Why Big Data Is Critical In Drug Discovery

Why Big Data Is Critical In Drug Discovery

Healthcare Tech Outlook | Tuesday, April 02, 2019

Discovery of drugs is an expensive and time-consuming process with a comparatively low success rate. Over the past decade, a big data approach has been expanding at an unprecedented pace.  It is generally based on the development of electronic databases of chemical substances, protein targets, and clinical information covering genetic variations and toxicities. This tremendous shift has enabled systematic and accelerated the identification of innovative drugs or repurposed indications of existing drugs.

Top pharma companies are using some open access tools that allow users to measure, mine and interactively explore morphological data from images of complex physiological samples in high throughput. It can also find unique morphological differences between diseased and healthy cells. This approach is showing clinical promise and is making an immediate impact on patients through personalized medicine.

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Big data can aggregate information from various sources and provide intelligence to assist decision-making. There are already lots of people working on applying these approaches more in the drug discovery mining clinical data.  The data points in the drug discovery process are large, diverse and not always standardized or meaningful. Traditional data management and analytical tools may fail to scale up studies and to make sense of various streams of data. But using complex algorithms to screen large databases containing biological, chemical and clinical information can help. Advanced analytics can help analyze the clinical imperative and candidate profiles to design a product development pipeline for pharmaceutical companies.

Big data analytics can also identify specific subpopulations for which a failed drug can still be the success. Patients suffering from rare or genetic diseases will be especially benefited with this practice of drug repurposing.

The dependence on big data will increase as the concept of personalized medicine comes to the fore. Artificial intelligence, predictive analytics, and machine learning will play more significant roles in drug development, revitalizing drug formulation and molecule design.

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