Nonlinear Mixed Effect Modeling과 $Na\ddot{i}ve$ Pooled Data 방법에 의한 Barnidipine Dose-Titration Trial의 분석

Background : naive pooled data(NPD) or two-stage method, the two traditional population approaches in pharmacokinetic/pharmacodynamic(PK/PD) analysis, have much limitation in their application. Nonlinear mixed-effect modeling approach has many advantages over the traditional approaches. It provides a solution for analysis of nonexperimetntal or observational data, such as those from clinical situations, which have characteristics of sparseness, unbalancedness, and fragmentariness and so on. In addition, it can be also adopted as a method for estimation of PK/PD parameters in clinical trial of less stringent and restrictive design condition and by doing so we can design clinical trial more flexibly, which will be helpful especially if PK/PD are to be investigated in patients. We analyzed a dose-titration trial for barnidipine, an antihypertensive agent, with nonlinear mixed effect modeling and naive pooled data method and based on the actual trial we simulated dose-titration trials of various conditions with Monte-Carlo method and analyzed with both nonlinear mixed effect modeling and naive pooled data method and compared the results. Methods : A dose-titration trial 'Blood-pressure lowering effect of barnidipine HCI in renal parenchymal hypertension' which was performed in three general hospitals of Seoul, was analyzed with nonlinear mixed effect modeling and naive pooled data method And based on the actual trial, we simulated dose-titration trials of various conditions for barnidipine with Trial Simulator $2.1l^{\circledR}(Pharsight)$ which uses Monte-Carlo method in simulation. We designed and simulated a total of 6 trials adopting linear model and Emax model with conditions of drop-outs of study subjects or high BSV/BOV. The simulated outcomes were analyzed with nonlinear mixed effect modeling and naive pooled data method respectively using $NONMEM^{\circledR}$. We used $NONMEM^{\circledR}$, a computer software for the analyses. Results : When we analyzed the actual dose-titration trial, linear model was superior with regard to model building criteria and serum creatinine level was found to be the sole covariate. When we analyzed the simulated data, 18 out of 21(85.7%) population parameters (6 out of 9 in linear model, 12 out of 12 in Emax model) estimated with nonlinear mixed effect modeling or naive pooled data method were statistically different from each other. And 16 of them (88.9%) showed values closer to the true values(those introduced for simul