Computational modeling identifies multitargeted kinase inhibitors as effective therapies for metastatic, castration-resistant prostate cancer

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dc.citation.articleNumbere2103623118
dc.citation.issueNumber40
dc.citation.journalTitleProceedings of the National Academy of Sciences
dc.citation.volumeNumber118
dc.contributor.authorBello, Thomas
dc.contributor.authorPaindelli, Claudia
dc.contributor.authorDiaz-Gomez, Luis A.
dc.contributor.authorMelchiorri, Anthony
dc.contributor.authorMikos, Antonios G.
dc.contributor.authorNelson, Peter S.
dc.contributor.authorDondossola, Eleonora
dc.contributor.authorGujral, Taranjit S.
dc.date.accessioned2021-10-21T17:53:23Z
dc.date.available2021-10-21T17:53:23Z
dc.date.issued2021
dc.description.abstractCastration-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.
dc.identifier.citationBello, 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.
dc.identifier.digitale2103623118-full
dc.identifier.doihttps://doi.org/10.1073/pnas.2103623118
dc.identifier.urihttps://hdl.handle.net/1911/111588
dc.language.isoeng
dc.publisherNational Academy of Sciences
dc.rightsThis open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleComputational modeling identifies multitargeted kinase inhibitors as effective therapies for metastatic, castration-resistant prostate cancer
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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