University College London, London, United Kingdom.
University College London, London, United Kingdom.
Biotechnol Adv. 2022 Jan-Feb;54:107797. doi: 10.1016/j.biotechadv.2021.107797. Epub 2021 Jul 11.
Pivotal work in recent years has cast light on the importance of the human microbiome in maintenance of health and physiological response to drugs. It is now clear that gastrointestinal microbiota have the metabolic power to promote, inactivate, or even toxify the efficacy of a drug to a level of clinically relevant significance. At the same time, it appears that drug intake has the propensity to alter gut microbiome composition, potentially affecting health and response to other drugs. Since the precise composition of an individual's microbiome is unique, one's drug-microbiome relationship is similarly unique. Thus, in the age of evermore personalised medicine, the ability to predict individuals' drug-microbiome interactions is highly sought. Machine learning (ML) offers a powerful toolkit capable of characterising and predicting drug-microbiota interactions at the individual patient level. ML techniques have the potential to learn the mechanisms operating drug-microbiome activities and measure patients' risk of such occurrences. This review will outline current knowledge at the drug-microbiota interface, and present ML as a technique for examining and forecasting personalised drug-microbiome interactions. When harnessed effectively, ML could alter how the pharmaceutical industry and healthcare professionals consider the drug-microbiome axis in patient care.
近年来的重要研究工作揭示了人类微生物组在维持健康和对药物的生理反应中的重要性。现在很清楚，胃肠道微生物组具有促进、失活甚至使药物毒性增强的代谢能力，其程度达到了具有临床相关意义的水平。与此同时，似乎药物的摄入有改变肠道微生物组组成的倾向，这可能会影响健康和对其他药物的反应。由于个体微生物组的精确组成是独特的，因此一个人的药物-微生物组关系也是独特的。因此，在个性化医学的时代，预测个体药物-微生物组相互作用的能力是非常需要的。机器学习 (ML) 提供了一个功能强大的工具包，能够在个体患者水平上对药物-微生物群相互作用进行特征描述和预测。ML 技术有可能了解药物-微生物组活动的运作机制，并衡量患者发生此类事件的风险。本文将概述药物-微生物群界面的现有知识，并将 ML 作为一种检查和预测个性化药物-微生物组相互作用的技术。如果能够有效地利用，ML 可以改变制药行业和医疗保健专业人员在患者护理中考虑药物-微生物群轴的方式。
