Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com
(CNN)Every morning, Dr. David Fajgenbaum takes three life-saving pills. He wakes up his 21-month-old daughter Amelia to help feed her. He usually grabs some Greek yogurt to eat quickly before sitting down in his home office. Then he spends most of the next 14 hours leading dozens of fellow researchers and volunteers in a systematic review of all the drugs that physicians and researchers have used so far to treat Covid-19. His team has already poredover more than 8,000 papers on how to treat coronavirus patients.
The 35-year-old associate professor at the University of Pennsylvania Perelman School of Medicine leads the school’s Center for Cytokine Storm Treatment & Laboratory. For the last few years, he has dedicated his life to studying Castleman disease, a rare condition that nearly claimed his life. Against epic odds, he found a drug that saved his own life six years ago, by creating a collaborative method for organizing medical research that could be applicable to thousands of human diseases. But after seeing how the same types of flares ofimmune-signaling cells, called cytokine storms, kill both Castleman and Covid-19 patients alike, his lab has devoted nearly all of its resources to aiding doctors fighting the pandemic.
A global repository for Covid-19 treatment data
Researchers working with his lab have reviewed published data on more than 150 drugs doctors around the world have to treat nearly 50,000 patients diagnosed with Covid-19. They’ve made their analysis public in a database called the Covid-19 Registry of Off-label & New Agents (or CORONA for short).
It’s a central repository of all available data in scientific journals on all the therapies used so far to curb the pandemic. This information can help doctors treat patients and tell researchers how to build clinical trials.The team’s process resembles that of the coordination Fajgenbaum used as a medical student to discover that he could repurpose Sirolimus, an immunosuppressant drug approved for kidney transplant patients, to prevent his body from producing deadly flares of immune-signaling cells called cytokines.The 13 members of Fajgenbaum’s lab recruited dozens of other scientific colleagues to join their coronavirus effort. And what this group is finding has ramifications for scientists globally.
This effort by Dr. Fajgenbaum’s lab and the resultant collaborative effort shows the power and speed at which a coordinated open science effort can achieve goals. Below is the description of the phased efforts planned and completed from the CORONA website.
CORONA (COvid19 Registry of Off-label & New Agents)
Drug Repurposing for COVID-19
Our overarching vision: A world where data on all treatments that have been used against COVID19 are maintained in a central repository and analyzed so that physicians currently treating COVID19 patients know what treatments are most likely to help their patients and so that clinical trials can be appropriately prioritized.
Our team reviewed 2500+ papers & extracted data on over 9,000 COVID19 patients. We found 115 repurposed drugs that have been used to treat COVID19 patients and analyzed data on which ones seem most promising for clinical trials. This data is open source and can be used by physicians to treat patients and prioritize drugs for trials. The CDCN will keep this database updated as a resource for this global fight. Repurposed drugs give us the best chance to help COVID19 as quickly as possible! As disease hunters who have identified and repurposed drugs for Castleman disease, we’re applying our ChasingMyCure approach to COVID19.
From Fajgenbaum, D.C., Khor, J.S., Gorzewski, A. et al. Treatments Administered to the First 9152 Reported Cases of COVID-19: A Systematic Review. Infect Dis Ther (2020). https://doi.org/10.1007/s40121-020-00303-8
The following is the Abstract and link to the metastudy. This study was a systematic review of literature with strict inclusion criteria. Data was curated from these published studies and a total of 9152 patients were evaluated for treatment regimens for COVID19 complications and clinical response was curated for therapies in these curated studies. Main insights from this study were as follows:
Key Summary Points
Why carry out this study?
Data on drugs that have been used to treat COVID-19 worldwide are currently spread throughout disparate publications.
We performed a systematic review of the literature to identify drugs that have been tried in COVID-19 patients and to explore clinically meaningful response time.
What was learned from the study?
We identified 115 uniquely referenced treatments administered to COVID-19 patients. Antivirals were the most frequently administered class; combination lopinavir/ritonavir was the most frequently used treatment.
This study presents the latest status of off-label and experimental treatments for COVID-19. Studies such as this are important for all diseases, especially those that do not currently have definitive evidence from randomized controlled trials or approved therapies.
The emergence of SARS-CoV-2/2019 novel coronavirus (COVID-19) has created a global pandemic with no approved treatments or vaccines. Many treatments have already been administered to COVID-19 patients but have not been systematically evaluated. We performed a systematic literature review to identify all treatments reported to be administered to COVID-19 patients and to assess time to clinically meaningful response for treatments with sufficient data. We searched PubMed, BioRxiv, MedRxiv, and ChinaXiv for articles reporting treatments for COVID-19 patients published between 1 December 2019 and 27 March 2020. Data were analyzed descriptively. Of the 2706 articles identified, 155 studies met the inclusion criteria, comprising 9152 patients. The cohort was 45.4% female and 98.3% hospitalized, and mean (SD) age was 44.4 years (SD 21.0). The most frequently administered drug classes were antivirals, antibiotics, and corticosteroids, and of the 115 reported drugs, the most frequently administered was combination lopinavir/ritonavir, which was associated with a time to clinically meaningful response (complete symptom resolution or hospital discharge) of 11.7 (1.09) days. There were insufficient data to compare across treatments. Many treatments have been administered to the first 9152 reported cases of COVID-19. These data serve as the basis for an open-source registry of all reported treatments given to COVID-19 patients at www.CDCN.org/CORONA. Further work is needed to prioritize drugs for investigation in well-controlled clinical trials and treatment protocols.
Our team continues to work diligently to maintain an updated listing of all treatments reported to be used in COVID19 patients from papers in PubMed. We are also re-analyzing publicly available COVID19 single cell transcriptomic data alongside our iMCD data to search for novel insights and therapeutic targets.
