Computational drug discovery for castration-resistant prostate cancers through in vitro drug response modeling

Weijie Zhang, Adam M. Lee, Sampreeti Jena, Yingbo Huang, Yeung Ho, Kiel T. Tietz, Conor R. Miller, Mei Chi Su, Joshua Mentzer, Alexander L. Ling, Yingming Li, Scott M. Dehm, R. Stephanie Huang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Prostate cancer (PC) is the most frequently diagnosed malignancy and a leading cause of cancer deaths in US men. Many PC cases metastasize and develop resistance to systemic hormonal therapy, a stage known as castration-resistant prostate cancer (CRPC). Therefore, there is an urgent need to develop effective therapeutic strategies for CRPC. Traditional drug discovery pipelines require significant time and capital input, which highlights a need for novel methods to evaluate the repositioning potential of existing drugs. Here, we present a computational framework to predict drug sensitivities of clinical CRPC tumors to various existing compounds and identify treatment options with high potential for clinical impact. We applied this method to a CRPC patient cohort and nominated drugs to combat resistance to hormonal therapies including abiraterone and enzalutamide. The utility of this method was demonstrated by nomination of multiple drugs that are currently undergoing clinical trials for CRPC. Additionally, this method identified the tetracycline derivative COL-3, for which we validated higher efficacy in an isogenic cell line model of enzalutamide-resistant vs. enzalutamide-sensitive CRPC. In enzalutamide-resistant CRPC cells, COL-3 displayed higher activity for inhibiting cell growth and migration, and for inducing G1-phase cell cycle arrest and apoptosis. Collectively, these findings demonstrate the utility of a computational framework for independent validation of drugs being tested in CRPC clinical trials, and for nominating drugs with enhanced biological activity in models of enzalutamide-resistant CRPC. The efficiency of this method relative to traditional drug development approaches indicates a high potential for accelerating drug development for CRPC.

Original languageEnglish (US)
Article numbere2218522120
JournalProceedings of the National Academy of Sciences of the United States of America
Volume120
Issue number17
DOIs
StatePublished - Apr 25 2023

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS. This study was supported by NIH/NCI Grants R01CA204856 (R.S.H) and the University of Minnesota (UMN) Office of Academic Clinical Affairs (OACA) Faculty Research Development grant (R.S.H and S. D.). R.S.H. also received support from NIH/NCI R01CA229618, a research grant from the Avon Foundation for Women and the UMN OACA Grant-in-Aid Program (GIA) award. W.Z. received the UMN Bioinformatics and Computational Biology first-year Fellowship, the UMN IDF Fellowship, and the UMN Clinical & Translational Science Institute A-PReP scholarship.

Publisher Copyright:
Copyright © 2023 the Author(s). Published by PNAS.

Keywords

  • castration-resistant prostate cancer
  • drug discovery
  • drug repurpose
  • drug response prediction
  • enzalutamide

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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