Computational prediction of gut microbiome-mediated drug metabolism

项目总结/摘要 尽管进行了临床前实验和临床试验以确定疗效、副作用和不良反应， 药物不良反应（ADR），只有25-60%的患者对处方药物有良好的反应， 在美国每年花费300 - 1300亿美元。ADR部分归因于肠道微生物组，即 胃肠道内微生物群落复杂而动态。肠道微生物组 与包括药物在内的不同类型的外源性物质相互作用，导致治疗剂生物转化为 代谢物的处置、功效和毒性发生改变。肠道微生物群介导的药物代谢导致 无效的治疗以及致畸、毒性和致死作用，在某些情况下， 直到药物进入市场。因此，领先的制药研究人员已经开始 认识到肠道微生物组在药物代谢中的作用， 治疗效果。然而，尽管肠道微生物组研究取得了广泛进展，但目前还没有 这是一种可靠的、具有成本效益的方法，将肠道介导的药物代谢整合到药物开发管道中。 第一阶段的提案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 设计和开发。我们将采用一系列最先进的计算生物学技术， 可靠地筛选可能代谢靶药物的微生物。这个项目的新奇在于 使用多种指标和方法筛选药物代谢酶/微生物的能力， 预测的可靠性，以达到临床和商业使用所需的准确性。这多- 方法平台将使用靶向体外试验以迭代方式构建、整合和验证 两种候选药物的实验，即抗疟疾药物胺碘酮和抗病毒药物 泛昔洛韦。该项目旨在促进我们目前对微生物组功能的理解， 药物-肠道相互作用的背景以及有助于加强公共卫生和经济 增长 该项目的价值主张包括利用公开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，可靠和廉价的工具， 微生物组介导的治疗药物代谢。该计算机平台可用于 当前的药物以及正在开发的药物。对于目前的药物，这个平台可以帮助增加 通过预测疗效和毒性机制来评估药物的安全性，因为它们可能因个体而异， 单独的.对于新药，该平台将降低药物开发的成本和时间表， 从而增加治疗剂本身的有效性。

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.