You can visit the following link to access a database viewer built and managed by Matt Chadsey, owner of Nonlinear Ventures.
If you are a physician treating COVID19 patients, please visit the FDA’s CURE ID app to report de-identified information about drugs you’ve used to treat COVID19 in just a couple minutes.
For more information on COVID19 on this Open Access Journal please see our Coronavirus Portal at
Real Time Coverage @BIOConvention #BIO2019: Issues of Risk and Reproduceability in Translational and Academic Collaboration; 2:30-4:00 June 3 Philadelphia PA
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.
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.
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
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 Biotechnologyvolume28, pages827–838 (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:
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.
Other Articles on Industry/Academic Research Partnerships and Translational Research on this Open Access Online Journal Include
The Vibrant Philly Biotech Scene: Focus on Computer-Aided Drug Design and Gfree Bio, LLC
Curator: Stephen J. Williams, Ph.D.
This post is the second in a series of posts highlighting interviews with Philadelphia area biotech startup CEO’s and show how a vibrant biotech startup scene is evolving in the city as well as the Delaware Valley area. Philadelphia has been home to some of the nation’s oldest biotechs including Cephalon, Centocor, hundreds of spinouts from a multitude of universities as well as home of the first cloned animal (a frog), the first transgenic mouse, and Nobel laureates in the field of molecular biology and genetics. Although some recent disheartening news about the fall in rankings of Philadelphia as a biotech hub and recent remarks by CEO’s of former area companies has dominated the news, biotech incubators like the University City Science Center and Bucks County Biotechnology Center as well as a reinvigorated investment community (like PCCI and MABA) are bringing Philadelphia back. And although much work is needed to bring the Philadelphia area back to its former glory days (including political will at the state level) there are many bright spots such as the innovative young companies as outlined in these posts.
In today’s post, I had the opportunity to talk with molecular modeler Charles H. Reynolds, Ph.D., founder and CEO of Gfree Bio LLC, a computational structure-based design and modeling company based in the Pennsylvania Biotech Center of Bucks County. Gfree is actually one of a few molecular modeling companies at the Bucks County Biotech Center (I highlighted another company RabD Biotech which structural computational methods to design antibody therapeutics).
Below is the interview with Dr. Reynolds of Gfree Bio LLC and Leaders in Pharmaceutical Business Intelligence (LPBI):
LPBI: Could you briefly explain, for non-molecular modelers, your business and the advantages you offer over other molecular modeling programs (either academic programs or other biotech companies)? As big pharma outsources more are you finding that your company is filling a needed niche market?
GfreeBio: Gfree develops and deploys innovative computational solutions to accelerate drug discovery. We can offer academic labs a proven partner for developing SBIR/STTR proposals that include a computational or structure-based design component. This can be very helpful in developing a successful proposal. We also provide the same modeling and structure-based design input for small biotechs that do not have these capabilities internally. Working with Gfree is much more cost-effective than trying to develop these capabilities internally. We have helped several small biotechs in the Philadelphia region assess their modeling needs and apply computational tools to advance their discovery programs. (see publication and collaboration list here).
LPBI: Could you offer more information on the nature of your 2014 STTR award?
GfreeBio: Gfree has been involved in three successful SBIR/STTR awards in 2014. I am the PI for an STTR with Professor Burgess of Texas A&M that is focused on new computational and synthetic approaches to designing inhibitors for protein-protein interactions. Gfree is also collaborating with the Wistar Institute and Phelix Therapeutics on two other Phase II proposals in the areas of oncology and infectious disease.
LPBI: Why did you choose the Bucks County Pennsylvania Biotechnology Center?
GfreeBio: I chose to locate my company at the Biotech Center because it is a regional hub for small biotech companies and it provides a range of shared resources that are very useful to the company. Many of my most valuable collaborations have resulted from contacts at the center.
LPBI: The Blumberg Institute and Natural Products Discovery Institute has acquired a massive phytochemical library. How does this resource benefit the present and future plans for GfreeBio?
GfreeBio: To date Gfree Bio has not been an active collaborator with the Natural Products Insititute, but I have a good relationship with the Director and that could change at any time.
LPBI: Was the state of Pennsylvania and local industry groups support GfreeBio’s move into the Doylestown incubator? Has the partnership with Ben Franklin Partners and the Center provided you with investment and partnership opportunities?
GfreeBio: Gfree Bio has not been actively seeking outside investors, at least to date. We have been focused on growing the company through collaborations and consulting relationships. However, we have benefitted from being part of the Keystone Innovation Zone, a state program that provides incentives for small technology-based businesses in Pennsylvania.
LPBI: You will be speaking at a conference in the UK on reinventing the drug discovery process through tighter collaborations between biotech, academia, and non-profit organizations. How do you feel the Philadelphia area can increase this type of collaboration to enhance not only the goals and missions of nonprofits, invigorate the Pennsylvania biotech industry, but add much needed funding to the local academic organizations?
GfreeBio: I think this type of collaboration across sectors appears to be one of the most important emerging models for drug discovery. The Philadelphia region has been in many ways hard hit by the shift of drug discovery from large vertically integrated pharmaceutical companies to smaller biotechs, since this area was at the very center of “Big Pharma.” But I think the region is bouncing back as it shifts more to being a center for biotech. The three ingredients for success in the new pharma model are great universities, a sizeable talent pool, and access to capital. The last item may be the biggest challenge locally. The KIZ program (Keystone Innovation Zone) is a good start, but the region and state could do more to help promote innovation and company creation. Some other states are being much more aggressive.
