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Updated: Jun 10, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
A novel hypothesis-generating computational workflow utilizing reverse pharmacophore mapping-A drug repurposing
Mietha Magdalena van der Walt1, Arnold Petrus Smith1
1Human Metabolomics, Faculty of Natural and Agricultural Sciences, Potchefstroom Campus, North-West University, Potchefstroom, South Africa.
Background And Purpose:
Drug repurposing (DR) offers a compelling alternative to traditional drug discovery's lengthy, resource-intensive process. DR is the process of identifying alternative clinical applications for pre-approved drugs as a low-risk and low-cost strategy. Computational approaches are crucial during the early hypothesis-generating stage of DR. However, 'large-scale' data retrieval remains a significant challenge. A computational workflow addressing such limitations might improve hypothesis generation, ultimately benefit patients and advance DR research.
Experimental Approach:
We introduce a novel computational workflow (combining free-accessible computational platforms) to provide 'proof-of-concept' of the pre-approved drug's suitability for repurposing. Three key phases are included: target fishing (via reverse pharmacophore mapping), target identification (via disease- and drug-target pathway identification) and retrospective literature and drug-like analysis (via in silico ADMET properties determination). Istradefylline is a Parkinson's disease-approved drug with literature-attributed antidepressant properties remaining unclear. Practically applied, istradefylline's antidepressant activity was assessed in the context of major depressive disorder (MDD).
Key Results:
Data mining aided by target identification resulted in istradefylline potentially representing a novel antidepressant drug class. Retrieved drug targets (KYNU, MAO-B, ALOX12 and PLCB2) associated with selected MDD pathways (tryptophan metabolism and serotonergic synapse) generated a hypothesis that istradefylline increased extracellular 5-HT levels (MAO-B inhibition) and reduced inflammation (KYNU, ALOX12 and PLCB2 inhibition).
Conclusion And Implications:
The practically applied workflow's generated hypothesis aligns with known experimental data, validating the effectiveness of this novel computational workflow. It is a low-risk and low-cost DR computational tool providing a bird's-eye view for exploring alternative clinical applications of pre-approved drugs.
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