alexa Welcome to the Special Edition of Recent Advances in Ph
ISSN: 2167-1052

Advances in Pharmacoepidemiology and Drug Safety
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Commentary

Welcome to the Special Edition of Recent Advances in Pharmacokinetics and Pharmacodynamics

Richard L Slaughter*
Associate Dean–Pharmacy, Professor of Pharmacy Practice, Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, NI 48201, USA
*Corresponding Author : Richard L Slaughter
Associate Dean–Pharmacy
Professor of Pharmacy Practice
Eugene Applebaum College of Pharmacy and Health Sciences
Wayne State University Detroit, NI 48201, USA
E-mail: [email protected]
Received March 01, 2013; Accepted March 04, 2013; Published March 06, 2013
Citation: Slaughter RL (2013) Welcome to the Special Edition of Recent Advances in Pharmacokinetics and Pharmacodynamics. Adv Pharmacoepidem Drug Safety S1:008. doi:10.4172/2167-1052.S1-008
Copyright: © 2013 Slaughter RL. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
 

Abstract

I am pleased to welcome you the Special Edition of Advances in Pharmacoepidemiology and Drug Safety that focuses on Recent Advances in Pharmacokinetics (PK) and Pharmacodynamics (PD). I do hope that you find the manuscripts in this edition of interest. This edition provides a wide scope of related articles in the area of PK/ PD. Articles range from highly theoretical articles related to Bayesian forecasting to very practical articles that deal with very common issues in clinical practice such as vancomycin doing in morbid obese patients. Specifically, readers who have an interest in Bayesian pharmacokinetics and pharmacodynamics will appreciate the article by Dr. Verotta on covariate modeling PK/PD studies and that of Drs. van der Meerm and Neef on optimum sampling strategies. This latter paper argues that Maximum A Posterioiri Bayesian (MAPB) estimation be used for pharmacokinetic parameter estimation over multiple regression analysis in Bayesian forecasting studies.

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