Computational prediction of gut microbiome-mediated drug metabolism

肠道微生物介导的药物代谢的计算预测

• 批准号: 10256102
• 负责人: MOHAMMAD RK MOFRAD
• 金额: $ 32.5万
• 依托单位: NEXILICO, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2023-04-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10256102
• 关键词: 3-Dimensional; Achievement; Address; Algorithms; Amiodarone; Anti-Arrhythmia Agents; Antiviral Agents; Bioinformatics; Biotechnology; California; Caregivers; Clinical; Clinical Data; Clinical Trials; Communities; Complex; Computational Biology; Computer Models; Data; Data Set; Databases; Development; Digoxin; Docking; Drug Design; Drug Industry; Drug Prescriptions; Drug Screening; Drug Targeting; Drug usage; Economic Burden; Effectiveness; Enzymes; Evaluation; Famciclovir; Gastrointestinal tract structure; Government; Growth; Healthcare Systems; Human; In Vitro; Individual; Industrialization; Infrastructure; Letters; Levodopa; Machine Learning; Mediating; Metabolic Biotransformation; Metabolism; Methods; Molecular; Outcome; Patients; Persons; Pharmaceutical Preparations; Pharmacogenomics; Pharmacologic Substance; Phase; Plug-in; Public Health; Research; Research Personnel; Resolution; Role; Small Business Technology Transfer Research; System; Techniques; Technology; Teratogens; Therapeutic; Toxic effect; Training; Treatment Effectiveness; Universities; Validation; Xenobiotics; adverse drug reaction; base; clinically relevant; computational platform; convolutional neural network; cost; cost effective; deep learning; design; drug candidate; drug development; drug metabolism; experimental study; gut microbiome; gut microbiota; health economics; high throughput screening; improved; in silico; medication safety; microbial; microbiome; microbiome research; microorganism; molecular dynamics; molecular modeling; novel; novel therapeutics; parallelization; pharmacokinetics and pharmacodynamics; pre-clinical; precision medicine; prototype; response; side effect; software as a service; therapeutic effectiveness; three dimensional structure; tool; web based interface

Project Summary / Abstract Notwithstanding pre-clinical experiments and clinical trials performed to identify efficacy, side effects, and adverse drug reactions (ADRs), only 25-60% of patients respond favorably to prescribed drugs, leading to a cost of $30-$130 billion in the US annually. ADRs are partially attributed to the gut microbiome, i.e. the complex and dynamic community of microorganisms residing in gastrointestinal tract. The gut microbiome interacts with different types of xenobiotics including drugs, resulting in biotransformation of therapeutics into metabolites with altered disposition, efficacy, and toxicity. Gut microbiome-mediated drug metabolism leads to non-effective treatments as well as teratogenic, toxic, and lethal effects that in some cases were not recognized until the drug was on the market. As a result, leading pharmaceutical researchers have begun to recognize that the role of gut microbiome in drug metabolism should be accounted for in attempts to improve treatment effectiveness. However, despite extensive progress in gut microbiome research, there is currently no reliable, cost-effective approach to integrate gut-mediated drug metabolism in drug development pipelines. This Phase I proposal aims to address this challenge by developing a new computational platform with the ability to predict microbial metabolism of therapeutic drugs and to leverage that information to enhance drug design and development. We will employ a range of state-of-the-art computational biology techniques to reliably screen for microorganisms that may metabolize the target drugs. The novelty of this project lies in the ability to screen drug-metabolizing enzymes/microorganisms using multiple metrics and methods to increase the reliability of predictions to achieve the accuracy necessary for clinical and commercial use. This multi- method platform will be built, integrated, and validated in an iterative fashion using targeted in vitro experiments on two candidate drugs, i.e. the anti-arrhythmic drug amiodarone and the anti-viral drug famciclovir. This project is designed to both advance our current understanding of microbiome function in the context of drug-gut interactions as well as inform strategies to help enhance public health and economic growth. The value proposition of this project includes leveraging publicly available bioinformatics databases as well as advances in computational biology techniques to develop a more precise, reliable, and inexpensive tool for gut microbiome-mediated metabolism of therapeutic drugs. This in-silico platform could be employed for both current drugs as well as drugs under development. For current drugs, this platform can help increase the safety of drugs by predicting the mechanisms of efficacy and toxicity as they may differ from individual-to- individual. For new drugs, the platform would reduce the cost and timeframe of drug development, while increasing the effectiveness of the therapeutics themselves.

项目摘要 /摘要 尽管进行了临床前实验和临床试验，以确定疗效，副作用和 不良药物反应（ADR），只有25-60％的患者对处方药的反应有利，导致了 每年在美国成本30亿美元。 ADR部分归因于肠道微生物组，即 位于胃肠道中的微生物的复杂而动态的微生物社区。肠道微生物组 与包括药物在内的不同类型的异生元相互作用，导致治疗剂的生物转化为 具有改变，功效和毒性改变的代谢产物。肠道微生物组介导的药物代谢导致 无效的治疗以及致死性，有毒和致命作用在某些情况下不是 直到该药物上市为止。结果，领先的药物研究人员已经开始 认识到肠道微生物组在药物代谢中的作用应考虑改善 治疗效率。但是，尽管肠道微生物组研究取得了很大进展，但目前尚无 可靠的，具有成本效益的方法来整合肠道介导的药物开发管道中的药物代谢。 该阶段I提出旨在通过开发一个新的计算平台来应对这一挑战 能够预测治疗药物的微生物代谢并利用该信息来增强药物 设计与开发。我们将采用一系列最新的计算生物学技术来 可靠的筛查微生物可能代谢目标药物。这个项目的新颖性在于 能够使用多种指标和方法筛选药物代谢酶/微生物 预测的可靠性，以达到临床和商业用途所需的准确性。这个多 方法平台将使用有针对性的体外构建，集成和验证 对两种候选药物的实验，即抗心律失常药物amiodarone和抗病毒药物 汉密维尔。该项目旨在提高我们当前对微生物组功能的理解 毒品互动的背景以及为帮助增强公共卫生和经济的策略提供信息 生长。 该项目的价值主张包括利用公开可用的生物信息学数据库以及 计算生物学技术的进步，以开发一种更精确，可靠和廉价的肠道工具 微生物组介导的治疗药物代谢。这两个可以用来使用此智能平台 当前的药物以及正在开发的药物。对于当前药物，该平台可以帮助增加 通过预测功效和毒性的机制，药物的安全性可能与个人到个体不同 个人。对于新药，该平台将减少药物开发的成本和时间表，而 提高治疗剂本身的有效性。