title: A proposal for scientific framework enabling specific population drug dosing recommendations.
abstract: Over the last 3 decades, there has been little change in the paradigm to derive dosing recommendations for specific populations (e.g., renal failure, elderly, or obese patients) despite better understanding of clearance pathways in these groups and availability of modeling and simulation tools. Dosing recommendations for specific populations are often incomplete or unavailable at the time of drug approval. Currently, there is no regulatory framework to incorporate model-based dosing recommendations for specific populations. This paper proposes a scientific framework for using modeling and simulation to support specific population dosing recommendations. This framework creates a knowledgebase of drug and population attributes where model-based approaches can be developed to inform dosing recommendations. The framework may benefit patients by having reliable dosing information at the time of drug approval. Patients with conditions where studies are difficult to perform would benefit from dosing based on state-of-the-art knowledge. Industry and regulators would benefit from a scientific and efficient approach to improve specific population prediction. A research approach to determine specific population dose prediction is discussed along with challenges and risks. We hope to initiate a dialogue to explore the role of modeling based on data for drugs with similar clearance mechanisms to predict drug dosing.
authors.affiliation: Quantitative Pharmacology and Pharmacometrics, Merck and Co., Kenilworth, NJ, USA.
authors.forename: Pravin R
authors.initials: PR
authors.lastname: Jadhav
authors.affiliation: Clinical Pharmacology, Pfizer Inc., Groton, CT, USA.
authors.forename: Jack
authors.initials: J
authors.lastname: Cook
authors.affiliation: Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Vikram
authors.initials: V
authors.lastname: Sinha
authors.affiliation: Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Ping
authors.initials: P
authors.lastname: Zhao
authors.affiliation: Systems Pharmacology, University of Manchester, Manchester, UK.
authors.forename: Amin
authors.initials: A
authors.lastname: Rostami-Hodjegan
authors.affiliation: Clinical Pharmacology, Pfizer Inc., Groton, CT, USA.
authors.forename: Vaishali
authors.initials: V
authors.lastname: Sahasrabudhe
authors.affiliation: Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Norman
authors.initials: N
authors.lastname: Stockbridge
authors.affiliation: Independent Consultant, Chapel Hill, NC, USA.
authors.forename: J Robert
authors.initials: JR
authors.lastname: Powell
mesh_terms: D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D004348:Drug Labeling; D004358:Drug Therapy; D006801:Humans; D007880:Legislation, Drug; D008954:Models, Biological; D010599:Pharmacokinetics
keywords: clinical pharmacology; dosing and administration; drug approval; drug development; labeling; modeling and simulation; specific populations