Artificial Intelligence and Machine Learning Approaches to Facilitate Therapeutic Drug Management and Model-Informed Precision Dosing

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.

Machine Learning‐Based Prediction of Escitalopram and Sertraline Side Effects With Pharmacokinetic Data in Children and Adolescents

“…[17][18][19][20] Recent studies have suggested that using artificial intelligence in tandem with pharmacometric approaches can improve patient outcomes. 21 Indeed, machine learning methodologies can process high dimensional data and have demonstrated good performance in predicting treatment outcomes, including antidepressant response. [22][23][24] Yet, no one has evaluated a machine learning approach leveraging pharmacokinetic data to predict antidepressant-related side effects.…”

“…For instance, therapeutic drug monitoring and model‐informed precision dosing can help optimize dosing to balance the efficacy‐tolerability tradeoff, and these strategies could benefit SSRI‐treated patients 17–20 . Recent studies have suggested that using artificial intelligence in tandem with pharmacometric approaches can improve patient outcomes 21 . Indeed, machine learning methodologies can process high dimensional data and have demonstrated good performance in predicting treatment outcomes, including antidepressant response 22–24 .…”

Reinforcement Learning and PK‐PD Models Integration to Personalize the Adaptive Dosing Protocol of Erdafitinib in Patients with Metastatic Urothelial Carcinoma

“…4,5 Recently, hybrid approaches integrating AI/ML with mathematical modeling and simulations have been proposed and gained great momentum in the pharmacometric landscape to solve different tasks [6][7][8][9][10] such as pharmacokinetic (PK) and pharmacodynamic (PD) predictions, 6,7 covariate selection 6,7,10,11 as well as treatment optimization, especially in a precision dosing context. 12 Precision dosing refers to tailoring the dose on each individual patient at each given time, in order to ensure the greatest benefits and least risks. It is recommended for classes of compounds characterized by a narrow therapeutic index and/or high interindividual variability (IIV).…”

“…AI/ML are now present throughout the entire continuum of pharmacology research, from the early drug discovery phases 1–3 to the clinical practise 4,5 . Recently, hybrid approaches integrating AI/ML with mathematical modeling and simulations have been proposed and gained great momentum in the pharmacometric landscape to solve different tasks 6–10 such as pharmacokinetic (PK) and pharmacodynamic (PD) predictions, 6,7 covariate selection 6,7,10,11 as well as treatment optimization, especially in a precision dosing context 12 …”

Artificial intelligence and machine learning for clinical pharmacology

“…Similar approaches have shown improvements in isavuconazole exposure prediction 76 . Therapeutic drug management and model‐informed precision dosing have been covered elsewhere 77 …”