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Komenkul V, Sukarnjanaset W, Komolmit P, Wattanavijitkul T. External validation of population pharmacokinetic models of tacrolimus in Thai adult liver transplant recipients. Eur J Clin Pharmacol 2024; 80:1229-1240. [PMID: 38695888 DOI: 10.1007/s00228-024-03692-8] [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: 01/09/2024] [Accepted: 04/17/2024] [Indexed: 07/06/2024]
Abstract
OBJECTIVE Several population pharmacokinetic models of tacrolimus in liver transplant patients were built, and their predictability was evaluated in their settings. However, the extrapolation in the prediction was unclear. This study aimed to evaluate the predictive performance of published tacrolimus models in adult liver transplant recipients using data from the Thai population as an external dataset. METHODS The selected published models were systematically searched and evaluated for their quality. The external dataset of patients who underwent the first liver transplant and received immediate-release tacrolimus was used to assess the predictive performance of each selected model. Trough concentrations between 3 and 6 months were retrospectively collected to evaluate the predictability of each model using prediction-based diagnostics, simulation-based diagnostics, and Bayesian forecasting. RESULTS Sixty-seven patients with 360 trough concentrations and eight selected published models were included in this study. None of the models met the predictive precision criteria in prediction-based diagnostics. Meanwhile, four published population pharmacokinetic models showed a normal distribution in NPDE testing. Regarding Bayesian forecasting, all models improved their forecasts with at least one prior information data point. CONCLUSION Bayesian forecasting is more accurate and precise than other testing methods for predicting drug concentrations. However, none of the evaluated models provides satisfactory predictive performance for generalization to Thai liver transplant patients. This underscores the need for future research to develop population PK models tailored to the Thai population. Such efforts should consider the inclusion of nonlinear pharmacokinetics and region-specific factors, including genetic variability, to improve model accuracy and applicability.
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Affiliation(s)
- Virunya Komenkul
- Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Waroonrat Sukarnjanaset
- Department of Pharmaceutical Care, College of Pharmacy, Rangsit University, Pathum Thani, Thailand
| | - Piyawat Komolmit
- Division of Gastro-enterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Center of Excellence in Liver Diseases, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Thitima Wattanavijitkul
- Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, Thailand.
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2
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Yang S, Wei J, Pan X, Li Z, Zhang X, Li Z, Dong X, Hua Z, Li X. Development and validation of individualized tacrolimus dosing software for Chinese pediatric liver transplantation patients: a population pharmacokinetic approach. Eur J Clin Pharmacol 2024:10.1007/s00228-024-03717-2. [PMID: 38904798 DOI: 10.1007/s00228-024-03717-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 06/14/2024] [Indexed: 06/22/2024]
Abstract
OBJECTIVE We aim to describe the population pharmacokinetics (PPK) of tacrolimus in Chinese pediatric patients under 4 years old after liver transplantation and to develop individualized tacrolimus dosing software. METHODS A total of 663 blood concentrations from 85 patients aged 4.57 months to 3.97 years were collected in this study. PPK analysis was performed using a nonlinear mixed effects modeling approach with the software, Phoenix. Using C#, an individualized tacrolimus dosing software was created. The software was then used to predict the concentrations of another ten pediatric liver transplantation patients to verify the accuracy of said software. The predictive error (PE) and the absolute predictive error (APE) for each predicted time point were computed. RESULTS A one-compartment model with first-order elimination best fitted the data. The apparent volume of distribution (V/F) and apparent clearance (CL/F) were 198.65 L and 2.41 L/h. Postoperative days (POD), total bilirubin (TBIL), and the use of voriconazole significantly influenced tacrolimus apparent clearance. The incorporation of an increasing number of actual blood drug concentrations into the prediction resulted in a decrease in both PE (72%, 17%, 7%) and APE (87%, 53%, 26%). CONCLUSIONS A qualified PPK model of tacrolimus was developed in Chinese pediatric patients. The individualized tacrolimus dosing software could be used as a suitable tool for the personalization of tacrolimus dosing for pediatric patients after liver transplantation.
