成人肝移植术后他克莫司个体化治疗的研究进展

doi:10.3969/j.issn.1006-5725.2026.07.024

Abstract

Abstract:

Tacrolimus（TAC） serves as a cornerstone immunosuppressive therapy following liver transplantation（LT）. However， its narrow therapeutic window， combined with significant inter- and intra-individual pharmacokinetic variability， often leads to blood concentrations deviating from target range， posing a major challenge for precision dosing. In recent years， with the widespread adoption of therapeutic drug monitoring（TDM）， research on individualized dosing strategies based on TAC blood concentrations has achieved substantial progress. This article provides a systematic review of the latest advances in personalized TAC dosing for adult LT recipients， with a focus on the clinical application value and current status of pharmacogenomics， population pharmacokinetics（PPK）， and machine learning（ML）. It further compares and summarizes the strengths and limitations of different modeling strategies. Studies demonstrate that model-informed precision dosing（MIPD） tools can not only significantly enhance the accuracy of blood concentration predictions but also generate patient-tailored dosing regimens， thereby contributing to the optimization of therapeutic outcomes and long-term prognosis in LT patients.

Key words: tacrolimus, liver transplantation, personalized dosing, population pharmacokinetics, machine learning

CLC Number:

R617

Yan JIANG,Guohui WANG,Cangsang SONG,Hanshu ZHANG,Xingde LI. Research advances in personalized tacrolimus therapy for adult liver transplant recipients[J]. The Journal of Practical Medicine, 2026, 42(7): 1294-1300.

