Predicting Reactions of Xenobiotic Compounds in Mammals

预测哺乳动物中异生化合物的反应

• 批准号: 7684034
• 负责人: Fangping Mu
• 金额: $ 30.63万
• 依托单位: LOS ALAMOS NAT SECTY-LOS ALAMOS NAT LAB
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-08 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7684034
• 关键词: Animal Model; Animals; Biological; Biological Assay; Biological Availability; CYP3A4 gene; Chemicals; Chronic; Computer Simulation; Databases; Drug Interactions; Drug toxicity; Drug usage; Environment; Enzymes; Excretory function; Failure; Goals; Human; Hydroxylation; In Vitro; Individual; Knowledge; Ligand Binding; Machine Learning; Mammals; Marketing; Metabolic; Metabolic Biotransformation; Metabolism; Methods; Modeling; Organism; Pharmaceutical Preparations; Pharmacologic Substance; Plant Roots; Probability; Process; Property; Rattus; Reaction; Research; Research Personnel; Route; Scanning; Screening procedure; Site; Source; Speed; Staging; System; Techniques; Testing; Time; Toxic effect; Training; Xenobiotic Metabolism; Xenobiotics; absorption; cost; design; drug candidate; drug development; drug discovery; drug metabolism; enzyme model; functional group; improved; in vivo; insight; meetings; metabolic abnormality assessment; novel; novel therapeutics; public health relevance; small molecule; tool; virtual

DESCRIPTION (provided by applicant): In pursuit of novel therapeutics, drug developers are scanning more compounds and covering more chemical space than ever before. The time required to bring a new drug into the market has not decreased, though the cost for drug discovery is steadily increasing. The root causes of this problem are related to efficacy, toxicity, and inappropriate absorption, distribution, metabolism and excretion, as shown by recent rigorous analyses. To focus on the ADMET(Absorption, Distribution, Metabolism, Elimination and Toxicity) issues, pharmaceutical research groups have, since the late 1990s, moved various physicochemical property screens earlier in the drug discovery process. The metabolic transformations of pharmaceuticals profoundly impact their bioavailability, efficacy, chronic toxicity, metabolic idiosyncrasies, excretion rates and routes. Metabolism is one of the major hurdles to overcome. In silico tools enable fast and virtual screening of large numbers of compounds before compounds are synthesized. Such tools enable researchers to recognize complicated metabolic processes, to eliminate poor candidates, and then to use the knowledge gained to discern possible deficiencies in compounds. Still poorly understood, metabolism is the most difficult to predict. The overall goal of this proposal is to develop a system to predict xenobiotic metabolism in mammals, and to gain insights into metabolism mechanisms (aim 1), and to study the differences in metabolism between humans and model animals (aim 2). We will use MDL's Metabolite database as a source of information about drug metabolism reactions. For aim 1, we will develop both global metabolism prediction systems, which can be applied to diverse substrate without prior knowledge of enzymes, and local models for particular enzymes, when prior knowledge of the enzymes involved in reactions is available. Global metabolism prediction systems will comprise many individual models, each of which will focus on an animal species (e.g., humans), an enzyme (e.g., CYP3A4) and a specific biotransformation (e.g., hydroxylation). Machine learning techniques will be used to build each individual model using various features to characterize the chemical environments of functional groups within molecules. For local models for an enzyme, we assume that the tight binding of ligands and enzymes is not required, but rather that reactions occur at sites where enzymes can easily attack. We will design ways to model the probability that a particular site will be attacked by enzymes. In Aim 1, both the global metabolism prediction system and the local models are trained on human reactions, so the models are animal specific. In Aim 2, we will build models for rat, which is a model animal in drug development. Using rat reactions listed in MDL's Metabolite database, we will establish a global metabolism system for rats and local models for rats using the same methods outlined in Aim 1. Though the methods are the same, the training sets are different, and it is expected that the models will make different predictions. By using drugs that are known to be metabolized differently in humans and rats, we will study differences in the human and rat models. PUBLIC HEALTH RELEVANCE: In pursuit of novel therapeutics, drug developers are scanning more compounds and covering more chemical space than ever before. ADMET(Absorption, Distribution, Metabolism, Elimination and Toxicity) has assumed center stage in the drug discovery process. Predicting metabolism is one of the major challenges to be met, and metabolism is the most poorly understood of the ADMET processes, and the most difficult to predict. We propose a machine learning approach for improving metabolism prediction, and for gaining insights into metabolism mechanisms, as well as studying differences in metabolism between humans and model animals.

描述（由申请人提供）：为了追求新的治疗学，药物开发人员正在扫描更多的化合物，并涵盖比以往更多的化学空间。尽管药物发现的成本正在稳步增加，但将新药带入市场所需的时间并没有减少。该问题的根本原因与疗效，毒性和不适当的吸收，分布，代谢和排泄有关，如最近的严格分析所示。为了关注ADMET（吸收，分布，新陈代谢，消除和毒性）问题，制药研究小组自1990年代后期以来，在药物发现过程中早些时候移动了各种物理化学特性筛查。药物的代谢转化深刻影响其生物利用度，疗效，慢性毒性，代谢特质，排泄率和途径。代谢是要克服的主要障碍之一。在计算机工具中，在合成化合物之前，可以快速和虚拟筛选大量化合物。这样的工具使研究人员能够认识到复杂的代谢过程，消除候选者的差，然后利用所获得的知识来辨别化合物中可能的缺陷。新陈代谢仍然很难预测。该提案的总体目标是开发一种系统来预测哺乳动物中异生物代谢，并了解对代谢机制的见解（AIM 1），并研究人类和模型动物之间代谢的差异（AIM 2）。我们将使用MDL的代谢物数据库作为有关药物代谢反应的信息来源。对于AIM 1，我们将开发全球代谢预测系统，可以在没有酶的先验知识的情况下应用于不同的底物，也可以将特定酶的局部模型应用于局部模型。全球代谢预测系统将包括许多单个模型，每个模型将集中在动物物种（例如人类），酶（例如CYP3A4）和特定的生物转化（例如羟基化）上。机器学习技术将用于使用各种特征来构建每个单独的模型，以表征分子中官能团的化学环境。对于酶的局部模型，我们假设不需要配体和酶的紧密结合，而是在酶很容易攻击的位点发生反应。我们将设计方法来建模特定站点将受到酶攻击的可能性。在AIM 1中，全球代谢预测系统和局部模型均经过人类反应的培训，因此模型是特定于动物的。在AIM 2中，我们将建立大鼠模型，这是药物开发中的模型动物。使用MDL代谢物数据库中列出的等级反应，我们将使用AIM 1中概述的相同方法为大鼠和局部模型建立一个全球代谢系统。尽管这些方法是相同的，但训练集是不同的，并且预计该模型将做出不同的预测。通过使用已知在人类和大鼠中代谢不同的药物，我们将研究人类和大鼠模型中的差异。公共卫生相关性：为了追求新颖的治疗学，药物开发人员正在扫描更多的化合物，并涵盖比以往更多的化学空间。 ADMET（吸收，分布，代谢，消除和毒性）在药物发现过程中占据了中心阶段。预测新陈代谢是应满足的主要挑战之一，而新陈代谢是对ADMET过程最糟糕的理解，也是最难预测的。我们提出了一种用于改善新陈代谢预测的机器学习方法，并获得对代谢机制的见解，并研究人类与模型动物之间代谢的差异。