Periodic Reporting for period 3 - SyBil-AA (Systems Biology of Alcohol Addiction: Modeling and validating disease state networks in human and animal brains for understanding pathophysiolgy, predicting outcomes and improving therapy)
Reporting period: 2019-01-01 to 2019-12-31
Alcohol addiction is characterized by cycles of excessive alcohol consumption, interspersed with intervals of abstinence, and frequent relapses. The vulnerability to relapse is a key element of the disease process and blocking relapse is therefore the most important treatment objective. Despite many efforts, the outcomes for even the best studied treatment programs are not impressive, with about half of the patients relapsing within a year. The few pharmacotherapies available for alcohol addiction have not changed this situation because these medications have small effect sizes and only a minority of the patients do benefit from such treatments.
The urgency to understand more about the changes in the brain of alcohol addicts and to find effective treatments is obvious. Considerable research efforts have been made all over the world and led to increased understanding of the psychological and neurobiological processes that underlie addictive disorders. Despite substantial enthusiasm about the prospect of developing novel, mechanism-based therapies, these prospects have not played out so far, indicating the challenge of translating scientific knowledge into the clinical praxis of addiction therapy. While this translational gap has complex causes, we think that one important limitation of the current neurobiological view on alcohol addiction is the focus on only a few processes and brain regions, albeit the broad systemic actions of alcohol are commonly acknowledged.
The SyBil-AA project addressed this problem by adopting a global perspective on brain network organization – the connectome – by making use of mathematical and network theoretical methods. These advanced computational tools allowed us to better describe the dynamics of brain networks in the addicted state and to compare these with healthy brains. Predictive models of ‘relapse-prone’ states of the brain connectome were built based on magnetic resonance imaging (MRI) studies in patient populations and healthy control subjects. MRI is widely employed in basic and clinical research to map both the functional and structural organization of the brain. Hence, MRI is principally well suited to obtain a global view on brain activity. MRI information from humans and laboratory animals is complemented by electrophysiology and neurochemical data. For this, we systematically mined public knowledge databases that contain experimental reports from decades of research. Our highly interdisciplinary team included leading experts on animal models of alcoholism with the ability to ascertain molecular and cellular processes in great detail. The concepts develop by this group were then validated and extended in human brain imaging and intervention studies by the most established clinical alcohol researchers in Europe. Furthermore, several young teams with outstanding expertise in systems biology and network science joined to analyze the findings from the animal and human experiments. They proposed connectome models for the alcoholic brain as well as potential access points for guiding the network to more normal functioning. Together, we achieved our translational goal by setting up a theoretical and experimental framework for making predictions based on MRI and mathematical modeling, which is verified in animals, and which was transferred to human research. Thereby we provide a rational discovery strategy based on the principles of systems medicine for design and development of novel, evidence based therapies for alcohol addiction.
In a wider perspective, our interdisciplinary consortium will also have strong impact on the way we will carried out research in the future. A new generation of scientists has been taught to better understand the overall picture, to communicate more easily with scientists from different fields, and to apply their own expertise within such a framework.