Artificial intelligence and machine learning in clinical pharmacological research.

IF 3.6 3区医学 Q2 PHARMACOLOGY & PHARMACY

Expert Review of Clinical Pharmacology Pub Date : 2024-01-01 Epub Date: 2024-01-23 DOI:10.1080/17512433.2023.2294005

Benjamin Mayer, Dario Kringel, Jörn Lötsch

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引用次数: 0

Abstract

Background: Clinical pharmacology research has always involved computational analysis. With the abundance of drug-related data available, the integration of artificial intelligence (AI) and machine learning (ML) methods has emerged as a promising way to enhance clinical pharmacology research.

Methods: Based on an accepted definition of clinical pharmacology as a field of research dealing with all aspects of drug-human interactions, the analysis included publications from institutes specializing in clinical pharmacology. Research topics and the most used machine learning methods in clinical pharmacology were retrieved from the PubMed database and summarized.

Results: ML was identified in 674 publications attributed to clinical pharmacology research, with a significant increase in publication activity over the last decade. Notable research topics addressed by ML/AI included Covid-19-related clinical pharmacology research, clinical neuropharmacology, drug safety and risk assessment, clinical pharmacology related to cancer research, and antimicrobial and antiviral research unrelated to Covid-19. In terms of ML methods, neural networks, random forests, and support vector machines were frequently mentioned in the abstracts of the retrieved papers.

Conclusions: ML, and AI in general, is increasingly being used in various research areas within clinical pharmacology. This report presents specific examples of applications and highlights the most used ML methods.

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人工智能和机器学习在临床药理研究中的应用。

背景：临床药理学研究一直涉及计算分析。随着药物相关数据的丰富，人工智能（AI）和机器学习（ML）方法的整合已成为加强临床药理学研究的一种有前途的方法：根据公认的临床药理学定义，临床药理学是涉及药物与人体相互作用各个方面的研究领域。从 PubMed 数据库中检索并总结了临床药理学的研究主题和最常用的机器学习方法：结果：在 674 篇临床药理学研究论文中发现了机器学习方法，过去十年间论文数量显著增加。ML/AI涉及的重要研究课题包括与Covid-19相关的临床药理研究、临床神经药理学、药物安全性和风险评估、与癌症研究相关的临床药理以及与Covid-19无关的抗菌和抗病毒研究。在 ML 方法方面，神经网络、随机森林和支持向量机在检索到的论文摘要中被频繁提及：结论：ML 和一般人工智能正越来越多地应用于临床药理学的各个研究领域。本报告介绍了具体的应用实例，并重点介绍了最常用的 ML 方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Expert Review of Clinical Pharmacology PHARMACOLOGY & PHARMACY-

CiteScore

7.30

自引率

2.30%

发文量

127

期刊介绍： Advances in drug development technologies are yielding innovative new therapies, from potentially lifesaving medicines to lifestyle products. In recent years, however, the cost of developing new drugs has soared, and concerns over drug resistance and pharmacoeconomics have come to the fore. Adverse reactions experienced at the clinical trial level serve as a constant reminder of the importance of rigorous safety and toxicity testing. Furthermore the advent of pharmacogenomics and ‘individualized’ approaches to therapy will demand a fresh approach to drug evaluation and healthcare delivery. Clinical Pharmacology provides an essential role in integrating the expertise of all of the specialists and players who are active in meeting such challenges in modern biomedical practice.