Through the use of artificial intelligence and Machine learning, numerous companies aspire to bring more efficiency to drug discovery technologies and preclinical development processes…
Drug development is an expensive and slow process which can last 10 to 15 years and can cost more than 1 billion dollars. Preclinical trials are a key element in drug discovery and development. These studies aim to confirm the safety and efficacity of candidate drugs. Or a lack of effectiveness is the reason for 30% of failures during clinical trials. That is why a more efficient preclinical process would represent a substantial gain of time and money for industrials.
Therefore, french companies have developed technologies based on artificial intelligence and machine learning. Using quantitative data from pharmaceutical industrials, their algorithms will be able to enhance drug design or define and measure drugs’ biological activities… A numerical analysis, in opposition to a visual assessment by pathologist, will reduce costs, delays and interindividual variability.
Big pharma companies have already expressed their interest. For example, Servier, Pierre Fabre and Merck are now working in collaboration with Iktos, a company which develops deep learning based technologies. Beside, Pierre Fabre stages, each year, an AI Challenge in order to reveal innovative projects and initiate partnerships. Last edition challenge « a new algorithm able to detect the minimum erythema dose » was won by Quantacell, a contract research organization based in Bordeaux.
Pharmacists and scientists should continue to monitor this fast growing field. This source of innovation raises the question : Can an artificial intelligence supersedes scientists’ experience?
To learn more :
– the chemicalengineer- using AI to improve drug development
– ticpharma-iktos la start up qui met l’IA au service de la recherche pharmaceutique
– actuia – les lauréats du 2eme IA santé challenge organisé par Pierre Fabre