A novel method to predict fatty liver drugs in context of metabolic
network
Priyanka Banerjee1,2, Nikolaus Berndt, Robert Preissner, Hermann-Georg Holzhütter1,21Computational Systems Biochemistry, Institute of Biochemistry, Charité – Universitätsmedizin Berlin,Germany;
2
Computational Systems Biology, Humboldt-Universität zu Berlin, Berlin, Germany

Hepatocytes exhibits a wide range of functions extending from removal of toxic
substances, homeostatic regulations and synthesis of most plasma membrane
constituents as well as production of bile and hormones. Hepatocytes have higher
metabolic activity in human and play an important role in human metabolism. Deficiency
or alterations in the metabolism of hepatocytes can lead to complicated liver diseases
like hepatitis, non alcoholic fatty liver disease (NAFLD), cirrhosis and liver cancer, andcan be serious threats to public health. NAFLD is considered as the hepatic
manifestation of obesity and metabolic syndrome as a result of series of pathologicalchanges, which ranges from reversible fatty liver (steatosis) to a non-alcoholicsteatohepatitis (NASH).
In recent years, the most common cause of chronic liver disease in USA is contributedby NAFLD. A recent study shows the need and cost associated with medication related
to liver diseases as well as organ transplantation. Increase rates of obesity diabetes andhigh cholesterol have results in growing concern for liver diseases. NAFLD and
Steatohepatitis are well linked but rare forms of drugs induced liver injury. In addition,
fatty liver is often chronic than acute even when drug induced. Even though it is wellknown that the lipid accumulation in the liver is a hallmark of the NAFLD; the underlying
mechanism leading to steotosis and further transition to NASH is still remains elusive. Itis therefore difficult to track the onset and progression or to diagnose and design
effective therapeutic techniques. The adverse outcome of this pathology may bepossibly prevented once the molecular mechanisms involved in the metabolism of
Hepatocytes are unraveled. On the other hand, this requires understanding of thecoordinated behavior of a very large number of interconnected networks of drugs,
molecular target as well as off-target, pathways, metabolic network and metabolites.
Recent developments in the field of computational systems biology made it possible topredict the functional effects of systems perturbations using large scale network models.
Subsequently, advances in the field of structural bioinformatics and chem-informatics
have led to the prediction of protein-drug off-target effects based on their ligand
structures and binding site information. Integration of these expertises provides aplatform for evaluating metabolic drug response in silico. The combination of theseapproaches was applied to investigate the drugs that can cause fatty liver disease in
human. Currently, there is no efficient treatment or explanation involved in themechanism of NAFLD.

This study represents a novel integration of the trio ’computational systems biology,
structural bioinformatics and cheminformatics’ approaches to predict drugs involved inmetabolic syndrome and the possible underlying mechanism.