{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T09:57:55Z","timestamp":1785491875317,"version":"3.56.0"},"reference-count":66,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Tacrolimus (TAC) is a core immunosuppressant used to prevent transplant rejection after organ transplantation. However, its clinical use is limited by a narrow therapeutic window and substantial interindividual pharmacokinetic variability. Subtherapeutic TAC concentrations are closely associated with acute or chronic transplant rejection, whereas supratherapeutic concentrations often cause severe drug toxicity. As precision medicine advances, integrating multidimensional patient clinical characteristics with machine learning (ML) provides important support for individualized TAC dosing adjustments after liver and kidney transplantation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>This review summarizes the application of ML in determining the initial dose of TAC and in recommending early blood drug concentrations in liver and kidney transplant patients, analyzes the core challenges in model extrapolation and clinical translation in existing research, compares their characteristics with traditional models, and looks ahead to the construction direction of long-term immunosuppression monitoring and TAC dynamic adjustment models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This narrative review summarizes the benefits of ML for tacrolimus dosing in liver and kidney transplantation. Its key strength is integrating patients\u2019 multidimensional clinical data to enable personalized dosing. We focus on ML applications in initial tacrolimus dose selection, early post-transplant concentration range determination, and dosage form conversion. We also systematically discuss current model limitations and future directions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ML has been widely applied to develop precise models for TAC immunosuppressive drug administration in patients after liver and kidney transplantation, providing a reference for individualized treatment. However, such models have not yet been widely applied in clinical practice and generally lack the ability to dynamically regulate drug concentrations over the long term. How to promote the model\u2019s true application in clinical practice remains to be explored in depth, but it holds significant prospects for application and research value in individualized treatment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1822904","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T04:21:22Z","timestamp":1784521282000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning driven precision medicine in tacrolimus dosing: current research and future perspectives in liver and kidney transplantation"],"prefix":"10.3389","volume":"17","author":[{"given":"Hanbei","family":"Lv","sequence":"first","affiliation":[{"name":"School of Medicine, Zhejiang Chinese Medical University","place":["Hangzhou, China"]},{"name":"Department of Hepatobiliary Pancreatic Surgery, Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University","place":["Hangzhou, China"]},{"name":"Department of Hepatobiliary Pancreatic Surgery, Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhangpeng","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Medicine, Zhejiang Chinese Medical University","place":["Hangzhou, China"]},{"name":"Department of Hepatobiliary Pancreatic Surgery, Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University","place":["Hangzhou, China"]},{"name":"Department of Hepatobiliary Pancreatic Surgery, Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shusen","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary Pancreatic Surgery, Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University","place":["Hangzhou, China"]},{"name":"Department of Hepatobiliary Pancreatic Surgery, Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoping","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary Pancreatic Surgery, Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University","place":["Hangzhou, China"]},{"name":"Department of Hepatobiliary Pancreatic Surgery, Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bpg.2020.101681","article-title":"Immunosuppression in liver transplant","author":"Di Maira","year":"2020","journal-title":"Best Pract Res Clin Gastroenterol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.14110","article-title":"Toward a robust tool for pharmacokinetic\u2010based personalization of treatment with tacrolimus in solid organ transplantation: a model\u2010based meta\u2010analysis approach","volume":"85","author":"Nanga","year":"2019","journal-title":"Br J Clin Pharmacol"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jconrel.2020.11.055","article-title":"Predicting drug release and degradation kinetics of long-acting microsphere formulations of tacrolimus for subcutaneous injection","volume":"329","author":"Gao","year":"2021","journal-title":"J Controlled Release"},{"key":"B4","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1097\/FTD.0000000000000640","article-title":"Therapeutic drug monitoring of tacrolimus-personalized therapy: Second consensus report","volume":"41","author":"Brunet","year":"2019","journal-title":"Ther Drug Monit"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1111\/ajt.16002","article-title":"A comprehensive review of the impact of tacrolimus intrapatient variability on clinical outcomes in kidney transplantation","volume":"20","author":"Gonzales","year":"2020","journal-title":"Am J Transplant"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.3389\/ti.2023.11149","article-title":"Development of a radiomics-based model to predict graft fibrosis in liver transplant recipients: a pilot study","volume":"36","author":"Qazi Arisar","year":"2023","journal-title":"Transplant Int"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.3390\/pharmaceutics14091755","article-title":"Insights