Details

Autor(en) / Beteiligte

• Gong, Xiajing
• Hu, Meng
• Liu, Jinzhong
• Kim, Geoffrey
• Xu, James
• McKee, Amy
• Palmby, Todd
• de Claro, R. Angelo
• Zhao, Liang

Titel

Decoding kinase-adverse event associations for small molecule kinase inhibitors

Ist Teil von

• Nature communications, 2022-07, Vol.13 (1), p.4349-4349, Article 4349

Ort / Verlag

London: Nature Publishing Group

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Abstract Small molecule kinase inhibitors (SMKIs) are being approved at a fast pace under expedited programs for anticancer treatment. In this study, we construct a multi-domain dataset from a total of 4638 patients in the registrational trials of 16 FDA-approved SMKIs and employ a machine-learning model to examine the relationships between kinase targets and adverse events (AEs). Internal and external (datasets from two independent SMKIs) validations have been conducted to verify the usefulness of the established model. We systematically evaluate the potential associations between 442 kinases with 2145 AEs and made publicly accessible an interactive web application “Identification of Kinase-Specific Signal” ( https://gongj.shinyapps.io/ml4ki ). The developed model (1) provides a platform for experimentalists to identify and verify undiscovered KI-AE pairs, (2) serves as a precision-medicine tool to mitigate individual patient safety risks by forecasting clinical safety signals and (3) can function as a modern drug development tool to screen and compare SMKI target therapies from the safety perspective.