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Affiliation(s)
- Siyu Yang
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Jian Wei
- Department of Interventional Radiography, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - Xueqiang Pan
- Pharmacy Department of Beijing Health Vocational College, No. 128, Jiukeshu East Road, Tongzhou District, Beijing, 101101, China
| | - Ze Li
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Xuanling Zhang
- Department of Pharmacy, Peking University First Hospital, Beijing, China
| | - Zhe Li
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Xianzhe Dong
- Department of Pharmacy, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Zixin Hua
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China
| | - Xingang Li
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing, 100050, China.
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Wang YP, Lu XL, Shao K, Shi HQ, Zhou PJ, Chen B. Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients. Front Pharmacol 2024; 15:1389271. [PMID: 38783953 PMCID: PMC11111944 DOI: 10.3389/fphar.2024.1389271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 04/15/2024] [Indexed: 05/25/2024] Open
Abstract
Aims The population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients. Methods Conventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group. Results The final PPK model, incorporating hematocrit and CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance. Conclusion The PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.
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Affiliation(s)
- Yu-Ping Wang
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Xiao-Ling Lu
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Kun Shao
- Center for Organ Transplantation, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Hao-Qiang Shi
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Pei-Jun Zhou
- Center for Organ Transplantation, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
| | - Bing Chen
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, China
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4
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Du Y, Zhang Y, Yang Z, Li Y, Wang X, Li Z, Ren L, Li Y. Artificial Neural Network Analysis of Determinants of Tacrolimus Pharmacokinetics in Liver Transplant Recipients. Ann Pharmacother 2024; 58:469-479. [PMID: 37559252 DOI: 10.1177/10600280231190943] [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] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND The efficacy and toxicity of tacrolimus are closely related to its trough blood concentrations. Identifying the influencing factors of pharmacokinetics of tacrolimus in the early postoperative period is conducive to the optimization of the individualized tacrolimus administration protocol and to help liver transplant (LT) recipients achieve the target blood concentrations. OBJECTIVE This study aimed to develop an artificial neural network (ANN) for predicting the blood concentration of tacrolimus soon after liver transplantation and for identifying determinants of the concentration based on Shapley additive explanation (SHAP). METHODS In this retrospective study, we enrolled 31 recipients who were first treated with liver transplantation from the Department of Liver Transplantation and Hepatic Surgery, the First Affiliated Hospital of Shandong First Medical University (Shandong Provincial Qianfoshan Hospital) from November 2020 to May 2021. The basic information, biochemical indexes, use of concomitant drugs, and genetic factors of organ donors and recipients were used for the ANN model inputs, and the output was the steady-state trough concentration (C0) of tacrolimus after oral administration in LT recipients. The ANN model was established to predict C0 of tacrolimus, SHAP was applied to the trained model, and the SHAP value of each input was calculated to analyze quantitatively the influencing factors for the output C0. RESULTS A back-propagation ANN model with 3 hidden layers was established using deep learning. The mean prediction error was 0.27 ± 0.75 ng/mL; mean absolute error, 0.60 ± 0.52 ng/mL; correlation coefficient between predicted and actual C0 values, 0.9677; and absolute prediction error of all blood concentrations obtained by the ANN model, ≤3.0 ng/mL. The results indicated that the following factors had the most significant effect on C0: age, daily drug dose, genotype at CYP3A5 polymorphism rs776746 in both recipient and donor, and concomitant use of caspofungin. The predicted C0 value of tacrolimus in LT recipients increased in a dose-dependent manner when the daily dose exceeded 3 mg, whereas it decreased with age when LT recipients were older than 48 years. The predicted C0 was higher when recipients and donors had the genotype CYP3A5*3*3 than when they had the genotype CYP3A5*1. The predicted C0 value also increased with the use of caspofungin or Wuzhi capsule. CONCLUSION AND RELEVANCE The established ANN model can be used to predict the C0 value of tacrolimus in LT recipients with high accuracy and good predictive ability, serving as a reference for personalized treatment in the early stage after liver transplantation.
