随着个体化给药的理论与研究不断发展,涌现出很多各具特色的辅助决策系统。通过文献检索及网络查找,对目前常见的个体化给药计算机程序、网络平台及移动设备应用进行汇总整理,对比分析各个系统的通用属性和专业功能。本研究共纳入25款个体化辅助决策系统,涵盖了抗菌药物和抗病毒药物、免疫抑制剂和抗肿瘤药物、神经系统药物、心血管系统药物、呼吸系统药物等,多采用贝叶斯估算法进行参数估算。其中商业计算机程序MwPharm++综合性能最佳。此外商业计算机程序Precise PK、 APK、免费计算机程序JPKD、BestDose和网络平台SmartDose也展现出良好的性能。随着互联网和高性能计算工具的发展,移动智能设备应用大量兴起,可以预期个体化给药辅助决策系统在未来不断发展和完善,并为临床个体化给药提供更多选择和参考。
Abstract
With the progress of dose individualization theory and research, decision-making systems have increasingly emerged in recent years. In this paper, common individualized dosing computer program, web platform and application on mobile devices are identified and summarized by searching literatures and internet, and compared with each other in terms of general characteristics and professional characteristics. Twenty-five systems are included in total. These systems, which estimate parameters mostly by Bayesian algorithm, cover anti-bacterial drugs and antiviral drugs, immunosuppressants, anti-tumor drugs, nervous system drugs, cardiovascular system drugs, respiratory system drugs and so on. MwPharm++, a commercial computer program, has the best comprehensive performance among all these. Besides, highlighted advantages are showed in commercial computer program Precise PK, APK, free computer program JPKD, BestDose and web platform SmartDose. Along with the development of internet and the high performance computing tools, mobile apps are booming. It is expected that the decision-making systems to be developed and promoted continuously in the future and could provide more options and references for clinical individualized dosing.
关键词
个体化给药 /
辅助决策系统 /
治疗药物监测 /
软件
{{custom_keyword}} /
Key words
individualized dosing /
decision-making system /
therapeutic drug monitoring /
software
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] LING J, JIAO Z, ZHONG M K. Overviews and advances in studies of target concentration intervention [J]. Chin Pharm J(中国药学杂志), 2013, 48(16):1337-1342.
[2] HAMBERG A K, HELLMAN J, DAHLBERG J, et al. A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children [J]. BMC Med Inform Decis Mak, 2015,15(7):1-9.
[3] GAO Y C, JIAO Z, HUANG H, et al. Development of decision system for individualization of vancomycin dosage [J]. Acta Pharm Sin(药学学报), 2018, 53(1):104-110.
[4] BUFFINGTON D E, LAMPASONA V, CHANDLER M H. Computers in pharmacokinetics: choosing software for clinical decision making [J]. Clin Pharmacokinet, 1993, 25(3):205-216.
[5] FUCHS A, CSAJKA C, THOMA Y, et al. Benchmarking therapeutic drug monitoring software: a review of available computer tools [J]. Clin Pharmacokinet, 2013, 52(1):9-22.
[6] FU S S, DUAN J L. Progress in application of therapeutic drug monitoring softwares [J]. Chin J Clin Pharmacol(中国临床药理学杂志), 2012, 28(9):701-703, 713.
[7] GAGE B F, EBY C, JOHNSON J A, et al. Use of pharmacogenetic and clinical factors to predict the therapeutic dose of warfarin [J]. Clin Pharmacol Ther, 2008, 84(3):326-331.
[8] PROOST J H, MEIJER D K. MW/Pharm, an integrated software package for drug dosage regimen calculation and therapeutic drug monitoring [J]. Comput Biol Med, 1992, 22(3):155-163.
[9] JELLIFFE R W, NEELY M. Individualized Drug Therapy for Patients: Basic Foundations, Relevant Software and Clinical Applications[M]. Netherlands: Elsevier, 2016.
[10] JELLIFFE R W. The USC*PACK PC programs for population pharmacokinetic modeling, modeling of large kinetic/dynamic systems, and adaptive control of drug dosage regimens [J]. Proc Annu Symp Comput Appl Med Care, 1991:922-924.
[11] MEDIWARE. MwPharm++ presentation[EB/OL]. Groningen: Mediware [2018-03-31]. http://mediware.cz/presentation_mwpharm_plus_plus.pdf.
[12] DOSEME. Drug packages[EB/OL]. Australia: DoseMe [2018-03-31]. https://doseme-rx.com/our-platform#drug_packages.
