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Wang CB, Zhang YJ, Zhao MM, Zhao L. Dosage optimization of tacrolimus based on the glucocorticoid dose and pharmacogenetics in adult patients with systemic lupus erythematosus. Int Immunopharmacol 2023; 124:110866. [PMID: 37678026 DOI: 10.1016/j.intimp.2023.110866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/25/2023] [Accepted: 08/26/2023] [Indexed: 09/09/2023]
Abstract
BACKGROUND The purpose of the study was to develop a genotype-incorporated population pharmacokinetic (PPK) model of tacrolimus (TAC) in adults with systemic lupus erythematosus (SLE) to investigate the factors influencing TAC pharmacokinetics and to develop an individualized dosing regimen based on the model. In addition, a non-genotype-incorporated model was also established to assess its predictive performance compared to the genotype-incorporated model. METHODS A total of 365 trough concentrations from 133 adult SLE patients treated with TAC were collected to develop a genotype-incorporated PPK model and a non-genotype-incorporated PPK model of TAC using a nonlinear mixed-effects model (NONMEM). External validation of the two models was performed using data from an additional 29 patients. Goodness-of-fit diagnostic plots, bootstrap method, and normalized predictive distribution error test were used to validate the predictive performance and stability of the final models. The goodness-of-fit of the two final models was compared using the Akaike information criterion (AIC). The dosing regimen was optimized using Monte Carlo simulations based on the developed optimal model. RESULTS The typical value of the apparent clearance (CL/F) of TAC estimated in the final genotype-incorporated model was 14.3 L h-1 with inter-individual variability of 27.6%. CYP3A5 polymorphism and coadministered medication were significant factors affecting TAC-CL/F. CYP3A5 rs776746 GG genotype carriers had only 77.3% of the TAC-CL/F of AA or AG genotype carriers. Omeprazole reduced TAC-CL/F by 3.7 L h-1 when combined with TAC, while TAC-CL/F increased nonlinearly as glucocorticoid dose increased. Similar findings were demonstrated in the non-genotype-incorporated PPK model. Comparing these two models, the genotype-incorporated PPK model was superior to the non-genotype-incorporated PPK model (AIC = 643.19 vs. 657.425). Monte Carlo simulation based on the genotype-incorporated PPK model indicated that CYP3A5 rs776746 AA or AG genotype carriers required a 1/2-1 fold higher dose of TAC than GG genotype carriers to achieve the target concentration. And as the daily dose of prednisone increases, the dose of TAC required to reach the target concentration increases appropriately. CONCLUSIONS We developed the first pharmacogenetic-based PPK model of TAC in adult patients with SLE and proposed a dosing regimen based on glucocorticoid dose and CYP3A5 genotype according to the model, which could facilitate individualized dosing for TAC.
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Affiliation(s)
- Cheng-Bin Wang
- Department of Pharmacy, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yu-Jia Zhang
- Department of Pharmacy, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ming-Ming Zhao
- Department of Pharmacy, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
| | - Limei Zhao
- Department of Pharmacy, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
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Individualization of Mycophenolic Acid Therapy through Pharmacogenetic, Pharmacokinetic and Pharmacodynamic Testing. Biomedicines 2022; 10:biomedicines10112882. [DOI: 10.3390/biomedicines10112882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/03/2022] [Accepted: 11/08/2022] [Indexed: 11/12/2022] Open
Abstract
Mycophenolic acid (MPA) is a widely used immunosuppressive agent and exerts its effect by inhibiting inosine 5′-monophosphate dehydrogenase (IMPDH), the main regulating enzyme of purine metabolism. However, significant unexplained differences in the efficacy and tolerability of MPA therapy pose a clinical challenge. Therefore, broad pharmacogenetic, pharmacokinetic, and pharmacodynamic approaches are needed to individualize MPA therapy. In this prospective cohort study including 277 renal transplant recipients, IMPDH2 rs11706052 SNP status was assessed by genetic sequencing, and plasma MPA trough levels were determined by HPLC and IMPDH enzyme activity in peripheral blood mononuclear cells (PBMCs) by liquid chromatography–mass spectrometry. Among the 277 patients, 84 were identified with episodes of biopsy-proven rejection (BPR). No association was found between rs11706052 SNP status and graft rejection (OR 1.808, and 95% CI, 0.939 to 3.479; p = 0.076). Furthermore, there was no association between MPA plasma levels and BPR (p = 0.69). However, the patients with graft rejection had a significantly higher predose IMPDH activity in PBMCs compared to the controls without rejection at the time of biopsy (110.1 ± 50.2 vs. 95.2 ± 45.4 pmol/h; p = 0.001), and relative to the baseline IMPDH activity before transplantation (p = 0.042). Our results suggest that individualization of MPA therapy, particularly through pharmacodynamic monitoring of IMPDH activity in PBMCs, has the potential to improve the clinical outcomes of transplant patients.
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Rong Y, Patel V, Kiang TKL. Recent lessons learned from population pharmacokinetic studies of mycophenolic acid: physiological, genomic, and drug interactions leading to the prediction of drug effects. Expert Opin Drug Metab Toxicol 2022; 17:1369-1406. [PMID: 35000505 DOI: 10.1080/17425255.2021.2027906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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, 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.
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Affiliation(s)
- Yan Rong
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Vrunda Patel
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Tony K L Kiang
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada
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Wang X, Wu Y, Huang J, Shan S, Mai M, Zhu J, Yang M, Shang D, Wu Z, Lan J, Zhong S, Wu M. Estimation of Mycophenolic Acid Exposure in Heart Transplant Recipients by Population Pharmacokinetic and Limited Sampling Strategies. Front Pharmacol 2021; 12:748609. [PMID: 34867352 PMCID: PMC8640522 DOI: 10.3389/fphar.2021.748609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/14/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: The aim of this study is i) to establish a strategy to estimate the area under the curve of the dosing interval (AUC0-12h) of mycophenolic acid (MPA) in the heart transplant recipients and ii) to find the covariates that significantly affect the pharmacokinetics of MPA exposure. Methods: This single-center, prospective, open-label, observational study was conducted in 91 adult heart transplant recipients orally taking mycophenolate mofetil dispersible tablets. Samples collected intensively and sparsely were analyzed by the enzyme-multiplied immunoassay technique, and all the data were used in PPK modeling. Potential covariates were tested stepwise. The goodness-of-fit plots, the normalized prediction distribution error, and prediction-corrected visual predictive check were used for model evaluation. Optimal sampling times by ED-optimal strategy and multilinear regression (MLR) were analyzed based on the simulated data by the final PPK model. Moreover, using intensive data from 14 patients, the accuracy of AUC0-12h estimation was evaluated by Passing-Bablok regression analysis and Bland-Alman plots for both the PPK model and MLR equation. Results: A two-compartment model with first-order absorption and elimination with a lag time was chosen as the structure model. Co-medication of proton pump inhibitors (PPIs), estimated glomerular filtration rate (eGFR), and albumin (ALB) were found to significantly affect bioavailability (F), clearance of central compartment (CL/F), and the distribution volume of the central compartment (V2/F), respectively. Co-medication of PPIs decreased F by 27.6%. When eGFR decreased by 30 ml/min/1.73 m2, CL/F decreased by 23.7%. However, the impact of ALB on V2/F was limited to MPA exposure. The final model showed an adequate fitness of the data. The optimal sampling design was pre-dose and 1 and 4 h post-dose for pharmacokinetic estimation. The best-fit linear equation was finally established as follows: AUC0-12h = 3.539 × C0 + 0.288 × C0.5 + 1.349 × C1 + 6.773 × C4.5. Conclusion: A PPK model was established with three covariates in heart transplant patients. Co-medication of PPIs and eGFR had a remarkable impact on AUC0-12h of MPA. A linear equation was also concluded with four time points as an alternative way to estimate AUC0-12h for MPA.