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Affiliation(s)
- Yue Du
- Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
- Department of Pharmacy, Zibo Central Hospital, Zibo, China
| | - Yundi Zhang
- School of Pharmaceutical Sciences, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhiyan Yang
- Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Yue Li
- Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Xinyu Wang
- School of Pharmaceutical Sciences, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ziqiang Li
- Department of Liver Transplantation and Hepatic Surgery, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Lei Ren
- Department of Liver Transplantation and Hepatic Surgery, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Yan Li
- Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
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Schagen MR, Ulu AN, Francke MI, van de Wetering J, van Buren MC, Schoenmakers S, Matic M, van Schaik RHN, Hesselink DA, de Winter BCM. Modelling changes in the pharmacokinetics of tacrolimus during pregnancy after kidney transplantation: A retrospective cohort study. Br J Clin Pharmacol 2024; 90:176-188. [PMID: 37596793 DOI: 10.1111/bcp.15886] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/19/2023] [Accepted: 08/05/2023] [Indexed: 08/20/2023] Open
Abstract
AIMS Pregnancy after kidney transplantation is realistic but immunosuppressants should be continued to prevent rejection. Tacrolimus is safe during pregnancy and is routinely dosed based on whole-blood predose concentrations. However, maintaining these concentrations is complicated as physiological changes during pregnancy affect tacrolimus pharmacokinetics. The aim of this study was to describe tacrolimus pharmacokinetics throughout pregnancy and explain the changes by investigating covariates in a population pharmacokinetic model. METHODS Data of pregnant women using a twice-daily tacrolimus formulation following kidney transplantation were retrospectively collected from 6 months before conception, throughout gestation and up to 6 months postpartum. Pharmacokinetic analysis was performed using nonlinear mixed effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates. The final model was evaluated using goodness-of-fit plots, visual predictive checks and a bootstrap analysis. RESULTS A total of 260 whole-blood tacrolimus predose concentrations from 14 pregnant kidney transplant recipients were included. Clearance increased during pregnancy from 34.5 to 41.7 L/h, by 15, 19 and 21% in the first, second and third trimester, respectively, compared to prior to pregnancy. This indicates a required increase in the tacrolimus dose by the same percentage to maintain the prepregnancy concentration. Haematocrit and gestational age were negatively correlated with tacrolimus clearance (P ≤ 0.01), explaining 18% of interindividual and 85% of interoccasion variability in oral clearance. CONCLUSIONS Tacrolimus clearance increases during pregnancy, resulting in decreased exposure to tacrolimus, which is explained by gestational age and haematocrit. To maintain prepregnancy target whole-blood tacrolimus predose concentrations during pregnancy, increasing the dose is required.
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Affiliation(s)
- Maaike R Schagen
- Erasmus MC Transplant Institute, Department of Internal Medicine, Division of Nephrology and Transplantation, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Rotterdam Clinical Pharmacometrics Group, Rotterdam, the Netherlands
| | - Asiye Nur Ulu
- Department of Hospital Pharmacy, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Marith I Francke
- Erasmus MC Transplant Institute, Department of Internal Medicine, Division of Nephrology and Transplantation, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Rotterdam Clinical Pharmacometrics Group, Rotterdam, the Netherlands
| | - Jacqueline van de Wetering
- Erasmus MC Transplant Institute, Department of Internal Medicine, Division of Nephrology and Transplantation, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Marleen C van Buren
- Erasmus MC Transplant Institute, Department of Internal Medicine, Division of Nephrology and Transplantation, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Sam Schoenmakers
- Department of Obstetrics and Gynaecology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Maja Matic
- Department of Clinical Chemistry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Ron H N van Schaik
- Department of Clinical Chemistry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Dennis A Hesselink
- Erasmus MC Transplant Institute, Department of Internal Medicine, Division of Nephrology and Transplantation, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Brenda C M de Winter
- Erasmus MC Transplant Institute, Department of Internal Medicine, Division of Nephrology and Transplantation, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Rotterdam Clinical Pharmacometrics Group, Rotterdam, the Netherlands
- Department of Hospital Pharmacy, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
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6
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Chen D, Yao Q, Chen W, Yin J, Hou S, Tian X, Zhao M, Zhang H, Yang L, Zhou T, Jin P. Population PK/PD model of tacrolimus for exploring the relationship between accumulated exposure and quantitative scores in myasthenia gravis patients. CPT Pharmacometrics Syst Pharmacol 2023; 12:963-976. [PMID: 37060188 PMCID: PMC10349186 DOI: 10.1002/psp4.12966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 04/16/2023] Open
Abstract
Tacrolimus is an important immunosuppressant used in the treatment of myasthenia gravis (MG). However, the population pharmacokinetic (PK) characteristics together with the exposure-response of tacrolimus in the treatment of MG remain largely unknown. In this study, we aimed to develop a population PK/pharmacodynamic (PK/PD) model of tacrolimus in patients with MG, in order to explore the relationships among tacrolimus dose, exposure, and its therapeutic efficacy. The genotype of CYP3A5, Osserman's classification, and status of thymus, as well as demographic characteristics and other biomarkers from laboratory testing were tested as covariate, and simulations were performed based on the final model. The population PK model was described using a one-compartment model with first-order elimination and fixed absorption parameters. CYP3A5 genotype significantly influenced the apparent clearance, and total protein (TP) influenced the apparent volume of distribution as covariates. The quantitative MG scores were characterized by the cumulated area under curve of tacrolimus in a maximum effect function. Osserman's classification was a significant covariate on the initial score of patients with MG. The simulations demonstrated that tacrolimus showed an unsatisfying effect possibly due to insufficient exposure in some patients with MG. A starting dose of 2 mg/d and even higher dose for patients with CYP3A5 *1/*1 and *1/*3 and lower TP level were required for the rapid action of tacrolimus. The population PK/PD model quantitatively described the relationships among tacrolimus dose, exposure, and therapeutic efficacy in patients with MG, which could provide reference for the optimization of tacrolimus dosing regimen at the individual patient level.
