Leemann T, Dayer P
Division de Pharmacologie clinique, Hôpital cantonal universitaire, Genève.
Schweiz Med Wochenschr. 1992 Dec 12;122(50):1930-2.
Interactions are an important cause of adverse drug effects. Because of the large number of substances and the complexity of the mechanisms involved, computers can be of great help in the detection, prediction and management of interactions. We propose a new complementary approach to the prediction of interactions through a key mechanism, hepatic drug biotransformation. The originality of the approach consists in integrating in vitro enzymatic data and pharmacokinetic data to make in vivo predictions. An Inhibition Index (II) is defined to characterize the interaction potential of an inhibitor for a specific isozyme independently of the isozyme's substrates. It is thus possible, even in the absence of prior clinical observations, to make individualized qualitative as well as quantitative predictions of potential in vivo interactions. Q-DIPS is a prototype computer system under development on a Macintosh to manage the large amount of multi-dimensional data and facilitate the investigation and validation of extrapolations. The kinetics of II for two antifungal drugs, fluconazole and ketoconazole, are simulated and compared in order to illustrate the potential of the approach.
药物相互作用是药物不良反应的一个重要原因。由于涉及的物质数量众多且机制复杂，计算机在药物相互作用的检测、预测和管理方面能提供很大帮助。我们提出一种通过关键机制——肝脏药物生物转化来预测药物相互作用的新的补充方法。该方法的独特之处在于整合体外酶学数据和药代动力学数据以进行体内预测。定义了一个抑制指数（II）来独立于同工酶的底物表征抑制剂对特定同工酶的相互作用潜力。因此，即使在没有先前临床观察的情况下，也能够对潜在的体内相互作用进行个体化的定性和定量预测。Q - DIPS是一个正在Macintosh上开发的原型计算机系统，用于管理大量的多维数据，并促进外推法的研究和验证。为了说明该方法的潜力，对两种抗真菌药物氟康唑和酮康唑的II动力学进行了模拟和比较。
