How is Crowdsourcing being used in Pharma?

Crowdsourcing is a method of soliciting, obtaining or collaborating on projects or procuring goods through a large community rather than by traditional suppliers.

Why would Pharma utilize Crowdsourcing?

Using crowdsourcing for pharmaceutical endeavors seems obvious, as the venture is high-risk.  For example, the number of new drugs approved per billion dollars spent has halved about every nine years since 1950. It is no wonder, since companies look into an unimaginable number of potential drug-like molecules – between 1023 and 1060 for hits.

Thus a common strategy to manage risk is the adoption of a portfolio strategy. This is a balance of exploitation versus exploration. This can be thought of as what can be taken on internally versus externally. The external is where “the crowd” comes into play.

The modern pharmaceutical industry has its 19th century origins in apothecaries (that turned into drug manufacturers) and chemical companies (that turned into research laboratories). The early collaboration between the two is argued to be an early form of crowdsourcing in the pharmaceutical space.

A famous example of crowdsourcing before the internet was the British government’s prize to map longitude to prevent ships being lost at sea. John Harrison, watchmaker and carpenter, devised the chronometer and won the prize. Thus crowdsourcing rewarded innovation, not expertise.

How is Crowdsourcing used Outside of Pharma?

Crowdsourcing projects including Amazon’s Mechanical Turk, InnoCentive, Kaggle, Topcoder, mySoil, The American Gut Project, and Resilience Project.

NASA has used crowdsourcing to optimally position the solar panels on the International Space Station (ISS Longeron Challenge) to increase the power generated while eliminating shadows.

The Environmental Protection Agency (EPA) has used crowdsourcing to develop predictive analytic in predicting the toxicity of compounds.  The EPA has also used crowdsourcing to detect algae blooms.

What are the Examples of Pharma utilizing Crowdsourcing?

Crowd Computing in silico models to find a relationship between observable chemical structures and genotoxicity with The Kaggle Approach.

Speeding up logical regression modeling of genome-wide association studies (GWAS) to analyze single-nucleotide polymorphisms in thousands of individuals to locate genetic variants that are associated with phenotypes of interest.

Computer scientist and statistician researchers from the University of Toronto won the top prize at a contest sponsored my Merck to design software to predict the activity of potential drugs. They used a subfield of machine learning called deep-learning, which is wired like neurons.

Crowd Analytix had a 2014 contest to build a model that could predict if a lung disease patient’s symptoms would worsen based on demographic, clinical, imaging, genetic and other biomarker data.

What is Abstraction?

If a problem can be taken out of context, it can broaden participation. Foldit is a protein folding game that a broad, lay audience can “play” without certain expertise.

EteRNA allows users to perform remote experiments to validate their RNA folding predictions in a web-based game format.

How can Crowdsourcing in Pharma be categorized?

The crowd can be measured by “coupling constants” and arranged in how strongly they are coupled.

Strongly coupled would be post-doctorates to pharma. The top five pharmaceutical conpanies (by sales) all proclaim strong bonds with post-docs- Novartis, Pfizer, Merck etc.

Post-doctorates are considered abstractions because they do not provide the end product.

Partially coupled refers to consortia situations where other organizations are involved including European Innovative Medicines Initiative (IMI), a public and private partnership to develop new technologies for pharmaceutical R&D.

Weakly coupled refers to a loose connection of individuals, usually internet-enabled.

Article based on:

Bentzien, Jörg, Ragu Bharadwaj, and David C. Thompson. “Crowdsourcing in pharma: a strategic framework.” Drug discovery today (2015).
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