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NEXUS-Rx
Our Framework
Nexus Rx is a comprehensive framework for drug repositioning, de-risking and optimization, encompassing various stages from clinical data analysis to drug mechanism exploration.
It is comprised of different modules:
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Genoptimizer
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Pheno-connect
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RxFinder
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Opti-Mouse
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Clinicalyzer
Genoptimizer
Understanding the intricate relationship between gene expression, disease, and biological networks is fundamental to understand diseases of high unmet need.
Publications
+ 4 articles in Preparation / Communication
Pheno-Connect
Connecting gene network information to clinical features is pivotal in advancing our understanding of disease pathogenesis, diagnosis, prognosis, and treatment. This understanding offers a comprehensive view of disease complexity and enables the identification of novel therapeutic targets.
Publications
Project in progress in rare diseases of high unmet need. Publications will be updated at the end of the project
Rx Finder
By identifying key factors involved in disease pathways, we can prioritize targets based on their centrality and influence within the gene and phenotypic network. Analyzing drug-gene interactions enables discovery of new therapeutics.
Publications
Internal proprietary drug database comprising of semi-manually curated enrichment, encompassing ~5 million drug-target combinations
Opti- Mouse
Genetic animal models mimic human diseases, allowing studies of disease progression, which can provide valuable insights into identification of potential therapeutic targets. They help determine optimal drug dosages, identify potential side effects, and assess drug safety.
Publications
Nathalie Barreto Lefevre, Aleksandra Polosukhina, et al., A novel computational framework to identify translational potential of genetic mouse models in rare diseases.
(In communication)
Clinicalyzer
Publications
Clinical trial intelligence provides the foundation for data-driven decision-making throughout the drug development process. They help in the identification of optimal trial designs, patient populations, and endpoints. Understanding past trial failures can help identify potential risks and develop mitigation strategies.
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