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Affiliation(s)
- Xipei Wang
- Research Center of Medical Sciences, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Clinical Pharmacology, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yijin Wu
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jinsong Huang
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Songgui Shan
- Department of Pharmacy, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Mingjie Mai
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jiade Zhu
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Min Yang
- Guangdong Provincial Key Laboratory of Clinical Pharmacology, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Dewei Shang
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zheng Wu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Jinhua Lan
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Shilong Zhong
- Department of Pharmacy, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangzhou, China
| | - Min Wu
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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Population Pharmacokinetic Models of Tacrolimus in Adult Transplant Recipients: A Systematic Review. Clin Pharmacokinet 2021; 59:1357-1392. [PMID: 32783100 DOI: 10.1007/s40262-020-00922-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND AND OBJECTIVES Numerous population pharmacokinetic (PK) models of tacrolimus in adult transplant recipients have been published to characterize tacrolimus PK and facilitate dose individualization. This study aimed to (1) investigate clinical determinants influencing tacrolimus PK, and (2) identify areas requiring additional research to facilitate the use of population PK models to guide tacrolimus dosing decisions. METHODS The MEDLINE and EMBASE databases, as well as the reference lists of all articles, were searched to identify population PK models of tacrolimus developed from adult transplant recipients published from the inception of the databases to 29 February 2020. RESULTS Of the 69 studies identified, 55% were developed from kidney transplant recipients and 30% from liver transplant recipients. Most studies (91%) investigated the oral immediate-release formulation of tacrolimus. Few studies (17%) explained the effect of drug-drug interactions on tacrolimus PK. Only 35% of the studies performed an external evaluation to assess the generalizability of the models. Studies related variability in tacrolimus whole blood clearance among transplant recipients to either cytochrome P450 (CYP) 3A5 genotype (41%), days post-transplant (30%), or hematocrit (29%). Variability in the central volume of distribution was mainly explained by body weight (20% of studies). CONCLUSION The effect of clinically significant drug-drug interactions and different formulations and brands of tacrolimus should be considered for any future tacrolimus population PK model development. Further work is required to assess the generalizability of existing models and identify key factors that influence both initial and maintenance doses of tacrolimus, particularly in heart and lung transplant recipients.
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Yang CL, Sheng CC, Liao GY, Su Y, Feng LJ, Xia Q, Jiao Z, Xu DJ. Genetic polymorphisms in metabolic enzymes and transporters have no impact on mycophenolic acid pharmacokinetics in adult kidney transplant patients co-treated with tacrolimus: A population analysis. J Clin Pharm Ther 2021; 46:1564-1575. [PMID: 34312870 DOI: 10.1111/jcpt.13488] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 05/05/2021] [Accepted: 07/01/2021] [Indexed: 12/17/2022]
Abstract
WHAT IS KNOWN AND OBJECTIVE Mycophenolate mofetil, an ester prodrug of mycophenolic acid (MPA), is widely used to prevent graft rejection after kidney transplantation. The pharmacokinetic (PK) of MPA has been extensively studied, which revealed a high degree of variability. An integrated population PK (PopPK) model of MPA and its main metabolite mycophenolic acid glucuronide (MPAG) was developed using the adult patients who underwent kidney transplant and were administered oral mycophenolate mofetil combined with tacrolimus. METHODS In total, 917 MPA and 740 MPAG concentrations in191 adult patients were analysed via nonlinear mixed-effects modelling. The concentration-time data were adequately described using a chain compartment model, including central and peripheral compartments for MPA and a central compartment for MPAG. Stepwise forward inclusion and backward elimination procedures were used to investigate the effects of genetic polymorphisms, including in UGT1A8, UGT1A9, UGT2B7, ABCB1, ABCC2, ABCG2, SLCO1B1, SLCO1B3, and HNF1α. RESULTS AND DISCUSSION These genetic polymorphisms in metabolic enzymes and transporters have no obvious impact on the PK of MPA in adult patients who underwent kidney transplant and were co-treated with tacrolimus. The post-transplant time, serum albumin, and creatinine clearance were identified as significant covariates affecting the PK of MPA and MPAG, which should be considered in the clinical use of mycophenolate mofetil. WHAT IS NEW AND CONCLUSION We established a PopPK model of MPA and MPAG in Chinese adult patients who underwent kidney transplant and were co-treated with tacrolimus. Genetic polymorphisms in metabolic enzymes and transporters showed no obvious impact on MMF PK. A model-informed dosing strategy was proposed by the established model, and MMF dose adjustment should be based on ALB levels and the post-transplantation time.
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Affiliation(s)
- Chun-Lan Yang
- Department of Pharmacy, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Chang-Cheng Sheng
- Department of Pharmacy, Guizhou Provincial People's Hospital, Guiyang, China
| | - Gui-Yi Liao
- Department of Urology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yong Su
- Department of Pharmacy, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Li-Juan Feng
- Department of Pharmacy, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Quan Xia
- Department of Pharmacy, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zheng Jiao
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Du-Juan Xu
- Department of Pharmacy, the First Affiliated Hospital of Anhui Medical University, Hefei, China
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Zwart TC, Guchelaar HJ, van der Boog PJM, Swen JJ, van Gelder T, de Fijter JW, Moes DJAR. Model-informed precision dosing to optimise immunosuppressive therapy in renal transplantation. Drug Discov Today 2021; 26:2527-2546. [PMID: 34119665 DOI: 10.1016/j.drudis.2021.06.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 04/21/2021] [Accepted: 06/04/2021] [Indexed: 12/18/2022]
Abstract
Immunosuppressive therapy is pivotal for sustained allograft and patient survival after renal transplantation. However, optimally balanced immunosuppressive therapy is challenged by between-patient and within-patient pharmacokinetic (PK) variability. This could warrant the application of personalised dosing strategies to optimise individual patient outcomes. Pharmacometrics, the science that investigates the xenobiotic-biotic interplay using computer-aided mathematical modelling, provides options to describe and quantify this PK variability and enables identification of patient characteristics affecting immunosuppressant PK and treatment outcomes. Here, we review and critically appraise the available pharmacometric model-informed dosing solutions for the typical immunosuppressants in modern renal transplantation, to guide their initial and subsequent dosing.
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Affiliation(s)
- Tom C Zwart
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, the Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, the Netherlands; Leiden Network for Personalised Therapeutics, Leiden, the Netherlands
| | - Paul J M van der Boog
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, the Netherlands; LUMC Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
| | - Jesse J Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, the Netherlands; Leiden Network for Personalised Therapeutics, Leiden, the Netherlands
| | - Teun van Gelder
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, the Netherlands
| | - Johan W de Fijter
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, the Netherlands; LUMC Transplant Center, Leiden University Medical Center, Leiden, the Netherlands
| | - Dirk Jan A R Moes
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, the Netherlands; Leiden Network for Personalised Therapeutics, Leiden, the Netherlands.
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Bergan S, Brunet M, Hesselink DA, Johnson-Davis KL, Kunicki PK, Lemaitre F, Marquet P, Molinaro M, Noceti O, Pattanaik S, Pawinski T, Seger C, Shipkova M, Swen JJ, van Gelder T, Venkataramanan R, Wieland E, Woillard JB, Zwart TC, Barten MJ, Budde K, Dieterlen MT, Elens L, Haufroid V, Masuda S, Millan O, Mizuno T, Moes DJAR, Oellerich M, Picard N, Salzmann L, Tönshoff B, van Schaik RHN, Vethe NT, Vinks AA, Wallemacq P, Åsberg A, Langman LJ. Personalized Therapy for Mycophenolate: Consensus Report by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology. Ther Drug Monit 2021; 43:150-200. [PMID: 33711005 DOI: 10.1097/ftd.0000000000000871] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 01/29/2021] [Indexed: 12/13/2022]
Abstract
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.