into the pharmacogenetics of tacrolimus pharmacokinetics and pharmacodynamics","volume":"14","author":"Brunet","year":"2022","journal-title":"Pharmaceutics"},{"key":"B8","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1097\/hep.0000000000000045","article-title":"Checkpoint inhibitor hepatotoxicity: pathogenesis and management","volume":"79","author":"Cunningham","year":"2024","journal-title":"Hepatology"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1097\/TP.0000000000002052","article-title":"High intrapatient variability of tacrolimus exposure in the early period after liver transplantation is associated with poorer outcomes","volume":"102","author":"Rayar","year":"2018","journal-title":"Transplantation"},{"key":"B10","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.cgh.2016.07.035","article-title":"Acute rejection increases risk of graft failure and death in recent liver transplant recipients","volume":"15","author":"Levitsky","year":"2017","journal-title":"Clin Gastroenterol Hepatol"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1097\/tp.0000000000002147","article-title":"International liver transplantation society consensus statement on immunosuppression in liver transplant recipients","volume":"102","author":"Charlton","year":"2018","journal-title":"Transplantation"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.2123","article-title":"Tacrolimus exposure prediction using machine learning","volume":"110","author":"Woillard","year":"2021","journal-title":"Clin Pharmacol Ther"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2023.01.006","article-title":"Artificial intelligence, machine learning, and deep learning in liver transplantation","volume":"78","author":"Bhat","year":"2023","journal-title":"J Hepatol"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1038\/srep42192","article-title":"Application of machine-learning models to predict tacrolimus stable dose in renal transplant recipients","volume":"7","author":"Tang","year":"2017","journal-title":"Sci Rep"},{"key":"B15","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1007\/s12072-021-10291-7","article-title":"Application of machine learning in liver transplantation: a review","volume":"16","author":"Tran","year":"2022","journal-title":"Hepatol Int"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2019.103957","article-title":"Machine learning in predicting graft failure following kidney transplantation: a systematic review of published predictive models","volume":"130","author":"Senanayake","year":"2019","journal-title":"Int J Med Inf"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1002\/hep.31103","article-title":"Applying machine learning in liver disease and transplantation: a comprehensive review","volume":"71","author":"Spann","year":"2020","journal-title":"Hepatology"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2020.09.015","article-title":"Multicenter validation of the liver graft assessment following transplantation (L-GrAFT) score for assessment of early allograft dysfunction","volume":"74","author":"Agopian","year":"2021","journal-title":"J Hepatol"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1080\/07853890.2024.2410404","article-title":"Development and validation of a model for early survival prediction following liver transplantation based on donor and recipient characteristics","volume":"56","author":"Xie","year":"2024","journal-title":"Ann Med"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.3389\/fsurg.2021.753056","article-title":"Prediction of graft survival post-liver transplantation by L-GrAFT risk score model, EASE score, MEAF scoring, and EAD","volume":"8","author":"Chen","year":"2021","journal-title":"Front Surg"},{"key":"B21","doi-asserted-by":"publisher","first-page":"12909","DOI":"10.1038\/s41598-021-92298-6","article-title":"Prediction model for early graft failure after liver transplantation using aspartate aminotransferase, total bilirubin and coagulation factor","volume":"11","author":"Rhu","year":"2021","journal-title":"Sci Rep"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.l4923","article-title":"Prediction system for risk of allograft loss in patients receiving kidney transplants: International derivation and validation study","volume":"366","author":"Loupy","year":"2019","journal-title":"BMJ"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1080\/07853890.2025.2458212","article-title":"dentification of a postoperative survival scoring index for adult liver transplantation","volume":"57","author":"Wang","year":"2025","journal-title":"Ann Med"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2024.1427845","article-title":"A machine learning approach towards assessing consistency and reproducibility: an application to graft survival across three kidney transplantation eras","volume":"6","author":"Achilonu","year":"2024","journal-title":"Front Digital Health"},{"key":"B25","doi-asserted-by":"publisher","first-page":"4768","DOI":"10.1038\/s41598-025-89570-4","article-title":"Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients","volume":"15","author":"Andishgar","year":"2025","journal-title":"Sci Rep"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1111\/ajt.15172","article-title":"Development and validation of an optimized prediction of mortality for candidates awaiting liver transplantation","volume":"19","author":"Bertsimas","year":"2019","journal-title":"Am J Transplant"},{"key":"B27","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1111\/tri.13818","article-title":"Machine learning in liver transplantation: a tool for some unsolved questions","volume":"34","author":"Ferrarese","year":"2021","journal-title":"Transplant Int"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2014.05.039","article-title":"Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter spanish study","volume":"61","author":"Brice\u00f1o","year":"2014","journal-title":"J