浏览全部资源
扫码关注微信
陆军军医大学第一附属医院药剂科,重庆 400038
Published:15 January 2023,
Received:27 July 2022,
Revised:18 November 2022,
扫 描 看 全 文
詹世鹏,马攀,刘芳.机器学习在治疗药物监测与个体化用药中的应用 Δ[J].中国药房,2023,34(01):117-121.
ZHAN Shipeng,MA Pan,LIU Fang.Application of machine learning in the therapeutic drug monitoring and individual drug therapy[J].ZHONGGUO YAOFANG,2023,34(01):117-121.
詹世鹏,马攀,刘芳.机器学习在治疗药物监测与个体化用药中的应用 Δ[J].中国药房,2023,34(01):117-121. DOI: 10.6039/j.issn.1001-0408.2023.01.23.
ZHAN Shipeng,MA Pan,LIU Fang.Application of machine learning in the therapeutic drug monitoring and individual drug therapy[J].ZHONGGUO YAOFANG,2023,34(01):117-121. DOI: 10.6039/j.issn.1001-0408.2023.01.23.
机器学习由于其强大的数据分析与预测能力,在医学领域的研究与应用不断深入。近年来,越来越多的研究将其应用于免疫抑制剂、抗感染药物、抗癫痫药物等的治疗药物监测与个体化用药中。相较于传统的群体药动学建模方法,基于机器学习构建的模型能更精准地预测血药浓度和给药剂量,提高临床精准用药水平,减少不良反应的发生。基于此,本文就机器学习在治疗药物监测与个体化用药中的应用予以综述,为临床精准用药提供理论依据和技术支撑。
Machine learning has been applied in the medical field due to its powerful data analysis and exploration capabilities. In recent years, more and more studies have applied it to therapeutic drug monitoring and individual drug therapy of immunosuppressants, anti-infective drugs, antiepileptic drugs, etc. Compared with the traditional population pharmacokinetic modeling methods, the constructed models based on machine learning can predict blood drug concentration and drug dose more accurately, improve the level of clinical precision drug use and reduce the occurrence of adverse drug reactions. Based on this, this article reviews the application of machine learning in therapeutic drug monitoring and individual drug therapy, with a view to providing theoretical basis and technical support for clinical precise drug use.
机器学习治疗药物监测免疫抑制剂抗感染药物个体化用药
therapeutic drug monitoringimmunosuppressantsanti-infective drugsindividual drug therapy
DEO R C.Machine learning in medicine[J]. Circulation,2015,132(20):1920-1930.
KOLLURI S,LIN J,LIU R,et al. Machine learning and artificial intelligence in pharmaceutical research and development:a review[J]. AAPS J,2022,24(1):19.
刘雨安,杨小文,李乐之.机器学习在疾病预测的应用研究进展[J].护理学报,2021,28(7):30-34.
刘子暖,杨俊杰,陈韵岱.机器学习在冠状动脉计算机断层扫描领域的应用及进展[J].解放军医学杂志,2021,46(3):286-293.
张景奇,史文宝,纪秀娟.机器学习在医疗和公共卫生中应用[J].中国公共卫生,2019,35(10):1449-1452.
VAMATHEVAN J,CLARK D,CZODROWSKI P,et al. Applications of machine learning in drug discovery and development[J]. Nat Rev Drug Discov,2019,18(6):463-477.
韦炳华,叶毅芳,罗美娟,等.肝移植患者他克莫司个体化给药研究[J].中国临床药理学与治疗学,2012,17(7):791-796.
傅晓华,叶毅芳,罗美娟,等.人工神经网络预测肝移植术受者他克莫司血药浓度[J].药学学报,2012,47(9):1134-1140.
BADILLO S,BANFAI B,BIRZELE F,et al. An introduction to machine learning[J]. Clin Pharmacol Ther,2020,107(4):871-885.
KOZA J R,BENNETT F H,ANDRE D,et al. Automated design of both the topology and sizing of analog electrical circuits using genetic programming[J]. Artif Intell Des,1996:123-134.
PUNCHOO R,BHOORA S,PILLAY N. Applications of machine learning in the chemical pathology laboratory[J]. J Clin Pathol,2021,74(7):435-442.
LI T F,HU L,MA X L,et al. Population pharmacokine- tics of cyclosporine in Chinese children receiving hematopoietic stem cell transplantation[J]. Acta Pharmacol Sin,2019,40(12):1603-1610.
XUE L,ZHANG W W,DING X L,et al. Population pharmacokinetics and individualized dosage prediction of cyclosporine in allogeneic hematopoietic stem cell transplant patients[J]. Am J Med Sci,2014,348(6):448-454.
CANDELA-BOIX M R,RAMÓN-LÓPEZ A,NALDA-MOLINA R,et al. Population pharmacokinetics models of sirolimus in renal transplant patients:a systematic review[J]. Farm Hosp,2021,45(7):77-83.
傅晓华,洪晓丹,刘石带,等.人工神经网络模型在肾移植患者他克莫司个体化给药中的应用[J].中国药学杂志,2013,48(12):1000-1004.
