Nanoscale drug testing: Sweden as a test bed for revolutionising drug discovery
|Coordinator||Karolinska Institutet - Institutionen för Onkologi och Patologi (OnkPat)|
|Funding from Vinnova||SEK 10 000 000|
|Project duration||December 2018 - December 2020|
|Venture||Utmaningsdriven innovation – steg 2 samverkansprojekt|
|Call||Challenge-driven innovation - Stage 2 Collaboration 2018 (autumn)|
Purpose and goal
While the need for new therapies to address global health challenges is apparent, the pharmaceutical industry is struggling to increase its R&D productivity. This highlights a fundamental lack of tools in pharma and diagnostics industries to accurately predict efficacy and toxicity in patients. Such tools need to be implemented early in drug discovery pipeline to ensure all R&D investments reflect the variability of the patient population. However, there is currently no solution to cost-effectively embrace biological complexity at the scale required for drug development.
Expected results and effects
A paradigm shift in drug discovery is imperative to address global health challenges. A “near-patient drug development” strategy will ensure sustainability of the industry; address unmet patient needs and improve efficiency of healthcare by utilizing patient-derived cells to match the right drug development leads with the right patients to reduce clinical failures. This strategy enables development of safer, more effective treatments at lower cost and generates new offerings brought to global markets by Swedish biotechs, and establishes Sweden as a testbed for R&D investments.
Planned approach and implementation
Our consortium will co-create and implement new nanoscale concepts for drug testing and predictive diagnostics. The core of our proposal consists of: 1) addressing sourcing of patient-relevant cell models reflecting patient variability; 2) miniaturization of test systems for cost-effective implementation in the drug discovery process; 3) integration of measurements on interpatient drug binding variablity. Our aim is to discriminate very early on between drug candidates that should be stopped or pursued, by linking potential to cure and side effects in the same patient cohort.