Three Reasons Biopharma Companies Can’t Afford To Miss The Digital Revolution

August 20, 2021

Contributed Commentary by Claire Hill

August 20, 2021 | In the biopharmaceutical industry, there’s no shortage of data—the issue organizations more often face is how to capture data in ways that derive insight from it. As more and more companies are now recognizing, a fundamentally different approach is needed to realize the strategic benefits of data in making exciting and transformative new applications, such as digital twins, a reality. Rather than stitching together a patchwork of data from disparate systems after the fact, more emphasis should be placed on capturing data in context, right at the point of execution. Applying the same approach across labs, and even across departments and disciplines, ensures data are captured in a consistent and structured way, so that they are usable for those who need it. 

Unfortunately, many biopharma companies are still spending significant amounts of time on ineffective data administration. My company’s survey of leading biopharma companies found that half the respondents used a mix of paper, Excel, and standalone software to record development work; the other half used legacy applications such as Electronic Lab Notebooks, which lack the context that allows data to be fully searchable and reusable. At best, this is inefficient; at worst, it can result in problems encountered as late as the point of biologics license application (BLA), requiring repetition of development studies involving months of additional time and money spent on rework. 

While there are many reasons not to miss out on the digital revolution, here are three of the most critical reasons biopharmaceutical companies should reassess their data strategy.

1. Increasing Capacity And Reducing Manufacturing Bottlenecks 

Manufacturing bottlenecks have been a significant issue in recent years, and were exacerbated during the pandemic, as items from raw materials to active pharmaceutical ingredients (APIs) have undergone shortages. Biopharmaceutical production can be at particular risk because of the long timescales and high capital costs inherent in building out manufacturing; biopharma facilities can take years to construct, and capital costs are generally orders of magnitude higher than for small molecule API facilities. 

With the right context, well-managed data can help predict, prevent, and circumvent supply chain problems. For instance, modeling and simulation can use small-scale data and collective process understanding to predict productivity and performance at production scale with greater accuracy. It’s also much more efficient to leverage bioprocess data to make the right design choices earlier rather than later in process development: for example, to find alternatives for scarce materials or support process intensification strategies, such as continuous manufacturing, which can significantly reduce facility footprint and costs. Finally, reference data from experimental studies can support increasing collaboration across external partners, including contract development and manufacturing organizations (CDMOs), which are playing a more prominent role in providing clinical trial materials and enhancing commercial supply.

2. Reducing Time And Cost Through In Silico Simulation 

Another clear benefit is the potential to reduce the time and cost required to bring a drug to market, which are still, respectively, more than a decade and a billion dollars. While biologics are highly complex both in terms of structure and mode of action, it’s now increasingly possible to use computing power for in silico simulations to speed up the development process.

One promising example is the recent publication of the AlphaFold Protein Structure Database, created by DeepMind and the European Molecular Biology Laboratory; the system uses amino acid sequences to predict the 3D structure of every protein in the human proteome and is the most comprehensive protein database to date. While most of the excitement has been around the potential to understand and treat diseases, this kind of advanced modeling can also be applied to predict how new biological products such as monoclonal antibodies (mAb) will perform under various process development conditions. Doing more process optimization work through modeling and simulation, rather than time-consuming and resource-intensive lab work, can shorten the time and cost associated with development. Mechanistic modelling of purification steps such as chromatography, for example, can help minimize product degradation and optimize yield and purity which in turn reduces resource requirements and costs. 

3. Producing Cell And Gene Therapies To Meet The Growing Demand 

Advanced therapies, including cell and gene therapies, increasingly hold promise not just to treat, but to cure disease. Not surprisingly, interest in these technologies has taken off in recent years, with more and more companies devoting research to them and BLA approvals accelerating. But because most rely on viral vectors for delivery or gene modification, vector production has struggled to meet demand. Current manufacturing methods for viral vectors are plagued with problems such as limited scalability and low yield; therefore, there’s a race to improve productivity with techniques such as continuous processing. Moving to continuous manufacture not only enhances production, but also reduces manufacturing costs. This of course requires data—in particular, real-time data—to provide insight into making the switch and provide the necessary assurances of process control and product quality for regulatory submissions.

Other aspects of the development of advanced therapies can also be aided by data: for instance, to solve engineering and logistical challenges. Manufacturing methods such as stirred-tank bioreactors, which work well for conventional biologics, can’t be used for cell and gene therapies without extensive modification. Cross-disciplinary collaboration between engineers, cell biologists, biochemists, clinicians, and more is the best way to approach these challenges, as each can provide unique insights and ideas the others may have overlooked. But it is only with effective data and process management that “old” knowledge, gained from conventional biologics such as mAbs, can be applied to these exciting new products.

The digital revolution holds great promise for the biopharma industry and combining learnings, experiences and insights into a single system is really the key to all of this. For the three reasons discussed here, along with many others, data can be thought of as the cornerstone of drug development in the coming years and should be treated as the valuable resource it truly is.


Claire Hill leads IDBS’ market engagement for BioPharma Lifecycle Management. With over 15 years of business analysis and consulting experience, Claire has in-depth knowledge of the data management challenges in the biopharmaceutical industry. Claire has an MSc in Biochemical Engineering and an MBA and previously worked as an analyst for IBM and as a consultant for BioPharm Services.  She can be reached at