Figures/Tables 2

Tab.1

Tab.2

References 40

[1]	BONNEY G K， CHEW C A， LODGE P， et al. Liver transplantation for non-resectable colorectal liver metastases： The International Hepato-Pancreato-Biliary Association consensus guidelines［J］. Lancet Gastroenterol Hepatol， 2021， 6（11）： 933-946. doi： 10.1016/S2468-1253（21）00219-3 . doi: 10.1016/S2468-1253（21）00219-3
[2]	蒋勇，冯啸，刘炜，等. 2024年全球肝移植研究年度盘点——技术突破、精准管理与未来挑战［J］. 器官移植， 2025， 16（3）： 350-358. doi： 10.12464/j.issn.1674-7445.2025099 . doi: 10.12464/j.issn.1674-7445.2025099
[3]	HUANG H， LU Y， ZHOU T， et al. Innate Immune Cells in Immune Tolerance After Liver Transplantation［J］. Front Immunol， 2018， 9： 2401. doi： 10.3389/fimmu.2018.02401 . doi: 10.3389/fimmu.2018.02401
[4]	OHM B， GIANNOU A D， HARRIMAN D， et al. Chimerism and immunological tolerance in solid organ transplantation［J］. Semin Immunopathol， 2025， 47（1）： 27. doi： 10.1007/s00281-025-01052-x . doi: 10.1007/s00281-025-01052-x
[5]	LAN T， WU G， ZUO B， et al. Regulation of the immune microenvironment and immunotherapy after liver transplantation［J］. Front Immunol， 2025， 16： 1602877. doi： 10.3389/fimmu.2025.1602877 . doi: 10.3389/fimmu.2025.1602877
[6]	LEVITSKY J， GOLDBERG D， SMITH A R， et al. Acute Rejection Increases Risk of Graft Failure and Death in Recent Liver Transplant Recipients［J］. Clin Gastroenterol Hepatol， 2017， 15（4）： 584-593.e582. doi： 10.1016/j.cgh.2016.07.035 . doi: 10.1016/j.cgh.2016.07.035
[7]	HARRINGTON CR， BITTERMANN T， GOLDBERG D， et al. The Continued Impact of Acute Rejection in the Last Decade of Liver Transplantation［J］. Gastro Hep Adv， 2022， 1（5）： 686-688. doi： 10.1016/j.gastha.2022.04.021 . doi: 10.1016/j.gastha.2022.04.021
[8]	ONG S C， GASTON R S. Thirty Years of Tacrolimus in Clinical Practice［J］. Transplantation， 2021， 105（3）： 484-495. doi： 10.1097/TP.0000000000003350 . doi: 10.1097/TP.0000000000003350
[9]	陈冰，左笑丛，李新刚，等. 模型引导的抗排异治疗患者他克莫司精准用药专家共识［J］. 中国临床药理学与治疗学， 2025， 30（04）： 433-445. doi： 10.12092/j.issn.1009-2501.2025.04.001 . doi: 10.12092/j.issn.1009-2501.2025.04.001
[10]	CAMPAGNE O， MAGER D E， TORNATORE K M. Population Pharmacokinetics of Tacrolimus in Transplant Recipients： What Did We Learn About Sources of Interindividual Variabilities？［J］. J Clin Pharmacol， 2019， 59（3）： 309-325. doi： 10.1002/jcph.1325 . doi: 10.1002/jcph.1325
[11]	KHONG J， LEE M， WARREN C， et al. Tacrolimus dosing in liver transplant recipients using phenotypic personalized medicine： A phase 2 randomized clinical trial［J］. Nat Commun， 2025， 16（1）： 4558. doi： 10.1038/s41467-025-59739-6 . doi: 10.1038/s41467-025-59739-6
[12]	ZHOU X， TANG R， ZHANG D， et al. Effect of CYP 3A41B and CYP3A53 Gene Polymorphisms in Antirejection of Tacrolimus in Liver Transplant Patients［J］. Transplant Proc， 2025， 57（2）： 298-304. doi： 10.1016/j.transproceed.2024.10.030 . doi: 10.1016/j.transproceed.2024.10.030
[13]	TANG H L， XIE H G， YAO Y， et al. Lower tacrolimus daily dose requirements and acute rejection rates in the CYP3A5 nonexpressers than expressers［J］. Pharmacogenet Genomics， 2011， 21（11）： 713-720. doi： 10.1097/FPC.0b013e32834a48ca . doi: 10.1097/FPC.0b013e32834a48ca
[14]	BUENDíA J A， HALAC E， BOSALEH A， et al. Frequency of CYP3A5 Genetic Polymorphisms and Tacrolimus Pharmacokinetics in Pediatric Liver Transplantation［J］. Pharmaceutics， 2020， 12（9）. doi： 10.3390/pharmaceutics12090898 . doi: 10.3390/pharmaceutics12090898
[15]	KIM J M， RYU J H， LEE K W， et al. Effect of CYP3A5 on the Once-Daily Tacrolimus Conversion in Stable Liver Transplant Patients［J］. J Clin Med， 2020， 9（9）. doi： 10.3390/jcm9092897 . doi: 10.3390/jcm9092897
[16]	SHAO J， WANG C， FU P， et al. Impact of Donor and Recipient CYP3A5*3 Genotype on Tacrolimus Population Pharmacokinetics in Chinese Adult Liver Transplant Recipients［J］. Ann Pharmacother， 2020， 54（7）： 652-661. doi： 10.1177/1060028019897050 . doi: 10.1177/1060028019897050
[17]	DONG Y， XU Q， LI R， et al. CYP3A7， CYP3A4， and CYP3A5 genetic polymorphisms in recipients rather than donors influence tacrolimus concentrations in the early stages after liver transplantation［J］. Gene， 2022， 809： 146007. doi： 10.1016/j.gene.2021.146007 . doi: 10.1016/j.gene.2021.146007
[18]	LINDARTE E F， DE JESUS VASQUEZ G， TORO RENDóN L G， et al. Effect of CYP3A5 genotypes on pharmacokinetic of tacrolimus in colombian liver transplant patients［J］. Front Pharmacol， 2025， 16： 1505490. doi： 10.3389/fphar.2025.1505490 . doi: 10.3389/fphar.2025.1505490
[19]	NALDI G A R， MINARI A B， PEREIRA T D M， et al. CYP3A5 and POR gene polymorphisms as predictors of infection and graft rejection in post-liver transplant patients treated with tacrolimus - a cohort study［J］. Pharmacogenomics J， 2025， 25（2）： 4. doi： 10.1038/s41397-025-00363-4 . doi: 10.1038/s41397-025-00363-4
[20]	SHI B， LIU Y， LIU D， et al. Genotype-guided model significantly improves accuracy of tacrolimus initial dosing after liver transplantation［J］. E Clinl Med， 2023， 55： 101752. doi： 10.1016/j.eclinm.2022.101752 . doi: 10.1016/j.eclinm.2022.101752
