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药物相互作用处理方面的改进。

Improvement in the handling of drug-drug interactions.

作者信息

Fuhr Uwe

机构信息

Department of Pharmacology, Clinical Pharmacology Unit, University of Cologne, Köln, Germany.

出版信息

Eur J Clin Pharmacol. 2008 Feb;64(2):167-71. doi: 10.1007/s00228-007-0436-8. Epub 2008 Jan 3.

Abstract

Approximately one in 200 hospitalised patients has a serious adverse drug effect caused by drug-drug interactions (DDIs). Such adverse effects should be avoidable, but current information provided on DDIs is often incomplete and difficult or even impossible to translate into true risk and appropriate tangible action. Clinicians need to know the mean and maximal expected effect of a DDI on clinical endpoints, any dose adjustments required, and how to monitor tolerability and efficacy in patients subject to a DDI. To this end, improved study designs should take the objective of improving treatment explicitly into account, and any existing DDI data should be publicly accessible. Modelling needs to be used more extensively in order to quantitatively predict the effects of DDIs on clinical endpoints in patients and to relate clinical endpoint effects considered as acceptable to respective changes in experimental and clinical studies. Computer-based expert systems will be required to convert such DDI data into recommendations applicable to the individual patient. Therefore, the incorporation of DDIs in a more general procedure for personalisation of drug therapy is desirable.

摘要

每200名住院患者中约有1人会因药物相互作用(DDIs)产生严重的药物不良反应。此类不良反应应是可以避免的,但目前提供的有关药物相互作用的信息往往不完整,难以甚至无法转化为真正的风险以及适当的切实可行的措施。临床医生需要了解药物相互作用对临床终点的平均和最大预期影响、所需的任何剂量调整,以及如何监测药物相互作用患者的耐受性和疗效。为此,改进的研究设计应明确考虑改善治疗这一目标,并且任何现有的药物相互作用数据都应公开可用。需要更广泛地使用建模方法,以便定量预测药物相互作用对患者临床终点的影响,并将被视为可接受的临床终点影响与实验和临床研究中的相应变化联系起来。将需要基于计算机的专家系统,以便将此类药物相互作用数据转化为适用于个体患者的建议。因此,将药物相互作用纳入更通用的药物治疗个体化程序是可取的。

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