The mechanistic functional landscape of Retinitis Pigmentosa: a Machine Learning driven approach to drug Repurposing.

Abstract

Abstract 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 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. Six of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1, Slc12a5, KCC2, Grin1, Glr2a. As a result, we provide a resource to evaluate drugs with the potential to be repurposed 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 target candidates for drug repositioning. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying six promising target candidates for drug repositioning. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.