[21]	YOON S B， LEE J M， JUNG C W， et al. Machine-learning model to predict the tacrolimus concentration and suggest optimal dose in liver transplantation recipients： A multicenter retrospective cohort study［J］. Sci Rep， 2024， 14（1）： 19996. doi： 10.1038/s41598-024-71032-y . doi: 10.1038/s41598-024-71032-y
[22]	WOILLARD J B， LABRIFFE M， DEBORD J， et al. Tacrolimus Exposure Prediction Using Machine Learning［J］. Clin Pharmacol Ther， 2021， 110（2）： 361-369. doi： 10.1002/cpt.2123 . doi: 10.1002/cpt.2123
[23]	沈黎，李智平. 儿童他克莫司个体化给药模型的研究进展［J］. 中国药房， 2025， 36（01）： 124-128. doi 10.6039/j.issn.1001-0408.2025.01.21.
[24]	SONG X， LIU F， GAO H， et al. Compare the performance of multiple machine learning models in predicting tacrolimus concentration for infant patients with living donor liver transplantation［J］. Pediatr Transplant， 2023， 27（1）： e14379. doi： 10.1111/petr.14379 . doi: 10.1111/petr.14379
[25]	刘迎春，杨斌. 人工智能在肝移植中的应用研究进展［J］. 器官移植， 2024， 15（06）： 883-888. doi： 10.3969/j.issn.1674-7445.2024084 . doi: 10.3969/j.issn.1674-7445.2024084
[26]	LU Z， BONATE P， KEIRNS J. Population pharmacokinetics of immediate- and prolonged-release tacrolimus formulations in liver， kidney and heart transplant recipients［J］. Br J Clin Pharmacol， 2019， 85（8）： 1692-1703. doi： 10.1111/bcp.13952 . doi: 10.1111/bcp.13952
[27]	CHEN B， SHI H Q， LIU X X， et al. Population pharmacokinetics and Bayesian estimation of tacrolimus exposure in Chinese liver transplant patients［J］. J Clin Pharm Ther， 2017， 42（6）： 679-688. doi： 10.1111/jcpt.12599 . doi: 10.1111/jcpt.12599
[28]	JALIL M H， HAWWA A F， MCKIERNAN P J， et al. Population pharmacokinetic and pharmacogenetic analysis of tacrolimus in paediatric liver transplant patients［J］. Br J Clin Pharmacol， 2014， 77（1）： 130-140. doi： 10.1111/bcp.12174 . doi: 10.1111/bcp.12174
[29]	LEE D R， HO M J， JUNG H J， et al. Enhanced dissolution and oral absorption of tacrolimus by supersaturable self-emulsifying drug delivery system［J］. Int J Nanomedicine， 2016， 11： 1109-1117. doi： 10.2147/IJN.S102991 . doi: 10.2147/IJN.S102991
[30]	ZHANG Y， SHANGGUAN C， ZHANG X， et al. Computer-Aided Diagnosis of Complications After Liver Transplantation Based on Transfer Learning［J］. Interdiscip Sci， 2024， 16（1）： 123-140. doi： 10.1007/s12539-023-00588-6 . doi: 10.1007/s12539-023-00588-6
[31]	ZHANG H X， SHENG C C， LIU L S， et al. Systematic external evaluation of published population pharmacokinetic models of mycophenolate mofetil in adult kidney transplant recipients co-administered with tacrolimus［J］. Br J Clin Pharmacol， 2019， 85（4）： 746-761. doi： 10.1111/bcp.13850 . doi: 10.1111/bcp.13850
[32]	CAI X， LI R， SHENG C， et al. Systematic external evaluation of published population pharmacokinetic models for tacrolimus in adult liver transplant recipients［J］. Eur J Pharm Sci， 2020， 145： 105237. doi： 10.1016/j.ejps.2020.105237 . doi: 10.1016/j.ejps.2020.105237
[33]	LI Z R， LI R D， NIU W J， et al. Population Pharmacokinetic Modeling Combined With Machine Learning Approach Improved Tacrolimus Trough Concentration Prediction in Chinese Adult Liver Transplant Recipients［J］. J Clin Pharmacol， 2023， 63（3）： 314-325. doi： 10.1002/jcph.2156 . doi: 10.1002/jcph.2156
[34]	HOFFERT Y， DIA N， VANUYTSEL T， et al. Correction： Model-Informed Precision Dosing of Tacrolimus： A Systematic Review of Population Pharmacokinetic Models and a Benchmark Study of Software Tools［J］. Clin Pharmacokinet， 2024， 63（10）： 1511. doi： 10.1007/s40262-024-01438-4 . doi: 10.1007/s40262-024-01438-4
[35]	ZHU L， YANG J， ZHANG Y， et al. Effects of CYP3A5 genotypes， ABCB1 C3435T and G2677T/A polymorphism on pharmacokinetics of Tacrolimus in Chinese adult liver transplant patients ［J］. Xenobiotica， 2015， 45（9）： 840-846. doi： 10.3109/00498254.2015.1021733 . doi: 10.3109/00498254.2015.1021733
[36]	JI E， KIM M G， OH J M. CYP3A5 genotype-based model to predict tacrolimus dosage in the early postoperative period after living donor liver transplantation［J］. Ther Clin Risk Manag， 2018， 14： 2119-2126. doi： 10.2147/TCRM.S184376 . doi: 10.2147/TCRM.S184376
[37]	DU Y， SONG W， XIONG X， et al. Population pharmacokinetics and dosage optimisation of tacrolimus coadministration with Wuzhi capsule in adult liver transplant patients［J］. Xenobiotica， 2022， 52（3）： 274-283. doi： 10.1080/00498254.2022.2073851 . doi: 10.1080/00498254.2022.2073851
[38]	HOU J， YANG S， LIU W， et al. Tacrolimus population pharmacokinetic model-informed precision dosing in adult liver transplant patients［J］. Naunyn Schmiedebergs Arch Pharmacol， 2025， 398（8）： 10719-10729. doi： 10.1007/s00210-025-03982-7 . doi: 10.1007/s00210-025-03982-7
[39]	KOMENKUL V， SUKARNJANASET W， KOMOLMIT P， et al. External validation of population pharmacokinetic models of tacrolimus in Thai adult liver transplant recipients［J］. Eur J Clin Pharmacol， 2024， 80（8）： 1229-1240. doi： 10.1007/s00228-024-03692-8 . doi: 10.1007/s00228-024-03692-8
[40]	ZHU H， WANG M， XIONG X， et al. Plasma metabolomic profiling reveals factors associated with dose-adjusted trough concentration of tacrolimus in liver transplant recipients［J］. Front Pharmacol， 2022， 13： 1045843. doi： 10.3389/fphar.2022.1045843 . doi: 10.3389/fphar.2022.1045843

