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个体化用药就是在最适的时间，对最适的患者给予最适的药物和最适的剂量。目前，个体化用药的主要依据是治疗药物监测和药物基因组检测。既往的治疗药物监测应用研究主要基于群体药动学模型、药动学/药效学模型等给出剂量建议，然而这些模型在计算用药方案时仅基于少量临床参数和指标，无法适用于情况复杂的特殊人群，如覆盖多种群体特征的危重症患者等。人工智能（AI）技术可以挖掘分析海量真实世界用药数据，通过对数据的多层次挖掘，筛选出更多影响药物作用的特征，从而构建实用性更强的个体化用药模型，同时也可用于药物不良反应预测等方面。课题组研发的i Pharma个体化精准用药系统是基于真实世界的用药大数据，运用AI技术实现个体化用药指导。该文结合课题组的前期工作，概述AI指导个体化用药的研究技术与实践案例，以期为临床个体化用药提供更多的参考。
Abstract：[1]周颖,刘晓,马凌云,等.万古霉素的临床应用及急性肾损伤监测现状分析[J].中国临床药理学杂志,2019,35(12):1298.
[2]HAMET P,TREMBLAY J.Artificial intelligence in medicine[J].Metabolism,2017,69S:S36.
[4]焦正,李新刚,尚德为,等.模型引导的精准用药:中国专家共识(2021版)[J].中国临床药理学与治疗学,2021,26(11):1215.
[5]HUANG X H,YU Z,BU S H,et al.An ensemble model for prediction of vancomycin trough concentrations in pediatric patients[J].Drug Des Devel Ther,2021,15:1549.
[6]YU Z,JI H H,XIAO J W,et al.Predicting adverse drug events in Chinese pediatric inpatients with the associated risk factors:a machine learning study[J].Front Pharmacol,2021,12:659099.
[7]HUANG X H,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.
[8]CHEN H,MA Y Y,HONG N,et al.Early warning of citric acid overdose and timely adjustment of regional citrate anticoagulation based on machine learning methods[J].BMC Med Inform Decis Mak,2021,21(Suppl 2):126.
[9]LIU Y,CHEN J H,YOU Y,et al.An ensemble learning based framework to estimate warfarin maintenance dose with cross-over variables exploration on incomplete data set[J].Comput Biol Med,2021,131:104242.
[10]WOILLARD J B,LABRIFFE M,DEBORD J,et al.Mycophenolic acid exposure prediction using machine learning[J].Clin Pharmacol Ther,2021,110(2):370.
[11]Van LOOY S,VERPLANCKE T,BENOIT D,et al.A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression[J].Crit Care,2007,11(4):R83.
[12]ZHENG P,YU Z,LI L R,et al.Predicting blood concentration of tacrolimus in patients with autoimmune diseases using machine learning techniques based on real-world evidence[J].Front Pharmacol,2021,12:727245.
[13]GUO W,YU Z,GAO Y,et al.A machine learning model to predict risperidone active moiety concentration based on initial therapeutic drug monitoring[J].Front Psychiatry,2021,12:711868.
[14]HATMAL M M,AL-HATAMLEH M A I,OLAIMAT A N,et al.Side effects and perceptions following COVID-19 vaccination in Jordan:a randomized,cross-sectional study implementing machine learning for predicting severity of side effects[J].Vaccines(Basel),2021,9(6):556.
[15]MO X L;CHEN X J,IEONG C,et al.Early prediction of clinical response to etanercept treatment in juvenile idiopathic arthritis using machine learning[J].Front Pharmacol,2020,11:1164.
[16]MCMASTER C,LIEW D,KEITH C,et al.A machine-learning algorithm to optimise automated adverse drug reaction detection from clinical coding[J].Drug Saf,2019,42(6):721.
[17]KRUPPA J,ZIEGLER A,KONIG I R.Risk estimation and risk prediction using machine-learning methods[J].Hum Genet,2012,131(10):1639.
[18]MINERALI E,FOIL D H,ZORN K M,et al.Comparing machine learning algorithms for predicting drug-induced liver injury(DILI)[J].Mol Pharm,2020,17(7):2628.
[19]FENG C L,CHEN H W,YUAN X Q,et al.Gene expression data based deep learning model for accurate prediction of drug-induced liver injury in advance[J].J Chem Inf Model,2019,59(7):3240.
[20]CUPLOV V,ANDRE N.Machine learning approach to forecast chemotherapy-induced haematological toxicities in patients with rhabdomyosarcoma[J].Cancers,2020,12(7):1944.
[21]LEE H C, YOON S B,YANG S M,et al.Prediction of acute kidney injury after liver transplantation:machine learning approaches vs.logistic regression model[J].J Clin Med,2018,7(11):E428.
[22]HE X,YAO P L,LI M T,et al.A risk scoring model for highdose methotrexate-induced liver injury in children with acute lymphoblastic leukemia based on gene polymorphism study[J].Front Pharmacol,2021,12:726229.
[23]SHEN N L,HUANG X L,WANG P P.Application research of artificial intelligence in medical field[J].Nurs Integr Tradit Chin West Med,2019,5(11):141.
[24]LI H C,Promotion of the integrated application of artificial intelligence in the medical field[J].China Digit Med,2019,14(11):1.
基本信息:
DOI：10.19577/j.1007-4406.2022.02.017
中图分类号:R95
引用信息:
[1]张颖,于泽,许本善,等.人工智能指导个体化用药的研究与实践[J].中国临床药学杂志,2022,31(02):151-156.DOI:10.19577/j.1007-4406.2022.02.017.
基金信息:
国家重点研发计划项目（编号2020YFC2005502、2020YFC2005503）