Recent lessons learned from population pharmacokinetic studies of mycophenolic acid: physiological, genomic, and drug interactions leading to the prediction of drug effects

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.

KEYWORDS:

• Drug interactions
• mycophenolic acid
• non-linear mixed-effects modeling
• pharmacology
• population pharmacodynamics
• population pharmacokinetics

Abbreviation

%EHC, percentage of mycophenolic acid glucuronide recycled into the systemic circulation (represents the process of enterohepatic circulation);

a₁ and a₂, the shape of the gamma function;

ABC transporter, ATP-binding cassette transporter;

AcMPAG, mycophenolic acid acyl glucuronide;

ALP, alkaline phosphatase;

ALT, alanine aminotransferase;

AST, aspartate aminotransferase;

AUC, area under the concentration-time curve; b₁ and b₂, the scale of the gamma function;

BCRP, breast cancer resistance protein;

BMI, body mass indexC₀, trough concentration;

CKD-EPI equation, chronic kidney disease epidemiology collaboration equation;

CL/F, apparent total clearance;

CL_in/F, apparent clearance of unbound mycophenolic acid distributed from central compartment into peripheral blood mononuclear cell compartment;

CL_out/F, apparent clearance of mycophenolic acid eliminated from peripheral blood mononuclear cell compartment;

CrCL, creatinine clearance;

CV, coefficient of variation;

CXCL-10, interferon gamma inducible chemokine 10;

EC-MPS, enteric-coated mycophenolate sodium;

eGFR, estimated glomerular filtration rate;

EHC, entero-hepatic circulation;

F, bioavailability;

FAIV, the maximum concentration following an intravenous bolus administration of a unit dose;

f_MPA, metabolism fraction from mycophenolic acid to mycophenolic acid glucuronide;

FO, first-order algorithm;

FOCE, first-order conditional estimation algorithm;

FOCE-I, first-order conditional estimation with interaction algorithm;

f_u,MPA, unbound fraction of mycophenolic acid;

GFR, glomerular filtration rate;

GGT, γ-glutamyltransferase; HLA, human leukocyte antigen;

HNF1A, hepatic nuclear factor 1 alpha;

HPLC-UV-DAD, high-performance liquid chromatography with ultraviolet diode-array detector;

HPLC-UV, high-performance liquid chromatography with ultraviolet detection;

IIV, inter-individual variability;

IMPDH, inosine-5’-monophosphate dehydrogenase;

IOV, inter-occasion variability;

IQR, interquartile range;

k₁₂ and k₂₁, rate constants of intercompartment distribution;

k_a, absorption rate constant;

k_B, rate constant of protein binding (represents number of binding sites);

k_CG, transport rate constant from mycophenolic acid central compartment to gallbladder;

k_e0, elimination rate constant from unbound mycophenolic acid glucuronide central compartment;

k_EHC, first-order rate constant of enterohepatic circulation;

k_GB, rate constant of gallbladder emptying;

k_GG, transport rate constant from unbound mycophenolic acid glucuronide central compartment to gallbladder;

LCMS, liquid chromatography with mass spectrometry;

LC-MS/MS, liquid chromatography tandem mass spectrometry;

MDRD equation, modification of diet in renal disease;

MMF, mycophenolate mofetil;

MPA, mycophenolic acid;

MPA_cell peripheral blood mononuclear cell intracellular mycophenolic acid;

MPAG_u, unbound mycophenolic acid glucuronide;

MPA_t, total mycophenolic acid;

MPA_u, unbound mycophenolic acid;

MTIME1, meal-time; MTIME2 or T_GB, gallbladder emptying duration;

N/A, not applicable;

NPDE, normalized prediction distribution errors;

PBMC, peripheral blood mononuclear cell;

PD, pharmacodynamic;

PK, pharmacokinetic;

Q/F, apparent intercompartmental clearance;

r, the dose fraction absorbed from the first gamma function;

RSE, relative standard error;

RUV, residual unexplained variability;

SAEM, stochastic approximation expectation maximization estimation method;

SCr, serum creatinine;

SD, standard deviation;

SE, standard error;

SLC, solute carrier family transporter;

Tk0, zero-order absorption rate constant;

t_lag, lag time;

UGT, uridine 5’-diphospho-glucuronosyltransferase; UPLC-MS/MS, ultra-performance liquid chromatography tandem mass spectrometry;

V_c/F, apparent volume of distribution in the central compartment;

V_p/F, apparent volume of distribution in the peripheral compartment;

VPC, visual predictive check;

α, elimination parameter;

β, covariate effect;

θ, population pharmacokinetic fixed-effect parameter estimate

Article highlights

• Various intrinsic (e.g. physiological variables and genetic polymorphisms) and extrinsic (e.g. drug–drug interactions) factors have been identified to influence the pharmacology of mycophenolic acid (MPA) using population pharmacokinetic/dynamic models.

• The covariate effects contributing to the variabilities in MPA metabolism and excretion have been extensively characterized in general, while factors influencing absorption and distribution would warrant further investigations.

• Although more studies have now incorporated pharmacogenomic data, the effects of various genetic polymorphisms on the population pharmacokinetics of MPA still require confirmation.

• Only a limited number of population pharmacodynamic models characterizing MPA efficacy and toxicity are available.

• An ideal population pharmacokinetic-dynamic model of MPA should consist of total/unbound MPA concentrations, metabolite data, plasma/urinary concentrations, inosine-5′-monophosphate dehydrogenase activities, and clinical outcomes.

• While the majority of the data have been derived from kidney transplant patients, population models of MPA with unique characteristics in other patient populations are also being reported. We foresee this field of research to continue to expand rapidly in the next 5 to 10 years.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.