The screening process for new drugs can be made more efficient by using in silico methods that exploit existing datasets that represent the response of cells to a large collection of drugs.
In the CBF we have experience in this area and have access to a database of 20,000 drug treatments, which represent a wide range of pharmaceuticals and chemicals.
Our algorithms allow for a disease-specific expression signature to be matched with one of the existing databases looking for single drugs or a combination of drugs that may induce an opposite signature to the disease fingerprint. This approach has been used successfully to identify etacrynic acid, a diuretic used to treat high blood pressure, as a candidate drug to treat Chronic Lymphocytic Leukaemia (CLL).
The heatmap shows the result of the in silico screening for the drug pair. The drug pair induces an expression signature that is the reverse of the two defined disease signatures. The figure only shows a small number of relevant genes. Red is up-regulation, blue is downregulation.
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