a Tripartite Network based Drug Repositioning Algorithm
1 2 2 2
1 Department of Mathematics and Computer Science, University of Catania, 2 Department of Clinical and Molecular Biomedicine, University of Catania
Motivations
Developing new drugs is an extremely expensive, time‐consuming, and risky process. Current knowledge of existing drugs is limited, and new discoveries concerning possible new applications or unknown side effects are very common. Consequently, the development of algorithmic techniques for the repositioning of existing drugs may be a way to make this process more rational, yielding a better understanding of the underlying mechanisms . In the favourable cases this may greatly reduce the failure risk together with the new drugs development cost and time.
Methods
Here we propose a method called TuNDRA for the repositioning of existing drugs based on an extension of the recommendation technique described in Alaimo et al. 2013 for drug‐target interaction prediction.
Following Lee et al. 2013, we can represent our knowledge through a tripartite network, whose nodes can be drugs, targets, or diseases. Interactions in the network associate each drug with a disease through its targets. Our algorithm, starting from such a network, computes a weight for each possible pair of diseases, measuring how reliable is to claim that a drug, which is related the first disease, can be also associated with the second one. This weight is then used to compute recommendations and assign new diseases to each drug. These recommendations are then filtered producing a smaller set from which novel biological insight can be discovered. This process is applied to each drug by computing a measure of correlation between the aforementioned diseases and the known ones. Next we select a subset of diseases minimizing the probability of obtaining by chance a better correlation than the predicted one.
Finally we can use these filtered predictions to guide the experimental activity reducing time and cost of the development process.
For further information please contact apulvirenti@dmi.unict.it
References
Salvatore Alaimo, Alfredo Pulvirenti, Rosalba Giugno, and Alfredo Ferro. “Drug‐target
interaction prediction through domain‐tuned network‐based inference.” Bioinformatics 2013 29: 2004‐2008.
Lee, Hee, et al. "Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug." BMC systems biology 6.1 (2012): 80.
Travel Salvatore
(PB
Travel Grant awarded to Salvatore Alaimo has been provided by the Flagship "InterOmics" project (PB.P05)
