Open AccessComputational modeling identifies multitargeted kinase inhibitors as effective therapies for metastatic, castration-resistant prostate cancer
Author(s) -
Thomas R. Bello,
Claudia Paindelli,
Luis Díaz-Gómez,
Anthony J. Melchiorri,
Antonios G. Mikos,
Peter S. Nelson,
Eleonora Dondossola,
Taranjit S. Gujral
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2103623118
Subject(s) - prostate cancer , docetaxel , kinome , in vivo , ex vivo , medicine , cancer research , prostate , cabozantinib , pharmacology , oncology , cancer , kinase , biology , microbiology and biotechnology
Significance 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.