首页|Data on Retinitis Pigmentosa Reported by Carlos Loucera and Colleagues (The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery)
Data on Retinitis Pigmentosa Reported by Carlos Loucera and Colleagues (The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Springer Nature
New research on Eye Diseases and Conditions - Retinitis Pigmentosa is the subject of a report. According to news reporting out of Seville, Spain, by NewsRx editors, research stated, “Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms.” Financial supporters for this research include Consejeria de Salud y Consumo, Junta de Andalucia, H2020 Marie Sklodowska-Curie Actions, Ministerio de Ciencia e Innovacion, Instituto de Salud Carlos III. Our news journalists obtained a quote from the research, “The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP. By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa. A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARa1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa. The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug.”
SevilleSpainEuropeCyborgsDrug DevelopmentDrug TargetsDrugs and TherapiesEmerging TechnologiesEye Diseases and ConditionsGeneticsHealth and MedicineHereditary Eye Diseases and ConditionsMachine LearningPharmaceuticalsRetinal DegenerationRetinal Diseases and ConditionsRetinitisRetinitis Pigmentosa
NETL
NSTL
Springer Nature