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Affiliation(s)
- Di Chen
- Department of PharmacyBeijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical ScienceBeijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital)BeijingChina
| | - Qingyu Yao
- Department of PharmaceuticsSchool of Pharmaceutical SciencesPeking UniversityBeijingChina
| | - Wenjun Chen
- Department of PharmaceuticsSchool of Pharmaceutical SciencesPeking UniversityBeijingChina
| | - Jian Yin
- Department of NeurologyBeijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijingChina
| | - Shifang Hou
- Department of NeurologyBeijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijingChina
| | - Xiaoxin Tian
- Department of PharmacyBeijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical ScienceBeijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital)BeijingChina
| | - Ming Zhao
- Department of PharmacyBeijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical ScienceBeijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital)BeijingChina
| | - Hua Zhang
- Department of NeurologyBeijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical SciencesBeijingChina
| | - Liping Yang
- Department of PharmacyBeijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical ScienceBeijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital)BeijingChina
| | - Tianyan Zhou
- Department of PharmaceuticsSchool of Pharmaceutical SciencesPeking UniversityBeijingChina
| | - Pengfei Jin
- Department of PharmacyBeijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical ScienceBeijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital)BeijingChina
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7
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Wang CB, Zhang YJ, Zhao MM, Zhao LM. Population pharmacokinetic analyses of tacrolimus in non-transplant patients: a systematic review. Eur J Clin Pharmacol 2023:10.1007/s00228-023-03503-6. [PMID: 37261481 DOI: 10.1007/s00228-023-03503-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 04/30/2023] [Indexed: 06/02/2023]
Abstract
BACKGROUND AND OBJECTIVES Tacrolimus (TAC) has been increasingly used in patients with non-transplant settings. Because of its large between-subject variability, several population pharmacokinetic (PPK) studies have been performed to facilitate individualized therapy. This review summarized published PPK models of TAC in non-transplant patients, aiming to clarify factors affecting PKs of TAC and identify the knowledge gap that may require further research. METHODS The PubMed, Embase databases, and Cochrane Library, as well as related references, were searched from the time of inception of the databases to February 2023, to identify TAC population pharmacokinetic studies modeled in non-transplant patients using a non-linear mixed-effects modeling approach. RESULTS Sixteen studies, all from Asian countries (China and Korea), were included in this study. Of these studies, eleven and four were carried out in pediatric and adult patients, respectively. One-compartment models were the commonly used structural models for TAC. The apparent clearance (CL/F) of TAC ranged from 2.05 to 30.9 L·h-1 (median of 14.9 L·h-1). Coadministered medication, genetic factors, and weight were the most common covariates affecting TAC-CL/F, and variability in the apparent volume of distribution (V/F) was largely explained by weight. Coadministration with Wuzhi capsules reduced CL/F by about 19 to 43%. For patients with CYP3A5*1*1 and *1*3 genotypes, the CL/F was 39-149% higher CL/F than patients with CYP3A5*1*1. CONCLUSION The optimal TAC dosage should be adjusted based on the patient's co-administration, body weight, and genetic information (especially CYP3A5 genotype). Further studies are needed to assess the generalizability of the published models to other ethnic groups. Moreover, external validation should be frequently performed to improve the clinical practicality of the models.
