We have currently progressed to a state where there is too much information for one person to analyze. What we now call “Big data”- composed of more significant volumes, variety, and velocity of data than ever before, is just more data. With big data analytics, we can now process and make sense of that data, thus providing excellent opportunities for every industry.

Over the years, the demand for data has grown exponentially, and quick integration is now considered a business requirement. This is particularly true for pharmaceutical industries as they have always trusted empirical data to identify patterns, test theories, and understand the efficacy of treatments.

For big data analysis, one can manage an extensive collection of digital health records, and administrative data, including medicine safety reports, drug prescriptions, and hospital discharge datasets.

Effectively leveraging these data will enable pharmaceutical companies to identify new potential drug candidates better and develop them into effective, approved, and reimbursed medicines more quickly.

Exhibit 1. Big data analytics enables the pharmaceutical industry to make smarter and more effective decisions.

How Is Big Data Impacting the Pharma Sector?

• Sustainable Research and Development: The introduction of big data in the pharmaceutical industry helps researchers gain insights about the potential therapeutic molecules and increase R&D productivity.
• Better Clinical Trials: The data such as personality traits, genetic information, and disease status help recruit patients for effective clinical trials.
• Expedite Drug Discovery: Researchers can apply predictive modeling to expedite the drug discovery process by utilizing big data in the pharmaceutical industry.
• Controlling Adverse Drug Reactions (ADRs): Pharmaceuticals can leverage social media platforms, medical forums, and patient reviews to generate analytics around ADRs and build strategies to control these.
• Empowering Personalized Medicine: Big data can be the key enabler of personalized medicine. Diagnosis and treatment of diseases are carried out using relevant data about a patient’s genetic makeup, environmental factors, and behavioral patterns.

Exhibit 2. Omics data integration and analytics have the potential to promote personalized medicine.

• Gain Insights on Sales and Marketing: Big data analytics will allow business leaders to examine large volumes of data about customer behavior, the impact of ad campaigns, customer retention, and competitors.
• Boost Collaboration: Big data will help develop collaboration between pharmaceutical companies and healthcare institutions.

Big Data and Big Pharma Players!

Several pharma companies have already progressed by implementing big data technology into research and development, commercialization, and supply all phases are equipped with it.

• GlaxoSmithKline recently announced a radical change in R&D spending, wherein the company will refocus on data analytics and the link between the immune system and human disease. GSK is trying to uncover bigger trends by studying the genetic profiles of patients with diseases. Scientists are starting to understand the cause that makes humans ill in the first place.
• GE Healthcare and Roche Diagnostics have partnered to offer a comprehensive data dashboard to help providers coordinate care and target therapies to individuals and their needs.
• Pfizer delivers Precision Medicine Analytics Ecosystem, a program that connects the dots among genomic, electronic medical record (EMR), and clinical trial data. And then rapidly delivers new drugs for specific patient populations.
  Each of the three platforms Pfizer developed under this program represents a huge achievement.
  1. The genomics research repository is built on tranSMART, an open-source data management system. It combines genomic data sets from internal and external sources, using the platform’s data standards and processing capabilities.
  2. Pfizer worked with Oracle and other partners to create a cloud-based clinical database called the Clinical Cloud.
  3. The EMR module of Pfizer’s data ecosystem contains “hundreds of millions” of anonymized records. Pfizer uses Tableau Software for data exploration and visualization for this module.
• Johnson & Johnson can utilize data to learn more about who might be most at risk of getting sick by analyzing longitudinal data from a global COVID-19 registry about areas where the disease is most prevalent and hospitals in New York City.

Big Data Consortium

Paywalls and patents have long restricted the flow of information in the hypercompetitive industry. So, an interesting trend is growing collaboration amongst industry parties. For example, the non-profit Structural Genomics Consortium partners with labs and pharma companies (Exhibit 3), who have promised to share their drug wish lists, results in open-access journals, and experimental samples to speed up discovery.

Exhibit 3. Labs and pharma companies partner with the Structural Genomics Consortium to promote open science.

Similarly, MedChemica, a UK startup, is at the core of a collaboration designed to speed up development using data mining while preserving each partner’s intellectual property. MedChemica explores partners’ databases of molecules to find closely matched pairs, and performs an analysis between the two, and the output is consequently used to create rules that can be applied to virtual molecules to predict the impacts of similar structural changes. All partners in the consortium can advise where additional data is needed and can even agree to share costs for further testing.

Big Data Into Therapy Approach

Big data has changed the perspective of the treatment approach. The approach was based on ‘One drug fits all medicine’ before big data, wherein the outcome was decided based on trial and error. However, after big data, the treatment outcome was based on precision medicine (Exhibit 4). It resulted in minimizing the treatment error due to an evidence-based approach.

Exhibit 4. Big data enables the development of precision medicine over the conventional therapy approach.

Conclusion

Big data and analytics are crucial communication tools for effective decision-making. As a continuous process, pharmaceutical companies are looking for ways to better the odds of getting a new drug in the market. They strive to go through drug discovery to market as quickly as possible and that too in the most cost-effective way and confronting numerous risks and legalities, at the same time to provide maximum benefit to patients.
To help companies achieve their business objectives and overcome challenges, big data analytics which includes data integration, data mining, and analytics will help the pharmaceutical industry make smarter and quicker decisions. With diverse data sources being added constantly, possibilities are immense. Undoubtedly, big data will advance over time, and we will deliver more excellent value to all the stakeholders. Applying these technology-assisted ways to benefit from big data, the healthcare sector or pharmaceutical could steadily change the tide of declining success rates and dormant pipelines.