ORIGINAL PAPER Quantitative systems pharmacology as an extension of PK/PD modeling in CNS research and development Hugo Geerts • Athan Spiros • Patrick Roberts • Robert Carr Received: 5 October 2012 / Accepted: 10 January 2013 / Published online: 22 January 2013 Ó Springer Science+Business Media New York 2013 Abstract Quantitative systems pharmacology (QSP) is a recent addition to the modeling and simulation toolbox for drug discovery and development and is based upon math- ematical modeling of biophysical realistic biological pro- cesses in the disease area of interest. The combination of preclinical neurophysiology information with clinical data on pathology, imaging and clinical scales makes it a real translational tool. We will discuss the specific character- istics of QSP and where it differs from PK/PD modeling, such as the ability to provide support in target validation, clinical candidate selection and multi-target MedChem projects. In clinical development the approach can provide additional and unique evaluation of the effect of comedi- cations, genotypes and disease states (patient populations) even before the initiation of actual trials. A powerful property is the ability to perform failure analysis. By giving examples from the CNS R&D field in schizophrenia and Alzheimer’s disease, we will illustrate how this approach can make a difference for CNS R&D projects. Keywords Quantitative systems pharmacology  CNS diseases  Alzheimer’s disease  Schizophrenia Abbreviations BOLDfMRI Blood oxygen level dependent functional magnetic resonance imaging CNS Central nervous system EEG Electro-encephalography EPS Extra-pyramidal symptoms GPCR G-protein coupled receptor GUI Graphical user interface MSN Medium spiny neuron QSP Quantitative systems pharmacology PANSS Positive and negative symptoms in schizophrenia Background Drug development in CNS is faced with a high rate of failure in clinical trials. As an example, the latest first-in- class molecule in schizophrenia, aripiprazole was approved in 2002 [1] and was only a minor variation of a basic mechanism that has been identified more than 50 years ago [2]. The recent failure of pomaglumetad methionil, a mGluR2/R3 modulator illustrates the difficulties of trans- lating animal results in animal models to the clinical situ- ation [3]. Similarly in Alzheimer’s disease, the last approved compound memantine [4] is already for more than 8 years on the market and the recent failures of pas- sive vaccination strategies [5] and dimebon [6] have added to the perception that these diseases are far too difficult to treat. All of these failed drugs have passed animal studies to the extent that they were deemed interesting to invest substantial amounts of resources for testing in the clinic. While animal models are very good at elucidating the underlying molecular biology of individual targets and H. Geerts (&)  A. Spiros  P. Roberts  R. Carr In Silico Biosciences, Berwyn, PA, USA e-mail: hugo-geerts@in-silico-biosciences.com H. Geerts Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA P. Roberts Oregon Health and Science University, Portland, OR, USA 123 J Pharmacokinet Pharmacodyn (2013) 40:257–265 DOI 10.1007/s10928-013-9297-1