New drugs can be developed from old ones.
This revolutionary pharmacological approach that has been gaining ground recently was confirmed by a study conducted by two major research centres of the University of Trento that was published today in Nature Communications.
The study, which investigated the relationship between diseases and pharmacological effects at molecular level, was jointly carried out by two research teams of the University: one at Cosbi (Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology), which is specialized in computer science application to big data, and one at Cibio Department, which is focused on biology and genomics.
Everything started from a big data analysis: the existing drugs and, in particular, their molecular properties, were analyzed with a new algorithm designed by Cosbi, to see whether they could be used to treat other conditions, to bring benefits to patients with conditions that are difficult or impossible to treat.
The technique of drug repositioning has been already used in the past, but it has become even more effective today thanks to new technologies, which facilitate the quick and systematic analysis of a vast amount of data.
With promising results, as explained by Enrico Domenici, president of Cosbi: "We have tested the new algorithm to search for new therapies to treat metabolic syndrome, a condition that increases the risk of heart disease and type 2 diabetes and which is characterized by obesity, elevated levels of cholesterol and/or triglycerides, high blood pressure and diabetes. We identified the mutated genes that are responsible for these alterations and then searched pharmaceutical databases for already approved molecules that could interact with the networks that these genes form in the adipose tissue, liver and muscles. To carry out such a complex study we adopted a new computational approach that measures the "distance" between the proteins targeted by the drug and those found in the networks affected by the disease. That is how we found a new application for an already existing drug: Ibrutinib, which was originally developed to treat completely different conditions, and specifically B-cell tumors like mantle cell lymphoma, chronic lymphocytic leukemia, and Waldenström macroglobulinemia. Other studies on this drug are under way to determine if it could be used to treat autoimmune diseases".
The results of the computational analysis were validated at Cibio on zebrafish larvae to confirm the response to the drug.
The head of the research team at Cibio department, Maria Caterina Mione, explained how it worked: "When we tested this drug in the laboratory we saw that it is possible to limit the detrimental impact of obesity induced by a high-fat diet. The drug succeeded in limiting the inflammatory condition associated with lipid accumulation. Testing the effectiveness of a drug that is already on the market makes it possible to skip a number of long and complex stages of the approval process for new medical products, because their tolerance and safety have already been demonstrated. Our data are just a start and more in-depth studies and clinical tests are required, but they demonstrate that combining experience in big data analysis and the ability to develop models for biological validation can boost drug repurposing research".
Finding out that a market approved drug can potentially be used to treat metabolic syndrome is great news. The incidence of the disease and seriousness of its consequences on health (cardiovascular diseases and diabetes complications), have made metabolic syndrome a social problem in recent decades, especially in wealthy societies, where many people have a diet high in sugars and fats and a sedentary lifestyle. In particular, the disease affects people over 50 years of age, about 30% of the male population and 35-40% of women. The metabolic syndrome becomes more common in women after menopause.
The study made the drug discovery process faster, but our findings need to be validated in a clinical context for the drug to be used to treat diseases other than the ones for which it was originally designed. More research is needed in the coming years to translate the experimental findings into a clinical application.
About the article:
A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome
Karla Misselbeck1,2, Silvia Parolo 1*, Francesca Lorenzini3, Valeria Savoca3, Lorena Leonardelli1, Pranami Bora1, Melissa J. Morine1, Maria Caterina Mione 3, Enrico Domenici 1,3* & Corrado Priami 1,4*
1 Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy. 2 Department of Mathematics, University of Trento, Trento, Italy. 3 Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy. 4 Department of Computer Science, University of Pisa, Pisa, Italy. *email: parolo [at] cosbi.eu; enrico.domenici [at] unitn.it; priami [at] cosbi.eu