In 2016, ROI for the top 12 pharmaceutical companies fell to just 3.7 percent. This sobering statistic is due to both declining pharmaceutical productivity (fewer drug approvals) and increasing research and development (R&D) costs. As the industry looks to the future, it is clear that it needs to consider how to offer the maximum value for money—not only to enhance their organizations, but also to improve the patients they serve.
One key area to be addressed is scientific productivity. While pharma output has remained reasonably stable over the years, a continued reliance on the same technologies and processes will not bring a much-needed boost in productivity or a reduction in costs. Many organizations are adopting a hybrid automation approach, which mixes digital data capture with manual processes.
This often leads to the maintenance of more complex manual procedures, increasing the potential for human errors and reducing data integrity. By moving to a more automated approach, pharma can take advantage of many benefits, including consistent quality, reduced manual interactions and human errors, the potential to scale quickly, improved responsiveness, lowered costs, and increased reliability.
Although many organizations are attempting to implement efficient streamlined systems, ever-increasing regulatory pressures are forcing the pharmaceutical industry to lag behind other industries in adopting new technologies, despite their urgent need to increase productivity.
Faced with cost-savings and economic pressures, organizations are starting to look again at how they can drive R&D efficiency and, in particular, many R&D organizations are considering the use of automated technologies to free up capacity and make the flow of information from hypothesis to product faster. But, as a data-intensive industry, they are facing several challenges.
Pharma’s Big Data Problem
“Big data” is a hot buzzword, and understandably so. Along with analytics, artificial intelligence (AI), and machine learning (ML), big data is part of a broader wave of technology sweeping through the industry that could lead to the ultimate form of automation. But, if pharma wants to ride the wave and reap the benefits, there are a few roadblocks that need to be overcome.
Consider the “3 Vs” of big data: volume, velocity, and variety. It is clear that there is a huge volume of data being produced across the pharmaceutical R&D value chain, and with a great velocity of change, from the reams of data being produced through clinical trials to experimental data being generated by more and more sophisticated equipment and sensors. However, it’s perhaps the variety of data produced that provides the biggest problem. Being able to integrate this data is crucial for organizations trying to gain a wider insight and maximize the value of the data being generated.
A Legacy Challenge
Having reliable, well-linked data is essential for any organization looking to automate and benefit from big data, AI, and ML technology trends. However, pharmaceutical companies have to deal with a vast array of legacy systems, each containing disparate data and a lack of standardized data formats.
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While there is still a great deal to be done to create standard data formats within the pharmaceutical industry, vast inroads already have been made.
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The issue they face is that these systems have been designed and implemented to answer specific questions of the data being collected, but therefore limiting potential other insights that can be found from interrogating this data.
In order to take advantage of more sophisticated analysis methodologies, and lead to the potential for wider insights, these legacy data silos need to be broken down and the data linked together in a clean way.
Standardization Progresses
Many legacy systems were built on “old” technology, making it very difficult and costly to extract the data from these systems into an integrated environment where advanced analytics can be performed. This has led to a skills gap in organizations, which now require data scientists capable of connecting this “noisy” data, extracting the value and presenting it to stakeholders to allow them to make insight-driven decisions.
While there is still a great deal to be done to create standard data formats within the pharmaceutical industry, vast inroads already have been made.
Examples include the work being performed by the Pistoia Alliance to standardize scientific data capture with initiatives such as their Unified Data Model (UDM) project, creating a data format for the storage and exchange of experimental information, or their HELM project to create a single notation that can encode the structure of all biomolecules. Other data standards such as the emerging Allotrope ADF and well developed AnIML standards are helping ease integration of analytical instrument data.
These, among others, are making the journey to an integrated and automated drug discovery process a closer reality. Although there are still significant hurdles for pharmaceutical organizations, we are beginning to see a change of mindset in the industry with organizations such as GlaxoSmithKline putting a stronger focus on automation, analytics, and AI to drive drug discovery.
What Does the Future Hold?
When looking to the future, the only certainty is that the volumes of data being produced through the Pharma R&D value chain is going to rise—and the technology that allows data to be interrogated and lead to new breakthroughs also will continue to develop at pace.
The only remaining question is this: Can an industry with such regulatory sensitivity and data silos move fast enough to keep up with the technology?
About the Author
Rory Quinn is Solution Consultant at IDBS. The company was acquired by the Danaher Group and joined their Life Sciences platform in late October 2017.
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This article, originally carried on PharmPro.com, appeared in the INTERPHEX 2018 Show Daily: Thursday, April 19.
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