ArticleJournal of clinical pharmacology2026
Population Pharmacokinetic Modeling of Niraparib to Assess Different Absorption Models.
Article in Journal of clinical pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Niraparib is a poly(ADP-ribose) polymerase inhibitor approved for maintenance treatment of advanced ovarian cancer. Using data from a previous niraparib population pharmacokinetic (PK) model with data from four studies, this model added phase 1 pharmacokinetic data from two studies. The previous model was a three-compartment model with linear elimination, zero-order drug release into the absorption compartment, lag time, and first-order absorption into the central compartment. With added data, several absorption models, including dual absorption, were explored. Model fit was assessed using standard diagnostics. The resulting updated population PK model had the same three-compartment structure; absorption delays were newly described by three transit compartments. Covariate effects included increase in clearance (CL/F) with increasing baseline albumin (ALB) and creatinine clearance (CrCL); decrease in CL/F with increasing baseline alkaline phosphatase (ALP); increase in relative bioavailability and mean transit time in fed versus fasted/unknown state; increase in central volume of distribution (Vc/F) with increasing ALB and body weight; and increase in first peripheral volume of distribution with increasing ALB. Estimated typical values of CL/F and Vc/F were 15.9 L/h and 450 L. Except for extreme values of ALB, ALP, or CrCL, covariates resulted in minor effects on exposure (≤25% change). A thorough evaluation of additional phase 1 data for absorption established the niraparib reference population PK model. Despite a different absorption structure, estimates of CL/F, total volume of distribution, and covariate effects were similar to the previous model. The niraparib reference population PK model will play a pivotal role for future model-informed drug development.
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