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Affiliation(s)
- Stein Bergan
- Department of Pharmacology, Oslo University Hospital and Department of Pharmacy, University of Oslo, Oslo, Norway
| | - Mercè Brunet
- Pharmacology and Toxicology Laboratory, Biochemistry and Molecular Genetics Department, Biomedical Diagnostic Center, Hospital Clinic of Barcelona, University of Barcelona, IDIBAPS, CIBERehd, Spain
| | - Dennis A Hesselink
- Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Kamisha L Johnson-Davis
- Department of Pathology, University of Utah Health Sciences Center and ARUP Laboratories, Salt Lake City, Utah
| | - Paweł K Kunicki
- Department of Drug Chemistry, Faculty of Pharmacy, Medical University of Warsaw, Warszawa, Poland
| | - Florian Lemaitre
- Univ Rennes, CHU Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail)-UMR_S 1085, Rennes, France
| | - Pierre Marquet
- INSERM, Université de Limoges, Department of Pharmacology and Toxicology, CHU de Limoges, U1248 IPPRITT, Limoges, France
| | - Mariadelfina Molinaro
- Clinical and Experimental Pharmacokinetics Lab, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Ofelia Noceti
- National Center for Liver Tansplantation and Liver Diseases, Army Forces Hospital, Montevideo, Uruguay
| | | | - Tomasz Pawinski
- Department of Drug Chemistry, Faculty of Pharmacy, Medical University of Warsaw, Warszawa, Poland
| | | | - Maria Shipkova
- Synlab TDM Competence Center, Synlab MVZ Leinfelden-Echterdingen GmbH, Leinfelden-Echterdingen, Germany
| | - Jesse J Swen
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, Leiden, The Netherlands
| | - Teun van Gelder
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, Leiden, The Netherlands
| | - Raman Venkataramanan
- Department of Pharmaceutical Sciences, School of Pharmacy and Department of Pathology, Starzl Transplantation Institute, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Eberhard Wieland
- Synlab TDM Competence Center, Synlab MVZ Leinfelden-Echterdingen GmbH, Leinfelden-Echterdingen, Germany
| | - Jean-Baptiste Woillard
- INSERM, Université de Limoges, Department of Pharmacology and Toxicology, CHU de Limoges, U1248 IPPRITT, Limoges, France
| | - Tom C Zwart
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, Leiden, The Netherlands
| | - Markus J Barten
- Department of Cardiac- and Vascular Surgery, University Heart and Vascular Center Hamburg, Hamburg, Germany
| | - Klemens Budde
- Department of Nephrology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Maja-Theresa Dieterlen
- Department of Cardiac Surgery, Heart Center, HELIOS Clinic, University Hospital Leipzig, Leipzig, Germany
| | - Laure Elens
- Integrated PharmacoMetrics, PharmacoGenomics and PharmacoKinetics (PMGK) Research Group, Louvain Drug Research Institute (LDRI), Université Catholique de Louvain (UCLouvain), Brussels, Belgium
| | - Vincent Haufroid
- Louvain Centre for Toxicology and Applied Pharmacology (LTAP), Institut de Recherche Expérimentale et Clinique, UCLouvain and Department of Clinical Chemistry, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Satohiro Masuda
- Department of Pharmacy, International University of Health and Welfare Narita Hospital, Chiba, Japan
| | - Olga Millan
- Pharmacology and Toxicology Laboratory, Biochemistry and Molecular Genetics Department, Biomedical Diagnostic Center, Hospital Clinic of Barcelona, University of Barcelona, IDIBAPS, CIBERehd, Spain
| | - Tomoyuki Mizuno
- Division of Clinical Pharmacology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Dirk J A R Moes
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, Leiden, The Netherlands
| | - Michael Oellerich
- Department of Clinical Pharmacology, University Medical Center Göttingen, Georg-August-University Göttingen, Göttingen, Germany
| | - Nicolas Picard
- INSERM, Université de Limoges, Department of Pharmacology and Toxicology, CHU de Limoges, U1248 IPPRITT, Limoges, France
| | | | - Burkhard Tönshoff
- Department of Pediatrics I, University Children's Hospital, Heidelberg, Germany
| | - Ron H N van Schaik
- Department of Clinical Chemistry, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Nils Tore Vethe
- Department of Pharmacology, Oslo University Hospital and Department of Pharmacy, University of Oslo, Oslo, Norway
| | - Alexander A Vinks
- Department of Pharmacy, International University of Health and Welfare Narita Hospital, Chiba, Japan
| | - Pierre Wallemacq
- Clinical Chemistry Department, Cliniques Universitaires St Luc, Université Catholique de Louvain, LTAP, Brussels, Belgium
| | - Anders Åsberg
- Department of Transplantation Medicine, Oslo University Hospital-Rikshospitalet and Department of Pharmacy, University of Oslo, Oslo, Norway; and
| | - Loralie J Langman
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
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Rong Y, Jun H, Kiang TKL. Population pharmacokinetics of mycophenolic acid in paediatric patients. Br J Clin Pharmacol 2021; 87:1730-1757. [PMID: 33118201 DOI: 10.1111/bcp.14590] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 09/07/2020] [Accepted: 09/22/2020] [Indexed: 12/14/2022] Open
Abstract
Mycophenolic acid (MPA) is widely used in paediatric kidney transplant patients and sometimes prescribed for additional indications. Population pharmacokinetic or pharmacodynamic modelling has been frequently used to characterize the fixed, random and covariate effects of MPA in adult patients. However, MPA population pharmacokinetic data in the paediatric population have not been systematically summarized. The objective of this narrative review was to provide an up-to-date critique of currently available paediatric MPA population pharmacokinetic models, with emphases on modelling techniques, pharmacological findings and clinical relevance. PubMed and EMBASE were searched from inception of database to May 2020, where a total of 11 studies have been identified representing kidney transplant (n = 4), liver transplant (n = 1), haematopoietic stem cell transplant (n = 1), idiopathic nephrotic syndrome (n = 2), systemic lupus erythematosus (n = 2), and a combined population consisted of kidney, liver and haematopoietic stem cell transplant patients (n = 1). Critical analyses were provided in the context of MPA absorption, distribution, metabolism, excretion and bioavailability in this paediatric database. Comparisons to adult patients were also provided. With respect to clinical utility, Bayesian estimation models (n = 6) with acceptable accuracy and precision for MPA exposure determination have also been identified and systematically evaluated. Overall, our analyses have identified unique features of MPA clinical pharmacology in the paediatric population, while recognizing several gaps that still warrant further investigations. This review can be used by pharmacologists and clinicians for improving MPA pharmacokinetic-pharmacodynamic modelling and patient care.
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Affiliation(s)
- Yan Rong
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Heajin Jun
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada.,College of Pharmacy, Seoul National University, Seoul, Republic of Korea
| | - Tony K L Kiang
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada
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10
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Catić-Đorđević A, Pavlović I, Spasić A, Stefanović N, Pavlović D, Damnjanović I, Mitić B, Veličković-Radovanović R. Assessment of pharmacokinetic mycophenolic acid clearance models using Monte Carlo numerical analysis. Xenobiotica 2021; 51:387-393. [PMID: 33416418 DOI: 10.1080/00498254.2020.1871532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
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.
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Affiliation(s)
| | - Ivan Pavlović
- Faculty of Mechanical Engineering, University of Nis, Nis, Serbia
| | - Ana Spasić
- Faculty of Medicine, University of Nis, Nis, Serbia
| | | | | | | | - Branka Mitić
- Faculty of Medicine, University of Nis, Nis, Serbia.,Clinic of Nephrology, Clinical Center Nis, Nis, Serbia
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11
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Early prognostic performance of miR155-5p monitoring for the risk of rejection: Logistic regression with a population pharmacokinetic approach in adult kidney transplant patients. PLoS One 2021; 16:e0245880. [PMID: 33481955 PMCID: PMC7822507 DOI: 10.1371/journal.pone.0245880] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/10/2021] [Indexed: 12/29/2022] Open
Abstract
Previous results from our group and others have shown that urinary pellet expression of miR155-5p and urinary CXCL-10 production could play a key role in the prognosis and diagnosis of acute rejection (AR) in kidney transplantation patients. Here, a logistic regression model was developed using NONMEM to quantify the relationships of miR155-5p urinary expression, CXCL-10 urinary concentration and tacrolimus and mycophenolic acid (MPA) exposure with the probability of AR in adult kidney transplant patients during the early post-transplant period. Owing to the contribution of therapeutic drug monitoring to achieving target exposure, neither tacrolimus nor MPA cumulative exposure was identified as a predictor of AR in the studied population. Even though CXCL-10 urinary concentration showed a trend, its effect on AR was not significant. In contrast, urinary miR155-5p expression was prognostic of clinical outcome. Monitoring miR155-5p urinary pellet expression together with immunosuppressive drug exposure could be very useful during routine clinical practice to identify patients with a potential high risk of rejection at the early stages of the post-transplant period. This early risk assessment would allow for the optimization of treatment and improved prevention of AR.
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12
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Degraeve AL, Moudio S, Haufroid V, Chaib Eddour D, Mourad M, Bindels LB, Elens L. Predictors of tacrolimus pharmacokinetic variability: current evidences and future perspectives. Expert Opin Drug Metab Toxicol 2020; 16:769-782. [PMID: 32721175 DOI: 10.1080/17425255.2020.1803277] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
INTRODUCTION In kidney transplantation, tacrolimus (TAC) is at the cornerstone of current immunosuppressive strategies. Though because of its narrow therapeutic index, it is critical to ensure that TAC levels are maintained within this sharp window through reactive adjustments. This would allow maximizing efficiency while limiting drug-associated toxicity. However, TAC high intra- and inter-patient pharmacokinetic (PK) variability makes it more laborious to accurately predict the appropriate dosage required for a given patient. AREAS COVERED This review summarizes the state-of-the-art knowledge regarding drug interactions, demographic and pharmacogenetics factors as predictors of TAC PK. We provide a scoring index for each association to grade its relevance and we present practical recommendations, when possible for clinical practice. EXPERT OPINION The management of TAC concentration in transplanted kidney patients is as critical as it is challenging. Recommendations based on rigorous scientific evidences are lacking as knowledge of potential predictors remains limited outside of DDIs. Awareness of these limitations should pave the way for studies looking at demographic and pharmacogenetic factors as well as gut microbiota composition in order to promote tailored treatment plans. Therapeutic approaches considering patients' clinical singularities may help allowing to maintain appropriate concentration of TAC.
