Data analysis and diagnostic testing company Ariana Pharma joined the IMODI (Innovative Models Initiative) consortium and will receive €1.2 million (roughly $1.55 million) in funding under the French Government's "Investing for the Future" program.
IMODI is a national collaborative project in France with the goal of enabling the development of new drugs and personalized medicine approaches in cancer by creating an industrial-scale pipeline that characterizes, standardizes, and makes use of predictive cancer models. Its total cost is estimated to be €41 million (approximately $53 million). The concept of personalized medicine is the cornerstone of the IMODI project, which will receive a public investment of €13.4 million (around $17.3 million). The IMODI project is coordinated by Oncodesign and now brings together four SMEs (Ariana Pharma, Oncodesign, Oncomedics, and CTI Biotech), six industrial groups (including Sanofi and Ipsen), and seven academic institutions (including INSERM [French National Institute for Health and Medical Research] and CNRS [French National Center for Scientific Research]).
Starting with the experimental data generated by the program, Ariana will develop algorithms that identify signals associated with cancer and implement them into predictive tools with the aim of improving patient management. Ariana will also deploy its KEM® (Knowledge Extraction & Management) technology, used by FDA since 2010, to analyze pharmacogenomic data in tandem with patient characteristics.
Using Ariana Pharma's technology, IMODI will develop diagnostic tests that match each individual patient to the appropriate treatment. Ariana Pharma aims to create predictive tools to identify tumor, immunological, and microbiotic biomarkers, and will also will help establish a centralized biobank.
"IMODI and Ariana Pharma share the goal of facilitating the selection of new and effective treatments using a personalized medicine approach," said Mohammad Afshar, CEO of Ariana Pharma. "Our solutions will make it possible to identify biomarker combinations that are impossible to detect using traditional statistical methods and to optimize data so as to best use them for patients."