LPBI: In addition, the Pennsylvania Biotechnology Center in Bucks County appears to have this ecosystem: nonprofit organizations, biotechs, and academic researchers. Does this diversity of researchers/companies under one roof foster the type of collaboration needed, as will be discussed at the UK conference? Do you feel collaborations which are in close physical proximity are more effective and productive than a “virtual-style” (online) collaboration model? Could you comment on some of the collaborations GfreeBio is doing with other area biotechs and academics?
GfreeBio: I do think the “ecosystem” at the Pennsylvania Biotechnology Center is important in fostering new innovative companies. It promotes collaborations that might not happen otherwise, and I think close proximity is always a big plus. As I mentioned before, many of the current efforts of Gfree have come from contacts at the center. This includes SBIR/STTR collaborations and contract work for local small biotech companies.
LPBI: Thompson Reuters just reported that China’s IQ (Innovation Quotient) has risen dramatically with the greatest patents for pharmaceuticals and compounds from natural products. Have you or your colleagues noticed more competition or business from Chinese pharmaceutical companies?
GfreeBio: The rise of Asia, particularly China, has been one of the most significant recent trends in the pharmaceutical industry. Initially, this was almost exclusively in the CRO space, but now China is aggressively building a fully integrated domestic pharmaceutical industry.
LPBI: How can the Philadelphia ecosystem work closer together to support greater innovation?
GfreeBio: A lot has happened in recent years to promote innovation and company creation in the region. There could always be more opportunities for networking and collaboration within the Philadelphia community. Of course the biggest obstacle in this business is often financing. Philadelphia needs more public and private sources for investment in startups.
LPBI: Thank you Dr. Reynolds.
Please look for future posts in this series on the Philly Biotech Scene on this site
Heroes in Medical Research: Green Fluorescent Protein and the Rough Road in Science
Curator: Stephen J. Williams, Ph.D.
In this series, “Heroes in Medical Research”, I like to discuss the people who made some important contributions to science and medicine which underlie the great transformative changes but don’t usually get the notoriety given to Nobel Laureates or who seem to fly under the radar of popular news. Their work may be the development of research tools which allowed a great discovery leading to a line of transformative research, a moment of serendipity leading to discovery of a cure, or just contributions to the development of a new field or the mentoring of a new generation of scientists and clinicians. One such discovery, which has probably been pivotal in many of our research, is the discovery of the green fluorescent protein (GFP), commonly used as an invaluable tool to monitor protein for cellular expression and localization studies. Although the development of research tools, whether imaging tools, vectors, animal models, cell lines, and such are not heralded, they always assist in the pivotal discoveries of our time. The following is a heartwarming story by Discover Magazine’s Yudhijit Bhattacharjee behind Dr. Douglas Prasher’s discovery of the green fluorescent protein, his successful efforts to sequence the gene and subsequent struggles in science and finally scientific recognition for his work. In addition the story describes Dr. Prather’s perseverance, a trait necessary for every scientist.
In summary, Dr. Prather had been working at Wood’s Hole in Massachusetts trying to discover, isolate, then clone the protein which allowed a species of jellyfish living in the cold waters of the North Pacific, Aequorea victoria, to emit a green glow. Eventually he cloned the GFP gene, but gave up on work to express the gene in mammalian cells. Before leaving Wood’s Hole he gave the gene to Dr. Roger Tsien, who with Dr. Martin Chalfie and Osamu Shimomura showed the utility of GFP as an intracellular tracer to visualize, in real time, the expression and localization of GFP-tagged proteins (all three shared the 2008 Nobel Prize for this work). Dr. Tsien however realized the importance of Douglas’s cloning work as pivotal for their research, contacted Douglas (who now due to the bad economy was working at a Toyota dealership in Alabama) and invited him to the Nobel Prize Award Ceremony in Sweden as his guest. Although Dr. Prasher had “left academic science” he never really stopped his quest for a scientific career, using his spare time to review manuscripts.
Other researchers have invited their colleagues who made important contributions to the ultimate Nobel work. One such guest was one of my colleagues Dr. Leonard Cohen, who worked with Dr. Irwin Rose and Avram Hershko at the Institute for Cancer Research in Philadelphia a cell-free system from clams to discover the mechanism how cyclin B is degraded during the exit from the cell cycle (from A. Hershko’s Nobel speech). Dr. Hershko had acknowledged a slew of colleagues and highlighted their contributions to the ultimate work. It shows how even small discoveries can contribute to the sphere of scientific knowledge and breakthrough.
1. Chalfie, M., Tu, Y., Euskirchen, G., Ward, W.W., Prasher, D.C., Green fluorescent protein as a marker for gene expression. Science, 263(5148), 802-805 (1994).
Scientific Curation Fostering Expert Networks and Open Innovation: Lessons from Clive Thompson
Curators and Writer: Stephen J. Williams, Ph.D. with input from Curators Larry H. Bernstein, MD, FCAP, Dr. Justin D. Pearlman, MD, PhD, FACC and Dr. Aviva Lev-Ari, PhD, RN
(this discussion is in a three part series including:
Using Scientific Content Curation as a Method for Validation and Biocuration
Using Scientific Content Curation as a Method for Open Innovation)
Every month I get my Wired Magazine (yes in hard print, I still like to turn pages manually plus I don’t mind if I get grease or wing sauce on my magazine rather than on my e-reader) but I always love reading articles written by Clive Thompson. He has a certain flair for understanding the techno world we live in and the human/technology interaction, writing about interesting ways in which we almost inadvertently integrate new technologies into our day-to-day living, generating new entrepreneurship, new value. He also writes extensively about tech and entrepreneurship.
