Pharma Matters Q&A

Reducing Product Loss in Sterile Manufacturing

Thermo Fisher Scientific’s Christy Eatmon discusses strategies to improve yield, efficiency, and recovery across the sterile lifecycle.

Released By Thermo Fisher Scientific Pharma Services

Everything Thermo Fisher Scientific does starts with its mission to enable its customers to make the world healthier, cleaner and safer. The company’s end-to-end, integrated CDMO capabilities are designed to help companies on their drug development and manufacturing journeys. From research and development to regulatory landscape support, supply chain management, and everywhere in between, its services and solutions support the entire life cycle of drug substances and drug products, from preclinical research to commercial manufacturing.

In this conversation with Contract Pharma, Christy Eatmon, Global Steriles Subject Matter Expert, Thermo Fisher Scientific, Pharma Services Group, discusses how Thermo Fisher Scientific can help biopharma manufacturers reduce product loss and improve yield across sterile manufacturing.

Contract Pharma: Why is product loss across the sterile lifecycle such a significant challenge for biopharma manufacturers today, and how have expectations around yield, efficiency, and sustainability changed the conversation in recent years?

Christy Eatmon: Line loss can have a significant impact at every stage of drug production, but more specifically in early phase where batches are small and API or drug substance qualities are limited. On most filling lines, the line losses and required testing volumes are a fixed volume, which translates into higher percentages of product loss for small batches.

CP: When companies evaluate sterile manufacturing, where do the biggest sources of product loss typically occur, and what metrics best reveal where product is actually being lost throughout sterile processing?

Eatmon: In steriles manufacturing we can typically account for where all the material goes and often have over 95% accountability of the material. However, yields may be impacted by testing and other processes that require material. These tests include: filter flushes, PUPSIT (pre-use post-sterilization integrity testing), prefiltration bioburden, in-process samples, and line purge. The amount of material that is truly lost to transfer tubing, residual material in tanks, filling line and filter hold up is usually relatively small. Additional product losses are normally incurred during the visual inspection process and can account for an additional few percent of the batch going to waste, most of these for non-critical or cosmetic defects.

CP: Is there a particular stage of sterile manufacturing where even small percentage improvements can have an outsized financial impact, especially for high-value biologics?

Eatmon: The biggest areas of impact are identifying where product loss occurs during the compounding and filling process as well as improving defect rate. Process improvements to reduce product loss could include; proper filter sizing, ensuring flush and sample bags are properly sized so that excess material is not purged and designing product delivery systems efficiently.

CP: What factors can contribute to product yield or recovery losses when scaling up from clinical-scale to commercial manufacturing, and how can these challenges be evaluated and controlled to maintain consistent product recovery?

Eatmon: As a product moves from clinical-scale to commercial manufacturing, maintaining product yield or recovery can become more challenging because the process does not always scale linearly. Changes in equipment size and configuration can affect mixing, flow dynamics, hold-up volumes, and product contact with processing surfaces. These differences may lead to increased product losses through adsorption, retention in equipment and transfer lines, filtration, sampling, or incomplete product recovery during transfers.

At commercial scale, additional processing steps, longer transfer paths, larger filters, and increased hold-up volumes may also contribute to yield loss. Product-specific characteristics, such as sensitivity to shear, temperature, interfaces, or extended hold times, can further affect recovery.

To maintain acceptable yields during scale-up, these factors should be evaluated during process development and characterization. Critical process parameters (CPPs) and other operating conditions that influence recovery should be identified and appropriately controlled. Engineering and scale-up studies can then be used to optimize equipment configuration, transfer and flushing strategies, filtration, mixing, and hold times. Ultimately, the goal is to establish a robust commercial process that consistently meets product quality requirements while minimizing avoidable product loss.

CP: Thermo Fisher Scientific works across multiple stages of biologics development and manufacturing. How does that end-to-end perspectives help identify opportunities to reduce product loss throughout the sterile lifecycle? 

Eatmon: Product loss is often associated with specific unit operations or process steps, but having an end-to-end perspective allows us to look beyond individual operations and understand how decisions made at one stage may affect recovery and performance at subsequent stages.

By working across development, scale-up, formulation, sterile manufacturing, and final product presentation, teams can share knowledge and identify potential sources of loss earlier. For example, formulation choices, filtration strategies, equipment configuration, transfer steps, hold-up volumes, and the final presentation can all influence overall product recovery. When these activities are integrated within the same network, there is greater visibility across the process and better communication when potential issues arise.

This broader perspective enables teams to evaluate opportunities holistically rather than optimizing a single step in isolation. The result can be better-informed decisions around process design, formulation, and product presentation that help minimize avoidable losses while also supporting product quality, manufacturing efficiency, and program timelines.

CP: How has Thermo Fisher’s experience supporting both emerging biotech companies and large pharmaceutical manufacturers shaped its approach to yield optimization?

Eatmon: Although large pharma may have more resources, experience or more sophisticated ways to measure performance, as a CDMO we take the same approach to management of a program, including product handling, no matter the size of the client. In this industry nearly every product we work on is very valuable and essential to our clients success and positive patient outcomes. Part of that success relies on optimized manufacturing so that we can deliver product that meets our clients expectations both in quality and in quantity.

CP: Which technological innovations are having the greatest impact on reducing product loss today?

Eatmon: The implementation of single use systems as part of both biologics and drug product fill finish has allowed us to be more flexible in our process design, scale appropriately and conserve material where possible. With the growth of drugs that treat orphan diseases and personal medicines, it’s important that we find ways to prepare very small commercial batches. Equipment vendors have also been key in creating ways to support low line loss and high yields. When it comes down to product loss, it’s important to understand the equipment parameters and control what is within our ability. Sometimes that looks like being creative in design of processes or having reliable equipment that we feel confident will produce few rejects.

CP: Looking ahead, where do you believe the next major opportunities for yield improvement will come from, and how do you expect the industry’s approach to yield optimization to evolve over the next five years?

Eatmon: Automation and robotics will certainly continue to play a big role in how we product pharmaceutical products. As technology improves, our yields will improve as well. We can also start to think about how AI can help predict issue in reliability or defects before they occur.

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