Descrizione del progetto
Mettere in collegamento la ricerca e il filtraggio collaborativo per lo screening dei farmaci
Le pipeline di sviluppo dei farmaci determinano spese elevate, sono caratterizzate da breve durata e presentano tassi di fallimento elevati, motivi che configurano il riposizionamento dei medicinali, un’operazione che prevede lo screening sistematico dei composti esistenti per scoprire nuovi usi terapeutici, come una soluzione efficace. Il filtraggio collaborativo sfrutta le associazioni consolidate tra farmaci e malattie per formulare nuove raccomandazioni. Tuttavia, le indagini sinora effettuate in tal ambito non pongono l’accento sulla supervisione umana e mostrano una certa resistenza all’incorporazione dei dati biologici. In quest’ottica, il progetto RECeSS, finanziato dal programma di azioni Marie Skłodowska-Curie, mira a colmare il divario tra la ricerca sui farmaci e il filtraggio collaborativo attraverso l’uso di un classificatore. Questo classificatore adotta un approccio di apprendimento semi-supervisionato per affrontare il problema dello squilibrio di classe all’interno delle coppie farmaco-malattia e la presenza di caratteristiche incomplete tra tali coppie, stabilendo connessioni tra le corrispondenze proiettate e le vie biologiche perturbate tramite analisi di arricchimento.
Obiettivo
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.
Campo scientifico
Programma(i)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Meccanismo di finanziamento
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinatore
18055 Rostock
Germania