研究者	国家	基因	人群	病例数/例	个体化给药模型	年份
LINDARTE等^[18]	哥伦比亚	CYP3A5	成人肝移植受者	16	CYP3A5表达型患者较非表达型患者C₀及C₀/D比值更低、所需剂量更高；CYP3A51/1为1.04，CYP3A53/3为1.46	2025
SHI等^[20]	中国	CYP3A5rs776746 SLCO1B1rs4149015 CHST10 rs3748930	建模队列	150	模型在建模队列与验证队列中的ROC和AUC分别为0.88和0.79；他克莫司血药浓度维持在4 ~ 10 ng/mL可显著降低新发糖尿病风险（P = 0.043）	2023
			独立验证队列	97
			前瞻性预临床试验人群	40
BUENDíA等^[14]	日本	CYP3A5*1	儿童肝移植受者	77	受者中CYP3A5*1 表达率为37.1%，供者中为32.2%	2020
NALDI^[19]	巴西	CYP3A5 POR*28	肝移植受者	97	携带POR基因T等位基因（表达型）的患者酶活性更高、他克莫司血药浓度更低、免疫抑制剂用量更高，更易发生感染	2025
DONG等^[17]	中国	CYP3A7 CYP3A4 CYP3A5	肝移植受者	138	携带CYP3A7、CYP3A4、CYP3A41G、CYP3A53基因型者，他克莫司C0/D比值较非携带者升高近2倍	2022
SHAO等^[16]	中国	CYP3A5*1	肝移植受者	43	供者与受者CYP3A5*1基因型与他克莫司清除率显著相关；供受双方均为表达型者CL/F升高1.70倍	2020
KIM等^[15]	中国	CYP3A5	肝移植受者	60	肝移植受者CYP3A5基因分型可预测他克莫司由每天2次方案转换为每天1次剂型后的药代动力学特征	2022