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Affiliation(s)
- Cheng-Bin Wang
- Department of Pharmacy, Shengjing Hospital of China Medical University, 36 Sanhao Street, Shenyang, 110004, Liaoning Province, People's Republic of China
| | - Yu-Jia Zhang
- Department of Pharmacy, Shengjing Hospital of China Medical University, 36 Sanhao Street, Shenyang, 110004, Liaoning Province, People's Republic of China
| | - Ming-Ming Zhao
- Department of Pharmacy, Shengjing Hospital of China Medical University, 36 Sanhao Street, Shenyang, 110004, Liaoning Province, People's Republic of China
| | - Li-Mei Zhao
- Department of Pharmacy, Shengjing Hospital of China Medical University, 36 Sanhao Street, Shenyang, 110004, Liaoning Province, People's Republic of China.
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8
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Teng F, Zhang W, Wang W, Chen J, Liu S, Li M, Li L, Guo W, Wei H. Population pharmacokinetics of tacrolimus in Chinese adult liver transplant patients. Biopharm Drug Dispos 2022; 43:76-85. [PMID: 35220592 DOI: 10.1002/bdd.2311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/23/2022] [Accepted: 02/03/2022] [Indexed: 12/27/2022]
Abstract
Tacrolimus is widely used in organ transplantation to prevent rejection. However, the narrow therapeutic window and the large inter-and intra-individual variability in the pharmacokinetics (PK) of tacrolimus make it difficult for individualization of dosing. This study aimed at developing a population pharmacokinetic model for estimating the oral clearance of tacrolimus in Chinese liver transplant patients, and identifying factors that contribute to the PK variability of tacrolimus. Data of 151 liver transplant patients who received tacrolimus were analyzed in this study. The population PK model was analyzed and the covariates including population demographic and biochemical characteristics, drug combination, and genetic polymorphism were explored using non-linear mixed-effects modeling approach. A single-compartment population PK model was developed, and the final model was CL/F = (14.6-2.38 × cytochrome P450 (CYP) 3A5-3.72 × WZC+1.04 × (POD/9)+2.48 × COR) × Exp(ηi ), where CYP3A5 was 1 for CYP3A5*3/*3, Wuzhi Capsule (WZC) was 1 when patients took tacrolimus combined with WZC, otherwise it was 0, corticosteroids (COR) was 1 when patients take tacrolimus combined with COR, otherwise, it was 0, POD was the post-operative day. Visual inspection and bootstrap indicated that the final model was stable and robust. In this study, we developed the first tacrolimus population PK model in Chinese adult liver transplant patients. We first determined the influence of WZC on tacrolimus in these people, which could provide useful PK information for the drug combination of tacrolimus and WZC. We also revealed the influence of genetic polymorphism of CYP3A5, POD, and a combination of COR on tacrolimus PK. Therefore, these significant factors should be taken into consideration in optimizing dosage regimens.
