Quantitative Evaluation and Prediction of Drug Effects and Toxicological Risk Using Mechanistic Multiscale Models. Issue 1 (14th November 2012)

• Record Type: Journal Article
• Authors: Niklas, Jens; Diaz Ochoa, Juan G; Bucher, Joachim; Mauch, Klaus
• Is Part Of: Molecular informatics. Volume 32:Issue 1(2013:Jan.)
• Page Start: 14
• Page End: 23
• Publication Date: 2012-11-14
• Subjects: Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201200043
• Languages: English
• ISSNs: 1868-1743

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>Integrating in vitro and in silico approaches has great potential for reducing experimental effort and delivering know‐how and intellectual property in drug development. Here, we focus on a possible framework for multiscale modeling in pharmaceutical drug development. Looking at the modeling frameworks at different scales, it is obvious that choosing the proper level of complexity and abstraction is not a trivial task. At cellular level, we consider that the application of validated kinetic models of cellular toxicity mechanisms of drugs is particularly important for deriving valid predictions. These kinetic models can be applied for integrating inter‐individual differences, e.g. obtained from data measured in surgical liver samples, into predictions of drug effects. Challenges identified include (i) the development of sufficiently detailed, structured organ models, (ii) definition of multiscale models that can be efficiently handled by available super‐computing facilities, and (iii) availability of validated cell‐type and organ‐specific kinetic metabolic models. Multiscale models can streamline drug development by facilitating the design of experiments and trials, by providing and testing hypotheses, and by reducing time and costs due to less experiments and improved decision‐making. In this review, we discuss the required pieces, possibilities, and challenges in multiscale modeling for the prediction of drug effects.</p> </abstract>