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Affiliation(s)
- Alexandra L Degraeve
- Integrated Pharmacometrics, Pharmacogenomics and Pharmacokinetics (PMGK), Louvain Drug Research Institute (LDRI), Université Catholique De Louvain , Brussels, Belgium.,Metabolism and Nutrition Research Group (Mnut), Louvain Drug Research Institute (LDRI), Université Catholique De Louvain , Brussels, Belgium
| | - Serge Moudio
- Integrated Pharmacometrics, Pharmacogenomics and Pharmacokinetics (PMGK), Louvain Drug Research Institute (LDRI), Université Catholique De Louvain , Brussels, Belgium.,Louvain Centre for Toxicology and Applied Pharmacology (LTAP), Institut De Recherche Expérimentale Et Clinique (IREC), Université Catholique De Louvain , Brussels, Belgium
| | - Vincent Haufroid
- Louvain Centre for Toxicology and Applied Pharmacology (LTAP), Institut De Recherche Expérimentale Et Clinique (IREC), Université Catholique De Louvain , Brussels, Belgium.,Department of Clinical Chemistry, Cliniques Universitaires Saint-Luc , Brussels, Belgium
| | - Djamila Chaib Eddour
- Kidney and Pancreas Transplantation Unit, Cliniques Universitaires Saint-Luc , Brussels, Belgium
| | - Michel Mourad
- Kidney and Pancreas Transplantation Unit, Cliniques Universitaires Saint-Luc , Brussels, Belgium
| | - Laure B Bindels
- Metabolism and Nutrition Research Group (Mnut), Louvain Drug Research Institute (LDRI), Université Catholique De Louvain , Brussels, Belgium
| | - Laure Elens
- Integrated Pharmacometrics, Pharmacogenomics and Pharmacokinetics (PMGK), Louvain Drug Research Institute (LDRI), Université Catholique De Louvain , Brussels, Belgium.,Louvain Centre for Toxicology and Applied Pharmacology (LTAP), Institut De Recherche Expérimentale Et Clinique (IREC), Université Catholique De Louvain , Brussels, Belgium
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13
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Nanga TM, Doan TTP, Marquet P, Musuamba FT. Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach. Br J Clin Pharmacol 2019; 85:2793-2823. [PMID: 31471970 DOI: 10.1111/bcp.14110] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 07/31/2019] [Accepted: 08/06/2019] [Indexed: 02/07/2023] Open
Abstract
AIMS The objective of this study is to develop a generic model for tacrolimus pharmacokinetics modelling using a meta-analysis approach, that could serve as a first step towards a prediction tool to inform pharmacokinetics-based optimal dosing of tacrolimus in different populations and indications. METHODS A systematic literature review was performed and a meta-model developed with NONMEM software using a top-down approach. Historical (previously published) data were used for model development and qualification. In-house individual rich and sparse tacrolimus blood concentration profiles from adult and paediatric kidney, liver, lung and heart transplant patients were used for model validation. Model validation was based on successful numerical convergence, adequate precision in parameter estimation, acceptable goodness of fit with respect to measured blood concentrations with no indication of bias, and acceptable performance of visual predictive checks. External validation was performed by fitting the model to independent data from 3 external cohorts and remaining previously published studies. RESULTS A total of 76 models were found relevant for meta-model building from the literature and the related parameters recorded. The meta-model developed using patient level data was structurally a 2-compartment model with first-order absorption, absorption lag time and first-time varying elimination. Population values for clearance, intercompartmental clearance, central and peripheral volume were 22.5 L/h, 24.2 L/h, 246.2 L and 109.9 L, respectively. The absorption first-order rate and the lag time were fixed to 3.37/h and 0.33 hours, respectively. Transplanted organ and time after transplantation were found to influence drug apparent clearance whereas body weight influenced both the apparent volume of distribution and the apparent clearance. The model displayed good results as regards the internal and external validation. CONCLUSION A meta-model was successfully developed for tacrolimus in solid organ transplantation that can be used as a basis for the prediction of concentrations in different groups of patients, and eventually for effective dose individualization in different subgroups of the population.
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Affiliation(s)
- Tom M Nanga
- INSERM UMR 1248, Université de Limoges, FHU support, Limoges Cédex, 87025, France
| | - Thao T P Doan
- INSERM UMR 1248, Université de Limoges, FHU support, Limoges Cédex, 87025, France
| | - Pierre Marquet
- INSERM UMR 1248, Université de Limoges, FHU support, Limoges Cédex, 87025, France
| | - Flora T Musuamba
- Federal Agency for Medicines and Health Products, Brussels, Belgium.,Faculté des sciences pharmaceutiques, Université de Lubumbashi, Lubumbashi, Democratic Republic of the Congo
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14
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Model based development of tacrolimus dosing algorithm considering CYP3A5 genotypes and mycophenolate mofetil drug interaction in stable kidney transplant recipients. Sci Rep 2019; 9:11740. [PMID: 31409869 PMCID: PMC6692323 DOI: 10.1038/s41598-019-47876-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 07/19/2019] [Indexed: 01/10/2023] Open
Abstract
This study quantifies the interaction between tacrolimus (TAC) and mycophenolate mofetil (MMF) in kidney transplant recipients. Concentrations of TAC, mycophenolic acid (MPA), and metabolites were analyzed and relevant genotypes were determined from 32 patients. A population model was developed to estimate the effect of interaction. Concentrations of TAC were simulated in clinical scenarios and dose-adjusted trough concentrations per dose (C/D) were compared. Effect of interaction was described as the inverse exponential relationship. Major determinants of trough levels of TAC were CYP3A5 genotype and interaction with MPA. The absolute difference in C/D of TAC according to co-administered MMF was higher in CYP3A5 non-expressers (0.55 ng/mL) than in CYP3A5 expressers (0.35 ng/mL). The effect of MMF in determining the TAC exposure is more pronounced in CYP3A5 non-expressers. Based on population pharmacokinetic model, we suggest the TAC dosing algorithm considering the effects of CYP3A5 and MMF drug interaction in stable kidney transplant recipients.
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15
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Abstract
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.
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16
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Therapeutic Drug Monitoring of Tacrolimus-Personalized Therapy: Second Consensus Report. Ther Drug Monit 2019; 41:261-307. [DOI: 10.1097/ftd.0000000000000640] [Citation(s) in RCA: 227] [Impact Index Per Article: 45.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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17
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Brunet M, van Gelder T, Åsberg A, Haufroid V, Hesselink DA, Langman L, Lemaitre F, Marquet P, Seger C, Shipkova M, Vinks A, Wallemacq P, Wieland E, Woillard JB, Barten MJ, Budde K, Colom H, Dieterlen MT, Elens L, Johnson-Davis KL, Kunicki PK, MacPhee I, Masuda S, Mathew BS, Millán O, Mizuno T, Moes DJAR, Monchaud C, Noceti O, Pawinski T, Picard N, van Schaik R, Sommerer C, Vethe NT, de Winter B, Christians U, Bergan S. Therapeutic Drug Monitoring of Tacrolimus-Personalized Therapy: Second Consensus Report. Ther Drug Monit 2019. [DOI: 10.1097/ftd.0000000000000640
expr 845143713 + 809233716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
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18
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Rong Y, Mayo P, Ensom MHH, Kiang TKL. Population Pharmacokinetics of Mycophenolic Acid Co-Administered with Tacrolimus in Corticosteroid-Free Adult Kidney Transplant Patients. Clin Pharmacokinet 2019; 58:1483-1495. [DOI: 10.1007/s40262-019-00771-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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19
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Chen B, Shao K, An H, Shi H, Lu J, Zhai X, Liu X, Wang X, Xu D, Zhou P. Population Pharmacokinetics and Bayesian Estimation of Mycophenolic Acid Exposure in Chinese Renal Allograft Recipients After Administration of EC‐MPS. J Clin Pharmacol 2018; 59:578-589. [DOI: 10.1002/jcph.1352] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 11/08/2018] [Indexed: 12/17/2022]
Affiliation(s)
- Bing Chen
- Department of PharmacyRuijin HospitalShanghai Jiao Tong University School of Medicine Shanghai China
| | - Kun Shao
- Organ Transplantation CenterRuijin HospitalShanghai Jiao Tong University School of Medicine Shanghai China
| | - Hui‐Min An
- Organ Transplantation CenterRuijin HospitalShanghai Jiao Tong University School of Medicine Shanghai China
| | - Hao‐Qiang Shi
- Department of PharmacyRuijin HospitalShanghai Jiao Tong University School of Medicine Shanghai China
| | - Jia‐Qian Lu
- Department of PharmacyRuijin HospitalShanghai Jiao Tong University School of Medicine Shanghai China
| | - Xiao‐Hui Zhai
- Department of PharmacyRuijin HospitalShanghai Jiao Tong University School of Medicine Shanghai China
| | - Xiao‐Xue Liu
- Department of PharmacyRuijin HospitalShanghai Jiao Tong University School of Medicine Shanghai China
| | - Xiang‐Hui Wang
- Organ Transplantation CenterRuijin HospitalShanghai Jiao Tong University School of Medicine Shanghai China