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Affiliation(s)
- Fei Teng
- Institute of Organ Transplantation, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Weiyue Zhang
- School of Nursing, Beijing University of Chinese Medicine, Beijing, China
| | - Wei Wang
- Medical Guarantee Center, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Jiani Chen
- Medical Guarantee Center, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Shiyi Liu
- Medical Guarantee Center, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Mingming Li
- Medical Guarantee Center, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Lujin Li
- Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wenyuan Guo
- Institute of Organ Transplantation, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Hua Wei
- Medical Guarantee Center, Second Affiliated Hospital of Naval Medical University, Shanghai, China
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9
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Huang L, Assiri AA, Wen P, Zhang K, Fan J, Xing T, Liu Y, Zhang J, Wang Z, Su Z, Chen J, Xiao Y, Wang R, Na R, Yuan L, Liu D, Xia J, Zhong L, Liu W, Guo W, Overholser BR, Peng Z. The CYP3A5 genotypes of both liver transplant recipients and donors influence the time-dependent recovery of tacrolimus clearance during the early stage following transplantation. Clin Transl Med 2021; 11:e542. [PMID: 34709766 PMCID: PMC8516335 DOI: 10.1002/ctm2.542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 07/07/2021] [Accepted: 08/08/2021] [Indexed: 11/21/2022] Open
Affiliation(s)
- Li Huang
- Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Abdullah A Assiri
- Department of Clinical Pharmacy, King Khalid University, Abha, Saudi Arabia.,Department of Pharmacy Practice, College of Pharmacy, Purdue University, West Lafayette, Indiana, USA
| | - Peihao Wen
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhengzhou, P. R. China
| | - Kun Zhang
- Department of General Surgery, School of Medicine, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, P. R. China
| | - Junwei Fan
- Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Tonghai Xing
- Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Yuan Liu
- Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Jinyan Zhang
- Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Zhaowen Wang
- Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Zhaojie Su
- Department of General Surgery, School of Medicine, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, P. R. China.,Organ Transplantation Institute, School of Medicine, Xiamen University, Xiamen, Fujian, P. R. China
| | - Jiajia Chen
- Department of General Surgery, School of Medicine, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, P. R. China.,Organ Transplantation Institute, School of Medicine, Xiamen University, Xiamen, Fujian, P. R. China
| | - Yi Xiao
- Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, P. R. China.,Department of General Surgery, School of Medicine, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, P. R. China.,Organ Transplantation Institute, School of Medicine, Xiamen University, Xiamen, Fujian, P. R. China
| | - Rui Wang
- Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, P. R. China.,Department of General Surgery, School of Medicine, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, P. R. China.,Organ Transplantation Institute, School of Medicine, Xiamen University, Xiamen, Fujian, P. R. China
| | - Risi Na
- Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, P. R. China.,Department of General Surgery, School of Medicine, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, P. R. China.,Organ Transplantation Institute, School of Medicine, Xiamen University, Xiamen, Fujian, P. R. China
| | - Liyun Yuan
- Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai Institutes for Biological Sciences, Shanghai, P. R. China
| | - Dehua Liu
- Department of General Surgery, School of Medicine, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, P. R. China
| | - Junjie Xia
- Organ Transplantation Institute, School of Medicine, Xiamen University, Xiamen, Fujian, P. R. China
| | - Lin Zhong
- Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Wanqing Liu
- Department of Pharmaceutical Sciences, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, Michigan, USA
| | - Wenzhi Guo
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhengzhou, P. R. China
| | - Brian R Overholser
- Department of Pharmacy Practice, College of Pharmacy, Purdue University, West Lafayette, Indiana, USA.,Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Zhihai Peng
- Department of General Surgery, School of Medicine, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, P. R. China.,Organ Transplantation Institute, School of Medicine, Xiamen University, Xiamen, Fujian, P. R. China
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10
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Zhu J, Campagne O, Torrice CD, Flynn G, Miller JA, Patel T, Suzuki O, Ptachcinski JR, Armistead PM, Wiltshire T, Mager DE, Weiner DL, Crona DJ. Evaluation of the performance of a prior tacrolimus population pharmacokinetic kidney transplant model among adult allogeneic hematopoietic stem cell transplant patients. Clin Transl Sci 2021; 14:908-918. [PMID: 33502111 PMCID: PMC8212733 DOI: 10.1111/cts.12956] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 12/02/2020] [Accepted: 12/03/2020] [Indexed: 12/11/2022] Open
Abstract
Abstract Tacrolimus is a calcineurin inhibitor used to prevent acute graft versus host disease in adult patients receiving allogeneic hematopoietic stem cell transplantation (HCT). Previous population pharmacokinetic (PK) models have been developed in solid organ transplant, yet none exists for patients receiving HCT. The primary objectives of this study were to (1) use a previously published population PK model in adult patients who underwent kidney transplant and apply it to allogeneic HCT; (2) evaluate model‐predicted tacrolimus steady‐state trough concentrations and simulations in patients receiving HCT; and (3) evaluate covariates that affect tacrolimus PK in allogeneic HCT. A total of 252 adult patients receiving allogeneic HCT were included in the study. They received oral tacrolimus twice daily (0.03 mg/kg) starting 3 days prior to transplant. Data for these analyses included baseline clinical and demographic data, genotype data for single nucleotide polymorphisms in CYP3A4/5 and ABCB1, and the first tacrolimus steady‐state trough concentration. A dosing simulation strategy based on observed trough concentrations (rather than model‐based predictions) resulted in 12% more patients successfully achieving tacrolimus trough concentrations within the institutional target range (5–10 ng/ml). Stepwise covariate analyses identified HLA match and conditioning regimen (myeloablative vs. reduced intensity) as significant covariates. Ultimately, a previously published tacrolimus population PK model in kidney transplant provided a platform to help establish a model‐based dose adjustment strategy in patients receiving allogenic HCT, and identified HCT‐specific covariates to be considered for future prospective studies. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?
