Bonnabry P, Sievering J, Leemann T, Dayer P
Division of Clinical Pharmacology and Pharmacy, University Hospitals, Geneva, Switzerland.
Eur J Clin Pharmacol. 1999 Jul;55(5):341-7. doi: 10.1007/s002280050638.
Drug biotransformation and interactions are a major source of variability in the response to drugs. The superfamily of cytochromes P450 plays a key role in this phenomenon but, because of the complexity of interactions between drugs and isozymes, it becomes more and more difficult for clinicians to master the knowledge required to predict the occurrence of such drug interactions. To predict and help manage the occurrence of cytochrome P450-dependent interactions, we developed an original computer application: Q-DIPS (quantitative drug interactions prediction system).
A multidisciplinary work team was created, associating clinical pharmacologists, pharmacists and a computer scientist. Major steps of investigation were: (1) the creation of a database to collect qualitative and quantitative data describing substrates, inhibitors and inducers of specific cytochrome P450 isozymes, with quality assessments; (2) the development of multi-access to these data and (3) their incorporation into extrapolation systems allowing the prediction of in vivo drug interactions on the basis of in vitro data. As an example, prediction and validation studies of CYP3A4 inhibition by ketoconazole and fluconazole will be discussed.
Q-DIPS gives up-to-date information, in dynamic tables, describing which specific P450 isozymes metabolise a given drug, as well as which drugs may inhibit or induce a given isozyme. To better answer common clinical questions and help to rapidly evaluate the risk of interactions, it is possible to obtain an overview of substances causing interactions with a specific drug or to focus on drugs taken by a patient ("clinical case"). For each question, key references, relevant quantitative data and quality indices are easily accessible. Two modules allowing input with commercial names and the anatomical therapeutic chemical classification were also included. On the basis of enzymatic and pharmacokinetic data generated in vitro or collected in vivo, the extrapolation module integrates quantitative models to predict the impact of a treatment on enzymatic activities. The simplest model predicted a strong but fluctuating inhibition of CYP3A4 by ketoconazole, whereas the impact of fluconazole was lower. Validations with published in vivo data suggested an appropriate prediction of the risk.
The current Q-DIPS prototype shows promising potential for helping to improve the management of drug interactions involving metabolism. Validation of extrapolation techniques need to be completed, in view of including important factors such as intrahepatocyte drug accumulation, contribution of metabolites to inhibition as well as in vitro non-specific binding to microsomal proteins. The final goal will be to help select the most judicious clinical studies to be performed so as to avoid useless, expensive and unethical investigations in man.
药物生物转化及相互作用是药物反应变异性的主要来源。细胞色素P450超家族在这一现象中起关键作用，但由于药物与同工酶之间相互作用的复杂性，临床医生越来越难以掌握预测此类药物相互作用发生所需的知识。为了预测并帮助管理细胞色素P450依赖性相互作用的发生，我们开发了一款原创计算机应用程序：Q-DIPS（定量药物相互作用预测系统）。
组建了一个多学科工作团队，成员包括临床药理学家、药剂师和一名计算机科学家。主要研究步骤如下：（1）创建一个数据库，收集描述特定细胞色素P450同工酶的底物、抑制剂和诱导剂的定性和定量数据，并进行质量评估；（2）开发对这些数据的多途径访问方式；（3）将这些数据纳入外推系统，以便根据体外数据预测体内药物相互作用。例如，将讨论酮康唑和氟康唑对CYP3A4抑制作用的预测及验证研究。
Q-DIPS以动态表格形式提供最新信息，描述哪些特定的P450同工酶代谢某一特定药物，以及哪些药物可能抑制或诱导某一同工酶。为了更好地回答常见临床问题并帮助快速评估相互作用风险，可以获取与某一特定药物发生相互作用的物质概述，或关注患者服用的药物（“临床病例”）。对于每个问题，关键参考文献、相关定量数据和质量指标都易于获取。还包括两个允许输入商品名和解剖学治疗学化学分类的模块。基于体外生成或体内收集的酶学和药代动力学数据，外推模块整合定量模型以预测一种治疗对酶活性的影响。最简单的模型预测酮康唑对CYP3A4有强烈但波动的抑制作用，而氟康唑的影响较小。与已发表的体内数据进行验证表明，对风险的预测是恰当的。
当前的Q-DIPS原型在帮助改善涉及代谢的药物相互作用管理方面显示出有前景的潜力。鉴于要纳入诸如肝内药物蓄积、代谢产物对抑制作用的贡献以及体外与微粒体蛋白的非特异性结合等重要因素，外推技术的验证需要完成。最终目标将是帮助选择最明智的临床研究来进行，以避免在人体上进行无用、昂贵且不道德的研究。
