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.

FIGURE 1.

Artificial intelligence, ML, and DL approaches in supporting TDM and MiPD applications.

FIGURE 2.

Diagram of an EHR-integrated CDS system for precision dosing. Patient data (eg, demographics, dosing history, laboratory results, physiological measurements, etc.) can be extracted from EHRs, which could additionally collect data from a smart device/biosensor and concentration measurements determined through liquid chromatography with tandem mass spectrometry (LC/MS/MS). These data can then be processed for use by an AI/ML model, PK model with Bayesian estimation, or hybrid ML/PK model. Implementation of a continuous learning mechanism could support automated or semi-automated refinement of model parameters and predictions as new patient data are added to the EHR. The results from the model would be displayed as a CDS application, which could be developed using the SMART on FHIR platform and would be accessible to clinicians to provide clinical guidance related to dose optimization for individual patients.