[00994] Mathematical modeling approach in pharmacokinetics/pharmacodynamics
Session Date & Time : 1D (Aug.21, 15:30-17:10)
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.
[01369] Distributional approaches expressing tumor delay of the transit compartment model
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
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.