Clive gives a wonderful example of Ory Okolloh, a young Kenyan-born law student who, after becoming frustrated with the lack of coverage of problems back home, started a blog about Kenyan politics. Her blog not only got interest from movie producers who were documenting female bloggers but also gained the interest of fellow Kenyans who, during the upheaval after the 2007 Kenyan elections, helped Ory to develop a Google map for reporting of violence (http://www.ushahidi.com/, which eventually became a global organization using open-source technology to affect crises-management. There are a multitude of examples how networks and the conversations within these circles are fostering new ideas. As Clive states in the article:
Our ideas are PRODUCTS OF OUR ENVIRONMENT.
They are influenced by the conversations around us.
However the article got me thinking of how Science 2.0 and the internet is changing how scientists contribute, share, and make connections to produce new and transformative ideas.
But HOW MUCH Knowledge is OUT THERE?
Clive’s article listed some amazing facts about the mountains of posts, tweets, words etc. out on the internet EVERY DAY, all of which exemplifies the problem:
154.6 billion EMAILS per DAY
400 million TWEETS per DAY
1 million BLOG POSTS (including this one) per DAY
2 million COMMENTS on WordPress per DAY
16 million WORDS on Facebook per DAY
TOTAL 52 TRILLION WORDS per DAY
As he estimates this would be 520 million books per DAY (book with average 100,000 words).
A LOT of INFO. But as he suggests it is not the volume but how we create and share this information which is critical as the science fiction writer Theodore Sturgeon noted “Ninety percent of everything is crap” AKA Sturgeon’s Law.
Internet live stats show how congested the internet is each day (http://www.internetlivestats.com/). Needless to say Clive’s numbers are a bit off. As of the writing of this article:
2.9 billion internet users
981 million websites (only 25,000 hacked today)
128 billion emails
385 million Tweets
> 2.7 million BLOG posts today (including this one)
The Good, The Bad, and the Ugly of the Scientific Internet (The Wild West?)
So how many science blogs are out there? Well back in 2008 “grrlscientist” asked this question and turned up a total of 19,881 blogs however most were “pseudoscience” blogs, not written by Ph.D or MD level scientists. A deeper search on Technorati using the search term “scientist PhD” turned up about 2,000 written by trained scientists.
So granted, there is a lot of
….. when it comes to scientific information on the internet!
I had recently re-posted, on this site, a great example of how bad science and medicine can get propagated throughout the internet:
Drs.Elena Cattaneo and Gilberto Corbellini document their long, hard fight against false and invalidated medical claims made by some “clinicians” about the utility and medical benefits of certain stem-cell therapies, sacrificing their time to debunk medical pseudoscience.
Using Curation and Science 2.0 to build Trusted, Expert Networks of Scientists and Clinicians
Establishing networks of trusted colleagues has been a cornerstone of the scientific discourse for centuries. For example, in the mid-1640s, the Royal Society began as:
“a meeting of natural philosophers to discuss promoting knowledge of the
natural world through observation and experiment”, i.e. science.
The Society met weekly to witness experiments and discuss what we
would now call scientific topics. The first Curator of Experiments
Indeed as discussed in “Science 2.0/Brainstorming” by the originators of OpenWetWare, an open-source science-notebook software designed to foster open-innovation, the new search and aggregation tools are making it easier to find, contribute, and share information to interested individuals. This paradigm is the basis for the shift from Science 1.0 to Science 2.0. Science 2.0 is attempting to remedy current drawbacks which are hindering rapid and open scientific collaboration and discourse including:
Slow time frame of current publishing methods: reviews can take years to fashion leading to outdated material
Level of information dissemination is currently one dimensional: peer-review, highly polished work, conferences
Current publishing does not encourage open feedback and review
Published articles edited for print do not take advantage of new web-based features including tagging, search-engine features, interactive multimedia, no hyperlinks
Published data and methodology incomplete
Published data not available in formats which can be readably accessible across platforms: gene lists are now mandated to be supplied as files however other data does not have to be supplied in file format
(put in here a brief blurb of summary of problems and why curation could help)
Curation in the Sciences: View from Scientific Content Curators Larry H. Bernstein, MD, FCAP, Dr. Justin D. Pearlman, MD, PhD, FACC and Dr. Aviva Lev-Ari, PhD, RN
Curation is an active filtering of the web’s and peer reviewed literature found by such means – immense amount of relevant and irrelevant content. As a result content may be disruptive. However, in doing good curation, one does more than simply assign value by presentation of creative work in any category. Great curators comment and share experience across content, authors and themes. Great curators may see patterns others don’t, or may challenge or debate complex and apparently conflicting points of view. Answers to specifically focused questions comes from the hard work of many in laboratory settings creatively establishing answers to definitive questions, each a part of the larger knowledge-base of reference. There are those rare “Einstein’s” who imagine a whole universe, unlike the three blind men of the Sufi tale. One held the tail, the other the trunk, the other the ear, and they all said this is an elephant!
In my reading, I learn that the optimal ratio of curation to creation may be as high as 90% curation to 10% creation. Creating content is expensive. Curation, by comparison, is much less expensive.
– Larry H. Bernstein, MD, FCAP
Curation is Uniquely Distinguished by the Historical Exploratory Ties that Bind –Larry H. Bernstein, MD, FCAP
The explosion of information by numerous media, hardcopy and electronic, written and video, has created difficulties tracking topics and tying together relevant but separated discoveries, ideas, and potential applications. Some methods to help assimilate diverse sources of knowledge include a content expert preparing a textbook summary, a panel of experts leading a discussion or think tank, and conventions moderating presentations by researchers. Each of those methods has value and an audience, but they also have limitations, particularly with respect to timeliness and pushing the edge. In the electronic data age, there is a need for further innovation, to make synthesis, stimulating associations, synergy and contrasts available to audiences in a more timely and less formal manner. Hence the birth of curation. Key components of curation include expert identification of data, ideas and innovations of interest, expert interpretation of the original research results, integration with context, digesting, highlighting, correlating and presenting in novel light.
