Abstract

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.

Details

Title
Synergistic drug combinations and machine learning for drug repurposing in chordoma
Author
Anderson, Edward 1 ; Havener, Tammy M 1 ; Zorn, Kimberley M 2 ; Foil, Daniel H 2 ; Lane, Thomas R 2 ; Capuzzi, Stephen J 1 ; Morris, Dave 1 ; Hickey, Anthony J 3 ; Drewry, David H 4 ; Ekins, Sean 5 

 University of North Carolina at Chapel Hill, UNC Catalyst for Rare Diseases, Eshelman School of Pharmacy, Chapel Hill, USA (GRID:grid.10698.36) (ISNI:0000000122483208) 
 Collaborations Pharmaceuticals, Inc., Raleigh, USA (GRID:grid.492575.8) 
 University of North Carolina at Chapel Hill, UNC Catalyst for Rare Diseases, Eshelman School of Pharmacy, Chapel Hill, USA (GRID:grid.10698.36) (ISNI:0000000122483208); RTI International, Research Triangle Park, USA (GRID:grid.62562.35) (ISNI:0000000100301493) 
 University of North Carolina at Chapel Hill, Structural Genomics Consortium, UNC Eshelman School of Pharmacy, Chapel Hill, USA (GRID:grid.10698.36) (ISNI:0000000122483208) 
 University of North Carolina at Chapel Hill, UNC Catalyst for Rare Diseases, Eshelman School of Pharmacy, Chapel Hill, USA (GRID:grid.10698.36) (ISNI:0000000122483208); Collaborations Pharmaceuticals, Inc., Raleigh, USA (GRID:grid.492575.8) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2429349086
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.