Descripción del proyecto
Combinar la investigación y el filtrado colaborativo para el cribado de fármacos
Los procesos de desarrollo de fármacos conllevan gastos elevados, tienen una duración corta y presentan elevadas tasas de fracaso. Por ello, la readaptación de fármacos, que consiste en el cribado sistemático de compuestos existentes para descubrir nuevos usos terapéuticos, es una solución. El filtrado colaborativo aprovecha los emparejamientos fármaco-enfermedad establecidos para formular nuevas recomendaciones. Sin embargo, las investigaciones previas han pasado por alto la supervisión humana y ha sido reacias a incorporar datos biológicos. Teniendo esto en cuenta, en el proyecto RECeSS, financiado por las acciones Marie Skłodowska-Curie, se pretende tender un puente entre la investigación farmacológica y el filtrado colaborativo mediante el uso de un clasificador. Este clasificador aborda el problema del desequilibrio de clases dentro de los emparejamientos fármaco-enfermedad, así como la presencia de características fármaco-enfermedad incompletas, mediante un enfoque de aprendizaje semisupervisado. Gracias a ello, se establecerán vínculos entre las coincidencias predichas y las rutas biológicas alteradas con un análisis de enriquecimiento.
Objetivo
In 2021, drug development pipelines last 10 years in average, and cost around $2 billion, while facing high failure rates, as only around 10% of Phase 0 drug candidates reach the commercialization stage. These issues can be mitigated through drug repurposing, where existent compounds are systematically screened for new therapeutic indications. Collaborative filtering is a semi-supervised learning framework that leverages known drug-disease matchings to make novel recommendations. However, prior works cannot be leveraged because of their lack of focus on human oversight and robustness to biological data.
This project aims at bridging the gap between drug research and collaborative filtering by implementing a RECeSS classifier, that is
(1) Robust: deals with class imbalance in drug-disease matchings, and missing drug/disease features, by semi-supervised learning;
(2) Explainable: connects predicted matchings to perturbed biological pathways through enrichment analyses, based on the learnt importance of features in the model;
(3) Controllable: guarantees a bound on the false positive rate using an adaptive learning scheme;
(4) Standard: algorithms are trained and tested by a standardized open-source pipeline.
Predicted matchings will be independently validated by structure-based methods. This innovative interdisciplinary project relies on a solid basis of newly curated data (up to 1,386 drugs, 1,599 diseases, 12 feature types). It is primarily supervised by Pr. Olaf Wolkenhauer, at SBI Rostock, whose team has an expertise in drug repurposing, in systems biology and data imbalance in machine learning. This project will help the fellow develop new skills, and enhance her professional maturity in academia.
In the short term, this would yield the first method that fully integrates biological interpretation and risk assessment to collaborative filtering-based repurposing. Long-term outcomes might help define sustainable and transparent drug development for rare diseases.
Ámbito científico
Palabras clave
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinador
18055 Rostock
Alemania