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Real Time Coverage @BIOConvention #BIO2019:  Issues of Risk and Reproduceability in Translational and Academic Collaboration; 2:30-4:00 June 3 Philadelphia PA

Posted in Computational Biology/Systems and Bioinformatics, Conference Coverage with Social Media, Global Partnering & Biotech Investment, Investment in Technological Breakthrough, Life Sciences Breakthrough Prize, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Informatics, Pharmaceutical R&D Investment, tagged , , , , , , , , , , on June 3, 2019| Leave a Comment »

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.

Speakers
Collaboration from Chicago area universities like U of Chicago, Northwestern, etc.  First phase was enhance collaboration between universities by funding faculty recruitment and basic research.  Access to core facilities across universities.  Have expanded to give alternatives to company formation.
Half of the partnerships from Harvard and companies have been able to spin out viable startups.
Most academic PI are not as savvy to start a biotech so they bring in biotechs and build project teams as well as developing a team of ex pharma and biotech experts.  Derisk as running as one asset project.  Partner as early as possible.  A third of their pipeline have been successfully partnered.  Work with investors and patent attorneys.
Focused on getting PIs to get to startup.  Focused on oncology and vaccines and I/O.  The result can be liscensing or partnership. Running around 50 to 60 projects. Creating a new company from these US PI partnerships.
Most projects from Harvard have been therapeutics-based.  At Harvard they have a network of investors ($50 million).   They screen PI proposals based on translateability and what investors are interested in.
In Chicago they solicit multiple projects but are agnostic on area but as they are limited they are focused on projects that will assist in developing a stronger proposal to investor/funding mechanism.
NYU goes around university doing due diligence reaching out to investigators. They shop around their projects to wet their investors, pharma appetite future funding.  At Takeda they have five centers around US.  They want to have more input so go into the university with their scientists and discuss ideas.
Challenges:

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

Speakers
Novel Approaches to Improve Reproducibility in Academia-Industry Collaborations

112AB, Level 100

Speakers
Dr. Freedman: Most scientists responding to Nature survey said yes a reproduceability crisis.  The reasons: experimental bias, lack of validation techniques, reagents, and protocols etc.
And as he says there is a great ECONOMIC IMPACT of preclinical reproducability issues: to the tune of $56 billion of irreproducable results (paper published in PLOS Biology).  If can find the core drivers of this issue they can solve the problem.  STANDARDS are constantly used in various industries however academic research are lagging in developing such standards.  Just the problem of cell line authentication is costing $4 billion.
Dr. Cousins:  There are multiple high throughput screening (HTS) academic centers around the world (150 in US).  So where does the industry go for best practices in assays?  Eli Lilly had developed a manual for HTS best practices and in 1984 made publicly available (Assay Guidance Manual).  To date there have been constant updates to this manual to incorporate new assays.  Workshops have been developed to train scientists in these best practices.
NIH has been developing new programs to address these reproducability issues.  Developed a method called
“Ring Testing Initiative” where multiple centers involved in sharing reagents as well as assays and allowing scientists to test at multiple facilities.
Dr.Tong: Reproduceability of Microarrays:  As microarrays were the only methodology to do high through put genomics in the early 2000s, and although much research had been performed to standardize and achieve best reproduceability of the microarray technology (determining best practices in spotting RNA on glass slides, hybridization protocols, image analysis) little had been done on evaluating the reproducibility of results obtained from microarray experiments involving biological samples.  The advent of Artificial Intelligence and Machine Learning though can be used to help validate microarray results.  This was done in a Nature Biotechnology paper (Nature Biotechnology 28, 827–838 (2010)) by an international consortium, the International MAQC (Microarray Quality Control) Society and can be found here
However Dr. Tong feels there is much confusion in how we define reproduceability.  Dr. Tong identified a few key points of data reproduceability:
  1. Traceability: what are the practices and procedures from going from point A to point B (steps in a protocol or experimental design)
  2. Repeatability:  ability to repeat results within the same laboratory
  3. Replicatablilty:  ability to repeat results cross laboratory
  4. 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.

Other Articles on Industry/Academic Research Partnerships and Translational Research on this Open Access Online Journal Include

Envisage-Wistar Partnership and Immunacel LLC Presents at PCCI

BIO Partnering: Intersection of Academic and Industry: BIO INTERNATIONAL CONVENTION June 23-26, 2014 | San Diego, CA

R&D Alliances between Big Pharma and Academic Research Centers: Pharma’s Realization that Internal R&D Groups alone aren’t enough

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On the Folly of Big Science

Posted in Academic Publishing, tagged , , , , on October 3, 2015| Leave a Comment »

On the Folly of Big Science

Larry H. Bernstein, MD

Principal, Triplex Medical Science

To the reader:

The very to the point and interesting OP-ED in the Sat, Oct 3, New York Times titled “The Folly of Big Science Awards” by Vinay Prasad is of considerable interest for discussing a problem that goes deeper than the awards.

It is a valid and important points that Dr. Prasad makes that the Dickson, Lasker-DeBakey, Canada Gairdner, Breakthrough and Nobel awards are expending significant resources in support for established investigators at the apex of their careers, that there is always a trailing of leading investigations that have anticipated the awards, and that young investigators are currently squeezed by this structure of scientific endeavor.

In a historical perspective, the tradition of major centers of research is at least 200 years old, and it precedes the Nobel Prize. Notable centers of research in Europe were Cambridge, Copenhagen, Italy, Berlin, and several universities in Germany, from which evolved theoretical and experimental physics,
organic and inorganic chemistry, and this had an impact on the basic science requirements for medical education that came from the Flexner Report, and the establishment of Johns Hopkins University Medical School, and the Rockefeller University.  In the evolution of the medical and supporting scientific disciplines there is a long audit trail of top investigators coming through the laboratories of one or more of the most respected laboratories. This is highlighted in Germany by the Kaiser-Wilhelm Institutes. It is important to emphasize the importance of mentoring to the development of young investigators as well as continued support of their career development. The observation is quite impressive that eighty percent of the funding to basic research goes to one fifth of researchers.

The developments in medicine and in preparatory scientific education and research have for a long time aggregate resources to a small number of highly productive centers. This was in part driven by the ties of these centers to major universities and the review committees that serve NIH funding allocation. In addition, there is also a clustering of major centers of discovery and the instrumentation and technical development industries. A major additional funding comes from a select group of billionaires whose support has been essential beyond the dwindling, politically fragile federal support. The large science awards are for recognition, and they follow the course set by the Nobel Prize. There are also Young Investigator awards that are given by professional organizations.

The idea of breaking down the largest awards perhaps needs consideration. However, the most important consideration is to make adequate funding available to promising investigators irrespective of their university affiliation.  That is also complicated by the fact that funding for research belongs to the institution, and not the investigator. I cite the recent lawsuit won by the Scripps Research Institute in a suit filed by an investigator who was recruited from University of California, San Diego (#16) to the Keck School of Medicine at University of Southern California (#47). The funds could not be transferred. An adjacent problem to that described is how funding is directed to established research, and some good research may be squeezed. Only recently has proteomics and metabolomics opened up after the many years of emphasis in biological research on the genome. This occurred because of better understanding of cell signaling and regulatory pathways.

A different problem is that important discoveries may lead to patents, but the cost of development is a multiple of the cost of the patent. Companies that are dominant in the industry might want to buy the patent, but then they might shelve it because it may compete with another method that has not had its return on investment, or they might try to redesign the method with no significant improvement prior to introduction. This is the way the world works.  The United States has become a leader, and it is also the most wasteful in its success.

Larry H. Bernstein, MD

 

 

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