Drug repurposing approach to fight Covid-19
Exscalate4CoV, a public-private consortium of European partners, leveraged the power of Exscalate’s supercomputing AI-driven platform in the fight against COVID-19. In just four months a low molecular weight negative allosteric modulator, Raloxifene, was identified and successfully entered Phase III clinical trials.
When the world was in the grip of Covid-19 in 2020 with various lockdowns taking place across the world, Exscalate brought together a consortium of partners to help fight it. This public-private consortium - Exscalate4Cov (E4C) - consisting of 18 institutions and research centers across Europe and backed by the European Commission’s Horizon 2020 program - worked together to rapidly address the rising threat of COVID-19, which had been spreading worldwide since December 2019.
The E4C project brought together academic and research institutions involved in life sciences (Fraunhofer Inst., Politecnico di Milano), high performance computing (Cineca, the Barcelona Computing Center, SIB Swiss Institute of Bioinformatics) as well as industrial partners (IBM, ENI, SAS and LEONARDO). The core of the project is Exscalate’s intelligent supercomputing platform. Adopting a repurposing approach, the platform would help rapidly identify active, ready to use in humans drugs.
Exscalate’s virtual screening software LiGen (Ligand Generator) is specifically designed to run on High Performance Computing (HPC) architectures, and this project would require huge computational resource. As such the project utilized four of the most powerful supercomputing centers in Europe - Eni’s supercomputing infrastructure HPC5, CINECA’s Marconi100 supercomputer, Barcelona Supercomputing Center and the Exascale Supercomputer at FZ Jülich. Leveraging the power of these supercomputers together with Exscalate’s platform - consisting of a comprehensive therapeutic target database (CTTD) and virtual ligand library of proprietary Tangible Chemical Database (TCDb) - more than 70 billion molecules were simulated on 15 active interaction sites of the virus for a total of more than a thousand billion interactions evaluated in just 60 hours (five million molecules simulated per second).
The data from the simulation was processed with the SAS Viya platform using artificial intelligence techniques and advanced analytics. Results were made available in real time on the open science portal mediate.exscalate4Cov.eu to allow scientists around the world to carry out their own simulations. This is the most comprehensive scientific wealth of knowledge on Sars Cov 2 virus available globally.
During Phase 1 of the E4C project, with Exscalate used to carry out the screening on 400 thousand molecules and a specific test to evaluate 9000 promising molecules, one molecule was identified in just four months. This molecule is the validated post-menopausal osteoporosis drug Raloxifene, which is an effective treatment for inflammatory processes induced by Covid-19.
By October 2020 the consortium had submitted this first candidate for approval by the AIFA (The Italian Medicines Agency). The molecule subsequently entered Phase III clinical trials in Europe, which is still ongoing. Additionally, three other back-up molecules validated to inhibit the ACE2 protein, which allows the Covid-19 virus to infect human cells, were also submitted for preclinical testing.
The results from the E4C project prove how the use of the Exscalate platform, which harnesses the power of supercomputing resources, can accelerate the drug discovery process. However, as well as the technology, the project also proves what can be achieved when the scientific community come together to collaborate on finding a solution.
Exscalate believes that the creation of public-private consortia such as E4C that bring a range of expertise and technologies together will be a key strategy in the future for bringing new and safer therapies to patients. It will enable the scientific research community to quicker and more efficiently face future global pandemics as well as finding solutions for rare diseases.