The trend of open collaboration has led to innovation across multiple industries. For decades, big pharma has been known as conservative and slow to change. Today however, there is a growing movement toward open access and crowdsourcing scientific information to accelerate research and development. Patients are no longer just research subjects or data providers, they are problem solvers participating in a global community of stakeholders. Online physician communities are breaking down competitive barriers in the name of patients.
A recent example comes from SERMO- a global social network for medical crowdsourcing, where members helped save a little girl’s life from a deadly e. coli strain that killed her brother two weeks ago. Doctors from around the world worked seamlessly as a team across borders on this case, recommending testing and treatment, and reducing the risk of infection in her community.
Another great example of the power of crowdsourcing comes from Stanford University, where a group of researchers stumbled upon an unexpected discovery- while examining autopsied brain tissue of multiple sclerosis (MS) patients, they noticed high levels of angiotensin enzymes, responsible for hypertension. That is how they came up with the hypothesis that angiotensin inhibitor (Lisinopril) would decrease MS flare-ups, and when they used it on mice with ML-like nerve damage, it reversed their paralysis.In 2012, the FDA approved Lisinopril as the first investigational new drug developed through crowdsourcing.
Experts say this phenomenon has emerged from three main trends that are changing the health research environment: 1) citizen science (non-expert trained individuals conducting science-related activities); 2) crowdsourcing (using web-based technologies to recruit clinical trial participants); and 3) medicine 2.0 (active participation of individuals in their health care particularly using the Internet and social media).
Among the leading health research crowdsourcing platforms are the personal genetics company 23andMe and the health information sharing website PatientsLikeMe. 23andMe has more than a million customers, 80% of which have allowed their DNA samples to be used in research. On a typical week, the company collects more than two million individual survey responses from its online community, contributing to over 230 research studies on the human genome.
PatientsLikeMe is the largest free network where patients with life-changing conditions can track and share their experiences to help each other, as well as researchers, pharmaceutical companies, regulators, providers and non-profits, develop better solutions to their problems. The network has a community of over 220,000 members who share their personal data on more than 2,000 medical conditions.
In addition, patients have also started to organize their own research studies with the help of health social networks and online communities such as Quantified Self, Genomera, and DIYgenomics . Recently, some 200 crowdsourced studies have been added to the PubMed database .
Crowd Data Mining
According to the Oxford Bioinformatics Journal, many recent studies on the adverse effects of drugs are mining crowd data from search logs, Twitter, online patient forums, FDA reports as well as electronic health records (EHRs).
While traditional reporting of drug adverse effects is slow and does not capture all cases, web search patterns can provide early clues to adverse drug interactions. For example, a recent study explored the possibility of two drugs (paroxetine and pravastatin) interacting adversely and causing hyperglycemia. The researchers analyzed 82 million drug queries from 6 million web searches in 2010. Patients opted to share their IP addresses and search activities through a Microsoft add-on browser application. The results confirmed that patients who took both drugs at the same time were more likely to search for hyperglycemia-related terms compared to those who searched for only one of the drugs.
Another study used Google Trends (online tool which gives statistics on search term use) to observe the seasonality of sleep disordered breathing. Using search terms such as “snoring”, “sleep apnea” and “snoring children”, researchers collected queries from a seven-year time span in both the United States and Australia. Using regression analysis, researchers found that peak searches happened in the early spring and early winter season in both countries.
As opposed to crowd data mining, active crowdsourcing uses community challenges, labor markets, open forums or games to encourage participation in pharmaceutical clinical trials. Labor market platforms such as the Amazon Mechanical Turk or Crowdflower allow researchers to design and submit crowdsourcing tasks or build entire sophisticated interfaces around a study in order to recruit participants.
A group of scientists recently evaluated the viability of using crowdsourcing for creating large data sets through the public health forums MedHelp and CureTogether. They conducted two different studies- one with 30 professional nurses recruited from oDesk (freelance labor market) and another with 50 crowd workers recruited from MTurk. The results showed that the crowd-labeled data achieved an accuracy of 84% compared to 78% for the expert-labeled data.
Yet another form of active crowd sourcing are scientific games such as “Foldit”- a protein folding game with over half a million registered players, which has dramatically improved protein folding algorithms. Foldit’s goal is to predict the structure of a protein through puzzle-solving scenarios which would aid the creation of designing new proteins to combat disease-related proteins found in HIV/AIDS, cancer or Alzheimer’s, for example.
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