Artificial intelligence and machine learning in therapeutic drug monitoring: Revolutionizing dosing and clinical decision support

Therapeutic drug monitoring (TDM) is such an area that focuses on ensuring the right doses are given to reach the maximum therapeutical efficacy and the lowest possible adverse effects in precision medicine. Until now, TDM was primarily based on classical pharmacokinetic (PK) and pharmacodynamic (PD) models, requiring extensive laboratory monitoring and manual adjustments for behavioral assessment, eventually perfected by artificial intelligence (AI) and machine learning (ML). The demand for real-time, data-derived doses and clinical decision support is fulfilled by TDM enabled by AI and ML technologies. Models based on AI use the enormous databases of patients such as drug metabolism combined with genetic and real-time physiological factors for fine dosing. The machine learning algorithms such as neural networks, Bayesian models, and decision trees have shown significant application in levels of optimising biomolecules over time across multiple therapeutic areas including antimicrobial therapy, antiepileptic drugs, immunosuppressants, and oncology treatments. In addition, the AI-based Clinical decision support systems also help personalize dosage through EHR integration into the system and hence improve treatment outcomes and reduce drug toxicity. However, these advances in technology do face various challenges such as poor quality of data, non-interpretability of models, regulatory challenges, and several ethical dilemmas on the generalization of these technologies in the clinical environment. This article reviews the contributions made by AI and ML in TDM, their applications, benefits, limitations, and future scope in furthering precision medicine. Keywords: Therapeutic Drug Monitoring (TDM), Artificial Intelligence (AI), Machine Learning (ML), Clinical Decision Support System (CDSS), Precision Medicine, Pharmacokinetics, Pharmacodynamics, Personalized Dosing, Drug Monitoring, AI in Healthcare

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Publication Details

Journal
International Journal of Current Research in Medical Sciences
Published
2025-02-28
DOI
https://doi.org/10.22192/ijcrms.2025.11.02.008
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Artificial intelligence and machine learning in therapeutic drug monitoring: Revolutionizing dosing and clinical decision support

Rohit Gupta, C. Babu
International Journal of Current Research in Medical Sciences
Computational Drug Discovery Methods
article

Artificial intelligence and machine learning in therapeutic drug monitoring: Revolutionizing dosing and clinical decision support

Rohit Gupta, C. Babu
article en

Abstract

Therapeutic drug monitoring (TDM) is such an area that focuses on ensuring the right doses are given to reach the maximum therapeutical efficacy and the lowest possible adverse effects in precision medicine. Until now, TDM was primarily based on classical pharmacokinetic (PK) and pharmacodynamic (PD) models, requiring extensive laboratory monitoring and manual adjustments for behavioral assessment, eventually perfected by artificial intelligence (AI) and machine learning (ML). The demand for real-time, data-derived doses and clinical decision support is fulfilled by TDM enabled by AI and ML technologies. Models based on AI use the enormous databases of patients such as drug metabolism combined with genetic and real-time physiological factors for fine dosing. The machine learning algorithms such as neural networks, Bayesian models, and decision trees have shown significant application in levels of optimising biomolecules over time across multiple therapeutic areas including antimicrobial therapy, antiepileptic drugs, immunosuppressants, and oncology treatments. In addition, the AI-based Clinical decision support systems also help personalize dosage through EHR integration into the system and hence improve treatment outcomes and reduce drug toxicity. However, these advances in technology do face various challenges such as poor quality of data, non-interpretability of models, regulatory challenges, and several ethical dilemmas on the generalization of these technologies in the clinical environment. This article reviews the contributions made by AI and ML in TDM, their applications, benefits, limitations, and future scope in furthering precision medicine. Keywords: Therapeutic Drug Monitoring (TDM), Artificial Intelligence (AI), Machine Learning (ML), Clinical Decision Support System (CDSS), Precision Medicine, Pharmacokinetics, Pharmacodynamics, Personalized Dosing, Drug Monitoring, AI in Healthcare

International Journal of Current Research in Medical SciencesVol. 11(2)
Openalex Percentile: Top 92%
Computational Drug Discovery Methods
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