Work of Original Expression what is the methodology of Curation in the context of Medical Research Findings Exposition of Synthesis and Interpretation of the significance of the results to Clinical Care
… leading to new, curated, and collaborative works by networks of experts to generate (in this case) ebooks on most significant trends and interpretations of scientific knowledge as relates to medical practice.
In Summary: How Scientific Content Curation Can Help
Given the aforementioned problems of:
I. the complex and rapid deluge of scientific information
II. the need for a collaborative, open environment to produce transformative innovation
III. need for alternative ways to disseminate scientific findings
CURATION MAY OFFER SOLUTIONS
I. Curation exists beyond the review: curation decreases time for assessment of current trends adding multiple insights, analyses WITH an underlying METHODOLOGY (discussed below) while NOT acting as mere reiteration, regurgitation
II. Curation providing insights from WHOLE scientific community on multiple WEB 2.0 platforms
III. Curation makes use of new computational and Web-based tools to provide interoperability of data, reporting of findings (shown in Examples below)
Therefore a discussion is given on methodologies, definitions of best practices, and tools developed to assist the content curation community in this endeavor.
Methodology in Scientific Content Curation as Envisioned by Aviva lev-Ari, PhD, RN
At Leaders in Pharmaceutical Business Intelligence, site owner and chief editor Aviva lev-Ari, PhD, RN has been developing a strategy “for the facilitation of Global access to Biomedical knowledge rather than the access to sheer search results on Scientific subject matters in the Life Sciences and Medicine”. According to Aviva, “for the methodology to attain this complex goal it is to be dealing with popularization of ORIGINAL Scientific Research via Content Curation of Scientific Research Results by Experts, Authors, Writers using the critical thinking process of expert interpretation of the original research results.” The following post:
Cardiovascular Original Research: Cases in Methodology Design for Content Curation and Co-Curation
demonstrate two examples how content co-curation attempts to achieve this aim and develop networks of scientist and clinician curators to aid in the active discussion of scientific and medical findings, and use scientific content curation as a means for critique offering a “new architecture for knowledge”. Indeed, popular search engines such as Google, Yahoo, or even scientific search engines such as NCBI’s PubMed and the OVID search engine rely on keywords and Boolean algorithms …
which has created a need for more context-driven scientific search and discourse.
To address this need, human intermediaries, empowered by the participatory wave of web 2.0, naturally started narrowing down the information and providing an angle of analysis and some context. They are bloggers, regular Internet users or community managers – a new type of profession dedicated to the web 2.0. A new use of the web has emerged, through which the information, once produced, is collectively spread and filtered by Internet users who create hierarchies of information.
.. where Célya considers curation an essential practice to manage open science and this new style of research.
As mentioned above in her article, Dr. Lev-Ari represents two examples of how content curation expanded thought, discussion, and eventually new ideas.
Curator edifies content through analytic process = NEW form of writing and organizations leading to new interconnections of ideas = NEW INSIGHTS
The Life Cycle of Science 2.0. Due to Web 2.0, new paradigms of scientific collaboration are rapidly emerging. Originally, scientific discovery were performed by individual laboratories or “scientific silos” where the main method of communicationwas peer-reviewed publication, meeting presentation, and ultimately news outlets and multimedia. In this digital era, data was organized for literature search and biocurated databases.In an era of social media, Web 2.0, a group of scientifically and medically trained “curators” organize the piles of data of digitally generated data and fit data into an organizational structure which can be shared, communicated, and analyzed in a holistic approach, launching new ideas due to changes in organization structure of data and data analytics.
The result, in this case, is a collaborative written work above the scope of the review. Currently review articles are written by experts in the field and summarize the state of a research are. However, using collaborative, trusted networks of experts, the result is a real-time synopsis and analysis of the field with the goal in mind to
In her paper, Curating e-Science Data, Maureen Pennock, from The British Library, emphasized the importance of using a diligent, validated, and reproducible, and cost-effective methodology for curation by e-science communities over the ‘Grid’:
“The digital data deluge will have profound repercussions for the infrastructure of research and beyond. Data from a wide variety of new and existing sources will need to be annotated with metadata, then archived and curated so that both the data and the programmes used to transform the data can be reproduced for use in the future. The data represent a new foundation for new research, science, knowledge and discovery”
— JISC Senior Management Briefing Paper, The Data Deluge (2004)
As she states proper data and content curation is important for:
Post-analysis
Data and research result reuse for new research
Validation
Preservation of data in newer formats to prolong life-cycle of research results
However she laments the lack of
Funding for such efforts
Training
Organizational support
Monitoring
Established procedures
Tatiana Aders wrote a nice article based on an interview with Microsoft’s Robert Scoble, where he emphasized the need for curation in a world where “Twitter is the replacement of the Associated Press Wire Machine” and new technologic platforms are knocking out old platforms at a rapid pace. In addition he notes that curation is also a social art form where primary concerns are to understand an audience and a niche.
Indeed, part of the reason the need for curation is unmet, as writes Mark Carrigan, is the lack of appreciation by academics of the utility of tools such as Pinterest, Storify, and Pearl Trees to effectively communicate and build collaborative networks.
And teacher Nancy White, in her article Understanding Content Curation on her blog Innovations in Education, shows examples of how curation in an educational tool for students and teachers by demonstrating students need to CONTEXTUALIZE what the collect to add enhanced value, using higher mental processes such as:
Although many tools are related to biocuration and building databases but the common idea is curating data with indexing, analyses, and contextual value to provide for an audience to generate NETWORKS OF NEW IDEAS.
“Nowadays, any organization should employ network scientists/analysts who are able to map and analyze complex systems that are of importance to the organization (e.g. the organization itself, its activities, a country’s economic activities, transportation networks, research networks).”
