  More than 200 generic drugs approved by the U.S. Food and Drug Administration
for non-cancer indications have shown promise for treating cancer. Due to their
long history of safe patient use, low cost, and widespread availability,
repurposing of generic drugs represents a major opportunity to rapidly improve
outcomes for cancer patients and reduce healthcare costs worldwide. Evidence on
the efficacy of non-cancer generic drugs being tested for cancer exists in
scientific publications, but trying to manually identify and extract such
evidence is intractable. In this paper, we introduce a system to automate this
evidence extraction from PubMed abstracts. Our primary contribution is to
define the natural language processing pipeline required to obtain such
evidence, comprising the following modules: querying, filtering, cancer type
entity extraction, therapeutic association classification, and study type
classification. Using the subject matter expertise on our team, we create our
own datasets for these specialized domain-specific tasks. We obtain promising
performance in each of the modules by utilizing modern language modeling
techniques and plan to treat them as baseline approaches for future improvement
of individual components.