| | - Da Xu
- Organ Transplantation CenterRuijin HospitalShanghai Jiao Tong University School of Medicine Shanghai China
| | - Pei‐Jun Zhou
- Organ Transplantation CenterRuijin HospitalShanghai Jiao Tong University School of Medicine Shanghai China
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20
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Hu C, Yin WJ, Li DY, Ding JJ, Zhou LY, Wang JL, Ma RR, Liu K, Zhou G, Zuo XC. Evaluating tacrolimus pharmacokinetic models in adult renal transplant recipients with different CYP3A5 genotypes. Eur J Clin Pharmacol 2018; 74:1437-1447. [PMID: 30019212 DOI: 10.1007/s00228-018-2521-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 07/06/2018] [Indexed: 10/28/2022]
Abstract
PURPOSE Numerous studies have been conducted on the population pharmacokinetics of tacrolimus in adult renal transplant recipients. It has been reported that the cytochrome P450 (CYP) 3A5 genotype is an important cause of variability in tacrolimus pharmacokinetics. However, the predictive performance of population pharmacokinetic (PK) models of tacrolimus should be evaluated prior to their implementation in clinical practice. The aim of the study reported here was to test the predictive performance of these published PK models of tacrolimus. METHODS A literature search of the PubMed and Web of Science databases ultimately led to the inclusion of eight one-compartment models in our analysis. We collected a total of 1715 trough concentrations from 174 patients. Predictive performance was assessed based on visual and numerical comparison bias and imprecision and by the use of simulation-based diagnostics and Bayesian forecasting. RESULTS Of the eight one-compartment models assessed, seven showed better predictive performance in CYP3A5 extensive metabolizers in terms of bias and imprecision. Results of the simulation-based diagnostics also supported the findings. The model based on a Chinese population in 2013 (model 3) showed the best and most stable predictive performance in all the tests and was more informative in CYP3A5 extensive metabolizers. As expected, Bayesian forecasting improved model predictability. Diversity among models and between different CYP3A5 genotypes of the same model was also narrowed by Bayesian forecasting. CONCLUSIONS Based on our results, we recommend using model 3 in CYP3A5 extensive metabolizers in clinical practice. All models had a poor predictive performance in CYP3A5 poor metabolizers, and they should be used with caution in this patient population. However, Bayesian forecasting improved the predictability and reduced differences, and thus the models could be applied in this latter patient population for the design of maintenance dose.
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Affiliation(s)
- Can Hu
- Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, People's Republic of China
| | - Wen-Jun Yin
- Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, People's Republic of China
| | - Dai-Yang Li
- Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, People's Republic of China
| | - Jun-Jie Ding
- Department of Pharmacy, Children's Hospital of Fudan University, Shanghai, 100029, People's Republic of China
| | - Ling-Yun Zhou
- Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, People's Republic of China
| | - Jiang-Lin Wang
- Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, People's Republic of China
| | - Rong-Rong Ma
- Department of Pharmacy, First Affiliated Hospital of Xinjiang Medical University, No. 137 Liyushan South Road, Urumqi, 830054, Xinjiang, People's Republic of China
| | - Kun Liu
- Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, People's Republic of China
| | - Ge Zhou
- Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, People's Republic of China
| | - Xiao-Cong Zuo
- Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, People's Republic of China.
- Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, People's Republic of China.
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21
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Chen B, Shi HQ, Liu XX, Zhang WX, Lu JQ, Xu BM, Chen H. Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in Chinese liver transplant patients. J Clin Pharm Ther 2017; 42:679-688. [PMID: 28833329 DOI: 10.1111/jcpt.12599] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 06/26/2017] [Indexed: 12/16/2022]
Abstract
WHAT IS KNOWN AND OBJECTIVES Tacrolimus (TAC) is widely used as part of immunosuppressive regimens. There is great interindividual variation on the disposition of TAC. The aim of this study was to develop a population pharmacokinetic (PPK) model for Chinese liver transplant patients and evaluate genetic polymorphism and other possible factors on the PK parameters. The exposure of TAC is to be estimated through Bayesian modelling. METHODS A total of 47 sets of rich-time PK and 1234 conventional therapeutic drug monitoring (TDM) data were collected from 125 Chinese liver transplant patients. The pathophysiological data of these patients were recorded. CYP3A5*3 and ABCB1 genotypes were determined for each patient. The PPK model for TAC was established by nonlinear mixed-effects modelling (nonmem). The impact of pathophysiology and genotype on PPK parameters was evaluated. Bayesian estimators for the area under concentration-time curve (AUC) of TAC were validated. RESULTS A two-compartment model with lag time was found to be the most suitable model for the pooled full PK and TDM data for Chinese liver transplant patients. The CL/F, V2 /F, Q/F, V3 /F, Ka and lag time were 17.4±0.81 L/h, 165±44.1 L, 54.9±25.8L/h, 594±87.5 L, 0.51±0.095 L/h and 1.57±0.34 h. Post-operative day (POD), creatinine clearance (CLcr) and ABCB1 C3435T genotypes were found to have significant influences on CL/F (P<.01). ABCB1 C3435T genotypes showed a significant correlation with V2 /F (P<.01). C0 -C2 and C0 -C2 -C4 were shown to be suitable for the estimation of AUC in Chinese liver transplant patients. WHAT IS NEW AND CONCLUSION A PPK model for TAC was established successfully in Chinese liver transplant patients. POD, CLcr and ABCB1 C3435T genotypes were shown to have significant effects on CL/F. The AUC of TAC in Chinese liver transplant patients could be estimated through Bayesian modelling, based on which individualized immunosuppressive regimens can be designed.
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Affiliation(s)
- B Chen
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - H-Q Shi
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - X-X Liu
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - W-X Zhang
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - J-Q Lu
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - B-M Xu
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - H Chen
- Organ Transplantation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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22
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Yoo EC, Alvarez-Elías AC, Todorova EK, Filler G. Developmental changes of MPA exposure in children. Pediatr Nephrol 2016; 31:975-82. [PMID: 26743220 DOI: 10.1007/s00467-015-3303-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Revised: 12/02/2015] [Accepted: 12/11/2015] [Indexed: 11/28/2022]
Abstract
BACKGROUND Developmental changes (ontogeny) of drug disposition of Mycophenolate mofetil (MMF) have been understudied. METHODS The charts of 37 pediatric renal transplant recipients (median age 7.3 years, median follow-up 7.8 (IQR 6.6, 14.3 years) who had regular mycophenolic acid (MPA) trough level monitoring in combination with tacrolimus (n = 31) or sirolimus (n = 6) therapy were analyzed retrospectively for their dose-normalized MPA exposure, steroid dose, albumin, hematocrit, and cystatin C estimated glomerular filtration rate (eGFR). Using appropriate univariate and multivariate methods, we determined whether MPA exposure was age dependent when controlling for the confounders. RESULTS Dose-normalized MPA trough levels could be calculated in 2,128 (median 45/patient) instances. Spearman rank correlation analysis revealed that age correlated with dose-normalized MPA trough level for both body weight and body surface area, as well as serum albumin, hematocrit, steroid dose, and eGFR. In the multivariate analysis, serum albumin and steroid dose were not significant, and hematocrit only being significant when the youngest group of patients < 6 years of age was compared. eGFR was the most important confounder, but age dependency remained significant when controlling for all confounders. CONCLUSIONS Small children are at a significantly greater risk for low MPA trough levels than adolescents, highlighting the need for pharmacokinetic monitoring of MPA.
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Affiliation(s)
- Elisa C Yoo
- Department of Pediatrics, Schulich School of Medicine & Dentistry, London, ON, Canada, N6A 5W9
| | - Ana Catalina Alvarez-Elías
- Department of Pediatrics, Schulich School of Medicine & Dentistry, London, ON, Canada, N6A 5W9.,Universidad Nacional Autónoma de México, Mexico City, Mexico, 04510
| | | | - Guido Filler
- Department of Pediatrics, Schulich School of Medicine & Dentistry, London, ON, Canada, N6A 5W9. .,Department of Pathology and Laboratory Medicine, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Ontario, Canada, N5A 5A5. .,Department of Medicine, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada, 5A 5A5. .,Department of Pediatrics, Children's Hospital, London Health Sciences Centre, University of Western Ontario, 800 Commissioners Road East, London, ON, Canada, N6A 5W9.