Creating Content Curation Communities: Breaking Down the Silos!
An article by Dr. Dana Rotman “Facilitating Scientific Collaborations Through Content Curation Communities” highlights how scientific information resources, traditionally created and maintained by paid professionals, are being crowdsourced to professionals and nonprofessionals in which she termed “content curation communities”, consisting of professionals and nonprofessional volunteers who create, curate, and maintain the various scientific database tools we use such as Encyclopedia of Life, ChemSpider (for Slideshare see here), biowikipedia etc. Although very useful and openly available, these projects create their own challenges such as
information integration (various types of data and formats)
social integration (marginalized by scientific communities, no funding, no recognition)
The authors set forth some ways to overcome these challenges of the content curation community including:
standardization in practices
visualization to document contributions
emphasizing role of information professionals in content curation communities
maintaining quality control to increase respectability
recognizing participation to professional communities
A few great presentations and papers from the 2012 DICOSE meeting are found below
Judith M. Brown, Robert Biddle, Stevenson Gossage, Jeff Wilson & Steven Greenspan. Collaboratively Analyzing Large Data Sets using Multitouch Surfaces. (PDF) NotesForBrown
Bill Howe, Cecilia Aragon, David Beck, Jeffrey P. Gardner, Ed Lazowska, Tanya McEwen. Supporting Data-Intensive Collaboration via Campus eScience Centers. (PDF) NotesForHowe
Kerk F. Kee & Larry D. Browning. Challenges of Scientist-Developers and Adopters of Existing Cyberinfrastructure Tools for Data-Intensive Collaboration, Computational Simulation, and Interdisciplinary Projects in Early e-Science in the U.S.. (PDF) NotesForKee
Betsy Rolland & Charlotte P. Lee. Post-Doctoral Researchers’ Use of Preexisting Data in Cancer Epidemiology Research. (PDF) NoteForRolland
Dana Rotman, Jennifer Preece, Derek Hansen & Kezia Procita. Facilitating scientific collaboration through content curation communities. (PDF) NotesForRotman
Nicholas M. Weber & Karen S. Baker. System Slack in Cyberinfrastructure Development: Mind the Gaps. (PDF) NotesForWeber
Indeed, the movement of Science 2.0 from Science 1.0 had originated because these “silos” had frustrated many scientists, resulting in changes in the area of publishing (Open Access) but also communication of protocols (online protocol sites and notebooks like OpenWetWare and BioProtocols Online) and data and material registries (CGAP and tumor banks). Some examples are given below.
This project looked at what motivates researchers to work in an open manner with regard to their data, results and protocols, and whether advantages are delivered by working in this way.
The case studies consider the benefits and barriers to using ‘open science’ methods, and were carried out between November 2009 and April 2010 and published in the report Open to All? Case studies of openness in research. The Appendices to the main report (pdf) include a literature review, a framework for characterizing openness, a list of examples, and the interview schedule and topics. Some of the case study participants kindly agreed to us publishing the transcripts. This zip archive contains transcripts of interviews with researchers in astronomy, bioinformatics, chemistry, and language technology.
2. cBIO -cBio’s biological data curation group developed and operates using a methodology called CIMS, the Curation Information Management System. CIMS is a comprehensive curation and quality control process that efficiently extracts information from publications.
3. NIH Topic Maps – This website provides a database and web-based interface for searching and discovering the types of research awarded by the NIH. The database uses automated, computer generated categories from a statistical analysis known as topic modeling.
4. SciKnowMine (USC)- We propose to create a framework to support biocuration called SciKnowMine (after ‘Scientific Knowledge Mine’), cyberinfrastructure that supports biocuration through the automated mining of text, images, and other amenable media at the scale of the entire literature.
OpenWetWare– OpenWetWare is an effort to promote the sharing of information, know-how, and wisdom among researchers and groups who are working in biology & biological engineering. Learn more about us. If you would like edit access, would be interested in helping out, or want your lab website hosted on OpenWetWare, pleasejoin us. OpenWetWare is managed by the BioBricks Foundation. They also have a wiki about Science 2.0.
6. LabTrove: a lightweight, web based, laboratory “blog” as a route towards a marked up record of work in a bioscience research laboratory. Authors in PLOS One article, from University of Southampton, report the development of an open, scientific lab notebook using a blogging strategy to share information.
7. OpenScience Project– The OpenScience project is dedicated to writing and releasing free and Open Source scientific software. We are a group of scientists, mathematicians and engineers who want to encourage a collaborative environment in which science can be pursued by anyone who is inspired to discover something new about the natural world.
8. Open Science Grid is a multi-disciplinary partnership to federate local, regional, community and national cyberinfrastructures to meet the needs of research and academic communities at all scales.
9. Some ongoing biomedical knowledge (curation) projects at ISI
IICurate This project is concerned with developing a curation and documentation system for information integration in collaboration with the II Group at ISI as part of the BIRN.
BioScholar It’s primary purpose is to provide software for experimental biomedical scientists that would permit a single scientific worker (at the level of a graduate student or postdoctoral worker) to design, construct and manage a shared knowledge repository for a research group derived on a local store of PDF files. This project is funded by NIGMS from 2008-2012 ( RO1-GM083871).
VIVO for scientific communities– In order to connect this information about research activities across institutions and make it available to others, taking into account smaller players in the research landscape and addressing their need for specific information (for example, by proving non-conventional research objects), the open source software VIVO that provides research information as linked open data (LOD) is used in many countries. So-called VIVO harvesters collect research information that is freely available on the web, and convert the data collected in conformity with LOD standards. The VIVO ontology builds on prevalent LOD namespaces and, depending on the needs of the specialist community concerned, can be expanded.
