Artificial intelligence and machine learning in clinical pharmacological research

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.

KEYWORDS:

• Clinical pharmacology
• computational drug discovery
• data science
• machine learning
• applied artificial intelligence

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Data availability statement

Data has been taken from public sources that are precisely referenced in the report.

Author contributions

B Mayer – Programming, information retrieval and processing, writing of the manuscript, data analyses, conceptualization of the figures; D Kringel – Information retrieval and processing, writing of the manuscript; J Lötsch – Conceptualization of the project, programming, information retrieval and processing, writing of the manuscript, data analyses, conceptualization and creation of the figures, funding acquisition.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17512433.2023.2294005.

Additional information

Funding

JL was supported by the Deutsche Forschungsgemeinschaft (DFG LO 612/16-1).