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Drug repurposing has attracted increasing attention from both thepharmaceutical industry and the research community. Many existing computationaldrug repurposing methods rely on preclinical data (e.g., chemical structures,drug targets), resulting in translational problems for clinical trials. In thisstudy, we propose a clinical connectivity map framework for drug repurposing byleveraging laboratory tests to analyze complementarity between drugs anddiseases. We establish clinical drug effect vectors (i.e., drug-laboratory testassociations) by applying a continuous self-controlled case series model on alongitudinal electronic health record data. We establish clinical disease signvectors (i.e., disease-laboratory test associations) by applying a Wilcoxonrank sum test on a large-scale national survey data. Finally, we compute arepurposing possibility score for each drug-disease pair by applying a dotproduct-based scoring function on clinical disease sign vectors and clinicaldrug effect vectors. We comprehensively evaluate 392 drugs for 6 importantchronic diseases (e.g., asthma, coronary heart disease, type 2 diabetes, etc.).We discover not only known associations between diseases and drugs but alsomany hidden drug-disease associations. Moreover, we are able to explain thepredicted drug-disease associations via the corresponding complementaritybetween laboratory tests of drug effect vectors and disease sign vectors. Theproposed clinical connectivity map framework uses laboratory tests fromelectronic clinical information to bridge drugs and diseases, which isexplainable and has better translational power than existing computationalmethods. Experimental results demonstrate the effectiveness of the proposedframework and suggest that our method could help identify drug repurposingopportunities, which will benefit patients by offering more effective and safertreatments.

Clinical connectivity map for drug repurposing: using laboratory tests to bridge drugs and diseases