11. Examples of scientific curation in different areas of Science/Pharma/Biotech/Education
From Science 2.0 to Pharma 3.0 Q&A with Hervé Basset
Hervé Basset, specialist librarian in the pharmaceutical industry and owner of the blog “Science Intelligence“, to talk about the inspiration behind his recent book entitled “From Science 2.0 to Pharma 3.0″, published by Chandos Publishing and available on Amazon and how health care companies need a social media strategy to communicate and convince the health-care consumer, not just the practicioner.
How the Internet of Things is Promoting the Curation Effort
Update by Stephen J. Williams, PhD 3/01/19
Up till now, curation efforts like wikis (Wikipedia, Wikimedicine, Wormbase, GenBank, etc.) have been supported by a largely voluntary army of citizens, scientists, and data enthusiasts. I am sure all have seen the requests for donations to help keep Wikipedia and its other related projects up and running. One of the obscure sister projects of Wikipedia, Wikidata, wants to curate and represent all information in such a way in which both machines, computers, and humans can converse in. About an army of 4 million have Wiki entries and maintain these databases.
Enter the Age of the Personal Digital Assistants (Hellooo Alexa!)
In a March 2019 WIRED article “Encyclopedia Automata: Where Alexa Gets Its Information” senior WIRED writer Tom Simonite reports on the need for new types of data structure as well as how curated databases are so important for the new fields of AI as well as enabling personal digital assistants like Alexa or Google Assistant decipher meaning of the user.
As Mr. Simonite noted, many of our libraries of knowledge are encoded in an “ancient technology largely opaque to machines-prose.” Search engines like Google do not have a problem with a question asked in prose as they just have to find relevant links to pages. Yet this is a problem for Google Assistant, for instance, as machines can’t quickly extract meaning from the internet’s mess of “predicates, complements, sentences, and paragraphs. It requires a guide.”
Enter Wikidata. According to founder Denny Vrandecic,
Language depends on knowing a lot of common sense, which computers don’t have access to
A wikidata entry (of which there are about 60 million) codes every concept and item with a numeric code, the QID code number. These codes are integrated with tags (like tags you use on Twitter as handles or tags in WordPress used for Search Engine Optimization) so computers can identify patterns of recognition between these codes.
Now human entry into these databases are critical as we add new facts and in particular meaning to each of these items. Else, machines have problems deciphering our meaning like Apple’s Siri, where they had complained of dumb algorithms to interpret requests.
The knowledge of future machines could be shaped by you and me, not just tech companies and PhDs.
But this effort needs money
Wikimedia’s executive director, Katherine Maher, had prodded and cajoled these megacorporations for tapping the free resources of Wiki’s. In response, Amazon and Facebook had donated millions for the Wikimedia projects. Google recently gave 3.1 million USD$ in donations.
Future postings on the relevance and application of scientific curation will include:
Using Scientific Content Curation as a Method for Validation and Biocuration
Using Scientific Content Curation as a Method for Open Innovation
Other posts on this site related to Content Curation and Methodology include:
Innovation: Drug Discovery, Medical Devices and Digital Health
Curator: Larry H. Bernstein, MD, FCAP
The following discussuions are related to postings presenting on innovation by Dr. Aviva Lav-Ari. It is painfull on this week that the Federal Funding for research necessary for maintaining a fruitful and dominant position of US universities and scientific organizations is hanging on the vine. What resources will be available to ripen the fruit? Despite the serious fracturing of serious issues debated in the republican “Tea Parrty” led House of Representatives, The actual productivity of scientific discovery has increased with falling budgets since the Vietnam War, mainly because of great postdocs and great mentoring – in both “ivy league”, fluorishing non-ivy league (Duke, Vanderbilt, University of Chicago), and strong state and land-grant universities. The difference now is that states are struggling with budgets and the decline of municipalities, and research is no longer an individual exploring an idea because of the need for many scientists with different technologies and different approaches to collaborate, across worldwide and state borders. Michelangelo as an example. 3-D printing revolution.
This Will Save Us Years — Lean LaunchPad for Life Science Oct 14, 2013
Steve Blank
Part 1 of this post described the issues in the drug discovery. Part 2 covered medical devices and digital health. Part 3 described what we’re going to do about it.
This is post is a brief snapshot of our progress.
Vitruvian is one of the 28 teams in the class. The team members are:
Dr. Hobart Harris Chief of General Surgery, Vice-Chair of the Department of Surgery, and a Professor of Surgery at UCSF. Dr. Harris is also a Principal Investigator in the UCSF Surgical Research Laboratory at San Francisco General Hospital.
Dr. David Young, Professor of Plastic Surgery at UCSF. His area of expertise includes wound healing, microsurgery, and reconstruction after burns and trauma. His research interests include the molecular mechanisms of wound healing and the epidemiology and treatment of soft tissue infections.
Sarah Seegal is at One Medical. Sarah is interested in increasing the quality and accessibility of healthcare services. Sarah worked with Breakthrough.com to connect individuals with professional therapists for online sessions.
Cindy Chang is an Enzymologist investigating novel enzymes involved in biofuel and chemical synthesis in microbes at LS9
Vitruvian’s first product, MyoSeal, promotes wound repair via biocompatible microparticles plus a fibrin tissue sealant that has been shown to prevent incisional hernias through enhanced wound healing. The team believed that surgeons would embrace the product and pay thousands to use it. In week 2 of the class 14 of their potential customers (surgeons) told the team otherwise.
Watch and find out how the Lean LaunchPad class saved them years. https://media.licdn.com/mpr/mpr/shrink_80_80/p/8/000/1c3/112/01bd323.jpg
Image: A derived drawing from Vitruvian Man by Leonardo da Vinci, via Wikimedia Commons
What Michelangelo Can Teach Us about Innovation and Competition
Daniel Burrus Oct 14, 2013
On a recent trip to Italy I had the opportunity to visit both Florence and Rome, and to see the work of some of history’s greatest artists, including Michelangelo.
