Recent lessons learned from population pharmacokinetic studies of mycophenolic acid: physiological, genomic, and drug interactions leading to the prediction of drug effects. (2nd December 2021)

• Record Type: Journal Article
• Authors: Rong, Yan; Patel, Vrunda; Kiang, Tony K.L.
• Is Part Of: Expert opinion on drug metabolism and toxicology. Volume 17:Number 12(2021)
• Page Start: 1369
• Page End: 1406
• Publication Date: 2021-12-02
• Subjects: Drug interactions -- mycophenolic acid -- non-linear mixed-effects modeling -- pharmacology -- population pharmacodynamics -- population pharmacokinetics; Drugs -- Toxicology -- Periodicals; Drugs -- Metabolism -- Periodicals; 615.7
• DOI: 10.1080/17425255.2021.2027906
• Languages: English
• ISSNs: 1742-5255

Abstract

ABSTRACT: Introduction: Mycophenolic acid (MPA) is a widely used immunosuppressant in transplantation and autoimmune disease. Highly variable pharmacokinetics have been observed with MPA, but the exact mechanisms remain largely unknown. Areas covered: The current review provided a critical, comprehensive update of recently published population pharmacokinetic/dynamic models of MPA (n = 16 papers identified from PubMed and Embase, inclusive from January 2017 to August 2021), with specific emphases on the intrinsic and extrinsic factors influencing the pharmacology of MPA. The significance of the identified covariates, potential mechanisms, and comparisons to historical literature have been provided. Expert opinion: While select covariates affecting the population pharmacokinetics of MPA are consistently observed and mechanistically supported (e.g. cyclosporine and post-transplant time on MPA clearance), some variables have not been regularly reported and/or lacked mechanistic explanation (e.g. diarrhea and several genetic polymorphisms). Very few pharmacodynamic models were available, pointing to the need to extrapolate pharmacokinetic findings. Ideal models of MPA should consist of: i) utilizing optimal sampling points to allow the characterizations of absorption, re-absorption, and elimination phases; ii) characterizing unbound/total MPA, MPA metabolites, plasma/urinary concentrations, and genetic polymorphisms to facilitate mechanistic interpretations; and iii) incorporating actual outcomes (e.g. rejection, leukopenia, infections) and pharmacodynamic data (e.g. inosine-5′-monophosphate dehydrogenase activities) to establish clinical relevance. We anticipate the field will continue to expand in the next 5 to 10 years.