Computational modeling identifies multitargeted kinase inhibitors as effective therapies for metastatic, castration-resistant prostate cancer
Abstract
Metastatic, castration-resistant prostate cancer (mCRPC) is an advanced prostate cancer with limited therapeutic options and poor patient outcomes. To investigate whether multitargeted kinase inhibitors (KIs) represent an opportunity for mCRPC drug development, we applied machine learning–based functional screening and identified two KIs, PP121 and SC-1, which demonstrated strong suppression of CRPC growth in vitro and in vivo. Furthermore, we show the marked ability of these KIs to improve on standard-of-care chemotherapy in both tumor response and survival, suggesting that combining multitargeted KIs with chemotherapy represents a promising avenue for mCRPC treatment. Overall, our findings demonstrate the application of a multidisciplinary strategy that blends bench science with machine-learning approaches for rapidly identifying KIs that result in desired phenotypic effects.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 30, 2021
- Source ID
- 10.1073/pnas.2103623118
Entities
People
- Anthony Melchiorri
- Antonios Mikos
- Claudia Paindelli
- Eleonora Dondossola
- Luis A. Diaz-gomez
- Peter S Nelson
- Taran Gujral
- Thomas Bello
Organizations
- Fred Hutchinson Cancer Center
- National Cancer Institute
- National Institutes of Health
- Rice University
- United States Department of Defense
- University of Texas at Austin
- University of Washington