Beating the Capacity Crunch: Rethinking the Biopharma Supply Chain to Avoid Shortages

One of the lasting legacies of the COVID-19 pandemic is heightened awareness of manufacturing challenges and supply chain vulnerabilities in the biopharma industry and the impact this can have on public health. Issues have occurred at every stage of the supply chain, from the availability of raw materials to quality concerns with active pharmaceutical ingredients (APIs), and finished dosage forms and restrictive cold chain requirements. One well-known example of a key component shortage is viral vectors which are used in the AstraZeneca and Johnson & Johnson COVID-19 vaccines as well as gene therapies and gene-modified cell therapies. Viral vectors are difficult to make and were already in short supply before the pandemic, yet demand continues to grow. More general biopharma capacity shortages are also predicted as manufacturers struggle to keep up with demand for vaccines and therapeutics such as aducanumab, the newly approved Alzheimer’s drug from Biogen. FDA analysis shows that the number of ongoing drug shortages has been increasing since 2017 and that drug shortages have also been lasting longer, in some cases more than eight years.

One of the unique challenges of biopharmaceutical production is the long timescales and high level of uncertainty involved in building out manufacturing capacity. Manufacturing facilities take years to build and validate so construction is often started well before clinical performance has been established and production amounts and timings can be defined with any degree of accuracy. Late failures in the clinic or other setbacks can leave expensive manufacturing facilities and equipment lying idle, waiting to be repurposed for another drug through a lengthy and expensive retrofit. Even when clinical trials go to plan, multiple engineering runs to troubleshoot problems and ensure a stable manufacturing process can delay the start of commercial manufacturing by six months or more.

So, how can the industry prevent shortages from happening? In the US, the Biden administration just announced plans to strengthen critical supply chains for key products including advanced pharmaceutical ingredients. Unsurprisingly, the recommendations include diversification and redundancy of supply chains, improving quality management, and adopting new manufacturing technologies. The government is also committing about $60 million to “develop novel platform technologies to increase domestic manufacturing capacity for API”. While there are technology improvements including process intensification and the use of prefabricated modular cGMP environments such as KUBio or G-CON that offer the potential to significantly reduce time to market from a capacity expansion perspective, these will only work when coupled with effective process development, tech transfer, and validation.

The ugly truth is that biopharmaceutical lifecycle management is still in the dark ages for most companies. In a recent survey of biopharma development teams, participants reported spending an average of one day per week on data administration, and 50% reported using paper, Excel, and standalone software solutions to record process development work (1). The other 50% reported using legacy applications such as Electronic Lab Notebooks (ELNs) which capture data electronically but without the context needed to enable effective searching and reporting. As a result, problems encountered as late as biologics licence application (BLA) submission can uncover missing or unsupported development data which in turn require entire teams to stop ongoing projects and repeat previous studies to address the discrepancies.

The answer to speeding up development and avoiding shortages is, ultimately, data. Data to predict the productivity and yield of new manufacturing processes more accurately, for example using small-scale studies and scale-up models. Data to justify the use of advanced manufacturing technologies such as continuous processing in regulatory submissions. Data to enable substitution or reuse of critical components, for example as Pfizer increased COVID-19 vaccine production by reusing filters two or three times.

The real problem behind the problem is the usefulness of the data that’s currently being generated. The volume of data generated is constantly increasing, particularly with the use of high-throughput techniques and real-time monitoring, but if the data lacks context it’s difficult or even impossible to gain meaningful insight through analysis. The lack of standardization in data formats including metadata and descriptors and lab instrument interfaces is a known problem in the industry. While there are technological solutions to overcoming the lab connectivity challenge, the more fundamental challenge of ensuring valuable data is captured in context at the point of execution requires a solution that considers not only the operational workflow but also the data backbone that underpins the biopharmaceutical development lifecycle. This is essential to enable teams to share knowledge, gain insight from collective experience, and support strategic objectives such as quality by design and design for manufacturability. Design for manufacturability is especially important in the context of overall supply chain management as it’s far more efficient to make the right design choices early in process development, for example if it’s possible to find alternatives for difficult-to-source materials or workarounds for potential bottlenecks at commercial scale. Some of the key tools to support this include modeling and simulation to predict large-scale performance based on small-scale data and collective process understanding.

The need for more effective collaboration across an increasingly complex network of external partners and suppliers is another reason why fundamental changes in biopharma lifecycle management are urgently required. Contract development and manufacturing (CDMO) organizations continue to be a driving force in the industry and the expertise and flexibility they provide is critical both for supplying clinical trial materials and boosting commercial supply when needed. Tech transfer, especially to a CDMO, is a complex process involving equipment, raw material/consumables, and regulatory considerations and requires in-depth process understanding as well as comprehensive documentation and clear, frequent communications. While collaboration doesn’t necessarily demand a cloud environment, cloud integrated technology and applications are a key enabler of more holistic end-to-end digital workflows which can be rolled out quickly, enabling more fluid collaboration across teams.

In summary, while it’s great to see that global supply chain and manufacturing challenges are getting the attention they deserve, lasting change requires addressing the core problem of biopharma lifecycle management. Bringing together the combined expertise in R&D, manufacturing, and supply chain management across all organizations involved in vaccine and drug development could make all the difference for current and future global health challenges.

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References:

1. IDBS (2021). Aspen Survey. IDBS.