In Silico Assesment of Drug Metabolism and Toxicity

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可归因于不良的ADME/TOX性质。这些特性通常在药物发现的早期、成本较低的阶段是可以预测的。这项工作的目标是使用名为MetaDrug的计算套件来预测新陈代谢和毒性。它整合了人类内源性和外源代谢以及信号通路，还可以整合基因表达和实验数据。在第一阶段，产生了预测主要CYP介导的途径的新算法，并成功地验证了预测可能有毒的代谢物和反应性代谢物形成的规则。这种算法的发展使得能够预测底物和代谢物、亲和力和代谢率以及与其他内源、代谢和信号通路的相互作用。在第二阶段的资助下，我们将开发大型综合数据集(&gt；1000个分子)，用于药物与主要环磷酰胺的体外相互作用，并使用这些数据集来生成这些人类药物代谢酶的机器学习算法。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节，捕获动力学数据，这些数据也可以用于预测模型的建立。我们还将使用第一阶段的代谢物规则生成一种新的算法，用于准确预测代谢物，以生成已知药物的分子指纹。然后，含有已知人类代谢物的分子数据库将被用作机器学习算法的输入。我们将结合我们各种QSAR模型对酶亲和力和代谢率、这些酶的相对贡献及其组织分布的预测，最终预测药物的清除性。拟议的工作将使GeneGo能够开发一种独特的工具，改善新陈代谢和毒性的预测。然后，这些新功能和数据库内容将被销售给制药公司和学术界。

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.