洪晓丹,李碧虹,罗美娟,等.肝移植受者他克莫司血药浓度早期预测方案及评估[J].中国医院药学杂志,2013,33(5):381-385.
CHEN H Y,CHEN T C,MIN D I,et al. Prediction of tacrolimus blood levels by using the neural network with genetic algorithm in liver transplantation patients[J]. Ther Drug Monit,1999,21(1):50-56.
THISHYA K,VATTAM K K,NAUSHAD S M,et al. Artificial neural network model for predicting the bioavailabi- lity of tacrolimus in patients with renal transplantation[J]. PLoS One,2018,13(4):e0191921.
CAI N,ZHANG X,ZHENG C,et al. A novel random fo- rest integrative approach based on endogenous CYP3A4 phenotype for predicting tacrolimus concentrations and dosages in Chinese renal transplant patients[J]. J Clin Pharm Ther,2020,45(2):318-323.
SEELING W,PLISCHKE M,DE BRUIN J S,et al. Knowledge-based immunosuppressive therapy for kidney transplant patients:from theoretical model to clinical integration[J]. Stud Health Technol Inform,2015,216:1119.
TANG J,LIU R,ZHANG Y L,et al. Application of machine-learning models to predict tacrolimus stable dose in renal transplant recipients[J]. Sci Rep,2017,7:42192.
WOILLARD J B,LABRIFFE M,DEBORD J,et al. Tacrolimus exposure prediction using machine learning[J]. Clin Pharmacol Ther,2021,110(2):361-369.
WOILLARD J B,LABRIFFE M,PRÉMAUD A,et al. Estimation of drug exposure by machine learning based on simulations from published pharmacokinetic models:the example of tacrolimus[J]. Pharmacol Res,2021,167:105578.
余俊先,史丽敏,李珊,等.肾移植受者的环孢素剂量预测[J].中国医院药学杂志,2010,30(17):1451-1454.
余俊先,史丽敏,王汝龙,等.基于人工神经网络的环孢素A个体化给药设计[J].中国药学杂志,2010,45(12):927-930.
余俊先,夏杰,史丽敏,等.人工神经网络建立的环孢素A血药浓度预测模型[J].中国药物应用与监测,2010,7(1):52-55.
徐楚鸿,艾又生,陈华庭.人工神经网络法预测肾移植术后患者环孢素A的血药浓度[J].中国医院药学杂志,2008,28(4):276-278.
张靖悦,冯植,张佳成,等.基于改进曲线回归模型的人全血中环孢素A谷浓度预测[J].中国医院药学杂志,2021,41(15):1491-1495,1506.
CAMPS-VALLS G,PORTA-OLTRA B,SORIA-OLIVAS E,et al. Prediction of cyclosporine dosage in patients after kidney transplantation using neural networks[J]. IEEE Trans Biomed Eng,2003,50(4):442-448.
HODA M R,GRIMM M,LAUFER G. Prediction of cyclosporine blood levels in heart transplantation patients using a pharmacokinetic model identified by evolutionary algorithms[J]. J Heart Lung Transplant,2005,24(11):1855-1862.
LECLERC V,BLEYZAC N,CERAULO A,et al. A decision support tool to find the best cyclosporine dose when switching from intravenous to oral route in pediatric stem cell transplant patients[J]. Eur J Clin Pharmacol,2020,76(10):1409-1416.
任斌,何秋毅,许琼,等.人工神经网络预测肾移植受者霉酚酸体内暴露药量[J].药学学报,2009,44(12):1397-1401.
叶毅芳,容颖慈,李敏薇,等.肾移植患者霉酚酸血药浓度人工神经网络预测模型[J].中国药学杂志,2013,48(14):1200-1203.
WOILLARD J B,LABRIFFE M,DEBORD J,et al. Mycophenolic acid exposure prediction using machine lear-ning[J]. Clin Pharmacol Ther,2021,110(2):370-379.
HUANG X,YU Z,BU S,et al. An ensemble model for prediction of vancomycin trough concentrations in pedia-tric patients[J]. Drug Des Devel Ther,2021,15:1549-1559.
HUANG X,YU Z,WEI X,et al. Prediction of vancomycin dose on high-dimensional data using machine learning techniques[J]. Expert Rev Clin Pharmacol,2021,14(6):761-771.
IMAI S,TAKEKUMA Y,MIYAI T,et al. A new algorithm optimized for initial dose settings of vancomycin using machine learning[J]. Biol Pharm Bull,2020,43(1):188-193.
唐颖莹,陆璐,周东.中国癫痫诊断治疗现状[J].癫痫杂志,2019(3):161-164.
HIEMKE C,BERGEMANN N,CLEMENT H W,et al. Consensus guidelines for therapeutic drug monitoring in neuropsychopharmacology:update 2017[J]. Pharmacopsychiatry,2018,51(1/2):e1.
马攀,贾运涛,刘芳,等.基于支持向量机技术预测丙戊酸钠血药浓度[J].安徽医药,2021,25(1):35-39.
ZHU X,HUANG W,LU H,et al. A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters[J]. Sci Rep,2021,11(1):5568.
0
Views
9
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution