The Indian
pharmaceutical segment holds an important position in the global pharmaceutical industry. According to the Indian Brand Equity Foundation (IBEF), the sector supplies over 50% of worldwide demand for different vaccines, 40% of generic demand in the United States, and 25% of all pharmaceuticals in the United Kingdom. India is now the world's third-largest producer of pharmaceuticals by volume and the fourteenth-largest producer by value.However, when it comes to the formulation of new drugs, the Indian pharma industry is considerably lagging behind. A research paper published in the CHEMMEDCHEM journal in 2017 states that since 1990, only 200 formulations have reached the point of pre- clinical and clinical stage, while only a handful have made it to the market. The reason being low investment in R&D due to the high risk and high costs involved.This is where Big Data comes into play. With proper implication of big data, the total procedure of drug formulation, starting with - R&D to drug discovery and clinical trials to sale and marketing, can be exponentially accelerated.Having said that., in this article lets unearth the future possibilities that big data holds for the pharma industry.
Predictive Modeling to boost Drug DiscoveryIn the past, researchers used natural plant or animal compounds as the basis for candidate drugs. Drug development has historically been an iterative process using high-throughput screening (HTS) labs to physically test thousands of compounds a day, with an expected hit rate of 1% or less. But now scientists are creating new molecules with computers. Predictive modeling, both sophisticated and basic, can help predict candidate drug interaction, inhibition, and toxicity. A widespread method is pharmacokinetic modeling, which uses advanced mathematical modeling and simulations to predict how the compound will act in the body. Even without available protein structure information, screening of virtual compound libraries allows researchers to consider as many as 10,000 compounds, and narrow it down to 10 or 20.These capabilities do not necessarily have to be built in-house. Recently, IBM Watson Health and Pfizer forged a partnership to help researchers discover new drug targets. While the average researcher reads 250-300 articles in a year, Watson has processed 25 million Medline abstracts, over one million full-text medical journal articles, and four million patents. Watson can even be augmented with an organization’s private data to reveal hidden patterns.
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