Drug pharmacokinetics in the obese population: challenging common assumptions on predictors of obesity-related parameter changes. (3rd October 2022)

• Record Type: Journal Article
• Authors: Zhang, Tan; Krekels, Elke H.J.; Smit, Cornelis; Knibbe, Catherijne A.J.
• Is Part Of: Expert opinion on drug metabolism and toxicology. Volume 18:Number 10(2022)
• Page Start: 657
• Page End: 674
• Publication Date: 2022-10-03
• Subjects: Clearance -- drug dosing -- obesity -- pharmacokinetics -- volume of distribution; Drugs -- Toxicology -- Periodicals; Drugs -- Metabolism -- Periodicals; 615.7
• DOI: 10.1080/17425255.2022.2132931
• Languages: English
• ISSNs: 1742-5255

Abstract

ABSTRACT: Introduction: Obesity is associated with many physiological changes. We review available evidence regarding five commonly accepted assumptions to a priori predict the impact of obesity on drug pharmacokinetics (PK). Areas covered: The investigated assumptions are: 1) lean body weight is the preferred descriptor of clearance and dose adjustments; 2) volume of distribution increases for lipophilic, but not for hydrophilic drugs; 3) CYP-3A4 activity is suppressed and UGT activity is increased, implying decreased and increased dose requirements for substrates of these enzyme systems, respectively; 4) glomerular filtration rate is enhanced, necessitating higher doses for drugs cleared through glomerular filtration; 5) drug dosing information from obese adults can be extrapolated to obese adolescents. Expert opinion: Available literature contradicts, or at least limits the generalizability, of all five assumptions. Clinical studies should focus on quantifying the impact of duration and severity of obesity on drug PK in adults and adolescents, and also include oral bioavailability and pharmacodynamics in these studies. Physiologically based PK approaches can be used to predict PK changes for individual drugs but can also be used to define in general terms based on patient characteristics and drug properties, when certain assumptions can or cannot be expected to be systematically accurate.