Document Type
Article
Publication Date
7-2018
Keywords
Rexinoids, RXR, Gene expression, Microarrays, Analytics, Cancer
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.steroids.2018.04.009
Abstract
Rexinoids are powerful ligands that bind to retinoid-X-receptors (RXRs) and show great promise as therapeutics for a wide range of diseases, including cancer. However, only one rexinoid, bexarotene (Targretin TM) has been successfully transitioned from the bench to the clinic and used to treat cutaneous T-cell lymphoma (CTCL). Our goal is to develop novel potent rexinoids with a less untoward side effect profile than bexarotene. To this end, we have synthesized a wide array of rexinoids with EC50 values and biological activity similar to bexarotene. In order to determine their suitability for additional downstream analysis, and to identify potential candidate analogs for clinical translation, we treated human CTCL cells in culture and employed microarray technology to assess gene expression profiles. We analyzed twelve rexinoids and found they could be stratified into three distinct categories based on their gene expression: similar to bexarotene, moderately different from bexarotene, and substantially different from bexarotene. Surprisingly, small changes in the structure of the bexarotene parent compound led to marked differences in gene expression profiles. Furthermore, specific analogs diverged markedly from our hypothesis in expression of genes expected to be important for therapeutic promise. However, promoter analysis of genes whose expression was analyzed indicates general regulatory trends along structural frameworks. Our results suggest that certain structural motifs, particularly the basic frameworks found in analog 4 and analog 9, represent important starting points to exploit in generating additional rexinoids for future study and therapeutic applications.
Rights Information
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Steroids, v. 135, p. 36-49
This article is the post-print author version. Final version available at: https://doi.org/10.1016/j.steroids.2018.04.009
Scholar Commons Citation
Hanish, Bentley J.; Hackney Price, Jennifer F.; Kaneko, Ichiro; Ma, Ning; van der Vaart, Arjan; Wagner, Carl E.; Jurutka, Peter W.; and Marshall, Pamela A., "A Novel Gene Expression Analytics-based Approach to Structure Aided Design of Rexinoids for Development as Next-Generation Cancer Therapeutics" (2018). Chemistry Faculty Publications. 58.
https://digitalcommons.usf.edu/chm_facpub/58
