Importance of Data & Analytics in Pharmaceutical Product Development

Introduction

Research and development (R&D) has always been the backbone of the pharmaceutical industry. In 2001, there were 1,198 pharmaceutical companies with active R&D pipelines. And in 2020, this figure spiked to 4,800. Due to the COVID-19 pandemic, companies have doubled their efforts in creating new products. In fact, more than 17,700 drugs were recorded in the global R&D pipeline in 2020—and a lot of them made headlines.

The world got a glimpse of how complex and costly it is to create new drugs. Moreover, that also sparked an unprecedented wave of data sharing and access not just in the healthcare and pharmaceutical industries. But in the IT sector as well. In fact, 96% of all companies will have hired a data specialist by 2022, according to “The Future of Jobs Report” by the World Economic Forum. This is why there has already been a huge uptick in the number of professionals pursuing data analytics careers, with worldwide big data revenue already predicted to have passed $203 billion.

In the pharmaceutical industry, multiple organizations came together to share and exchange data: Google Cloud provided researchers free access to critical information through its COVID-19 Public Dataset Program, and Rensselaer Polytechnic Institute offered government entities. And researchers access to innovative AI tools and experts in data and public health.

Never before has data been so readily accessible and this has helped speed up the R&D efforts of many companies. In addition, data also helps the pharmaceutical products development in many other ways:

Improves research efficiency

Several companies were able to develop a COVID-19 vaccine in under a year—a record time—currently making it the fastest vaccine to ever be developed. It helped that pertinent data and information were exchanged freely between pharmaceutical companies, government agencies, and data analytics organizations.

Free-flowing data sharing, as seen during the pandemic response, makes the development of drugs easier as it cuts down several steps in the R&D process. And with the available information, researchers have a better understanding of the recipients of the product. This makes it possible for trials to acquire smaller sample sizes with higher accuracy, lower expenses, and in less time.

Creates precision medicine

Precision medicine is an approach to patient care that allows doctors to create diagnoses and treatments based on data on genetic make-up, environmental factors, and behavioral patterns. This approach allows companies to create personalized medicine for individuals’ genes and lifestyles. This data-driven approach also helps drug makers identify patients’ susceptibility to certain disorders, enhancing disorder detection. Since precision medicine has a higher probability of success compared to more conventional approaches, it also reduces trial costs.

One such example is Pfizer’s Xalkori (crizotinib), which they produced after combing through data from electronic medical records, clinical trials, and genomic data. They found that a small subset of lung cancer patients had a mutation in their ALK gene. And using this insight, they developed a personalized drug. “Had this compound been tested against a broad spectrum of lung cancer patients, it likely would not have been found to be effective. With this analytics-based approach, it was found to be very effective,” says Pfizer CIO Jeff Keisling.

Provides real-time analysis

It’s now possible to access real-time information—a feature that greatly benefits trials. With this, it’s easier to respond to issues in a timely manner, and create more accurate safety measures for trial participants, all leading to higher success rates from the R&D standpoint. Additionally, data can now be collected from real-world information such as health records, insurance claims, and even social media. This provides drug makers with evidence on how medicines work in an uncontrolled setting and across a wider demographic. This lets them make adjustments and improvements to the drugs.

Major pharmaceutical companies now have dedicated teams collecting data from studies and trials across different diseases. Their analysis of this information helps them formulate their drugs to be more potent and effective while combating the rising costs of traditional clinical trials and parallel development programs.

Simplify production plans

After developing a product, it needs to be mass-produced and distributed. You need to know the appropriate targets for the best ROI. With the right data, companies are able to create a more solid production plan, reduce labor costs, eliminate waste, decrease the need for excessive inventories, and optimize equipment usage. This ease of production will only increase in the future both within the healthcare industry and companies connected to it. And with the pharmaceutical industry predicted to grow to $1.57 trillion in value, the role of data in streaming lining production processes will only increase.

Smoother supply chains

Today’s pharma companies are breaking away from traditional practices and are embracing digital transformation and pharmaceutical data analysis on a much bigger scale. This move allows them to understand and cater to the needs of both their customers and stakeholders. As we mentioned in our previous write-up on the ways to enhance customer experience. Using data analytics, you can improve your supply chain efficiency by easily validating data, detecting anomalies, benchmarking operations, and accessing mobile and logistic reports.

Moreover, data analytics for pharma development offers real-time route optimization and improved inventory management, freeing up man-hours which otherwise would’ve been spent tracking and monitoring business operations.

The use of data in developing pharmaceutical products is very beneficial. It helps prevent health issues and strengthen the patient care sector.

Article specially written for xcelpros.com By Nina Ross