Team : DECISION
Arrival date : 09/15/2020
- Sorbonne Université - LIP6
Boîte courrier 169
Couloir 26-00, Étage 4, Bureau 401
4 place Jussieu
75252 PARIS CEDEX 05
Tel: +33 1 44 27 70 07,
Supervision : Pierre-Henri WUILLEMIN
Co-supervision : Pierre-Henri WUILLEMIN
Data-driven drug repositioning
De novo drug discovery is an expensive, time-consuming, and high-risk process. Despite increased investments in Research & Development, advances in technology and enhanced knowledge of human disease, the number of new drug approvals has stagnated. Drug repositioning is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. It has numerous advantages, most of all, the risk of failure for safety is often lower and the time frame for drug development can be reduced.
During the two last decades, the exponential accumulation of biomedical data and knowledge into more and more structured databases along with the improvement of computational techniques, have encouraged the development of “data-driven” approaches in order to shed light on links between diseases and therapeutic compounds. These approaches involve the analysis and integration of data of any type (such as chemical structure, omics, protein-protein or drug-targets interactions...) and have resulted in the identification of a number of promising candidate drugs, some of which are in advanced stages of clinical trials.
As the way to link drugs to disease can take several paths and different kind of data, we will remain open to different directions but will focus on the construction and analysis of bio-molecular networks obtained from the integration of knowledge such as protein-protein interactions or inferred from omics data such as rna-seq. Networks provide an intuitive framework to integrate a wide variety of information sources, capturing both quantitative and qualitative relationships between entities, such as gene expression correlation, or the presence or absence of an interaction.