DATA STRATEGIES - As We Shift Toward Biologics, We Also Need to Shift Toward Smarter Data Management

INTRODUCTION

Despite the curveball the pandemic threw us, 2020 was a year for the books in the way of FDA approvals — 53 new med­icines were approved, second only to the all-time high of 59 in 2018.¹ Throughout the past few years, there’s been a clear trend in the makeup of approvals, with biologics representing a grow­ing percentage.² But even within biologics, newer technologies like mRNA, cell, and gene therapies are gaining ground. The ap­provals are not one-off events, but evidence of an exciting long-term trend — these technologies will have massive impacts on human health throughout the next decades, and we should expect to see innovative biologics accounting for more and more of the drug landscape in the coming years.

The trend is also reflected in many of the individual portfolios of biopharmaceutical organizations: biologics may make up 50% to 60% of a company’s new products. Among the top 20 phar­maceutical companies, almost all are making monoclonal antibodies. For cell and gene therapies, at least two dozen companies are now well established in the area, and more small companies are moving in that direction. And whereas about 12 companies were researching mRNA therapies before the pandemic, this number has now increased significantly.

These trends should make it clear to organizations that we’re at an inflection point — this is truly the century of biology, and biology is much richer and more varied than it was just years ago. Investors have certainly pricked their ears and are putting more money into the area.

We should therefore expect to see increased competition be­tween the players who are developing these therapies. Biologics have quickly become billion-dollar revenue streams that need to be moved out of R&D and into the market and patients. Speed is important, not just for patients, but also for the organization — the first monoclonal antibody to serve unmet need X has a much greater competitive advantage over the second monoclonal anti­body to do so. It’s clear that companies large and small need to start building capacity for the future and prepare to get new ther­apies faster to market.

A key consideration in this competition is that lifecycle devel­opment is much more complicated for biologics than for small mol­ecule drugs. Whereas the chemical compound itself is what’s patented in small molecules, for a biologic, much of the IP is in the know-how associated with the process to produce a biologic. “The process is the product” is a phrase often used in the industry, mean­ing the value has shifted from the therapy to the process that can repeatedly produce that therapy safely and at scale. This highlights the importance of data and a company’s ability to capture it across the entire development lifecycle.

To support biologics development and speed it along, a new strategy to collect, manage, store, and draw insight from data is required: a biopharmaceutical lifecycle management (BPLM) sys­tem.³ This type of system captures data at the point of execution across the entire development lifecycle and creates a contextualized data backbone, which deepens the insights that are drawn from the data and makes them easier to interpret. This is a fundamen­tally different way of thinking about data, and one that involves bringing tech transfer into the equation right from the beginning.

Unfortunately, a startling number of organizations still rely on antiquated methods of data collection. In a recent survey, 50% of participants were using legacy applications such as electronic lab notebooks (ELN) to record process development work; the other 50% were using a mix of paper and Excel spreadsheets and stand­alone instrument software.⁴ In addition, 62% of participants re­ported spending at least 5 hours a week on data administration, and in some cases, more than 20 hours a week. Too often, there is an attempt to make one process or one lab more productive with an ELN or laboratory information management system (LIMS) — but sets of data still become siloed and difficult to integrate.

In contrast, collecting data across the entire development life­cycle has several advantages: one is that collection occurs not only from beginning to end, but also across researchers and depart­ments. Having data all gathered and available in one place is extremely helpful, particularly as early data are important later on, as in regulatory fil­ing. Additionally, a serious problem in R&D is data that are lost due to the use of disparate systems. With BPLM, the rework needed to replace lost data is greatly re­duced, which saves an organization both time and money, particularly in biologics, where development and scaling are inher­ently more complex. Saving time is incred­ibly important in this field because delays put IP exclusivity at risk.

While these benefits are clear, BPLM can also help set the stage for even more exciting ventures like the generation of digital twins. The ability to interlink vari­ables at multiple time points and structure data allows an organization to develop in­creasingly complex algorithms, which in turn, can lead to the creation of full digital twins of instruments, assets, and processes. Biopharma is catching up to other industries that have been using dig­ital twins for some time to create in silico representations that provide powerful pre­dictive capabilities. In biopharma, we want to move increasingly toward a place where phases of drug development are predic­tive, which cuts down on time to market. The older, siloed methods of data collec­tion are not conducive to movement in this direction and into biopharma 4.0.

The pandemic has accelerated trends already underway, and the digital divide was clear: digitally savvy companies hardly missed a beat, while those still using paper fell behind. There’s been a rise in mid-size and small biotechs coming to market that are primed to grow and are looking for digital solutions that support the cutting edge; they often have the ad­vantage of not being handcuffed by legacy systems that older companies are using. Big investments are going into mRNA and cell therapies, so it’s especially beneficial for these organizations to think differently about their data. Each new generation of these therapies can move faster if learn­ings from the first ones are captured. This is the digital mindset that must be adopted — thinking about software and data as central, in a way that older companies may still be trying to retrofit.

Finally, CMOs and CDMOs are also transforming fast — from service organi­zations to major drivers of innovation and process development. They’re becoming more strategic in how they serve customers and how they can help accelerate the crit­ical business milestones like regulatory fil­ing and tech transfer that are so crucial to their customers. The volume of the work they do and the nature of the IP they work on for their customers creates unique re­quirements for workflow support and data management.

It’s time for organizations of any size and type to think differently about their data, and how to accrue it and structure it across time and different entities in the lab. The competition that comes with the move toward biologics will require companies to move faster. Lifecycle data management won’t change the probability of success, but it will make a difference in whether you’re first or second to market. Adopting a new kind of data strategy might be disruptive to the expectation of how biologics development is carried out — but it may also be transformational.

REFERENCES

1. 2020 FDA drug approvals https://media.nature.com/original/magazine-assets/d41573-021-00002-0/d41573-021-00002-0.pdf.
2. Drug Approval Trends: Significant Ac­celeration in Recent Years https://www.pharmasalmanac.com/articles/drug-approval-trends-signifi­cant-acceleration-in-recent-years.
3. BioPharma Lifecycle Management, IDBS https://www.idbs.com/polar/bplm/.
4. IDBS, 2020 Aspen Survey, manuscript in preparation.

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Christian Marcazzo is the General Manager of IDBS. He earned his BA in Molecular and Cell Biology from the University of California, Berkeley. He is based at the IDBS headquarters in Guildford, UK, and has spent 25 years at the interface of biology and software, leading organizations like Spotfire and LION bioscience through the genomics revolution and the digitization of R&D.