, van Zandvoort P, et al. KH176 Safeguards Mitochondrial Diseased Cells from Redox Stress-Induced Cell Death by Interacting with the Thioredoxin System/Peroxiredoxin Enzyme Machinery. Sci Rep. 2018;8:6577. https://doi.org/10.1038/s41598-018-24900-3 [2] Smeitink J, Maanen R van, Boer L de, Ruiterkamp G, Renkema H. A randomised placebo-controlled, double-blind phase II study to explore the safety, efficacy, and pharmacokinetics of sonlicromanol in children with genetically confirmed mitochondrial disease and motor symptoms (“KHENERGYC”). BMC Neurol [Internet]. 2022 [cited 2023 Jan 4];22. Available from: https://link.springer.com/epdf/10.1186/s12883-022-02685-3https://doi.org/10.1186/s12883-022-02685-3 [3] Lippert J, Burghaus R, Edginton A, Frechen S, Karlsson M, Kovar A, et al. Open Systems Pharmacology community - an open access, open source, open science approach to modeling and simulation in pharmaceutical sciences. CPT Pharmacomet Syst Pharmacol. 2019; https://doi.org/10.1002/psp4.12473 [4] Edginton AN, Schmitt W, Willmann S. Development and evaluation of a generic physiologically based pharmacokinetic model for children. Clin Pharmacokinet. 2006;45:1013–34. https://doi.org/10.2165/00003088-200645100-00005
