03603nas a2200277 4500008004100000022001400041245013400055210006900189260001600258300000800274490000700282520262900289653001202918653000902930653002502939653002402964100002702988700002003015700001603035700002003051700003103071700002003102700002003122700002403142856015903166 2024 eng d a1479-587600aThe mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery.0 amechanistic functional landscape of retinitis pigmentosa a machi c2024 Feb 06 a1390 v223 a

BACKGROUND: 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. 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.

METHODS: 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.

RESULTS: 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 (GABARα1 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.

CONCLUSIONS: 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. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.

10aAnimals10aMice10aRetinitis pigmentosa10aSignal Transduction1 aEsteban-Medina, Marina1 aLoucera, Carlos1 aRian, Kinza1 aVelasco, Sheyla1 aOlivares-González, Lorena1 aRodrigo, Regina1 aDopazo, Joaquin1 aPeña-Chilet, Maria uhttps://www.clinbioinfosspa.es/content/mechanistic-functional-landscape-retinitis-pigmentosa-machine-learning-driven-approach-therapeutic-target-discovery