Pioneering automated drug design methodologies developed by researchers at the University of Dundee led to the spin out of ex scientia Ltd in 2012. The company provides technologies to enhance the efficacy and the efficiency of drug discovery for the pharmaceutical industry.
The pharmaceutical Industry is facing serious pressures, not least by the loss of $64-100 billion of revenue as key drugs lose patent protection and as the number of new approved drugs decreases steadily. Many drugs fail at the phase 2 and 3 stages, primarily because of a lack of efficacy and the risk of failure is higher if the drug has a novel mechanism of action. The high rate of drug attrition suggests that new approaches are needed to improve and expedite the drug discovery process. One approach to tackle this problem is to harness chemical, pharmacological and biological data for automated drug design. Professor Andrew Hopkins FRSC FLSW at the University of Dundee has been at the forefront of this research by combining chemoinformatics, chemogenomics and structural bioinformatics methodologies to tackle questions of target identification, polypharmacology and de novo compound design. In 2012, Professor Hopkins published the delivery of a new automated, adaptive methodology for designing drug ligands to multi-target profiles in the journal Nature (doi:10.1038/nature11691) and the technology received significant coverage in the press.
It was quickly realised that the computational technology platform invented by Professor Hopkins offered a highly scalable system to discover drugs against pharmacogenomic profiles of multiple drug targets. In addition to being a valuable new tool in drug discovery, the technology was also of interest for the potential repurposing of established drugs or anticipation of adverse drug reactions. ex scientia Ltd was created in 2012 in partnership with Frontier IP as a technology platform company operating at the IT/healthcare interface with the goal of revolutionising productivity by the use of data analytics and machine learning in drug design and pharmacogenomics. Three proof of concept agreements have already been signed and overall the company has signed deals worth $6.25 million (excluding milestones and licensing payments), with no dilution of equity.