Tacrolimus is a cornerstone immunosuppressant used in patients who undergo organ transplantations. However, because of its narrow therapeutic index and wide interpatient pharmacokinetic (PK) variability, optimizing its dose is crucial to maximize efficacy and minimize tacrolimus‐induced toxicities. Prior to this study, no tacrolimus population PK models have been developed for adult patients receiving allogeneic hematopoietic stem cell transplantation (HCT). Therefore, research effort was warranted to develop a population PK model that begins to propose more precision tacrolimus dosing and begins to address both a clinical and scientific gap in this patient population.
WHAT QUESTION DID THIS STUDY ADDRESS?
The study addressed whether there is value in utilizing the observed tacrolimus steady‐state trough concentrations from patients receiving allogeneic HCT within the context of a pre‐existing population PK model developed for kidney transplant. The study also addressed whether there are clinically relevant covariates specific to adult patients receiving allogeneic HCT.
WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?
Inclusion of a single steady‐state tacrolimus trough concentration is beneficial to model predictions. The dosing simulation strategy based on observed tacrolimus concentration, rather than the model‐predicted concentration, resulted in more patients achieving the target range at first steady‐state collection. Future studies should evaluate HLA matching and myeloablative conditioning versus reduced intensity conditioning regimens as covariates. These data and model‐informed dose adjustments should be included in future prospective studies. This research could also serve as a template as to how to assess the utility of prior information for other disease settings.
HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE?
The M2 model fitting method and D2 dosing simulation method can be applied to other clinical pharmacology studies where only a single steady‐state trough concentration is available per patient in the presence of a previously published population PK model.
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Affiliation(s)
- Jing Zhu
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA
| | - Olivia Campagne
- Department of Pharmaceutical Sciences, University at Buffalo, Buffalo, New York, USA.,Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Chad D Torrice
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA
| | - Gabrielle Flynn
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA
| | - Jordan A Miller
- Department of Pharmacy, University of North Carolina Hospitals and Clinics, Chapel Hill, North Carolina, USA
| | - Tejendra Patel
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA
| | - Oscar Suzuki
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA
| | - Jonathan R Ptachcinski
- Department of Pharmacy, University of North Carolina Hospitals and Clinics, Chapel Hill, North Carolina, USA.,Division of Practice Advancement and Clinical Education, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA
| | - Paul M Armistead
- Division of Hematology, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.,Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Tim Wiltshire
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA.,Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Donald E Mager
- Department of Pharmaceutical Sciences, University at Buffalo, Buffalo, New York, USA
| | - Daniel L Weiner
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA
| | - Daniel J Crona
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA.,Department of Pharmacy, University of North Carolina Hospitals and Clinics, Chapel Hill, North Carolina, USA.,Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USA
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11
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Shao J, Wang C, Fu P, Chen F, Zhang Y, Wei J. Impact of Donor and Recipient CYP3A5*3 Genotype on Tacrolimus Population Pharmacokinetics in Chinese Adult Liver Transplant Recipients. Ann Pharmacother 2019; 54:652-661. [PMID: 31888346 DOI: 10.1177/1060028019897050] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background: Tacrolimus (TAC) is widely used after liver transplantation, but the therapeutic window is narrow. Objective: The purpose was to study both donor and recipient CYP3A5*3 genotypes affecting TAC apparent clearance rate (CL/F) and investigate a TAC population pharmacokinetic (PPK) model in Chinese liver transplant recipients for potential starting-dose individualized medication. Methods: A data set of 721 TAC concentrations was obtained from 43 adult liver transplant recipients. The TAC PPK model was analyzed using nonlinear mixed-effects modeling. Potential covariates, including demographic characteristics, physiological and pathological data, concomitant medications, and CYP3A5*3 genotype, were evaluated. The final model was validated using normalized prediction distribution errors and bootstrapping. Results: A 2-compartment model with first-order absorption and elimination was used to describe TAC disposition. Population estimates of TAC, CL/F, apparent central distribution volume (V2/F), rate of absorption (Ka), and apparent peripheral distribution volume (V3/F) were 18.1 L/h (12%), 72.7 L (34%), 0.163 h−1 (17%), and 412 L (21%), respectively. The model and estimated parameters were found to be stable. Other covariates did not influence TAC CL/F. Both donor and recipient CYP3A5*1 genotypes were significantly correlated with TAC clearance, and CL/F was 1.70-fold higher in both donor and recipient CYP3A5*1 carriers than in noncarriers among Chinese liver transplant recipients. Conclusion and Relevance: A PPK model of TAC was established in Chinese adult liver transplantation recipients for starting-dose individualized medication, which can be expanded to optimize clinical efficacy and minimize toxicity with therapeutic drug monitoring.
