Understanding the Metabolic Impact of Aldehyde Oxidase on New Drug Design

了解醛氧化酶对新药设计的代谢影响

• 批准号: 8471732
• 负责人: Jeffrey P Jones
• 金额: $ 24.96万
• 依托单位: WASHINGTON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8471732
• 关键词: Acute Kidney Failure; Affinity; Aldehyde oxidase; Animal Model; Binding; Chemicals; Chlorpromazine; Clinic; Clinical Trials; Clozapine; Coenzymes; Complex; Computer Simulation; Cytosol; Data; Development; Drug Design; Drug Interactions; Enzymes; Failure; Grant; Heating; Human; In Vitro; Kidney; Kinetics; Knowledge; Lead; Life; Liver; Measures; Metabolic; Metabolism; Methods; Methotrexate; Modeling; Pharmaceutical Preparations; Phase I Clinical Trials; Publishing; Reaction; Reporting; Research; Role; Structure; Testing; Time; Toxic effect; Work; base; cost; drug candidate; drug development; drug metabolism; in vivo; inhibitor/antagonist; pharmacophore; pre-clinical; quantum; three-dimensional modeling; tool

DESCRIPTION (provided by applicant): This proposal aims to develop in silico models for aldehyde oxidase (AOX) binding affinities (Aim 1), and in vivo human clearance (Aim 2). Three compounds have failed recently in clinical trials as a result of the lack of knowledge of AOX metabolism prior to entering humans. One of these compounds led to kidney toxicity in the test subjects. These failures are a result of the inability of any preclinical species to predict AOX metabolism. Furthermore, it has been reported that human cytosol can also fail to accurately predict in vivo human clearance. Preliminary results support our efforts to accurately predict human clearance using in silico methods. We were able to predict human clearance with a reasonable amount of accuracy (r2 = 0.8) for 8 compounds that have been administered to humans. We also provide preliminary results for the inhibition of AOX for 8 compounds. All but one of these compounds show complex inhibition kinetics. From the inhibition results, we are able to determine that two compounds, clozapine and chlorpromazine, have high enough affinities to show drug-drug interactions. In Aim 1, we will develop quantitative structure inhibition relationships (QSIR) using data from 100 inhibitors of AOX. These QSIR models will have multiple uses, one of which will be predicting potential drug-drug interactions. In Aim 2, we will refine our models for AOX clearance. Again, these models will have multiple uses, and provide a strong understanding of how these enzymes work in drug metabolism. We hypothesize that the models from Aim 1 and Aim 2 can be used in drug development to help eliminate drug failures.

描述（由申请人提供）：该提案旨在开发醛氧化酶（AOX）结合亲和力（Aim 1）和体内人体清除（Aim 2）的硅模型。由于在进入人体之前缺乏对AOX代谢的了解，最近有三种化合物在临床试验中失败。其中一种化合物导致了试验对象的肾毒性。这些失败是任何临床前物种无法预测AOX代谢的结果。此外，据报道，人细胞质也不能准确预测人体内的清除率。初步结果支持我们使用计算机方法准确预测人体清除的努力。我们能够以合理的准确度（r2 = 0.8）预测8种已给药的化合物的人体清除率。我们还提供了8种化合物对AOX的抑制作用的初步结果。除一种化合物外，其余化合物均表现出复杂的抑制动力学。从抑制结果来看，我们可以确定氯氮平和氯丙嗪两种化合物具有足够高的亲和力，可以表现出药物-药物相互作用。在目标1中，我们将利用100种AOX抑制剂的数据建立定量结构抑制关系（QSIR）。这些QSIR模型将有多种用途，其中之一将是预测潜在的药物-药物相互作用。在目标2中，我们将完善我们的AOX清除模型。同样，这些模型将有多种用途，并提供对这些酶如何在药物代谢中起作用的深刻理解。我们假设Aim 1和Aim 2的模型可以用于药物开发，以帮助消除药物失败。