Columbia University Medical Center, New York, NY, USA.
Columbia University Medical Center, New York, NY, USA.
Trends Pharmacol Sci. 2019 Sep;40(9):624-635. doi: 10.1016/j.tips.2019.07.005. Epub 2019 Aug 2.
Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and prevents adverse drug reactions (ADRs). Safety efforts begin during the development process, using in vivo and in vitro studies, continue through clinical trials, and extend to postmarketing surveillance of ADRs in real-world populations. Future toxicity and safety challenges, including increased polypharmacy and patient diversity, stress the limits of these traditional tools. Massive amounts of newly available data present an opportunity for using artificial intelligence (AI) and machine learning to improve drug safety science. Here, we explore recent advances as applied to preclinical drug safety and postmarketing surveillance with a specific focus on machine and deep learning (DL) approaches.
介入药理学是医学对抗疾病的最有力武器之一。然而，这些药物可能会导致严重的副作用，因此必须密切监测。药物警戒学是监测、发现和预防药物不良反应（ADR）的科学领域。安全性工作始于开发过程，使用体内和体外研究，贯穿临床试验，并延伸至真实人群中对 ADR 的上市后监测。未来的毒性和安全性挑战，包括增加的多药治疗和患者多样性，对这些传统工具提出了挑战。大量新出现的数据为利用人工智能（AI）和机器学习来改善药物安全性科学提供了机会。在这里，我们探讨了最近在临床前药物安全性和上市后监测方面的应用进展，特别关注了机器和深度学习（DL）方法。