Hepatol"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1016\/j.hbpd.2022.03.001","article-title":"Artificial intelligence and liver transplantation: looking for the best donor-recipient pairing","volume":"21","author":"Brice\u00f1o","year":"2022","journal-title":"Hepatobiliary Pancreat Dis Int"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1016\/j.hbpd.2021.02.001","article-title":"Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation","volume":"20","author":"He","year":"2021","journal-title":"Hepatob Pancreat Dis"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.4254\/wjh.v13.i12.1977","article-title":"Artificial intelligence in hepatology, liver surgery and transplantation: emerging applications and frontiers of research","volume":"13","author":"Veerankutty","year":"2021","journal-title":"World J Hepatol"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2024.08.011","article-title":"Towards accurate and efficient diagnoses in nephropathology: an AI-based approach for assessing kidney transplant rejection","volume":"24","author":"Fayzullin","year":"2024","journal-title":"Comput Struct Biotechnol J"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2023.01.030","article-title":"Liver transplantation immunology: Immunosuppression, rejection, and immunomodulation","volume":"78","author":"Montano-Loza","year":"2023","journal-title":"J Hepatol"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2125.2011.04039.x","article-title":"Dosing equation for tacrolimus using genetic variants and clinical factors","volume":"72","author":"Passey","year":"2011","journal-title":"Br J Clin Pharmacol"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2021.06.001","article-title":"Model-informed precision dosing to optimise immunosuppressive therapy in renal transplantation","volume":"26","author":"Zwart","year":"2021","journal-title":"Drug Discov Today"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1111\/ctr.12345","article-title":"Population pharmacokinetics of tacrolimus in adult kidney transplant patients: impact of CYP3A5 genotype on starting dose","volume":"36","author":"Bergmann","year":"2014","journal-title":"Clin Transplant"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.3390\/pharmaceutics14020379","article-title":"The role of intra-patient variability of tacrolimus drug concentrations in solid organ transplantation: A focus on liver, heart, lung and pancreas","volume":"14","author":"Coste","year":"2022","journal-title":"Pharmaceutics"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1111\/ajt.15199","article-title":"Assessment of tacrolimus intrapatient variability in stable adherent transplant recipients: establishing baseline values","volume":"19","author":"Leino","year":"2019","journal-title":"Am J Transplant"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.2217\/pgs.12.98","article-title":"Validation of tacrolimus equation to predict troughs using genetic and clinical factors","volume":"13","author":"Passey","year":"2012","journal-title":"Pharmacogenomics"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.13838","article-title":"A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients","volume":"85","author":"Andrews","year":"2019","journal-title":"Br J Clin Pharmacol"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.2163","article-title":"Avoiding tacrolimus underexposure and overexposure with a dosing algorithm for renal transplant recipients: A single arm prospective intervention trial","volume":"110","author":"Francke","year":"2021","journal-title":"Clin Pharmacol Ther"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1007\/s00228-020-03058-w","article-title":"The influence of recipient SLCO1B1 rs2291075 polymorphism on tacrolimus dose\u2013corrected trough concentration in the early period after liver transplantation","volume":"77","author":"Wu","year":"2021","journal-title":"Eur J Clin Pharmacol"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.16172","article-title":"Steroid\u2013tacrolimus drug\u2013drug interaction and the effect of CYP3A genotypes","volume":"90","author":"Saqr","year":"2024","journal-title":"Br J Clin Pharmacol"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.3390\/pharmaceutics14102154","article-title":"Controversial interactions of tacrolimus with dietary supplements, herbs and food","volume":"14","author":"Miedziaszczyk","year":"2022","journal-title":"Pharmaceutics"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2024.1292163","article-title":"Clinical drug interactions between voriconazole and 38 other drugs: a retrospective analysis of adverse events","volume":"15","author":"Huo","year":"2024","journal-title":"Front Pharmacol"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2022.101752","article-title":"Genotype-guided model significantly improves accuracy of tacrolimus initial dosing after liver transplantation","volume":"55","author":"Shi","year":"2023","journal-title":"eClinicalMedicine"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2022.1031969","article-title":"Prospective population pharmacokinetic study of tacrolimus in adult recipients early after liver transplantation: A comparison of michaelis-menten and theory-based pharmacokinetic models","volume":"13","author":"Cai","year":"2022","journal-title":"Front Pharmacol"},{"key":"B48","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1002\/cpt.2915","article-title":"Tacrolimus and mycophenolic acid exposure are associated with biopsy\u2010proven acute rejection: a study to provide evidence for longer\u2010term target ranges","volume":"114","author":"Meziyerh","year":"2023","journal-title":"Clin Pharmacol Ther"},{"key":"B49","doi-asserted-by":"publisher","first-page":"22387","DOI":"10.1038\/s41598-023-50066-8","article-title":"Prediction of very early subclinical rejection with machine learning in kidney transplantation","volume":"13","author":"Jo","year":"2023","journal-title":"Sci