[13] WORRALL S P, ALMOND M K, DHILLON S. Pharmacokinetics of bupropion and its metabolites in haemodialysis patients who smoke. A single dose study [J]. Nephron Clin Pract, 2004, 97(3):83-89.
[14] PULLEN J, DE ROZARIO L, STOLK L M, et al. Population pharmacokinetics and dosing of flucloxacillin in preterm and term neonates [J]. Ther Drug Monit, 2006, 28(3):351-358.
[15] KERKHOFS T M, DERIJKS L J, ETTAIEB H, et al. Development of a pharmacokinetic model of mitotane: toward personalized dosing in adrenocortical carcinoma [J]. Ther Drug Monit, 2015, 37(1):58-65.
[16] WRIGHT D F, DUFFULL S B. Development of a bayesian forecasting method for warfarin dose individualization [J]. Pharm Res, 2011, 28(5):1100-1111.
[17] DENG C H, ZHANG G M, BI S S, et al. The therapeutic drug monitoring network server of tacrolimus for Chinese renal transplant patients [J]. Acta Pharm Sin(药学学报), 2011, 46(7):828-833.
[18] YIN Y Q, LIN W W, JIAO Z, et al. Development of software for individualized dosage regimen of valproic acid and carbamazepine [J]. Chin Pharm J(中国药学杂志), 2014, 49(2):163-166.
[19] HOPE W W, VANGUILDER M, DONNELLY J P, et al. Software for dosage individualization of voriconazole for immunocompromised patients [J]. Antimicrob Agents Chemother, 2013, 57(4):1888-1894.
[20] FELTON T W, ROBERTS J A, LODISE T P, et al. Individualization of piperacillin dosing for critically Ⅲ patients: dosing software to optimize antimicrobial therapy [J]. Antimicrob Agents Chemother, 2014, 58(7):4094-4102.
[21] NEELY M N, KATO L, YOUN G, et al. Prospective trial on the use of trough concentration versus area under the curve to determine therapeutic vancomycin dosing [J]. Antimicrob Agents Chemother, 2018, 62(2):e02042-17.
[22] MAREK E, MOMPER J D, HINES R N, et al. Prediction of warfarin dose in pediatric patients: an pvaluation of the predictive performance of several models [J]. J Pediatr Pharmacol Ther, 2016, 21(3):224-232.
[23] LIU Y, YANG J, XU Q, et al. Comparative performance of warfarin pharmacogenetic algorithms in Chinese patients [J]. Thromb Res, 2012, 130(3):435-440.
[24] CHUMNUMWAT S, YI K, LUCKSIRI A, et al. Comparative performance of pharmacogenetics-based warfarin dosing algorithms derived from Caucasian, Asian, and mixed races in Thai population [J]. Cardiovasc Ther, 2018, 36(2):e12315.
[25] STORSET E, ASBERG A, SKAUBY M, et al. Improved tacrolimus target concentration achievement using computerized dosing in renal transplant recipients-a prospective, randomized study [J]. Transplantation, 2015, 99(10):2158-2166.
[26] MOULD D R, D′HAENS G, UPTON R N. Clinical decision support tools: the evolution of a revolution [J]. Clin Pharmacol Ther, 2016, 99(4):405-418.
[27] DARWICH A S, OGUNGBENRO K, VINKS A A, et al. Why has model-informed precision dosing not yet become common clinical reality? Lessons from the past and a roadmap for the future [J]. Clin Pharmacol Ther, 2017, 101(5):646-656.
[28] ZHAO C Y, JIAO Z, MAO J J, et al. External evaluation of published population pharmacokinetic models of tacrolimus in adult renal transplant recipients [J]. Br J Clin Pharmacol, 2016, 81(5):891-907.
[29] MAO J J, JIAO Z, YUN H Y, et al. External evaluation of population pharmacokinetic models for ciclosporin in adult renal transplant recipients [J]. Br J Clin Pharmacol, 2018, 84(1):153-171.
[30] DENG C, LIU T, WU K, et al. Predictive performance of reported population pharmacokinetic models of vancomycin in Chinese adult patients [J]. J Clin Pharm Ther, 2013, 38(6):480-489.
[31] JIAO Z. The use of softwares for individualized dosing[EB/OL]. Community of clinical pharmacy, 2018 [2018-05-31]. http://www.linyaoquan.com/kecheng/detail_1557553?f=org_coursecenter.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
国家自然科学基金项目资助 (81573505);上海卫生计生系统重要薄弱学科建设计划项目资助 (2016ZB0301-01);上海市科委西医引导项目资助(15411968000)
{{custom_fund}}
PDF(1618 KB)
