Population pharmacokinetics of mycophenolic acid in lung transplant recipients with and without cystic fibrosis

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

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, some variables have not been regularly reported and/or lacked mechanistic explanation. 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 and pharmacodynamic data to establish clinical relevance. We anticipate the field will continue to expand in the next 5 to 10 years.

Personalized Therapy for Mycophenolate: Consensus Report by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology

ABSTRACT

When mycophenolic acid (MPA) was originally marketed for immunosuppressive therapy, fixed doses were recommended by the manufacturer. Awareness of the potential for a more personalized dosing has led to development of methods to estimate MPA area under the curve based on the measurement of drug concentrations in only a few samples. This approach is feasible in the clinical routine and has proven successful in terms of correlation with outcome. However, the search for superior correlates has continued, and numerous studies in search of biomarkers that could better predict the perfect dosage for the individual patient have been published. As it was considered timely for an updated and comprehensive presentation of consensus on the status for personalized treatment with MPA, this report was prepared following an initiative from members of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT). Topics included are the criteria for analytics, methods to estimate exposure including pharmacometrics, the potential influence of pharmacogenetics, development of biomarkers, and the practical aspects of implementation of target concentration intervention. For selected topics with sufficient evidence, such as the application of limited sampling strategies for MPA area under the curve, graded recommendations on target ranges are presented. To provide a comprehensive review, this report also includes updates on the status of potential biomarkers including those which may be promising but with a low level of evidence. In view of the fact that there are very few new immunosuppressive drugs under development for the transplant field, it is likely that MPA will continue to be prescribed on a large scale in the upcoming years. Discontinuation of therapy due to adverse effects is relatively common, increasing the risk for late rejections, which may contribute to graft loss. Therefore, the continued search for innovative methods to better personalize MPA dosage is warranted.

Assessment of pharmacokinetic mycophenolic acid clearance models using Monte Carlo numerical analysis

Previously, we performed population pharmacokinetic analysis and indicated age, mycophenolate mofetil (MMF)/mycophenolic acid (MPA) daily dose, and presence of nifedipine in patient therapy as significant predictors of MPA apparent clearance (CL/F) variability. This study aimed to determine the reliability of previously published population pharmacokinetic models derived from similar studies. Furthermore, this study investigated correspondence between chosen population models from the literature.By means of the Monte Carlo simulation method, pharmacokinetic models from different studies are simulated and analysed in the range of standard deviations of measured system parameters as well as the range of observed model parameters taken from the comparison studies.The 1000 numerical simulations were performed for every analysed model in order to calculate the most possible MPA CL/F values according to the expected values from the performed experiment. Fitting our results with other models showed how the presence of nifedipine makes difference in MPA CL/F values.By testing the data from selected studies into our model, a similar range of expected CL/F values was obtained, which may confirm the validity of our model. The results of our population pharmacokinetic study are partially applicable in models by other researchers.

Population Pharmacokinetics of Mycophenolic Acid: An Update

The most recent comprehensive reviews on the population pharmacokinetics of mycophenolic acid (MPA) were published in 2014. Since then, several population pharmacokinetic studies on MPA have been published. The majority of literature is still focused on the kidney transplant population, although studies have also been conducted in liver and lung transplantation, autoimmune diseases, and hematopoietic stem cell transplant. While the majority of the model building is still based on parametric non-linear mixed-effects modeling, recent studies suggest the suitability of other methodologies. Additionally, instead of just focusing on pharmacokinetic modeling, a trend toward describing the relationships between pharmacokinetic and pharmacodynamic parameters is observed. Given the importance of enterohepatic recirculation (EHR) in the pharmacokinetics of MPA, more authors have attempted to characterize this process in their models. Overall, the recent models have become more sophisticated and incorporate EHR, pharmacodynamic relationships, and metabolites while maintaining many of the population values and covariates identified previously. However, the number of MPA population pharmacokinetic models describing the enteric-coated formulation of MPA (EC-MPA) is still limited. Given the increasing use of EC-MPA, more studies are needed to fill this literature gap. In addition, few studies are yet available characterizing free MPA concentration or MPA metabolites. Given the extensive protein binding, low to intermediate extraction, and intrinsic clearance characteristics of MPA in humans, including these variables would improve the population structural models.

Immunosuppression Drug Therapy in Lung Transplantation for Cystic Fibrosis

Cystic fibrosis (CF) is a common indication for lung transplantation (LTx) in children and adults with severe and irreversible lung disease. In the setting of LTx in the CF population, immunosuppressive medications are used to prevent allograft rejection despite the majority of these patients being chronically infected with numerous, and often antibiotic-resistant, pathogens. There is limited evidence for the optimal post-LTx immunosuppression regimen in patients with CF, particularly in children. This article provides a review of immunosuppression regimens in the pediatric and adult CF post-LTx population, investigating drug dosing and monitoring, and medication combinations. Currently used immunosuppressive medications and related systemic adverse effects are reviewed. With limitations of data in the pediatric population, future research should address immunosuppression in these children to help guide pediatric drug management as a means to optimize clinical outcomes after LTx.