Digital Transformation in 2020: What Should Pharma Know?

Sponsored by LabVantage

January 6, 2020

Digital transformation is a business journey that uses technology as its vehicle, driving fragmented drug companies toward a harmonized ecosystem. At the heart of that ecosystem is the lab, harboring a wealth of data that could reveal the way forward through a turbulent marketplacebut only if the right tools are in place to turn that data into meaningful insight, and contextualize it within the enterprise as a whole. That’s the next frontier in pharma, and reaching it is increasingly essential for ongoing business success.

Pressure is rising

Pressure to accelerate innovation.  Pressure to optimize production.  Pressure to comply with regulators in a world of both big data and big concerns about that data’s integrity.

As these and other stressors contract around the pharmaceutical C-suite, many business leaders are re-examining their laboratory systems, searching for opportunities to reduce wasted effort and increase productivity. In R&D labs, where a single study might produce billions of lines of data, leaders want to know how to keep that data from lapsing into archives, where it’s difficult to access and offers no value. In QA, leaders are examining new ways to turn production data into insight, uncovering and removing manufacturing bottlenecks to achieve higher throughput.

Most of all, pharmaceutical business leaders are asking how they can layer LIMS data on top of data from their ERP systems, sales records, financial reports, and more, creating a meaningful, contextualized, and holistic story of their end-to-end operations and using that story to successfully navigate today’s business problems.

It all starts at the nerve center of every drug company: the laboratory.

How will digital transformation impact the lab?

The goalposts are shifting from data generation to advanced data analysis.

At this stage in our industry’s digital maturity, many pharmaceutical companies have invested heavily in digital warehouses for their R&D and QA data. The next frontier is to elevate our perspective on all of that data, translating it from inert information into organizational wisdom (see Figure 1). Advanced analytics platforms, powered by artificial intelligence and deep learning, are becoming increasingly ubiquitous, helping business leaders harmonize data from disparate sources—including, in many cases, retired legacy systems with valuable insights locked inside—and use it to uncover opportunities for growth or lucrative redirection.

The potential impacts on a business’ bottom line are huge. In a manufacturing environment, labs can use data analytics to visualize the historic performance of certain active ingredients to predict future outcomes, for example. These insights could eliminate costly rework while also helping labs better control their inventory—another opportunity to reduce costs.

In R&D environments, advanced data analytics can help teams predict the future outcomes of their experiments and develop lean and efficient clinical trials. A recent example emerged from a genomics company that sought to identify ideal study subjects within a large pool of potential candidates. Using advanced AI to analyze candidates’ genomics data, the company effectively cut their cost of recruitment by 10%. Such examples will abound in the near future, helping companies edge out wasteful spending and direct their R&D dollars toward the drug development pathways that are likely to deliver sustainable business outcomes.

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Figure 1: An ecosystem of digital tools is necessary for pharma companies to ascend the Data, Information, Knowledge, Wisdom (DIKW) Pyramid. Source: LabVantage Solutions


The nimblest companies will learn to extract continuous value from R&D activities.

Relative to other environments—like the QC lab, governed by its GMPs—the R&D lab is defined by its flexibility. The idea is to try something, to learn lessons, and to try again. It’s that second piece—learning lessons—that stands to gain the most from a digital transformation initiative. Using modern LIMS and ELN systems, R&D teams can record, search and analyze historic data to a depth and degree that’s simply not possible with paper records. And the deeper the insight, the more flexible the lab; free from the duplicated effort of having to re-learn what has already been demonstrated, R&D teams in a truly digital lab have more time to commit to new scientific endeavors. This benefits patients waiting for innovative therapies to emerge, as well as business leaders looking for ways to shorten drug development lifecycles and go to market sooner than the competition.

Truly digitized QC labs also have the advantage of better cost transparency. By integrating their LIMS data with data from other organizational systems (like a company’s ERP system, for example), quality teams can more accurately demonstrate ROI, helping them to solicit and retain necessary operating budgets. Business leaders, meanwhile, gain a consolidated view of profitability across the company’s whole ecosystem, helping them to allocate resources where they’re likely to yield results.

The manufacturing environment will delegate more tasks to tech.

Quality has a cost. Drug companies know this, and they know that the best way to keep delivering high-quality products is to drive costs down in every dimension of manufacturing. This means using modern digital tools to capture historic production data and make it available for analysis, giving QC teams the means to reduce wasted effort and focus on higher throughput, without compromising quality.

