Noori HR. BMBF - Bundesministerium für Bildung und Forschung 01ZX1503: e:Med Nachwuchsgruppe NeuroCon - Quantitative Konvergenz der neurochemischen und funktionellen Reaktionsnetzwerke psychiatrischer Medikamente. 04/2016-03/2021.
Psychiatric conditions such as depression, schizophrenia, addiction and others produce the largest global burden of disease. These disorders are difficult to treat and despite the fact that more than 100 psychiatric medications are in clinical use the treatment success is modest. For optimal drug development it is critical to understand the effects of drugs on brain network activity and neurochemistry. Preclinical functional magnetic resonance imaging (fMRI) is an emerging approach to study brain network function in relation to pharmacological manipulations and disease models with a high translational value. The neural basis of neuroimaging signals is probably neuronal activity but the role of the underlying neurochemistry, defined here by the interaction of synaptic inhibition, excitation, and the neurochemical responses of the targeted neurons, is not well understood. While both functional and neurochemical investigations of drug effects suggest the presence of substance-specific activity patterns, it remains unclear how the functional observations reflect the underlying neurochemical processes. The aim of this project is to develop a novel in silico framework to study convergent mechanisms of drug-induced MRI driven functional activity patterns and neurochemical fingerprints in the rat brain based on already existing databases for a variety of psychiatric medications. This approach will (i) integrate the different system levels and multi-dimensional network activity patterns into a unified framework, (ii) will link global and local network activity with underlying neurochemical events and changes in connectivity, (iii) will provide better predictions of the effects of a new potential drug on the circuitry, (iv) will indicate the best functional biomarker to monitor treatment efficacy, and (v) will provide a better understanding of preclinical functional neuroimaging data which can then be translated into human studies.
BMBF - Bundesministerium für Bildung und Forschung 01ZX1611A: e:Med II - SPs 8: Mathematical Modeling III: Global neurotransmitter dynamics and target predictions. 01/2017-12/2018.
SP8 utilizes state-of-the-art multi-scale in silico pharmacological methods in order to determine the chronic effects of ethanol on the neurochemical systems involved in mediating addictive behavior and aims thereby to identify pharmacological treatment strategies. In the first two years a mathematical model was developed to simulate the chronic effects of different alcohol intake patterns on the neurochemical dynamics of the rat brain. Hereby, the existing mathematical model comprised by delay differential equations was re-parametrized for long-term simulations and time-lags for drug effects were introduced. In particular, the drinking patterns deriving from the drinkometer system (SP5) were analysed by Fourier analysis and the sensitivity of the temporal patterns to pharmacological compounds was investigated (Vengeliene et al., 2015b). Furthermore, the alcohol intake pattern from the post-dependent model4 was mathematically modelled as an input function to simulate the effects of intermittent alcohol exposure on the brain neurochemistry. Numerical simulations suggest the up- and down-regulation of different neurotransmitters in critical brain regions. In particular, dopamine concentrations in nucleus accumbens converge to a level of approximately 150% of the basal concentration, an effect that has been validated by in vivo microdialysis experiments from SP11 (Hirth et al., 2015). In addition, further factors such as gender, age, stress, etc. were investigated with clustered meta-analyses with respect to their impact on alcohol consumption and relapse (Noori et al., 2014; Spanagel et al., 2014).