What is it about?
We developed a computational approach to facilitate quick drug discovery for advanced prostate cancer. By integrating patient biology data and large-scale drug screen databases we identified potentially efficacious drugs for therapy-resistant prostate cancer. Specifically, we identified a drug named COL-3 and validated its efficacy through additional experiments.
Featured Image
Photo by Nastya Dulhiier on Unsplash
Why is it important?
Traditional drug research and development pipelines are costly and time consuming, thus highlighting a need for quicker identification for aggressive diseases such as castration-resistant prostate cancer (CRPC). In this paper we utilized a computational method to repurpose currently existing drugs. Many existing drugs already have established safety profiles and/or have gained approval for patient care; consequently repurposing of these drugs can meet the urgent demand for treatments in CRPC.
Perspectives
This article showcased how collaborations between computational scientists and experimentalists have the potential to profoundly reshape the current landscape of translational oncology research. Amid the current climate of widely available biological data, powerful computational pipelines can be built to generate hypotheses and address specific research needs. Working in such an interdisciplinary team has been an exhilarating journey, and I look forward to further impactful science to be presented in the future.
Weijie Zhang
University of Minnesota Twin Cities
Read the Original
This page is a summary of: Computational drug discovery for castration-resistant prostate cancers through in vitro drug response modeling, Proceedings of the National Academy of Sciences, April 2023, Proceedings of the National Academy of Sciences,
DOI: 10.1073/pnas.2218522120.
You can read the full text:
Contributors
The following have contributed to this page
