Zisaki Aikaterini, Miskovic Ljubisa, Hatzimanikatis Vassily
Laboratory of Computational Systems Biotechnology (LCSB), Ecole Polytechnique Federale de Lausanne, EPFL/SB/ISIC/LCSB, CH H4 624/ Station 6/ CH-1015 Lausanne/ Switzerland.
Curr Pharm Des. 2015;21(6):806-22. doi: 10.2174/1381612820666141024151119.
Drug discovery and development is a high-risk enterprise that requires significant investments in capital, time and scientific expertise. The studies of xenobiotic metabolism remain as one of the main topics in the research and development of drugs, cosmetics and nutritional supplements. Antihypertensive drugs are used for the treatment of high blood pressure, which is one the most frequent symptoms of the patients that undergo cardiovascular diseases such as myocardial infraction and strokes. In current cardiovascular disease pharmacology, four drug clusters - Angiotensin Converting Enzyme Inhibitors, Beta-Blockers, Calcium Channel Blockers and Diuretics - cover the major therapeutic characteristics of the most antihypertensive drugs. The pharmacokinetic and specifically the metabolic profile of the antihypertensive agents are intensively studied because of the broad inter-individual variability on plasma concentrations and the diversity on the efficacy response especially due to the P450 dependent metabolic status they present. Several computational methods have been developed with the aim to: (i) model and better understand the human drug metabolism; and (ii) enhance the experimental investigation of the metabolism of small xenobiotic molecules. The main predictive tools these methods employ are rule-based approaches, quantitative structure metabolism/activity relationships and docking approaches. This review paper provides detailed metabolic profiles of the major clusters of antihypertensive agents, including their metabolites and their metabolizing enzymes, and it also provides specific information concerning the computational approaches that have been used to predict the metabolic profile of several antihypertensive drugs.
药物发现与开发是一项高风险事业，需要在资金、时间和科学专业知识方面进行大量投入。异源物质代谢研究仍是药物、化妆品和营养补充剂研发的主要课题之一。抗高血压药物用于治疗高血压，高血压是心肌梗死和中风等心血管疾病患者最常见的症状之一。在当前的心血管疾病药理学中，四类药物——血管紧张素转换酶抑制剂、β受体阻滞剂、钙通道阻滞剂和利尿剂——涵盖了大多数抗高血压药物的主要治疗特性。由于血浆浓度存在广泛的个体间差异，尤其是由于它们呈现出的依赖细胞色素P450的代谢状态导致疗效反应存在多样性，因此对抗高血压药物的药代动力学，特别是代谢特征进行了深入研究。已开发出多种计算方法，目的是：（i）建立模型并更好地理解人体药物代谢；（ii）加强对小异源生物分子代谢的实验研究。这些方法采用的主要预测工具是基于规则的方法、定量结构代谢/活性关系和对接方法。本文综述提供了主要抗高血压药物类别的详细代谢特征，包括它们的代谢产物及其代谢酶，还提供了有关用于预测几种抗高血压药物代谢特征的计算方法的具体信息。
