MegaTrans – human transporter machine learning models

MegaTrans — 人类运输机机器学习模型

• 批准号: 9768844
• 负责人: SEAN EKINS
• 金额: $ 21.07万
• 依托单位: COLLABORATIONS PHARMACEUTICALS, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9768844
• 关键词: Academia; Address; Angiotensin-Converting Enzyme Inhibitors; Bayesian Method; Behavior; Biological Assay; Cell Line; Cells; Characteristics; Clinical; Collaborations; Collection; Communities; Computer software; Data; Data Set; Databases; Drug Compounding; Drug Design; Drug Interactions; Drug Modelings; Ensure; Estradiol; Evaluation; Family; Fingerprint; Fluorescein; Glucuronides; Goals; Hepatocyte; Human; Imagery; In Vitro; Industry; International; Intuition; Investments; Kinetics; Libraries; Licensing; Literature; Machine Learning; Methods; Modeling; Natural Products; Online Systems; Organic Cation Transporter; Output; POU2F2 gene; Pharmaceutical Preparations; Phase; PubChem; Public Domains; Receiver Operating Characteristics; Risk; Savings; Software Tools; Structure; System; Testing; Time; Training; Trees; Work; Xenobiotics; clinically relevant; computerized tools; deep neural network; design; drug candidate; drug discovery; high throughput screening; human model; improved; in vitro testing; in vivo; inhibitor/antagonist; interest; learning strategy; machine learning algorithm; member; model building; novel therapeutics; prospective; side effect; software development; tool; tool development; uptake

Summary Being able to predict interactions with important human transporters would be of value to new drug design to avoid compounds that interact with them and cause undesirable side effects. OATP1B1 (SLCO1B1) and OATP1B3 (SLCO1B3) are `uptake' transporters largely restricted to the sinusoidal aspect of hepatocytes. They both transport a wide variety of structurally-unrelated compounds, including members of several clinically im- portant drug families such as statins, sartans and angiotensin converting enzyme (ACE) inhibitors. We now propose to test over 1000 drugs against 2 substrates for each transporter in vitro. We will then use these data to curate and validate machine learning models. We will also use an array of machine learning methods as well as multiple model evaluation metrics. This will enable us to develop a web-based software tool called MegaTrans that will encourage the user to input their own compound structures and generate predictions for interactions with transporter/s of interest and then visualize the similarity to the training set of each model using several different visualization methods. The return on investment of such a tool would be that it could assist in the design and selection of more favorable compounds that avoid transporters of interest while also saving time and money. It could also identify compounds that are already approved that might present a drug interaction risk. Predicting such behavior seen in vivo is ideal and will lead to the prioritization of compounds to test in vitro for potential drug-drug interactions. In Phase II we would greatly expand the number of transporters which we would generate data on and build models such that we could address all the major transporters of interest to drug discovery.

总结 能够预测与重要的人类转运蛋白的相互作用将对新药设计具有价值， 避免与它们相互作用并引起不良副作用的化合物。OATP1B1（SLCO1B1）和 OATP 1B3（SLCO 1B3）是“摄取”转运蛋白，主要限于肝细胞的窦状面。他们 两者都转运多种结构上不相关的化合物，包括几种临床上不相关的化合物的成员， 重要药物家族，如他汀类药物、沙坦类药物和血管紧张素转换酶（ACE）抑制剂。我们现在 建议在体外针对每种转运蛋白的2种底物测试1000多种药物。然后，我们将使用这些数据， 策划和验证机器学习模型。我们还将使用一系列机器学习方法， 多模型评估指标。这将使我们能够开发一种名为MegaTrans的基于网络的软件工具 这将鼓励用户输入他们自己的复合结构并生成交互预测 与感兴趣的运输商，然后使用几个可视化的相似性，每个模型的训练集 不同的可视化方法。这种工具的投资回报是它可以帮助设计 以及选择避免感兴趣的转运蛋白同时还节省时间和金钱的更有利的化合物。 它还可以识别已经批准的可能存在药物相互作用风险的化合物。预测 在体内观察到的这种行为是理想的， 药物相互作用在第二阶段，我们将大大增加运输机的数量， 数据和建立模型，这样我们就可以解决所有主要的药物发现感兴趣的转运。