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Commentary: Learning from the Wuhan coronavirus
Commentary: Learning from the Wuhan coronavirus
Collaboration leads the way to better understanding of pathogen
Barry Bunin of Collaborative Drug Discovery Inc.
As news about the Wuhan coronavirus (2019-nCoV) dominates the headlines, it is easy to get emotional and react to every latest development. However, we believe it is helpful to examine the facts and take a holistic view on this outbreak.
2019-nCoV is a coronavirus, the family of viruses traditionally associated with the common mild cold. It is genetically most related to, yet distinct from, the severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS) coronaviruses.
Researchers are evaluating countermeasures for 2019-nCoV using SARS-CoV and MERS-CoV as prototypes. For example, platform diagnostics are being rapidly adapted to include 2019-nCoV, allowing early recognition and isolation of cases. Broad-spectrum antivirals such as remdesivir, an RNA polymerase inhibitor, as well as lopinavir/ritonavir and interferon beta have shown promise against MERS-CoV in animal models and are being assessed versus 2019-nCoV. Vaccines, with nucleic acid vaccine platform approaches used for SARS-CoV or MERS-CoV, are being pursued at the National Institute of Allergy and Infectious Diseases Vaccine Research Center.
Coronavirus vaccine development
Vaccine (and antibody) development makes sense, given the potentially faster timeline than de-novo small-molecule drug discovery, although other antivirals have been used in SARS and MERS. The Wall Street Journal reported several drugmakers are racing to develop vaccines that could protect against the new respiratory virus originating in China. Moderna Inc., Inovio Pharmaceuticals Inc. and Novavax Inc. all plan to develop vaccines against the newly identified viral strain. Researchers at the University of Queensland in Australia are also trying to develop a vaccine against the strain.
More recently, FierceBiotech reported that both JNJ and Gilead have jumped into the accelerated coronavirus vaccine race.
Lessons for drug discovery collaboration?
As shared in a timely NIH JAMA Viewpoint from Drs. Catharine I. Paules, Hilary D. Marston, and Anthony S. Fauci, we know 2019-nCoV is similar to MERS and SARS thanks to rapid data sharing and international collaboration: “While MERS has not caused the international panic seen with SARS, the emergence of this second, highly pathogenic zoonotic HCoV illustrates the threat posed by this viral family. In 2017, the WHO placed SARS-CoV and MERS-CoV on its Priority Pathogen list, hoping to galvanize research and the development of countermeasures against CoVs. The action of the WHO proved prescient. On December 31, 2019, Chinese authorities reported a cluster of pneumonia cases in Wuhan, China, most of which included patients who reported exposure to a large seafood market selling many species of live animals. Emergence of another pathogenic zoonotic HCoV was suspected, and by January 10, 2020, researchers from the Shanghai Public Health Clinical Center & School of Public Health and their collaborators released a full genomic sequence of 2019-nCoV to public databases, exemplifying prompt data sharing in outbreak response.”
Publishers like the British Medical Journal (and, in a moment of solidarity, other publishers like Wiley and Elsevier) are providing information on the coronavirus freely on the internet to spur short-term global response efforts and support long-term research, in contrast to their usual paid-content business models. The British Medical Journal has also made information freely available on MERS and SARS.
One of the unique ways we can combat epidemics, not available to previous generations, is to leverage the free, global, instantaneous access to everyone across our species via the internet. We have only scratched the surface of the full potential of this mechanism for both response and research.
Collaboration can range from two scientists sharing data privately to publicly shared data with the international scientific community. Quantity has a quality all its own. In the case of an outbreak, publicly shared information allows the conversation to co-evolve with many brains (and technologies) rapidly in parallel—when additional data, analyses and insights are also shared in a timely manner.
When a timely response is needed, collaboratively sharing data allows the rate of learning to accelerate.
Within the commercial drug discovery arena, there are two counterbalances to immediate sharing. First, the data from diverse drug discovery assays are heterogeneous, complex and may require metadata from procedures to understand. Second, the data sharing, due to this heterogeneity requires sophisticated tools (i.e. sharing structure activity relationships from a series of primary and secondary high-throughput screens run on hundreds of thousands of compounds, at nine concentrations, in triplicate is not as trivial as, say, sharing a like on Facebook). Nonetheless, collaboration may be the key to quantum leaps in efficiency in drug discovery.
Open data (and idea) sharing is the purpose of the scientific literature. Scientific literature became a more global phenomena with the advent of the printing press.
We take the internet for granted today. However, the ability to instantaneously share information around the world is arguably the most fundamental paradigm shift for our species. We are no longer ants, but an ant colony. We can learn from the art of emergent, collective intelligence. Our memes traveling at the speed of the www to coordinate our collective thinking is our competitive advantage vs. the ancient relentless mechanisms of mutation, selection and horizontal gene transfer. The ace in our pocket is the ability to collectively learn and instantaneously share collective learnings. Prokaryotes have a fixed velocity of learning and information transfer (different in every case, but metaphorically speaking in general). Humans combining our intelligence with the Internet have the potential for uncapped, accelerated learning.
The next level of accelerated learning is integrating computers and algorithms together, via web-based platforms. Not only our own CDD Vault, which balances protecting intellectual property through secure data sharing while promoting maximum collaboration, but all the connecting web-based scientific data sharing platforms (with the majority sponsored by our publicly funded, government coordinated efforts such as PubMed, GenBank, ChEMBL, KEGG and PubChem, to mention just a handful of many impactful, web-based scientific data-sharing platforms). And there are highly impactful, community based efforts such as, well, Wikipedia (and its equally important cousin, DBpedia). We can and will collaborate better over time.
There is a need for accelerated data sharing and discovery for a number of viral diseases, including 2019-nCoV.
It is worth mentioning the rapid development of 2019-nCoV diagnostic kits, a number of which are already now available.
As with the response to the last Ebola epidemic and after this 2019-nCoV epidemic, we will need to consider general solutions to surveillance and response. The only thing we know for sure is that next time will be slightly different. In response, our tactics and tools can get better with each new epidemic via greater, web-coordinated collaboration.
In the near future, it is not difficult to imagine a time when emerging data and protocols are represented in FAIR (findable, accessible, interoperable, reusable) standardized formats for parallel computer analyses. Bioportal already has standardized, precisely defined terms for the new coronavirus. Future generations will be able to collaborate better, faster, longer term and smarter. We’re all in this together.
Adapted from “Coronavirus (2019-nCoV): The Facts” article at the Collaborative Drug Discovery Inc. website (www.collaborativedrug.com). You can link to the full article at www.collaborativedrug.com/coronavirus-2019-ncov-facts