ReviewClinical pharmacokinetics2011
The evolution of population pharmacokinetic models to describe the enterohepatic recycling of mycophenolic acid in solid organ transplantation and autoimmune disease.
Review in Clinical pharmacokinetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 69 citations in OpenAlex.
- Systematic external evaluation of published population pharmacokinetic models of mycophenolate mofetil in adult kidney transplant recipients co-administered with tacrolimus.British journal of clinical pharmacology · 2019Pooled it
- Population Pharmacokinetics of Mycophenolic Acid: An Update.Clinical pharmacokinetics · 2018Pooled it
- A Mycophenolate Pharmacokinetic Study with New Insights into Enterohepatic Recirculation in Kidney Transplant Recipients.Clinical pharmacokinetics · 2026Trial
- Population Pharmacokinetic Modeling of the Enterohepatic Recirculation of Fimasartan in Rats, Dogs, and Humans.The AAPS journal · 2015Trial
- Population pharmacokinetics of mycophenolic acid and its main glucuronide metabolite: a comparison between healthy Chinese and Caucasian subjects receiving mycophenolate mofetil.European journal of clinical pharmacology · 2015Trial
- Bayesian estimation of mycophenolate mofetil in lung transplantation, using a population pharmacokinetic model developed in kidney and lung transplant recipients.Clinical pharmacokinetics · 2012Trial
- Impacts of Uremic Toxins on the Population Pharmacokinetics of Total Mycophenolic Acid and its Glucuronide Metabolite in Adult Kidney Transplant Recipients.Clinical pharmacokinetics · 2026Observational
- Population Pharmacokinetic Modelling of Unbound Mycophenolic Acid and its Glucuronide in Adult Kidney Transplant Recipients in Early Post-Transplant.Clinical pharmacokinetics · 2026Observational
- Impact of body weight on mycophenolic acid population pharmacokinetics in paediatric lupus nephritis: a pharmacogenomic integration study.Lupus science & medicine · 2025Article
- Biopharmaceutical Factors Involved in the Disposition of Mycophenolic Acid: A Comprehensive Review of ADME Properties and Their Potential Impact on Mycophenolic Acid Plasma Exposure.Current drug metabolism · 2025Review
- Population pharmacokinetic analysis identifies an absorption process model for mycophenolic acid in patients with renal transplant.Clinical and translational science · 2024Article
- Pharmacokinetic Simulation and Area under the Curve Estimation of Drugs Subject to Enterohepatic Circulation.Pharmaceutics · 2024Article
- Importance of Considering Fed-State Gastrointestinal Physiology in Predicting the Reabsorption of Enterohepatic Circulation of Drugs.Pharmaceutical research · 2024Article
- Article
- Significant Effects of Renal Function on Mycophenolic Acid Total Clearance in Pediatric Kidney Transplant Recipients with Population Pharmacokinetic Modeling.Clinical pharmacokinetics · 2023Article
- Gut Microbiota-Mediated Pharmacokinetic Drug-Drug Interactions between Mycophenolic Acid and Trimethoprim-Sulfamethoxazole in Humans.Pharmaceutics · 2023Article
- Clinical Evidence on the Purported Pharmacokinetic Interactions between Corticosteroids and Mycophenolic Acid.Clinical pharmacokinetics · 2023Article
- Significant Correlations between p-Cresol Sulfate and Mycophenolic Acid Plasma Concentrations in Adult Kidney Transplant Recipients.Clinical drug investigation · 2022Observational
- Population Pharmacokinetics of Mycophenolic Acid in Renal Transplant Patients: A Comparison of the Early and Stable Posttransplant Stages.Frontiers in pharmacology · 2022Article
- Treatment optimization of maintenance immunosuppressive agents in pediatric renal transplant recipients.Expert opinion on drug metabolism & toxicology · 2021Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
With the increasing use of mycophenolic acid (MPA) as an immunosuppressant in solid organ transplantation and in treating autoimmune diseases such as systemic lupus erythematosus, the need for strategies to optimize therapy with this agent has become increasingly apparent. This need is largely based on MPA's significant between-subject and between-occasion (within-subject) pharmacokinetic variability. While there is a strong relationship between MPA exposure and effect, the relationship between drug dose, plasma concentration and exposure (area under the concentration-time curve [AUC]) is very complex and remains to be completely defined. Population pharmacokinetic models using various approaches have been proposed over the past 10 years to further evaluate the pharmacokinetic and pharmacodynamic behaviour of MPA. These models have evolved from simple one-compartment linear iterations to complex multi-compartment versions that try to include various factors, which may influence MPA's pharmacokinetic variability, such as enterohepatic recycling and pharmacogenetic polymorphisms. There have been major advances in the understanding of the roles transport mechanisms, metabolizing and other enzymes, drug-drug interactions and pharmacogenetic polymorphisms play in MPA's pharmacokinetic variability. Given these advances, the usefulness of empirical-based models and the limitations of nonlinear mixed-effects modelling in developing mechanism-based models need to be considered and discussed. If the goal is to individualize MPA dosing, it needs to be determined whether factors which may contribute significantly to variability can be utilized in the population pharmacokinetic models. Some pharmacokinetic models developed to date show promise in being able to describe the impact of physiological processes such as enterohepatic recycling. Most studies have historically been based on retrospective data or poorly designed studies which do not take these factors into consideration. Modelling typically has been undertaken using non-controlled therapeutic drug monitoring data, which do not have the information content to support the development of complex mechanistic models. Only a few recent modelling approaches have moved away from empiricism and have included mechanisms considered important, such as enterohepatic recycling. It is recognized that well thought-out sampling schedules allow for better evaluation of the pharmacokinetic data. It is not possible to undertake complex absorption modelling with very few samples being obtained during the absorption phase (which has often been the case). It is important to utilize robust AUC monitoring which is now being propagated in the latest consensus guideline on MPA therapeutic drug monitoring. This review aims to explore the biological factors that contribute to the clinical pharmacokinetics of MPA and how these have been introduced in the development of population pharmacokinetic models. An overview of the processes involved in the enterohepatic recycling of MPA will be provided. This will summarize the components that complicate absorption and recycling to influence MPA exposure such as biotransformation, transport, bile physiology and gut flora. Already published population pharmacokinetic models will be examined, and the evolution of these models away from empirical approaches to more mechanism-based models will be discussed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.