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In the United States, over 100,000 women are diagnosed with a gynecologic malignancy every year, with ovarian cancer being the most lethal. One of the hallmark characteristics of ovarian cancer is the development of resistance to chemotherapeutics. While the exact mechanisms of chemoresistance are poorly understood, it is known that changes at the cellular and molecular level make chemoresistance challenging to treat. Improved therapeutic options are needed to target these changes at the molecular level. Using a precision medicine approach, such as gene therapy, genes can be specifically exploited to resensitize tumors to therapeutics. This review highlights traditional and novel gene targets that can be used to develop new and improved targeted therapies, from drug efflux proteins to ovarian cancer stem cells. The review also addresses the clinical relevance and landscape of the discussed gene targets.
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CANDI: a web server for predicting molecular targets and pathways of cannabis-based therapeutics
Abstract BackgroundCannabis sativaL. with a rich history of traditional medicinal use, has garnered significant attention in contemporary research for its potential therapeutic applications in various human diseases, including pain, inflammation, cancer, and osteoarthritis. However, the specific molecular targets and mechanisms underlying the synergistic effects of its diverse phytochemical constituents remain elusive. Understanding these mechanisms is crucial for developing targeted, effective cannabis-based therapies. MethodsTo investigate the molecular targets and pathways involved in the synergistic effects of cannabis compounds, we utilized DRIFT, a deep learning model that leverages attention-based neural networks to predict compound-target interactions. We considered both whole plant extracts and specific plant-based formulations. Predicted targets were then mapped to the Reactome pathway database to identify the biological processes affected. To facilitate the prediction of molecular targets and associated pathways for any user-specified cannabis formulation, we developed CANDI (Cannabis-derived compound Analysis and Network Discovery Interface), a web-based server. This platform offers a user-friendly interface for researchers and drug developers to explore the therapeutic potential of cannabis compounds. ResultsOur analysis using DRIFT and CANDI successfully identified numerous molecular targets of cannabis compounds, many of which are involved in pathways relevant to pain, inflammation, cancer, and other diseases. The CANDI server enables researchers to predict the molecular targets and affected pathways for any specific cannabis formulation, providing valuable insights for developing targeted therapies. ConclusionsBy combining computational approaches with knowledge of traditional cannabis use, we have developed the CANDI server, a tool that allows us to harness the therapeutic potential of cannabis compounds for the effective treatment of various disorders. By bridging traditional pharmaceutical development with cannabis-based medicine, we propose a novel approach for botanical-based treatment modalities.
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- Award ID(s):
- 2210963
- PAR ID:
- 10574204
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Journal of Cannabis Research
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2522-5782
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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