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23
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Vadcharavivad S, Praisuwan S, Techawathanawanna N, Treyaprasert W, Avihingsanon Y. Population pharmacokinetics of tacrolimus in Thai kidney transplant patients: comparison with similar data from other populations. J Clin Pharm Ther 2016; 41:310-28. [DOI: 10.1111/jcpt.12396] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2015] [Accepted: 04/06/2016] [Indexed: 12/22/2022]
Affiliation(s)
- S. Vadcharavivad
- Faculty of Pharmaceutical Sciences; Chulalongkorn University; Bangkok Thailand
| | - S. Praisuwan
- Faculty of Pharmaceutical Sciences; Chulalongkorn University; Bangkok Thailand
| | | | - W. Treyaprasert
- Faculty of Pharmaceutical Sciences; Chulalongkorn University; Bangkok Thailand
| | - Y. Avihingsanon
- Faculty of Medicine; Chulalongkorn University; Bangkok Thailand
- Excellence Center of Organ Transplantation; King Chulalongkorn Memorial Hospital; Bangkok Thailand
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24
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Brooks E, Tett SE, Isbel NM, Staatz CE. Population Pharmacokinetic Modelling and Bayesian Estimation of Tacrolimus Exposure: Is this Clinically Useful for Dosage Prediction Yet? Clin Pharmacokinet 2016; 55:1295-1335. [DOI: 10.1007/s40262-016-0396-1] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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25
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Zhao CY, Jiao Z, Mao JJ, Qiu XY. External evaluation of published population pharmacokinetic models of tacrolimus in adult renal transplant recipients. Br J Clin Pharmacol 2016; 81:891-907. [PMID: 26574188 DOI: 10.1111/bcp.12830] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2015] [Revised: 11/04/2015] [Accepted: 11/11/2015] [Indexed: 11/29/2022] Open
Abstract
AIM Several tacrolimus population pharmacokinetic models in adult renal transplant recipients have been established to facilitate dose individualization. However, their applicability when extrapolated to other clinical centres is not clear. This study aimed to (1) evaluate model external predictability and (2) analyze potential influencing factors. METHODS Published models were screened from the literature and were evaluated using an external dataset with 52 patients (609 trough samples) collected by postoperative day 90 via methods that included (1) prediction-based prediction error (PE%), (2) simulation-based prediction- and variability-corrected visual predictive check (pvcVPC) and normalized prediction distribution error (NPDE) tests and (3) Bayesian forecasting to assess the influence of prior observations on model predictability. The factors influencing model predictability, particularly the impact of structural models, were evaluated. RESULTS Sixteen published models were evaluated. In prediction-based diagnostics, the PE% within ±30% was less than 50% in all models, indicating unsatisfactory predictability. In simulation-based diagnostics, both the pvcVPC and the NPDE indicated model misspecification. Bayesian forecasting improved model predictability significantly with prior 2-3 observations. The various factors influencing model extrapolation included bioassays, the covariates involved (CYP3A5*3 polymorphism, postoperative time and haematocrit) and whether non-linear kinetics were used. CONCLUSIONS The published models were unsatisfactory in prediction- and simulation-based diagnostics, thus inappropriate for direct extrapolation correspondingly. However Bayesian forecasting could improve the predictability considerably with priors. The incorporation of non-linear pharmacokinetics in modelling might be a promising approach to improving model predictability.
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Affiliation(s)
- Chen-Yan Zhao
- Department of Pharmacy, Huashan Hospital, Fudan University, 12 Middle Urumqi Road, Shanghai, 200040
| | - Zheng Jiao
- Department of Pharmacy, Huashan Hospital, Fudan University, 12 Middle Urumqi Road, Shanghai, 200040
| | - Jun-Jun Mao
- Department of Pharmacy, Huashan Hospital, Fudan University, 12 Middle Urumqi Road, Shanghai, 200040.,Department of Clinical Pharmacy, School of Pharmacy, Fudan University, 826 Zhang Heng Road, Shanghai, 201203, China
| | - Xiao-Yan Qiu
- Department of Pharmacy, Huashan Hospital, Fudan University, 12 Middle Urumqi Road, Shanghai, 200040
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Størset E, Holford N, Hennig S, Bergmann TK, Bergan S, Bremer S, Åsberg A, Midtvedt K, Staatz CE. Improved prediction of tacrolimus concentrations early after kidney transplantation using theory-based pharmacokinetic modelling. Br J Clin Pharmacol 2015; 78:509-23. [PMID: 25279405 PMCID: PMC4243902 DOI: 10.1111/bcp.12361] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Aims The aim was to develop a theory-based population pharmacokinetic model of tacrolimus in adult kidney transplant recipients and to externally evaluate this model and two previous empirical models. Methods Data were obtained from 242 patients with 3100 tacrolimus whole blood concentrations. External evaluation was performed by examining model predictive performance using Bayesian forecasting. Results Pharmacokinetic disposition parameters were estimated based on tacrolimus plasma concentrations, predicted from whole blood concentrations, haematocrit and literature values for tacrolimus binding to red blood cells. Disposition parameters were allometrically scaled to fat free mass. Tacrolimus whole blood clearance/bioavailability standardized to haematocrit of 45% and fat free mass of 60 kg was estimated to be 16.1 l h−1 [95% CI 12.6, 18.0 l h−1]. Tacrolimus clearance was 30% higher (95% CI 13, 46%) and bioavailability 18% lower (95% CI 2, 29%) in CYP3A5 expressers compared with non-expressers. An Emax model described decreasing tacrolimus bioavailability with increasing prednisolone dose. The theory-based model was superior to the empirical models during external evaluation displaying a median prediction error of −1.2% (95% CI −3.0, 0.1%). Based on simulation, Bayesian forecasting led to 65% (95% CI 62, 68%) of patients achieving a tacrolimus average steady-state concentration within a suggested acceptable range. Conclusion A theory-based population pharmacokinetic model was superior to two empirical models for prediction of tacrolimus concentrations and seemed suitable for Bayesian prediction of tacrolimus doses early after kidney transplantation.
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Affiliation(s)
- Elisabet Størset
- Department of Transplant Medicine, Oslo University Hospital RikshospitaletOslo, Norway
- Institute of Clinical Medicine, University of OsloOslo, Norway
- Correspondence: Ms Elisabet Størset MSc, Department of Transplant Medicine, Oslo University Hospital Rikshospitalet, Postbox 4950 Nydalen, Oslo 0424, Norway., Tel.: +47 2307 0000, Fax: +47 2307 3865, E-mail:
| | - Nick Holford
- Department of Pharmacology and Clinical Pharmacology, University of AucklandAuckland, New Zealand
| | - Stefanie Hennig
- School of Pharmacy, University of QueenslandBrisbane, Australia
- Australian Centre of PharmacometricsBrisbane, Australia
| | - Troels K Bergmann
- School of Pharmacy, University of QueenslandBrisbane, Australia
- Department of Clinical Pharmacology, Aarhus University HospitalAarhus, Denmark
| | - Stein Bergan
- Department of Pharmacology, Oslo University HospitalOslo, Norway
- School of Pharmacy, University of OsloOslo, Norway
| | - Sara Bremer
- Department of Medical Biochemistry, Oslo University HospitalOslo, Norway
| | - Anders Åsberg
- Department of Transplant Medicine, Oslo University Hospital RikshospitaletOslo, Norway
- School of Pharmacy, University of OsloOslo, Norway
| | - Karsten Midtvedt
- Department of Transplant Medicine, Oslo University Hospital RikshospitaletOslo, Norway
| | - Christine E Staatz
- School of Pharmacy, University of QueenslandBrisbane, Australia
- Australian Centre of PharmacometricsBrisbane, Australia
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Veličković-Radovanović RM, Janković SM, Milovanović JR, Catić-Đorđević AK, Spasić AA, Stefanović NZ, Džodić PL, Šmelcerović AA, Cvetković TP. Variability of mycophenolic acid elimination in the renal transplant recipients – population pharmacokinetic approach. Ren Fail 2015; 37:652-8. [DOI: 10.3109/0886022x.2015.1010442] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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Abd Rahman AN, Tett SE, Staatz CE. How accurate and precise are limited sampling strategies in estimating exposure to mycophenolic acid in people with autoimmune disease? Clin Pharmacokinet 2014; 53:227-245. [PMID: 24327238 DOI: 10.1007/s40262-013-0124-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Mycophenolic acid (MPA) is a potent immunosuppressant agent, which is increasingly being used in the treatment of patients with various autoimmune diseases. Dosing to achieve a specific target MPA area under the concentration-time curve from 0 to 12 h post-dose (AUC12) is likely to lead to better treatment outcomes in patients with autoimmune disease than a standard fixed-dose strategy. This review summarizes the available published data around concentration monitoring strategies for MPA in patients with autoimmune disease and examines the accuracy and precision of methods reported to date using limited concentration-time points to estimate MPA AUC12. A total of 13 studies were identified that assessed the correlation between single time points and MPA AUC12 and/or examined the predictive performance of limited sampling strategies in estimating MPA AUC12. The majority of studies investigated mycophenolate mofetil (MMF) rather than the enteric-coated mycophenolate sodium (EC-MPS) formulation of MPA. Correlations between MPA trough concentrations and MPA AUC12 estimated by full concentration-time profiling ranged from 0.13 to 0.94 across ten studies, with the highest associations (r (2) = 0.90-0.94) observed in lupus nephritis patients. Correlations were generally higher in autoimmune disease patients compared with renal allograft recipients and higher after MMF compared with EC-MPS intake. Four studies investigated use of a limited sampling strategy to predict MPA AUC12 determined by full concentration-time profiling. Three studies used a limited sampling strategy consisting of a maximum combination of three sampling time points with the latest sample drawn 3-6 h after MMF intake, whereas the remaining study tested all combinations of sampling times. MPA AUC12 was best predicted when three samples were taken at pre-dose and at 1 and 3 h post-dose with a mean bias and imprecision of 0.8 and 22.6 % for multiple linear regression analysis and of -5.5 and 23.0 % for maximum a posteriori (MAP) Bayesian analysis. Although mean bias was less when data were analysed using multiple linear regression, MAP Bayesian analysis is preferable because of its flexibility with respect to sample timing. Estimation of MPA AUC12 following EC-MPS administration using a limited sampling strategy with samples drawn within 3 h post-dose resulted in biased and imprecise results, likely due to a longer time to reach a peak MPA concentration (t max) with this formulation and more variable pharmacokinetic profiles. Inclusion of later sampling time points that capture enterohepatic recirculation and t max improved the predictive performance of strategies to predict EC-MPS exposure. Given the considerable pharmacokinetic variability associated with mycophenolate therapy, limited sampling strategies may potentially help in individualizing patient dosing. However, a compromise needs to be made between the predictive performance of the strategy and its clinical feasibility. An opportunity exists to combine research efforts globally to create an open-source database for MPA (AUC, concentrations and outcomes) that can be used and prospectively evaluated for AUC target-controlled dosing of MPA in autoimmune diseases.
