Capturing Knowledge about Drug-Drug Interactions to Enhance Treatment Effectiveness

Capturing knowledge about Drug-Drug Interactions (DDI) is a crucial factor to support clinicians in better treatments. Nowadays, public drug databases provide a wealth of information on drugs that can be exploited to enhance tasks, e.g., data mining, ranking, and query answering. However, all the interactions in the public database are focused on pairs of drugs. Since current treatments are composed of multi-drugs, it is extremely challenging to know which potential drugs affect the effectiveness of the treatment. In this work, we tackle the problem of discovering DDIs and reduce this problem to link prediction over a property graph represented in RDF-star. A deductive system captures knowledge about the conditions that define when a group of drugs interacts as Datalog rules. Extensional statements represent the property graph. Lastly, the intensional rules guide the deduction process to discover relationships in the graph and their properties. As a proof concept, we have implemented a graph traversal method on top of the property graph and the deduced edges. The technique aims to identify the combination of drugs whose interactions may reduce the effectiveness of a treatment or increase the number of toxicities. This traversal method relies on the computation of wedges in the property graph. Albeit illustrated in the context of DDI, this method could be generalized to other link traversal tasks. We conduct an experimental study on a DDIs property graph for different treatments. The results suggest that by capturing knowledge about DDIs, our approach can discover the drugs that decrease the effectiveness of the treatment. Our results are promising and suggest that clinicians can better understand the DDIs in treatment and prescribe improved treatments through the knowledge captured by our approach.