How Big Data Can Give Pharma Industry a New Direction to Success?
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How Big Data Can Give Pharma Industry a New Direction to Success?

By Pharma Tech Outlook | Friday, January 17, 2020

The big data has opened the door of new opportunities for the pharmaceutical industry, which includes enhanced R&D, enhanced clinical trials, and upgraded drug discovery.

FREMONT, CA: Big Data is taking big strides in the pharmaceutical industry at an exponential rate. It is helping in streamlining various intricate business processes and enhancing proficiency across the board. No wonder investors from healthcare and pharmaceutical industries are investing heavily in big data. With these constant investments, the pharmaceutical industry aims at developing numerous innovative applications.

Utilizing Big Data in the Pharmaceutical Industry

The utilization of big data will give birth to the following applications.

1. Clinical Trials

The complete process of clinical trials is extremely intricate and tangled with major procrastinations. Because of these delays, pharmaceutical companies suffer a loss of millions of dollars. Also, many clinical trials fail since recruiting the patients for the trial is not easy. To recruit, physicians need to manually review a list of patients that are eligible, and this process is very time-consuming as well as expensive.

Big data help doctors in recruiting patients by using data like personality traits, genetic information, and disease status. This approach empowers the physicians to comprehend multiple medical details of each patient and analyze whether a patient will be eligible for the clinical trial. This enables pharmaceutical companies to perform shorter and affordable clinical trials. Additionally, the doctors can also utilize electronic medical records as their primary source of data for the clinical trials, which minimizes the possibility of data entry errors and speeds up the medical procedures.

2. Drug Reactions

In many circumstances, medications may lead to adverse drug reactions (ADRs). Hence, many patients complain about the ADRs or its side effects on social networking websites like Twitter and Facebook or online medical forums.

Pharmaceutical companies mine medical forums and social media platforms for patient reviews and ADRs. To achieve this, pharmaceutical businesses can utilize sentiment analysis and natural language processing. Further, big data analytics can help in analyzing the collected data. This approach empowers pharmaceutical companies to gather insights about ADRs and simplify the process of drug reactions.

3. Research and Development

Big data has been proved to be very useful in research and development in the pharmaceutical industry. Pharmaceutical enterprises allocate huge volumes of data generated at various stages of the value chain from drug discovery to usage in the real-world. To obtain this, it is vital for pharmaceutical companies to detect suitable sources of clinical data and incorporate this data into their big data infrastructure. Using this approach, business leaders can connect disparate datasets together for enhancing research and development procedures.

Big data will empower business leaders in the pharmaceutical industry to gain insights about numerous drugs and their usage. These insights will help businesses in making informed decisions during any research and development.

4. Drug Discovery

Drug discovery needs an extreme amount of time and resources, which is not convenient for patients suffering from diseases like swine flu, ebola, and typhoid, especially during epidemics. Besides, developing such drugs can cost heavily to the doctors as well as patients. Therefore, pharmaceutical companies have to invest in the resources that are approved by clinical trials and have low production costs.

Through big data, researchers in the pharmaceutical industry can use predictive modeling for drug discovery. Predictive modeling empowers researchers to predict drug toxicity, inhibition, and interactions. For this objective, predictive models make use of upgraded mathematical models and simulations for predicting how a specific compound will respond to a human body. Historical data allocated from earlier clinical studies, post-marketing surveillance, and medical trials can also be used by predictive models. All this collected data can then be used in predicting FDA approval and patient outcomes.

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