title: Automated analysis of routinely generated preclinical pharmacokinetic and pharmacodynamic data.
abstract: Model-based analysis of routinely generated pharmacokinetic and pharmacodynamic (PK-PD) data is a key component of preclinical drug discovery. The work process of such analyses can be automated by properly designed computer programs that reduce the number of manual steps, resulting in time saving and significantly fewer errors. Critical decisions can still be made by modelers. Using concrete animal data examples this paper illustrates when, and demonstrates how, automated PK-PD approaches can be used and what benefits they offer to the modeling and simulation community. Specifically, we describe two compound optimization case studies from drug discovery projects, and also demonstrate how a subsequent optimization step to predict the human dose can be coupled to an automated approach.
authors: CVMD iMED DMPK AstraZeneca R&D, SE-431 83 Mölndal, Sweden.
authors: Emma
authors: E
authors: Lindhardt
authors: Peter
authors: P
authors: Gennemark
mesh_terms: D000818:Animals; D019295:Computational Biology; D003198:Computer Simulation; D055808:Drug Discovery / Q000706:statistics & numerical data*; D004353:Drug Evaluation, Preclinical / Q000706:statistics & numerical data*; D006801:Humans; D008954:Models, Biological; D010599:Pharmacokinetics; D000069437:Pharmacological Phenomena; D011955:Receptors, Drug / Q000187:drug effects / Q000378:metabolism; D012984:Software
chemical_list: D011955:Receptors, Drug
keywords: Mathematical modeling; automated PK–PD analysis; human dose prediction