D1 - Developing and validating models for an in-silico neuropharmacology
Prof. Dr. Daniel Durstewitz
RG Computational Neuroscience
School for Computing and Mathematics,
Plymouth University, Plymouth, UK
Projects within the BCCN:
The main research objective of this subproject was to establish a neurochemical model as a platform for testing and developing new psychopharmacological agents.
The starting point for the neurochemical model was the establishment of a global neurochemical connectome of the rat brain. This could be achieved by using clustering methods for large data sets, supervised machine learning and advanced network analyses. All neuroanatomical data published in the last 55 years were subjected to strict inclusion and exclusion criteria in order to ultimately develop a global neurochemical connectome with 25 different neurotransmitter systems from more than 1,500 studies with more than 37,000 rats. This connectome is composed of 188 cortical and subcortical regions linked by 3,712 neurochemical connections. All neurochemical connections were assigned to non-linear differential equations in the next step. This model now allows us to predict the acute and chronic global neurochemical effects of psychopharmacological compounds (Noori et al., 2012; 2017 in revision).
With this in-silico model, we were already able to predict a hyperdopaminergic mesocorticolimbic system in protracted alcohol deprivation, which could then be validated experimentally in rats as well as the human brain (Hirth et al., 2016). Our patented model for the first time allows predicting neurochemical changes in the brain after the administration of psychiatric medication or novel CNS compounds.
With increasing restrictions on animal experiments in Germany and the EU, 3R principles and in-silico pharmacology are gaining in importance. Our neurochemical rat connectome and our neurochemical modeling of acute and chronic effects of psychiatric medication and novel CNS compounds are therefore of utmost importance.
The specific use of our scientific results led to 13 peer-reviewed publications in scientific journals (including PNAS), the provision of the basic neurochemical connectome in a database (www.ChemNetDB.org), the prediction of experimental results (e.g. Fliegel et al. 2012, 2013, Hirth et al., 2016), as well as the creation of a data base on predicted interactions of psychoactive substances that are protected by patents. The commercial exploitation led to the establishment of a computer model as a platform for in-silico psychopharmacology. The further economic use is obvious: We want to use our patented computer model for the development and testing of new CNS compounds in cooperation with the pharmaceutical industry in the future.
Prof. Dr. Rainer Spanagel
PD. Thomas Hahn
Hirth N, Meinhardt MW, Noori HR, Uhrig S, Broccoli L, Perreau-Lenz S, Sommer WH, Spanagel R, Hansson AC equal contribution (2016) Convergent evidence from alcohol dependent humans and rats for a hyperdopaminergic state in protracted abstinence
Proc Natl Acad Sci U S A 113:3024-29
Spanagel R, Noori HR, Heilig M (2014) Stress and alcohol interactions: meta-analyses on animal studies and clinical significance
Trends Neurosci. 10.1016/j.tins.2014.02.006
Noori HR, Helinski S, Spanagel R (2014) Cluster and meta-analyses on factors influencing stress-induced alcohol drinking and relapse in rodents
Addict. Biol. 19:225-232
Brand I, Fliegel S, Spanagel R, Noori HR (2013) Global Ethanol-Induced Enhancements of the Monoaminergic Neurotransmission: A Meta-Analysis
Alcohol Clin Exp Res 37(12):2048-2057
Noori HR, Spanagel R, Hansson A (2012) Neurocircuitry for Modeling Drug Effects
Addict Biol 17(5):827-864
Board of Directors
Scientific Advisory Board
Teaching & Training
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Bernstein Center Heidelberg / Mannheim