Big Data : The Silver Bullet for Drug Discovery

Abhrasnata Das | Friday, 29 April 2022

The Indianpharmaceutical 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 organizationâ€™s private data to reveal hidden patterns.

Faster screening for Effective Clinical TrialsTrial failure is often caused by the inability to recruit enough eligible patients. Phase III trials are conducted at over 100 sites in ten or more countries. And, because drugs are now often designed for niche populations, companies compete to recruit the same patients. As a result, 37% of clinical trials fail to reach their recruitment goals and 11% of sites fail to recruit a single patient. According to the National Cancer Institute, only 5% of cancer patients join clinical trials. The traditional method of recruitment for eligible patients is manual review of physiciansâ€™ patient listsâ€”but itâ€™s expensive and slow.Hereâ€™s where electronic patient hospital data and big data can help. With analytics and data scientists, patients can be enrolled based on sources other than doctorsâ€™ visitors, like social media. The criteria for patient selection can now include factors like genetic information, disease status, and individual characteristics, enabling trials to be smaller, shorter, and less expensive. â€œItâ€™s like fishing with a fish finder,â€ said John Potthoff, chief executive officer at Elligo Health Research. â€œYou can really see where patients are and where to go to get them. Sales and MarketingThe utilization of big data in the pharmaceutical industry can enhance its sales and marketing efforts. With the help of big data, business leaders can analyze which geographical locations sell the highest number of promoted medicines. With such data, businesses may choose to supply more promoted products in those areas. Similarly, pharmaceutical companies can obtain critical data from various sources, helping them to make key decisions in their marketing and sales strategies.The advent of big data in the pharmaceutical industry will allow business leaders to analyze large volumes of data about customer behavior, the impact of ad campaigns, and customer retention. Using such data and machine learning, businesses can perform predictive analytics to find patterns within the acquired data and make accurate predictions regarding industry trends. Using this approach, business leaders can be better prepared for upcoming industry trends.Whatâ€™s Next?At this point, it can be concluded that Big data will undeniably change the shape of drug discovery. However, there are multiple challenges lying in the path. To begin with, effective data management has now turned out to be a grave source of concern. In between piles of unorganized data, the companies have to take robust steps to dig out the gold. Much before that, it is undoubtedly imperative for the companies to find the right talent.