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Affiliation(s)
- Azrin N Abd Rahman
- School of Pharmacy, Pharmacy Australia Centre of Excellence, University of Queensland, 20 Cornwall St, Woolloongabba, Brisbane, QLD, 4102, Australia.,School of Pharmacy, International Islamic University of Malaysia, Kuantan, Pahang, Malaysia
| | - Susan E Tett
- School of Pharmacy, Pharmacy Australia Centre of Excellence, University of Queensland, 20 Cornwall St, Woolloongabba, Brisbane, QLD, 4102, Australia
| | - Christine E Staatz
- School of Pharmacy, Pharmacy Australia Centre of Excellence, University of Queensland, 20 Cornwall St, Woolloongabba, Brisbane, QLD, 4102, Australia.
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Pharmacology and toxicology of mycophenolate in organ transplant recipients: an update. Arch Toxicol 2014; 88:1351-89. [PMID: 24792322 DOI: 10.1007/s00204-014-1247-1] [Citation(s) in RCA: 138] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2014] [Accepted: 04/15/2014] [Indexed: 12/22/2022]
Abstract
This review aims to provide an update of the literature on the pharmacology and toxicology of mycophenolate in solid organ transplant recipients. Mycophenolate is now the antimetabolite of choice in immunosuppressant regimens in transplant recipients. The active drug moiety mycophenolic acid (MPA) is available as an ester pro-drug and an enteric-coated sodium salt. MPA is a competitive, selective and reversible inhibitor of inosine-5'-monophosphate dehydrogenase (IMPDH), an important rate-limiting enzyme in purine synthesis. MPA suppresses T and B lymphocyte proliferation; it also decreases expression of glycoproteins and adhesion molecules responsible for recruiting monocytes and lymphocytes to sites of inflammation and graft rejection; and may destroy activated lymphocytes by induction of a necrotic signal. Improved long-term allograft survival has been demonstrated for MPA and may be due to inhibition of monocyte chemoattractant protein 1 or fibroblast proliferation. Recent research also suggested a differential effect of mycophenolate on the regulatory T cell/helper T cell balance which could potentially encourage immune tolerance. Lower exposure to calcineurin inhibitors (renal sparing) appears to be possible with concomitant use of MPA in renal transplant recipients without undue risk of rejection. MPA displays large between- and within-subject pharmacokinetic variability. At least three studies have now reported that MPA exhibits nonlinear pharmacokinetics, with bioavailability decreasing significantly with increasing doses, perhaps due to saturable absorption processes or saturable enterohepatic recirculation. The role of therapeutic drug monitoring (TDM) is still controversial and the ability of routine MPA TDM to improve long-term graft survival and patient outcomes is largely unknown. MPA monitoring may be more important in high-immunological recipients, those on calcineurin-inhibitor-sparing regimens and in whom unexpected rejection or infections have occurred. The majority of pharmacodynamic data on MPA has been obtained in patients receiving MMF therapy in the first year after kidney transplantation. Low MPA area under the concentration time from 0 to 12 h post-dose (AUC0-12) is associated with increased incidence of biopsy-proven acute rejection although AUC0-12 optimal cut-off values vary across study populations. IMPDH monitoring to identify individuals at increased risk of rejection shows some promise but is still in the experimental stage. A relationship between MPA exposure and adverse events was identified in some but not all studies. Genetic variants within genes involved in MPA metabolism (UGT1A9, UGT1A8, UGT2B7), cellular transportation (SLCOB1, SLCO1B3, ABCC2) and targets (IMPDH) have been reported to effect MPA pharmacokinetics and/or response in some studies; however, larger studies across different ethnic groups that take into account genetic linkage and drug interactions that can alter a patient's phenotype are needed before any clinical recommendations based on patient genotype can be formulated. There is little data on the pharmacology and toxicology of MPA in older and paediatric transplant recipients.
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Ogasawara K, Chitnis SD, Gohh RY, Christians U, Akhlaghi F. Multidrug resistance-associated protein 2 (MRP2/ABCC2) haplotypes significantly affect the pharmacokinetics of tacrolimus in kidney transplant recipients. Clin Pharmacokinet 2014; 52:751-62. [PMID: 23633119 DOI: 10.1007/s40262-013-0069-2] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND AND OBJECTIVE Tacrolimus is an immunosuppressive drug used for the prevention of the allograft rejection in kidney transplant recipients. It exhibits a narrow therapeutic index and large pharmacokinetic variability. Tacrolimus is mainly metabolized by cytochrome P450 (CYP) 3A4 and 3A5 and effluxed via ATP-binding cassette (ABC) transporters such as P-glycoprotein (P-gp), encoded by ABCB1 gene. The influence of CYP3A5*3 on the pharmacokinetics of tacrolimus has been well characterized. On the other hand, the contribution of polymorphisms in other genes is controversial. In addition, the involvement of other efflux transporters than P-gp in tacrolimus disposition is uncertain. The present study was designed to investigate the effects of genetic polymorphisms of CYP3As and efflux transporters on the pharmacokinetics of tacrolimus. SUBJECTS AND METHODS A total of 500 blood concentrations of tacrolimus from 102 adult stable kidney transplant recipients were included in the analyses. Genetic polymorphisms in CYP3A4 and CYP3A5 genes were determined. In addition, the genes of efflux transporters including P-gp (ABCB1), multidrug resistance-associated protein (MRP2/ABCC2) and breast cancer resistance protein (BCRP/ABCG2) were genotyped. For ABCC2 gene, haplotypes were determined as follows: H1 (wild type), H2 (1249G>A), H9 (3972C>T) and H12 (-24C>T and 3972C>T). Population pharmacokinetic analysis was performed using nonlinear mixed effects modeling. RESULTS Analyses revealed that the CYP3A5 expressers (CYP3A5*1 carriers) and MRP2 high-activity group (ABCC2 H2/H2 and H1/H2) showed a decreased dose-normalized trough concentration of tacrolimus by 2.3-fold (p < 0.001) and 1.5-fold (p = 0.007), respectively. The pharmacokinetics of tacrolimus were best described using a two-compartment model with first order absorption and an absorption lag time. In the population pharmacokinetic analysis, CYP3A5 expressers and MRP2 high-activity groups were identified as the significant covariates for tacrolimus apparent clearance expressed as 20.7 × (age/50)(-0.78) × 2.03 (CYP3A5 expressers) × 1.40 (MRP2 high-activity group). No other CYP3A4, ABCB1 or ABCG2 polymorphisms were associated with the apparent clearance of tacrolimus. CONCLUSIONS This is the first report showing that MRP2/ABCC2 has a crucial impact on the pharmacokinetics of tacrolimus in a haplotype-specific manner. Determination of the ABCC2 as well as CYP3A5 genotype may be useful for more accurate tacrolimus dosage adjustment.
