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Mon. Jul 7, 2025

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Machine Learning-Driven Risk Stratification for Early Graft Loss in Living Donor Liver Transplantation

Mathematical modeling and simulation (M&S) have become essential in advancing the discovery and clinical development of new drugs. Recent progress in the science and technology of M&S has enabled precise modeling of relationships among pharmacokinetics (PK), pharmacodynamics (PD), mechanisms of action, physiological processes, and disease progression, allowing for accurate predictions of clinical responses and true endpoints. This approach, known as Model-Informed Drug Development (MIDD), serves as a key driver for optimizing clinical study design, improving success rates of clinical studies, and accelerating the drug development process. M&S is also instrumental in precision dosing, supporting the implementation of personalized medicine in clinical settings.<br>In this session, we will focus on PK/PD modeling, Quantitative Systems Pharmacology (QSP), Physiologically-Based Pharmacokinetic (PBPK) modeling and disease progression modeling, all of which are key mathematical approaches within M&S that provide precise descriptions of biological systems and disease processes. We will discuss how these quantitative methods enhance drug development and healthcare, as well as how to advance current modeling methodologies.<br>Furthermore, we aim to foster collaboration between pharmaceutical researchers and academic experts in mathematical biology and applied mathematics, encouraging mutual understanding of research objectives and exploring ways to bring new hope to patients and families awaiting innovative treatments.

Enhancing Drug Development and Patient Care through Model Informed Drug Development

Joint Meeting of Asian Conference for Mathematical Biology and Annual Meeting of Japanese Society for Mathematical Biology (ACMB-JSMB2025)

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[SS09-05]Machine Learning-Driven Risk Stratification for Early Graft Loss in Living Donor Liver Transplantation

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