Chordoma is a devastating rare
cancer that affects one in a million people. With a mean-survival of just 6 years and no approved medicines, the primary treatments are surgery and radiation. In order to speed new medicines to
chordoma patients, a drug repurposing strategy represents an attractive approach. Drugs that have already advanced through human clinical safety trials have the potential to be approved more quickly than de novo discovered medicines on new targets. We have taken two strategies to enable this: (1) generated and validated machine learning models of
chordoma inhibition and screened compounds of interest in vitro. (2) Tested combinations of approved
kinase inhibitors already being individually evaluated for
chordoma. Several published studies of compounds screened against
chordoma cell lines were used to generate Bayesian Machine learning models which were then used to score compounds selected from the NIH NCATS industry-provided assets. Out of these compounds, the mTOR inhibitor
AZD2014, was the most potent against
chordoma cell lines (IC50 0.35 µM U-CH1 and 0.61 µM U-CH2). Several studies have shown the importance of the mTOR signaling pathway in
chordoma and suggest it as a promising avenue for targeted
therapy. Additionally, two currently FDA approved drugs,
afatinib and
palbociclib (EGFR and CDK4/6 inhibitors, respectively) demonstrated synergy in vitro (CI50 = 0.43) while
AZD2014 and afatanib also showed synergy (CI50 = 0.41) against a
chordoma cell in vitro. These findings may be of interest clinically, and this in vitro- and in silico approach could also be applied to other rare
cancers.