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Data Science Opportunities in the Pharmaceutical Industry
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swethakrishn017
Data Science Opportunities in the Pharmaceutical Industry
Big Data Promise
Five compounds are tested in humans for every 5,000 that begin in the laboratory, and only one makes it to market. Moreover, each new drug takes approximately ten years to develop and costs an average of $2-3 billion. This amounts to a massive amount of molecular and clinical data stored in proprietary networks, ready for analysis.
Enhanced Collaboration
In recent years, there has been a push to increase collaboration internally and with the outside world. Pharma is now collaborating: to gain a competitive advantage, increase its expertise, and expand its ever-growing databanks.
Academic Partners: Eli Lilly established the Phenotypic Drug Discovery Initiative to gain early access to compounds being developed outside of the company. External researchers submit their compounds for screening, and Lilly evaluates whether any of them have the potential to become drugs using proprietary tools and data.
Academic Collaborators: Contract Research Organizations (CROs) or data management companies are examples of external partners. GlaxoSmithKline, for example, announced a partnership with SAS in 2013 to provide a globally accessible private cloud where the pharmaceutical industry can securely collaborate around anonymous clinical trial data.
Predictive Analytics
Predicting the future has applications in drug development and avoiding negative outcomes. In terms of drug discovery, pharmaceutical companies spend a lot of money screening compounds for preclinical trials. Drug companies are using predictive models to speed up the process. To search vast virtual databases of molecular and clinical data, analysts narrow down potential drug candidates using criteria based on chemical structure, diseases/targets, and other factors.
(For further information on predictive analytics, please refer to the data science course and Artificial intelligence course.
For example, Numerate, which collaborates with companies such as Boehringer Ingelheim and Merck, develops predictive models with specific drug targets and treatment goals in mind.
Crowdsourced Contests
Pharmaceutical companies and institutions have sponsored crowdsourced contests in recent years to predict patient and clinical outcomes, sales patterns, molecule activity, and anything else involving big data.
Marketing and sales
Previously, pharmaceutical companies would send their representatives on lengthy doctor visits and invest in costly, broad-scale product promotion.
Some of that money is likely to be spent on tracking doctors' therapeutic preferences, geographic trends, peak prescription rates, and anything else that directly affects the sales cycle. This information is then fed into:
Predictive Analytics: Pharmaceutical companies use predictive methods to determine which consumers and physicians are most likely to use a drug and to create more targeted on-the-ground marketing efforts.
Sophisticated Sales: Pharmas are providing mobile devices and real-time analytics on their prospects to drug reps. Reps can then tailor their agenda to the physician's preferences. The sales team can then analyze the results to determine whether the approach was successful.
Regulations and Data Risks
The Upcoming Obstacles
By the 21st century, the pharmaceutical industry had amassed enormous amounts of structured and semi-structured data in separate, mutually inaccessible "silos."
Unfortunately, it is having difficulty connecting those silos.
Regardless of how analysts slice the numbers, the ultimate goal is to transform the data into information that can be used to make strategic business decisions. Making the data sources communicate with one another meaningfully is a prerequisite for this. Furthermore, pharmaceutical companies are dealing with a flood of unstructured information, including social sentiment, that is coming from outside sources. Integrating, manipulating, organizing, and interpreting this data to support a coherent course of action is giving me headaches.
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