Artificial Intelligence and Machine Learning Approaches to Facilitate Therapeutic Drug Management and Model-Informed Precision Dosing. Issue 2 (3rd April 2023)

• Record Type: Journal Article
• Authors: Poweleit, Ethan A.; Vinks, Alexander A.; Mizuno, Tomoyuki
• Is Part Of: Therapeutic drug monitoring. Volume 45:Issue 2(2023)
• Page Start: 143
• Page End: 150
• Publication Date: 2023-04-03
• Subjects: artificial intelligence -- machine learning -- therapeutic drug monitoring -- precision dosing -- precision medicine; Pharmacokinetics -- Periodicals; Patient monitoring -- Periodicals; Drugs -- Analysis -- Periodicals; Body fluids -- Analysis -- Periodicals; Drug Therapy -- Periodicals; Monitoring, Physiologic -- Periodicals; Pharmacology -- Periodicals; 615.7
• DOI: 10.1097/FTD.0000000000001078
• Languages: English
• ISSNs: 0163-4356

Abstract

Abstract : Background: Therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) have greatly benefitted from computational and mathematical advances over the past 60 years. Furthermore, the use of artificial intelligence (AI) and machine learning (ML) approaches for supporting clinical research and support is increasing. However, AI and ML applications for precision dosing have been evaluated only recently. Given the capability of ML to handle multidimensional data, such as from electronic health records, opportunities for AI and ML applications to facilitate TDM and MIPD may be advantageous. Methods: This review summarizes relevant AI and ML approaches to support TDM and MIPD, with a specific focus on recent applications. The opportunities and challenges associated with this integration are also discussed. Results: Various AI and ML applications have been evaluated for precision dosing, including those related to concentration or exposure prediction, dose optimization, population pharmacokinetics and pharmacodynamics, quantitative systems pharmacology, and MIPD system development and support. These applications provide an opportunity for ML and pharmacometrics to operate in an integrated manner to provide clinical decision support for precision dosing. Conclusions: Although the integration of AI with precision dosing is still in its early stages and is evolving, AI and ML have the potential to work harmoniously and synergistically with pharmacometric approaches to support TDM and MIPD. Because data are increasingly shared between institutions and clinical networks and aggregated into large databases, these applications will continue to grow. The successful implementation of these approaches will depend on cross-field collaborations among clinicians and experts in informatics, ML, pharmacometrics, clinical pharmacology, and TDM.