研究者	国家	病例数/n	结构模型	药代参数	参数与公式	BSV（%）/ BOV（%）	年份
LU等^[26]	中国	112（86/26）	2CMT	CL/F	32.8 × 0.562 ×［EXP（ALT/40）×（-0.0237）］	46.6	2015
				V_c/F	22.7	47.3
				Ka	0.419	-
ZHU等^[35]	中国	95（73/22）	1CMT	CL/F	17.6 ×（POD/40.36）^0.205 ×（BUN/11.86）^-0.116 ×（ALP/149.77）^0.165 ×（TBIL/100.22）^-0.142 ×（HCT/99.09）^-0.789 ×（1.661，ifCYP3A5*1受者）	53.9	2015
				V_d/F	225 × POD^0.852 ×（HB/99.09）^-0.813	68
				Ka	4.48	-
CHEN等^[27]	中国	153（125/28）	2CMT	CL/F	21.9 × EXP（0.0102 × POD）× EXP（0.258 × CCR/113）× EXP（-0.148）（ifABCB13435CT受者）or EXP（-0.296）（ifABCB13435TT受者）	36.3	2017
				V_c/F	284 × EXP（-0.125）（ifABCB13435CT受者） or EXP（-0.25）（if ABCB1 3435TT受者）	89.4
				Ka	0.55	-
JI等^[36]	韩国	58 （46/12）	1CMT	CL/F	6.33 × POD^0.257 × 2.314（ifCYP3A51受者CYP3A51供者）× 1.523（ifCYP3A5l受者grafted from CYP3A53/*3 donor）	34.2	2018
				V_d/F	465 × POD^0.322	45.5
				Ka	4.48（fixed）	-
DU等^[37]	中国	116（87/29）	1CMT	CL/F	（15.4 × （DDS/3）^0.377 + 0.0452 ×（eGFR-104.8）-3.88 × FLZ）e^{-0.0034x（DBIL-8）} ×（1-0.463×VCZ）× e^-0.171 ×（wzc-1）e^{-0.0034×（HGB-108）}e^{-0.0004×（ALT-22.4）} ×（POD/37）^0.0544	27	2022
DU等^[37]	中国	116（87/29）	1CMT	V/F	1210 × e^{-0006×（TBIL-9.8）} ×（1-0.602×VCZ）× e^{-0.0317×（UREA-7.6）} × e^{-0.0008 ×（ALT-22.4）}	23.6
HOU等^[38]		57（41/16）	1CMT	CL∕F	12.13 × POD^0.13 ×（GGT/183）^0.04（L.h-1）	-	2025
				V∕F	613.03（L）	-
				Ka	4.48	-
LI等^[33]	中国	145/36	1CMT	CL/F	19.2 ×（DD/2.5）^0.516 × e^（POD/10） × 0.124 ×（1-0.71×VRCZ）×（AST/44）^-1.66 × e^{（HCT/31.4）×-0.585}	32.1	2023
				V/F	885 × e^{（POD/10）× 0.633} × e^{（HCT/31.4）× -0.999}	67.6
				Ka	4.48	-
KOMEN-KUL^[39]	泰国	50（34/16）	1CMT	CL/F	26.2 ×（Hb/11）^0.802 ×（TBIL/1.7）^-0.096	40.10	2024
				V/F	890L	80.30
				Ka	4.48	-

Research advances in personalized tacrolimus therapy for adult liver transplant recipients

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