PREDIRE
Scheda del progetto
Dr. Chiara Sabena
Dr. Silvia Berto
Dr. Eugenio Alladio
Dr. Alberto Mazzoleni
Dr. Lorenzo Castellino
Dr. Martina Galletto
The PREDIRE project aims to develop software based on artificial intelligence and machine learning algorithms to reliably predict the formation of co-crystals between active pharmaceutical ingredients (APIs) and co-formers. This technology is crucial because 70% of new drugs exhibit low solubility, compromising therapeutic efficacy. Pharmaceutical co-crystals offer a solution by improving drug properties without altering their therapeutic action. However, selecting the right co-former from thousands is a complex process. The project aims to overcome this issue with a predictive approach, reducing time, costs, and the need for experimental trials. In collaboration with the EXPANDO project, the goal is to develop an integrated system for creating more effective and accessible drugs.
Contact:
Contact person
Roberto Gobetto
Email
roberto.gobetto@unito.it
Tel
+39 0116707520
+39 3343953536
The project aims to develop software based on AI and ML to predict the formation of pharmaceutical co-crystals, addressing the challenge of selecting from over 3,000 co-formers in an efficient and cost-effective manner. The goal is to replace the trial-and-error screening method with a predictive approach, reducing the number of syntheses, development time, and the use of reagents and solvents. Additionally, the project envisions two complementary computational tools: one for synthesis and the other for structural characterization. The ultimate goal is to achieve more efficient drugs with a faster and more sustainable approach.
The project aims to revolutionize the pharmaceutical co-crystal screening process by abandoning the traditional experimental approach in favor of a predictive forecasting method. This innovative approach allows for concentrating resources on the most promising co-formers for synthesis. Additionally, the project stands out for its adoption of sustainable synthetic methods and its potential extension to other industries, positioning it at the forefront of both basic and applied research.
The proposed technology promises an immediate reduction in costs, time, and environmental impact in the drug development process, enabling the faster creation of new crystalline forms with superior performance. In the long term, it is expected to revolutionize drug design, with the potential for personalized treatments tailored to individual patient needs, thereby improving overall population health and opening new perspectives in the pharmaceutical sector.