Rep"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1002\/lt.25804","article-title":"Drug\u2010induced liver injury after liver transplantation","volume":"26","author":"Jim\u00e9nez\u2010P\u00e9rez","year":"2020","journal-title":"Liver Transpl"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2024.1389271","article-title":"Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients","volume":"15","author":"Wang","year":"2024","journal-title":"Front Pharmacol"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.813117","article-title":"A prediction model for tacrolimus daily dose in kidney transplant recipients with machine learning and deep learning techniques","volume":"9","author":"Zhang","year":"2022","journal-title":"Front Med"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.1002\/jcph.1379","article-title":"Incorporation of gene\u2010environment interaction terms improved the predictive accuracy of tacrolimus stable dose algorithms in Chinese adult renal transplant recipients","volume":"59","author":"Tang","year":"2019","journal-title":"J Clin Pharmacol"},{"key":"B54","doi-asserted-by":"publisher","first-page":"4768","DOI":"10.1038\/s41598-024-71032-y","article-title":"Machine-learning model to predict the tacrolimus concentration and suggest optimal dose in liver transplantation recipients: a multicenter retrospective cohort study","volume":"14","author":"Yoon","year":"2024","journal-title":"Sci Rep"},{"key":"B55","doi-asserted-by":"publisher","DOI":"10.1111\/jcpt.13579","article-title":"Machine learning\u2010based method for tacrolimus dose predictions in chinese kidney transplant perioperative patients","volume":"47","author":"Fu","year":"2022","journal-title":"J Clin Pharm Ther"},{"key":"B56","doi-asserted-by":"publisher","DOI":"10.1007\/s11096-023-01545-5","article-title":"Developing supervised machine learning algorithms to evaluate the therapeutic effect and laboratory-related adverse events of cyclosporine and tacrolimus in renal transplants","volume":"45","author":"Sridharan","year":"2023","journal-title":"Int J Clin Pharm"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.1002\/jcph.2156","article-title":"Population pharmacokinetic modeling combined with machine learning approach improved tacrolimus trough concentration prediction in chinese adult liver transplant recipients","volume":"63","author":"Li","year":"2023","journal-title":"J Clin Pharmacol"},{"key":"B58","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.15953","article-title":"Development of a predictive model for nephrotoxicity during tacrolimus treatment using machine learning methods","volume":"90","author":"Noda","year":"2024","journal-title":"Br J Clin Pharmacol"},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.1038\/s41575-020-0334-4","article-title":"Understanding, predicting and achieving liver transplant tolerance: From bench to bedside","volume":"17","author":"Thomson","year":"2020","journal-title":"Nat Rev Gastro Hepat"},{"key":"B60","doi-asserted-by":"publisher","DOI":"10.1097\/JS9.0000000000001671","article-title":"Establishment and validation of a predictive model of immune tolerance after pediatric liver transplantation: A multicenter cohort study","volume":"110","author":"Wang","year":"2024","journal-title":"Int J Surg"},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.1007\/s00228-022-03445-5","article-title":"Application of machine learning to predict tacrolimus exposure in liver and kidney transplant patients given the MeltDose formulation","volume":"79","author":"Ponthier","year":"2023","journal-title":"Eur J Clin Pharmacol"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.1016\/j.phrs.2021.105578","article-title":"Estimation of drug exposure by machine learning based on simulations from published pharmacokinetic models: the example of tacrolimus","volume":"167","author":"Woillard","year":"2021","journal-title":"Pharmacol Res"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.3390\/pharmaceutics15122699","article-title":"CYP3A5*3 and CYP3A4*22 cluster polymorphism effects on LCP-tac tacrolimus exposure: population pharmacokinetic approach","volume":"15","author":"Mohammed Ali","year":"2023","journal-title":"Pharmaceutics"},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.1053\/j.ajkd.2017.07.018","article-title":"Results of ASERTAA, a randomized prospective crossover pharmacogenetic study of immediate-release versus extended-release tacrolimus in african american kidney transplant recipients","volume":"71","author":"Trofe-Clark","year":"2018","journal-title":"Am J Kidney Dis"},{"key":"B65","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.13952","article-title":"Population pharmacokinetics of immediate\u2010 and prolonged\u2010release tacrolimus formulations in liver, kidney and heart transplant recipients","volume":"85","author":"Lu","year":"2019","journal-title":"Br J Clin Pharmacol"},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.2165\/00003088-200443100-00001","article-title":"Clinical pharmacokinetics and pharmacodynamics of tacrolimus in solid organ transplantation","volume":"43","author":"Staatz","year":"2004","journal-title":"Clin Pharmacokinet"}],"updated-by":[{"DOI":"10.3389\/fimmu.2026.1946015","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000}}],"container-title":["Frontiers in Immunology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1822904\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:57:16Z","timestamp":1785488236000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fimmu.2026.1822904\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,20]]},"references-count":66,"alternative-id":["10.3389\/fimmu.2026.1822904"],"URL":"https:\/\/doi.org\/10.3389\/fimmu.2026.1822904","relation":{},"ISSN":["1664-3224"],"issn-type":[{"value":"1664-3224","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,20]]},"article-number":"1822904"}}