Real Time Coverage @BIOConvention #BIO2019: Issues of Risk and Reproduceability in Translational and Academic Collaboration; 2:30-4:00 June 3 Philadelphia PA
Reporter: Stephen J. Williams, PhD @StephenJWillia2
Derisking Academic Science: The Unmet Need
Translating academic research into products and new therapies is a very risky venture as only 1% of academic research has been successfully translated into successful products.
Takeda: Data Validation very important. Second there may be disconnect with the amount of equity the PI wants in the new company as well as management. Third PIs not aware of all steps in drug development.
Harvard: Pharma and biotech have robust research and academic does not have the size or scope of pharma. PIs must be more diligent on e.g. the compounds they get from a screen… they only focus narrowly
NYU: bring in consultants as PIs don’t understand all the management issues. Need to understand development so they bring in the experts to help them. Pharma he feels have to much risk aversion and none of their PIs want 100% equity.
Chicago: they like to publish at early stage so publication freedom is a challenge
It Starts with One Health: A Guide to Translational Science from Research to Investment
115B, Level 100
Novel Approaches to Improve Reproducibility in Academia-Industry Collaborations
112AB, Level 100
- Traceability: what are the practices and procedures from going from point A to point B (steps in a protocol or experimental design)
- Repeatability: ability to repeat results within the same laboratory
- Replicatablilty: ability to repeat results cross laboratory
- Transferability: are the results validated across multiple platforms?
The panel then discussed the role of journals and funders to drive reproduceability in research. They felt that editors have been doing as much as they can do as they receive an end product (the paper) but all agreed funders need to do more to promote data validity, especially in requiring that systematic evaluation and validation of each step in protocols are performed.. There could be more training of PIs with respect to protocol and data validation.