The “capturing” piece of that formula is where we’ll see the most acceleration in the coming year. Labs will increasingly embrace digital tools that automate data collection, freeing workers from time-consuming and error-prone clerical tasks and improving communication between teams. This could mean a tighter integration between R&D and QC through a modern LIMS implementation, eliminating the bottlenecks of more traditional (paper-based) knowledge transfer methodologies. Or it could be as simple as a sensor that automatically alerts the procurement team of low inventory on a consumable, reducing the risk of stalled production. Wherever these digital tools are deployed, the result is a more accurate means of data capture and a more streamlined connection between teams, which in turn introduces opportunities for sophisticated data analytics enabled by artificial intelligence and machine learning. From such analytics emerges the wisdom necessary for driving successful business outcomes.

In the same way that digital tools harmonize disparate teams within an organization, they also harmonize diverse teams outside of it, creating new engagement models that will play an increasingly important role in supporting collaboration across geographies.

In one recent example, a biotech manufacturer that had outgrown its urban headquarters added a state-of-the-art manufacturing facility outside the city limits. Anticipating the challenge of exchanging data between their downtown R&D labs and their new QC labs twenty miles away, business leaders invested in a digitally sophisticated enterprise ecosystem with a fully integrated, cloud-based LIMS at its core. This decision harmonized the company’s different operations, giving lab workers the data they needed to accelerate innovation and business leaders the wisdom they needed to outmaneuver the competition and emerge as a market leader.

Compliance will become more complex as data integrity regulations tighten.

Homegrown and heavily customized legacy lab systems, however ideal they may have been when deployed, provide inadequate protection against today’s regulatory scrutiny. At best, they slow progress by requiring users to sink time into manual data remediation; at worst, they perpetuate outdated and inconsistent data management practices, leaving companies vulnerable to security breaches and the costly penalties of non-compliance under the FDA’s 21 CFR Part 11 and Annex 11 regulations on electronic records and signatures.

That’s why the pharmaceutical industry will see a dramatic uptick in migration toward modern LIMS systems that are capable of capturing and protecting complex data according to today’s standards and guidelines likely to emerge in the future. This migration will help business leaders to reduce costs in the lab by eliminating manual data tasks while de-risking their data management practices, ensuring that all data is attributable, legible, contemporaneous, original, and accurate, as well as consistent, available, enduring, and complete (a principle known as ALCOA+).

What will pharma’s future digital leaders look like?

As more pharmaceutical companies join the surge toward fully digital labs, these four indicators will determine who will lead in 2020—and who might fall behind.

       1. Champions at the top.

For a digital transformation strategy to succeed, it requires the active support of a visionary CEO and an executive leadership team willing to embrace outside expertise and listen to those most impacted: the end users inside their R&D and QC labs.

       2. A rollout plan that balances ambitious goals with continuous learning. 

Labs have tremendous potential to lead a drug company’s digital overhaul—but only if their deployment strategy is designed for incremental rollout, ensuring smooth and informed adoption based on which lab functions are best positioned to transition first. For example, many digital transformation strategies start in the R&D lab, where IT can test, configure, and learn from new LIMS technology in a smaller, more manageable environment, then carry those learnings forward to the broader enterprise.

       3. A strategic partnership with the right technology innovators.

Pharmaceutical companies are in the business of drug discovery and manufacturing, not software development. Carefully selecting the right partner to fill that capability gap is key, ensuring the smooth implementation and validation of highly configurable, cloud-ready out-of-the-box digital systems.

       4. Clear and achievable KPIs, defined from the start.

The best way to ensure the continued success of a digital transformation strategy is to measure it. Every lab will have a unique set of KPIs to track their progress toward holistic digital transformation. The best of them will ensure that those KPIs are clear, specific, and measurable, and that they ladder up to overarching business objectives.

Conclusion

The meaning of “digital transformation” is often flattened to include any initiative that moves users off of paper and onto computers. True digital transformation is so much more than that. It’s about the way pharmaceutical companies use technology and data to build a business ecosystem, thereby reducing risk, accelerating innovation, and driving growth.

Some labs, born in this digital age, will never know anything but the power of advanced data analytics to achieve successful business outcomes; others will need to overhaul legacy systems in order to keep up. For both, the lesson is the same: to survive an increasingly competitive and regulated pharmaceutical environment, a flexible and integrated digital network is a must. That’s what true digital transformation in 2020 will be about. 

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