Anatoly Pokladyuk1, Veronika Voronova2, Gabriel Helmlinger3, Kirill Peskov2,4,5, Yuri Kosinsky2
1Sirius University of Science and Technology, 2M&S Decisions LLC, 3Quantitative Medicines, 4Research Center of Model-Informed Drug Development, I.M. Sechenov First Moscow State Medical University, 5Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences
Objectives: Poly ADP-ribose polymerase inhibitors (PARPi) have demonstrated significant efficacy in gynecological oncology. However, their clinical application is frequently limited by hematological adverse events, including thrombocytopenia, anemia, and neutropenia [1]. Investigating the pharmacokinetics of niraparib and other PARP inhibitors across diverse populations is critical for elucidating mechanisms underlying observed adverse events and offering tailored dose justification strategies. The key objective of the current work was to develop a physiologically based pharmacokinetic (PBPK) model of niraparib to predict optimal dosage in patients with hepatic impairment and evaluate main pharmacokinetic predictors of niraparib toxicity. Methods: A comprehensive systematic literature review was conducted in PubMed and ClinicalTrials.gov, to gather all sources reporting niraparib PK data, in accordance with PRISMA guidelines. The PBPK model of niraparib was built using the PK-Sim® software (version 11.3). Parameter calibration was performed using an Approximate Bayesian Computation Sequential Monte-Carlo (ABC-SMC) approach [2], which was implemented in R (version 4.0.2). Model development, analysis, validation, and forward simulations were performed using the OSP suite (version 11.3.520) in R. Results: The values of six drug-specific parameters: LogP, pKa, intestinal permeability (Pint), renal plasma clearance (CLren), as well as maximum reaction rate (Vmax) and Michaelis constant (Km) of carboxylesterase 2 (CE2) metabolic transformation were optimized to describe niraparib total plasma concentration-time profiles for various doses from the Phase 1 dose escalation study [3], while being consistent with drug urinary excretion data based on the niraparib human mass balance study [4]. Model validation was conducted against independent data obtained from the systematic literature review. Hepatic impairment categories were set using the National Cancer Institute-Organ Dysfunction Working Group (NCI-ODWG) criterion, which is based on a combination of total bilirubin (TBIL) and aspartate aminotransferase (AST) levels in plasma. To integrate the dependence of niraparib clearance on hepatic impairment severity, the Vmax parameter relevant to CE2 metabolism was considered as a power function of TBIL. The function was calibrated to describe mean niraparib plasma concentrations in patients with moderate hepatic impairment, based on the clinical study by Akce et al. [5]. PK simulations under a 300 mg QD regimen across four hepatic impairment groups categorized by µM values of TBIL (mild, moderate, severe60, severe120) demonstrated an increase in steady-state plasma AUCs vs. the predicted normal value of 5315 µM?min by, respectively, 38%, 65%, 81%, and 113%. Upon dose adjustments to 250 mg for mild, 200 mg for moderate, and 150 mg for severe60 and severe120 categories, simulations predicted mean AUCs which remained within a ±20% range for all compartments, as compared to patients with normal hepatic function receiving the full dose of 300 mg. A local sensitivity analysis was performed, to identify model parameters which affected exposures in bone and gonad compartments the most. The ratio of model-predicted intracellular unbound concentration AUCs in bone-to-gonads demonstrated that pKa and LogP were the most impactful predictors of niraparib toxicity. In particular, a 10% increase in niraparib pKa resulted in a 12% decrease in bone-to-gonads ratio value. Conclusions: The developed PBPK model of niraparib adequately reproduced mean niraparib plasma concentration-time profiles for various drug regimens administered clinically. The model justified the strategy for optimizing dosing regimens in patients with hepatic impairment, by integrating drug concentration predictions from different compartments across varying degrees of hepatic dysfunction. The proposed model can be used further to explore additional niraparib dosing regimens, in support of other safety challenges beyond hepatic impairment.
1. GlaxoSmithKline. Niraparib summary of product characteristics (SmPC). https://www.ema.europa.eu/en/documents/product-information/zejula-epar-product-information_en.pdf 2. Beaumont M, Cornuet J, Marin J, Robert CP. Adaptive approximate Bayesian computation. Biometrika. 2009;96(4):983-990. doi:10.1093/biomet/asp052 3. Sandhu SK, Schelman WR, Wilding G, et al. The poly(ADP-ribose) polymerase inhibitor niraparib (MK4827) in BRCA mutation carriers and patients with sporadic cancer: a phase 1 dose-escalation trial. Lancet Oncol. 2013;14(9):882-892. doi:10.1016/S1470-2045(13)70240-7 4. van Andel L, Zhang Z, Lu S, et al. Human mass balance study and metabolite profiling of 14C-niraparib, a novel poly(ADP-Ribose) polymerase (PARP)-1 and PARP-2 inhibitor, in patients with advanced cancer. Invest New Drugs. 2017;35(6):751-765. doi:10.1007/s10637-017-0451-2 5. Akce M, El-Khoueiry A, Piha-Paul SA, et al. Pharmacokinetics and safety of niraparib in patients with moderate hepatic impairment. Cancer Chemother Pharmacol. 2021;88(5):825-836. doi:10.1007/s00280-021-04329-8
Reference: PAGE 33 (2025) Abstr 11633 [www.page-meeting.org/?abstract=11633]
Poster: Drug/Disease Modelling - Absorption & PBPK