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Affiliation(s)
- Jia Shao
- Tianjin First Central Hospital, Tianjin, China
| | - Chenyu Wang
- Huashan Hospital, Fudan University, Shanghai, China
| | - Peng Fu
- Tianjin First Central Hospital, Tianjin, China
| | - Fan Chen
- Tianjin First Central Hospital, Tianjin, China
| | - Yi Zhang
- Tianjin First Central Hospital, Tianjin, China
| | - Jinxia Wei
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
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12
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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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13
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Riff C, Debord J, Monchaud C, Marquet P, Woillard JB. Population pharmacokinetic model and Bayesian estimator for 2 tacrolimus formulations in adult liver transplant patients. Br J Clin Pharmacol 2019; 85:1740-1750. [PMID: 30973981 DOI: 10.1111/bcp.13960] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 03/26/2019] [Accepted: 04/08/2019] [Indexed: 12/01/2022] Open
Affiliation(s)
- Camille Riff
- Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France
| | - Jean Debord
- Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France.,IPPRITT, Univ. Limoges, Limoges, France.,IPPRITT, U1248, INSERM, Limoges, France
| | - Caroline Monchaud
- Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France.,IPPRITT, Univ. Limoges, Limoges, France.,IPPRITT, U1248, INSERM, Limoges, France
| | - Pierre Marquet
- Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France.,IPPRITT, Univ. Limoges, Limoges, France.,IPPRITT, U1248, INSERM, Limoges, France
| | - Jean-Baptiste Woillard
- Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France.,IPPRITT, Univ. Limoges, Limoges, France.,IPPRITT, U1248, INSERM, Limoges, France
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14
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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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15
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Lu YX, Su QH, Wu KH, Ren YP, Li L, Zhou TY, Lu W. A population pharmacokinetic study of tacrolimus in healthy Chinese volunteers and liver transplant patients. Acta Pharmacol Sin 2015; 36:281-8. [PMID: 25500866 DOI: 10.1038/aps.2014.110] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Accepted: 09/25/2014] [Indexed: 11/09/2022] Open
Abstract
AIM To develop a population pharmacokinetic (PopPK) model of tacrolimus in healthy Chinese volunteers and liver transplant recipients for investigating the difference between the populations, and for potential individualized medication. METHODS A set of 1100 sparse trough concentration data points from 112 orthotopic liver transplant recipients, as well as 851 dense data points from 40 healthy volunteers receiving a single dose of tacrolimus (2 mg, p.o.) were collected. PopPK model of tacrolimus was constructed using the program NONMEM. Related covariates such as age, hepatic and renal functions that were potentially associated with tacrolimus disposition were evaluated. The final model was validated using bootstrapping and a visual predictive check. RESULTS A two-compartment model of tacrolimus could best describe the data from the two populations. The final model including two covariates, population (liver transplant recipients or volunteers) and serum ALT (alanine aminotransferase) level, was verified and adequately described the pharmacokinetic characteristics of tacrolimus. The estimates of V2/F, Q/F and V3/F were 22.7 L, 76.3 L/h and 916 L, respectively. The estimated CL/F in the volunteers and liver transplant recipients was 32.8 and 18.4 L/h, respectively. Serum ALT level was inversely related to CL/F, whereas age did not influence CL/F. Thus, the elderly (≥65 years) and adult (<65 years) groups in the liver transplant recipients showed no significant difference in the clearance of tacrolimus. CONCLUSION Compared with using the sparse data only, the integrating modeling technique combining sparse data from the patients and dense data from the healthy volunteers improved the PopPK analysis of tacrolimus.
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