Bello, ThomasPaindelli, ClaudiaDiaz-Gomez, Luis A.Melchiorri, AnthonyMikos, Antonios G.Nelson, Peter S.Dondossola, EleonoraGujral, Taranjit S.2021-10-212021-10-212021Bello, Thomas, Paindelli, Claudia, Diaz-Gomez, Luis A., et al.. "Computational modeling identifies multitargeted kinase inhibitors as effective therapies for metastatic, castration-resistant prostate cancer." <i>Proceedings of the National Academy of Sciences,</i> 118, no. 40 (2021) National Academy of Sciences: https://doi.org/10.1073/pnas.2103623118.https://hdl.handle.net/1911/111588Castration-resistant prostate cancer (CRPC) is an advanced subtype of prostate cancer with limited therapeutic options. Here, we applied a systems-based modeling approach called kinome regularization (KiR) to identify multitargeted kinase inhibitors (KIs) that abrogate CRPC growth. Two predicted KIs, PP121 and SC-1, suppressed CRPC growth in two-dimensional in vitro experiments and in vivo subcutaneous xenografts. An ex vivo bone mimetic environment and in vivo tibia xenografts revealed resistance to these KIs in bone. Combining PP121 or SC-1 with docetaxel, standard-of-care chemotherapy for late-stage CRPC, significantly reduced tibia tumor growth in vivo, decreased growth factor signaling, and vastly extended overall survival, compared to either docetaxel monotherapy. These results highlight the utility of computational modeling in forming physiologically relevant predictions and provide evidence for the role of multitargeted KIs as chemosensitizers for late-stage, metastatic CRPC.engThis open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).Journal articlee2103623118-fullhttps://doi.org/10.1073/pnas.2103623118