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In Silico Assesment of Drug Metabolism and Toxicity

药物代谢和毒性的计算机评估

基本信息

批准号：
7100969
负责人：
Yuri V. Nikolsky
金额：
$ 32.67万
依托单位：
GENEGO, INC.
依托单位国家：
美国
项目类别：
财政年份：
2003
资助国家：
美国
起止时间：
2003-07-15 至 2007-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7100969
关键词：
Silico Assesment Drug Metabolism Toxicity

项目摘要

DESCRIPTION (provided by applicant): Failure of molecules in the late stages of drug development are to a large extent attributable to poor ADME/Tox properties. These properties are generally predictable in the earlier, cheaper stages of drug discovery. The goal of this work is to predict metabolism and toxicity using a computational suite called MetaDrug. This integrates human endogenous and xenobiotic metabolic as well as signalling pathways and can also incorporate gene expression, and experimental data. Under phase I, novel algorithms for predicting major CYP-mediated pathways were generated and successfully validated along with rules for predicting metabolites and reactive metabolites formed which are likely to be toxic. This algorithm development enabled the prediction of substrates and metabolites, the affinity and the rate of metabolism as well as interactions with other endogenous, metabolic and signalling pathways. With phase II funding we will develop large comprehensive datasets (>1000 molecules) for in vitro drug-drug interactions with the major CYPs, and use these for generating machine learning algorithms for these human drug metabolizing enzymes. We will also annotate rat and mouse data for drug metabolism and the transcriptional regulation of these enzymes, capturing the kinetic data which can also be used for predictive model building. We will also generate a novel algorithm for the accurate prediction of metabolites using the metabolite rules from phase I to produce a molecular fingerprint for known drugs. The database of molecules with known human metabolites will then be used as an input for a machine learning algorithm. We will combine the predictions from our various QSAR models for enzyme affinity and rate of metabolism, the relative contributions of these enzymes and their tissue distribution, to ultimately predict the clearance of a drug. The proposed work will enable GeneGo to develop a unique tool that will improve the prediction of metabolism and toxicity. These new features and database content will then be marketed to pharmaceutical companies and academia.

描述（由申请人提供）：药物开发后期分子的失败在很大程度上归因于不良的ADME/Tox特性。这些特性通常在药物发现的早期，更便宜的阶段是可预测的。这项工作的目标是使用称为MetaDrug的计算套件来预测代谢和毒性。这整合了人类内源性和外源性代谢以及信号传导途径，也可以纳入基因表达和实验数据。在I期，生成了预测主要CYP介导途径的新算法，并与预测代谢物和可能有毒的反应性代谢物形成的规则一起沿着成功验证。该算法的开发使得能够预测底物和代谢物、亲和力和代谢速率以及与其他内源性、代谢和信号传导途径的相互作用。在第二阶段的资助下，我们将开发大型综合数据集（>1000个分子），用于体外药物与主要CYP的相互作用，并使用这些数据集为这些人类药物代谢酶生成机器学习算法。我们还将注释大鼠和小鼠的药物代谢数据和这些酶的转录调控，捕获动力学数据，这些数据也可用于预测模型的建立。我们还将生成一种新的算法，用于使用第一阶段的代谢物规则准确预测代谢物，以生成已知药物的分子指纹。然后，具有已知人类代谢物的分子数据库将用作机器学习算法的输入。我们将结合联合收割机从我们的各种QSAR模型的预测酶的亲和力和代谢率，这些酶的相对贡献和它们的组织分布，最终预测药物的清除率。拟议的工作将使GeneGo能够开发一种独特的工具，以改善对代谢和毒性的预测。这些新功能和数据库内容将随后向制药公司和学术界推广。