  The drug development pipeline for a new compound can last 10-20 years and
cost over 10 billion. Drug repurposing offers a more time- and cost-effective
alternative. Computational approaches based on biomedical knowledge graph
representations have recently yielded new drug repurposing hypotheses. In this
study, we present a novel, disease-specific hypergraph representation learning
technique to derive contextual embeddings of biological pathways of various
lengths but that all start at any given drug and all end at the disease of
interest. Further, we extend this method to multi-disease hypergraphs. To
determine the repurposing potential of each of the 1,522 drugs, we derive
drug-specific distributions of cosine similarity values and ultimately consider
the median for ranking. Cosine similarity values are computed between (1) all
biological pathways starting at the considered drug and ending at the disease
of interest and (2) all biological pathways starting at drugs currently
prescribed against that disease and ending at the disease of interest. We
illustrate our approach with Alzheimer's disease (AD) and two of its risk
factors: hypertension (HTN) and type 2 diabetes (T2D). We compare each drug's
rank across four hypergraph settings (single- or multi-disease): AD only, AD +
HTN, AD + T2D, and AD + HTN + T2D. Notably, our framework led to the
identification of two promising drugs whose repurposing potential was
significantly higher in hypergraphs combining two diseases: dapagliflozin
(antidiabetic; moved up, from top 32$\%$ to top 7$\%$, across all considered
drugs) and debrisoquine (antihypertensive; moved up, from top 76$\%$ to top
23$\%$). Our approach serves as a hypothesis generation tool, to be paired with
a validation pipeline relying on laboratory experiments and semi-automated
parsing of the biomedical literature.
