[00994] Mathematical modeling approach in pharmacokinetics/pharmacodynamics
Session Time & Room : 1D (Aug.21, 15:30-17:10) @D515
Type : Proposal of Minisymposium
Abstract : Pharmacokinetics/pharmacodynamics (PK/PD) modeling is an essential component of drug discovery and development. PK modeling describes the relationship between dose and drug concentration while PD modeling quantifies the relationship between drug concentration and therapeutic effects. A model-based simulation could provide a scientific decision-making information in new drug development process and the prediction power for the success of clinical trial. The session is dedicated to discuss recent advances and challenges in PK/PD modeling and simulation to overcome fundamental limitation and conventional approaches.
[05562] Principles and applications of clinical pharmacology and pharmacometrics in the drug development
Format : Talk at Waseda University
Author(s) :
Sungpil Han (The Catholic University of Korea)
Abstract : In the rapidly evolving landscape of pharmaceutical research, clinical pharmacology and pharmacometrics have emerged as pivotal disciplines in the drug development process. This presentation will delve into the principles and applications of these disciplines, particularly focusing on the integration of mathematical modeling in pharmacokinetics/pharmacodynamics (PK/PD) to accelerate and optimize drug development.
Clinical pharmacology plays a crucial role in understanding the effects of a drug and the mechanisms of its actions in humans, while pharmacometrics aids in quantifying drug, disease, and trial information to aid efficient drug development and regulatory decisions. This presentation will discuss the synergy between these two disciplines, highlighting how they can form the bedrock for creating more effective, safer drugs.
A key element in this context is the employment of mathematical models in PK/PD studies. The presentation will demonstrate the use of such models to predict the time course of drug concentration and its consequent effects, thereby guiding optimal dosage and timing strategies. Special emphasis will be placed on the role of mathematical modeling in minimizing adverse drug reactions and predicting drug-drug interactions.
[05550] Pharmacokinetic Model of Tacrolimus based on Stochastic Simulation and Estimation in Korean Adult Transplant Recipients
Format : Talk at Waseda University
Author(s) :
Suein Choi (Catholic university of Korea)
Seunghoon Han (Catholic university of Korea)
Abstract : Therapeutic drug monitoring (TDM) is a crucial clinical procedure that involves measuring drug concentrations in a patient's blood or other biological fluids to ensure optimal dosing. To achieve targeted exposure and improve dosing precision, the Bayesian estimation method is utilized, which optimizes individual pharmacokinetic (PK) parameters based on previous TDM data and a population PK model. The development of an accurate PK model is essential, as it integrates clinically relevant covariates and appropriate random effect parameters.
However, the nature of TDM data poses certain limitations for PK model development. Although it provides a wealth of real-world data reflecting a wide range of covariates, it primarily consists of trough concentrations, which restricts the information available for model building. To overcome these limitations, we employed the stochastic simulation and estimation (SSE) method, enabling the integration of published PK models with acquired real-world TDM data, even in the absence of raw data from the published models. This approach also allowed us to evaluate clinically meaningful covariates.
Using the SSE method, we successfully developed a population PK model for tacrolimus that encompasses both published PK models and newly collected TDM data from the Korean population. This model serves as a robust framework for practical TDM procedures, as it incorporates clinically relevant covariates and reflects real-world settings. Despite the inherent limitations associated with TDM data, the SSE method proved invaluable in leveraging the information contained within TDM data by integrating published PK models while accounting for model variability.
Overall, the developed population PK model for tacrolimus, utilizing the SSE method, represents a significant advancement in TDM practices. It enhances dosing precision, incorporates relevant covariates, and provides a solid foundation for guiding therapeutic strategies in clinical settings. By addressing the challenges posed by TDM data limitations, this research contributes to the refinement and optimization of pharmacokinetic modeling for improved patient outcomes.
[01369] Distributional approaches expressing tumor delay of the transit compartment model
Format : Talk at Waseda University
Author(s) :
Jong Hyuk Byun (Pusan National University)
Il Hyo Jung (Pusan National University)
Abstract : Transit compartment model describes the way in which drugs inhibit the growth of tumors, based on a system of ODEs describing damaged cells’ transition under the influence of the drug, using Erlang distribution. In our approach, Coxian distribution is used to model the various delays when the number of delay compartments is fixed. In the other approach, the delay compartments are combined into a single form using Mittag-Leffler distribution, without pre-specifying the number of compartments.
[01975] Accurate Prediction of Drug Interactions Through Cytochrome P450 Induction
Format : Talk at Waseda University
Author(s) :
Yun Min Song (KAIST)
Ngoc-Anh Thi Vu (Chungnam National University)
Quyen Thi Tran (Chungnam National University)
Hwi-yeol Yun (Chungnam National University)
Jung-woo Chae (Chungnam National University)
Sang Kyum Kim (Chungnam National University)
Jae Kyoung Kim (KAIST)
Abstract : FDA guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs). In particular, the ratio of substrate AUCs under and not under the effect of enzyme inducers is predicted by the Michaelis-Menten model, which is valid only in low-enzyme-concentration conditions. We found that such DDI predictions lead to severe errors. To resolve this, we derived a new equation that significantly improves clinical DDI prediction, which is critical to preventing drug toxicity and failure.