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Affiliation(s)
- Ken Ogasawara
- Clinical Pharmacokinetics Research Laboratory, Department of Biomedical and Pharmaceutical Sciences, University of Rhode Island, 7 Greenhouse Road, Kingston, RI 02881, USA
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Åsberg A, Midtvedt K, van Guilder M, Størset E, Bremer S, Bergan S, Jelliffe R, Hartmann A, Neely MN. Inclusion of CYP3A5 genotyping in a nonparametric population model improves dosing of tacrolimus early after transplantation. Transpl Int 2013; 26:1198-207. [PMID: 24118301 PMCID: PMC3852421 DOI: 10.1111/tri.12194] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2013] [Revised: 07/21/2013] [Accepted: 09/15/2013] [Indexed: 12/02/2022]
Abstract
Following organ engraftment, initial dosing of tacrolimus is based on recipient weight and adjusted by measured C0 concentrations. The bioavailability and elimination of tacrolimus are affected by the patients CYP3A5 genotype. Prospective data of the clinical advantage of knowing patient's CYP3A5 genotype prior to transplantation are lacking. A nonparametric population model was developed for tacrolimus in renal transplant recipients. Data from 99 patients were used for model development and validation. A three-compartment model with first-order absorption and lag time from the dosing compartment described the data well. Clearances and volumes of distribution were allometrically scaled to body size. The final model included fat-free mass, body mass index, hematocrit, time after transplantation, and CYP3A5 genotype as covariates. The bias and imprecision were 0.35 and 1.38, respectively, in the external data set. Patients with functional CYP3A5 had 26% higher clearance and 37% lower bioavailability. Knowledge of CYP3A5 genotype provided an initial advantage, but only until 3-4 tacrolimus concentrations were known. After this, a model without CYP3A5 genotype predicted just as well. The present models seem applicable for clinical individual dose predictions but need a prospective evaluation.
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Affiliation(s)
- Anders Åsberg
- Department of Pharmaceutical Biosciences, School of Pharmacy, University of Oslo, Oslo, Norway; Department of Transplant Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway
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Musuamba FT, Mourad M, Haufroid V, De Meyer M, Capron A, Delattre IK, Verbeeck RK, Wallemacq P. Statistical tools for dose individualization of mycophenolic acid and tacrolimus co-administered during the first month after renal transplantation. Br J Clin Pharmacol 2013; 75:1277-88. [PMID: 23072565 DOI: 10.1111/bcp.12007] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2011] [Accepted: 10/09/2012] [Indexed: 11/29/2022] Open
Abstract
AIM To predict simultaneously the area under the concentration-time curve during one dosing interval [AUC(0,12 h)] for mycophenolic acid (MPA) and tacrolimus (TAC), when concomitantly used during the first month after transplantation, based on common blood samples. METHODS Data were from two different sources, real patient pharmacokinetic (PK) profiles from 65 renal transplant recipients and 9000 PK profiles simulated from previously published models on MPA or TAC in the first month after transplantation. Multiple linear regression (MLR) and Bayesian estimation using optimal samples were performed to predict MPA and TAC AUC(0,12 h) based on two concentrations. RESULTS The following models were retained: AUC(0,12 h) = 16.5 + 4.9 × C1.5 + 6.7 × C3.5 (r(2) = 0.82, rRMSE = 9%, with simulations and r(2) = 0.66, rRMSE = 24%, with observed data) and AUC(0,12 h) = 24.3 + 5.9 × C1.5 + 12.2 × C3.5 (r(2) = 0.94, rRMSE = 12.3%, with simulations r(2) = 0.74, rRMSE = 15%, with observed data) for MPA and TAC, respectively. In addition, bayesian estimators were developed including parameter values from final models and values of concentrations at 1.5 and 3.5 h after dose. Good agreement was found between predicted and reference AUC(0,12 h) values: r(2) = 0.90, rRMSE = 13% and r(2) = 0.97, rRMSE = 5% with simulations for MPA and TAC, respectively and r(2) = 0.75, rRMSE = 11% and r(2) = 0.83, rRMSE = 7% with observed data for MPA and TAC, respectively. CONCLUSION Statistical tools were developed for simultaneous MPA and TAC therapeutic drug monitoring. They can be incorporated in computer programs for patient dose individualization.
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Affiliation(s)
- Flora T Musuamba
- Louvain Drug Research Institute, Université catholique de Louvain, Brussels, Belgium.
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Størset E, Holford N, Midtvedt K, Bremer S, Bergan S, Åsberg A. Importance of hematocrit for a tacrolimus target concentration strategy. Eur J Clin Pharmacol 2013; 70:65-77. [PMID: 24071959 PMCID: PMC3889505 DOI: 10.1007/s00228-013-1584-7] [Citation(s) in RCA: 88] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2013] [Accepted: 08/28/2013] [Indexed: 12/15/2022]
Abstract
Purpose To identify patient characteristics that influence tacrolimus individual dose requirement in kidney transplant recipients. Methods Data on forty-four 12-h pharmacokinetic profiles from 29 patients and trough concentrations in 44 patients measured during the first 70 days after transplantation (1,546 tacrolimus whole blood concentrations) were analyzed. Population pharmacokinetic modeling was performed using NONMEM 7.2®. Results Standardization of tacrolimus whole blood concentrations to a hematocrit value of 45 % improved the model fit significantly (p < 0.001). Fat-free mass was the best body size metric to predict tacrolimus clearance and volume of distribution. Bioavailability was 49 % lower in expressers of cytochrome P450 3A5 (CYP3A5) than in CYP3A5 nonexpressers. Younger females (<40 years) showed a 35 % lower bioavailability than younger males. Bioavailability increased with age for both males and females towards a common value at age >55 years that was 47 % higher than the male value at age <40 years. Bioavailability was highest immediately after transplantation, decreasing steeply thereafter to reach its nadir at day 5, following which it increased during the next 55 days towards an asymptotic value that was 28 % higher than that on day 5. Conclusions Hematocrit predicts variability in tacrolimus whole blood concentrations but is not expected to influence unbound (therapeutically active) concentrations. Fat-free mass, CYP3A5 genotype, sex, age and time after transplant influence the tacrolimus individual dose requirement. Because hematocrit is highly variable in kidney transplant patients and increases substantially after kidney transplantation, hematocrit is a key factor in the interpretation of tacrolimus whole blood concentrations. Electronic supplementary material The online version of this article (doi:10.1007/s00228-013-1584-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Elisabet Størset
- Department of Transplant Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway,
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Abd Rahman AN, Tett SE, Staatz CE. Clinical Pharmacokinetics and Pharmacodynamics of Mycophenolate in Patients with Autoimmune Disease. Clin Pharmacokinet 2013; 52:303-31. [DOI: 10.1007/s40262-013-0039-8] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Tolou-Ghamari Z. Nephro and neurotoxicity of calcineurin inhibitors and mechanisms of rejections: A review on tacrolimus and cyclosporin in organ transplantation. J Nephropathol 2012; 1:23-30. [PMID: 24475383 DOI: 10.5812/jnp.6] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2013] [Accepted: 02/10/2013] [Indexed: 12/30/2022] Open
Abstract
CONTEXT In the meadow of medical sciences substituting a diseased organ with a healthy one from another individual, dead or alive, to allow a human to stay alive could be consider as the most string event. In this article we review the history of transplantation, mechanisms of rejection, nephro-neurotoxicity of tacrolimus and cyclosporin in organ transplantations. EVIDENCE ACQUISITIONS Directory of Open Access Journals (DOAJ), Google Scholar, Pubmed (NLM), LISTA (EBSCO) and Web of Science have been searched. RESULTS The first reference to the concept of organ transplantation and replacement for therapeutic purposes appears to be to Hua-To (136 to 208 A.D), who replaced diseased organs with healthy ones in patients under analgesia induced with a mixture of Indian hemp. In 1936, the first human renal transplant performed by Voronoy in Russia. The first liver transplant in humans was performed on March 1, 1963 by Starzl in Denver, USA. Medawar was the first to assert that rejection was an immunological response, with the inflammatory reaction due to lymphocyte infiltration. Consequently, rational immunosuppressive therapies could inhibit deleterious T-cell responses in an antigen specific manner. CONCLUSIONS Searching related to the history of organ transplantation from mythic to modern times suggests that, to prevent graft rejection, minimize nephro and neuro toxicity monitoring of immunosupressive concentrations could provide an invaluable and essential aid in adjusting dosage to ensure adequate immunosuppression.
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Affiliation(s)
- Zahra Tolou-Ghamari
- Isfahan Neuroscience Research Centre, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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