Leveraging Artificial Intelligence and Machine Learning to Facilitate Individualized Precision Dosing

Session Description

This session will present novel concepts in the evolving area of AI/ML and their relevance to clinical pharmacokinetics (PK) and pharmacodynamics (PD), with the ultimate goal to foster precision dosing, i.e. dosing specifically tailored to individual needs based on drug exposure and response. Leading researchers in clinical pharmacometrics and related fields from academia and industry will elucidate diverse applications of AI and ML and their potential benefits compared with other traditional and emerging strategies of dosing individualization like model-informed precision dosing. Attendees will not only gain knowledge about the advantages of traditional PKPD models and ML models; they will also learn how to leverage the benefits of both approaches using hybrid PKPD/ML approaches. These concepts will be illustrated using examples for precision dosing in the areas of infectious diseases and oncology.

The session will also illustrate how ML can be utilized to accurately estimate the area-under-the-concentration-time-curve (AUC) from sparse concentration data, which can then be leveraged to determine individualized dosing. The session will also explore future perspectives including the development of a multiscale digital pharmacological twin framework. Furthermore, EHR-related AI and ML precision dosing applications will be discussed including a ML approach to predict medication-related adverse effects. Different concepts will be exemplified by real-world case studies, e.g. using a web-based application.

The advent of novel AI/ML methodologies holds immense potential to enhance precision dosing and improve the efficacy and safety of treatments in clinical care and drug development. The session will thus be relevant to healthcare professionals involved in dosing individualization, but also to researchers focusing on PKPD and precision dosing in diverse settings and locations.

Learning Objectives

1. Describe different applications of artificial intelligence (AI) and machine learning (ML) approaches for promoting individualized precision dosing and therapeutic optimization.
2. Learn about case studies of determining drug exposure and predicting medication-related adverse effects of selected drugs using a machine-learning algorithm.
3. Evaluate the benefits of combining ML with traditional PK/PD concepts in precision dosing for infectious diseases and oncology.

Session Schedule

1:30 PM - 1:32 PM MT
Introduction
Iris K. Minichmayr, PhD and Tomoyuki Mizuno, PhD

1:32 PM - 1:46 PM MT
Estimation of Drug Exposure Using Machine Learning: Toward a New Way to Perform Therapeutic Drug Monitoring
Jean-Baptiste Woillard, PharmD, PhD

1:46 PM - 1:51 PM MT
Interactive Session / Case-Study Presentation
Jean-Baptiste Woillard, PharmD, PhD

1:51 PM - 2:03 PM MT
The Best of Both Worlds: Hybrid PKPD/ML Models for Precision Dosing
Jasmine H. Hughes, PhD

2:03 PM - 2:15 PM MT
Realizing the Potential of Artificial Intelligence to Enhance Electronic Health Record-Based Model-Informed Precision Dosing
Ethan Poweleit, BS

2:15 PM - 2:30 PM MT
Q&A and Closing Remarks