Publication | Open Access
Physiologically based and population PK modeling in optimizing drug development: A predict–learn–confirm analysis
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Citations
12
References
2015
Year
PharmacotherapyPhysiologically-based Pharmacokinetic ModelingPharmacokineticsPharmacodynamic ModelingBiostatisticsChronic Kidney DiseaseClassical Population PharmacokineticPharmacokinetic ModelingDrug DevelopmentBiomedical ModelingPharmacologyPopulation Pk ModelingPredict–learn–confirm AnalysisUrologyPhysiologically Based PharmacokineticsRational Drug DesignDedicated Renal ImpairmentMedicineNephrologyDrug DiscoveryPharmaceutical ResearchSevere Ri
Physiologically based pharmacokinetic (PBPK) modeling and classical population pharmacokinetic (PK) model-based simulations are increasingly used to answer various drug development questions. In this study, we propose a methodology to optimize the development of drugs, primarily cleared by the kidney, using model-based approaches to determine the need for a dedicated renal impairment (RI) study. First, the impact of RI on drug exposure is simulated via PBPK modeling and then confirmed using classical population PK modeling of phase 2/3 data. This methodology was successfully evaluated and applied to an investigational agent, orteronel (nonsteroidal, reversible, selective 17,20-lyase inhibitor). A phase 1 RI study confirmed the accuracy of model-based predictions. Hence, for drugs eliminated primarily via renal clearance, this modeling approach can enable inclusion of patients with RI in phase 3 trials at appropriate doses, which may be an alternative to a dedicated RI study, or suggest that only a reduced-size study in severe RI may be sufficient.
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