In Florence, I saw David, Michelangelo’s amazing sculpture. I also refreshed my memory about the history of that sculpture which is a great story of innovation, courage, and reinvention. Historians have well documented the fact that Michelangelo was very competitive with other artists. When other sculptures looked at the large piece of marble that was selected for this sculpture that was being commissioned, they decided it was not a good piece of marble and would be too difficult to work with. So they passed on it.
But not Michelangelo. He said he could do it and he took it on. At that moment, he began to separate himself from the competition and he began his strategy to redefine sculpting. Therefore, he became the competition.
And that’s what business needs to do. In Michelangelo’s case, all of the depictions of David in the David and Goliath story, up to that point, depicted David as a very young boy. And, of course, he was clothed. Additionally, all of the sculptures up to that point were human-sized or slightly bigger. They weren’t overly large.
So Michelangelo did something very different from his peers. He did the opposite and created a 17-foot tall David, made him an adult, and kept him unclothed. The only thing he had with him was his slingshot to get Goliath.
After working each day on David, he would study cadavers to learn more of how the human body worked. Taking what he learned and applying it to his work, he became the first sculptor to show veins and arteries and detailed muscle structures.
The result, of course, was absolute mastery. Anyone who has ever seen David understands that.
Michelangelo changed everyone’s view. He redefined what sculpting was about and set a new standard. In other words, he went beyond the competition.
Years passed and Michelangelo had done some drawings and some paintings, but he considered himself, first and foremost, a sculptor. However, the Pope decided that he wanted Michelangelo to paint the ceiling of the Sistine Chapel. Interestingly, Michelangelo didn’t want to do it because he considered himself a sculptor. In a note to the Pope, Michelangelo even signed it, “The Sculptor, Michelangelo,” pointing out the fact that he wasn’t a painter; he was a sculptor. When the Pope wouldn’t take “no” for an answer, Michelangelo left Rome.
The Pope sent guards to get him and bring him back, essentially forcing him into painting the Sistine Chapel. So Michelangelo reluctantly agreed.
At that time, all of his competition was painting pictures in 2D. In other words, paintings were flat with no depth to them.
Anyone who has ever seen the ceiling of the Sistine Chapel knows that Michelangelo, once again, redefined what art was by putting in amazing—even by today’s standards—depth and 3D effects. Essentially, he once again went beyond the competition. As a matter of fact, while he was working on the Sistine Chapel, other great artists of the day would sneak in during Michelangelo’s breaks just to look at his techniques. They were floored, literally, by what he was doing. And from that point on, other artists started to incorporate depth and 3D techniques into their paintings.
So what’s the moral of the story? Look at what your competition is doing … and don’t do that. Why? Because they are already doing it.
Instead, raise the bar. Look at what the best of the best are doing … and then go beyond them. Think bigger. Don’t compete. Create. Innovate.
*****
DANIEL BURRUS is considered one of the world’s leading technology forecasters and innovation experts, and is the founder and CEO of Burrus Research, a research and consulting firm that monitors global advancements in technology driven trends to help clients understand how technological, social and business forces are converging to create enormous untapped opportunities. He is the author of six books including The New York Times best seller Flash Foresight.
3D Printing Is Turning the Impossible Into the Possible
Daniel Burrus Aug 22, 2013
Thanks to 3D Printing, you can!
I have been covering 3D Printing (also called Additive Manufacturing) for over 20 years in my Technotrends Newsletter,and at first the technology was used for rapid prototyping. Over the past few years, however, rapid advances in processing power, storage, and bandwidth have catapulted this technology into a tool for manufacturing finished products that include jewelry, shoes, dresses, car dashboards, parts for jet engines, jawbones for humans, replacement parts for synthesizers, and much more.
When people first hear that you can manufacture something by printing it, they have a hard time visualizing it. Think of it this way:
3D printers build things by depositing material, typically plastic or metal, layer by layer, until the prototype or final product is finished.
When the design is downloaded into the printer, a laser creates a layer of material and fuses it.
Then it adds another layer and fuses it…and then another and another…until the object is completed.
For example, a Belgian company, LayerWise, used 3D printing to create a jawbone that was recently implanted into an 83-year-old woman. An Australian company, Inventech, has created what they call their 3D BioPrinters to print tissue structures using human tissue. And Bespoke Innovations is using 3D printing to create prosthetic limb castings.
This amazing technology can also be used for on-demand printing of spare parts—something the U.S. military is already doing in the field. Knowing this,
it is not hard to see that in the future, a manufacturer could sell a machine or system to a company, and as part of their maintenance and support contract they can put their 3D printer on-site with the licensed software to print replacement parts as needed.
On a smaller level, it is easy to see that service mechanics will have portable 3D printers in their vans or at their main office. Original equipment manufacturers (OEM) will most likely sell and license these printers to their dealer network.
In addition, there are already a number of companies including Shapeways and Quirky that will use their 3D printers to print the design you send them, and then they’ll ship the final product to you. It’s not hard to see that at some point Amazon will provide this service too.
3D printing will definitely become more commonplace in the near future thanks to its many benefits, including the ability to print the complete part without assembly and the ability to print complex inner structures too difficult to be machined. Additionally, the entire process produces much less waste than traditional manufacturing where large amounts of material have to be trimmed away from the usable part.
Whether you call it 3D Printing or Additive Manufacturing, it is advancing quickly on a global level and offers something that up until recently was impossible: On-demand, anytime, anywhere, by anyone manufacturing.
Related references at Pharmaceutical Intelligence:
Healthcare Startups Accelerator is Reaching Out: Deadline November 11, 2013
2012pharmaceutical
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