Christopher Cole

High-speed autoinjector assembly integrates automated component feeding, orientation, spring loading, energy module integration, and housing assembly, followed by functional testing and quality inspection

Peptide or protein drug substances are produced under cGMP in sterile areas meeting 21 CFR Parts 210

 Class 5 standards. Active ingredients and excipients are mixed using validated procedures for uniformity and stability, and then sterile filtered through 0.2 μm filter per USP <797>.

Sterile solutions are filled into prefilled glass syringes in ISO Class 5 environments, often within isolators or RABS, following EU GMP Annex 1 and FDA guidance. Syringes are prepared per ISO 11040 with validated washing, siliconization, and depyrogenation, meeting USP <85>

 Accurate doses are dispensed with validated systems (ICH Q2,

), then stoppered with closures compliant with ISO 8362

 and container closure integrity verified per USP <1207>.

In-process controls include fill volume, stopper position, and particulate inspection per USP <788>

 Environmental monitoring is conducted per EU GMP Annex 1,

 with media fills for sterility assurance. Filled syringes undergo QC for sterility, endotoxins (USP <71>,

), dose accuracy, and visual quality before integration into autoinjectors built under ISO 13485

 Class 7 environments using validated equipment, and each unit is tested for injection force, delivery time, and safety per ISO 11608

 and FDA guidance. Stability studies follow ICH Q1A.

 Release occurs only after all sterility, functional, and quality checks pass.

Formulations maintain stability, dose accuracy, and device compatibility under ICH Q6B,

 Buffers, pH, stabilizers, and surfactants control degradation and adsorption. High peptide concentration increases viscosity, affecting filling, syringe performance, and needle injection, evaluated per ISO 11608.

 Particulates are controlled per USP<788>,

 and container compatibility verified via adsorption, extractables/leachables (USP<1663>,

), ensuring stability and safety throughout shelf life.

Having outlined formulation requirements, regulatory standards, and quality controls, the following sections discuss how these factors influence autoinjector performance, high-speed assembly, and factory acceptance testing. Table 1 summarizes relevant standards and guidance across the lifecycle of a peptide formulation in a prefilled syringe (PFS) and its integration into the autoinjector system.

Table 1-1 Lifecycle Stage Guidance, Regulations, and Standards Mapping

Table 1-2 Lifecycle Stage Guidance, Regulations, and Standards Mapping

Table 1-3 Lifecycle Stage Guidance, Regulations, and Standards Mapping

Factors Influencing Autoinjector Performance

Peptide formulations in PFS directly impact autoinjector performance. For PFS-based autoinjectors (Figure 1), viscosity, driven by peptide concentration, affects injection force, filling accuracy, and dose delivery. Buffers and pH must be compatible with syringe materials to prevent degradation, while surfactants like polysorbates minimize adsorption and particle formation (USP<788>14, USP<790>15, USP<787>28). Plunger lubrication ensures smooth motion, and formulations must withstand shipping and handling stresses.

Injection efficiency depends on the interaction of formulation, syringe mechanics, and device design. Fine needles improve comfort but increase resistance (Hagen–Poiseuille law; Shire, et al. 2004, 1390–1402).

 Autoinjectors use springs to push the plunger, overcoming viscosity and plunger friction (ISO 11040

). Shear stress, air bubbles, and temperature also influence performance. Design verification follows ISO 13485,

Engineers balance spring force, needle size, plunger friction, and viscosity to ensure reliable full-dose delivery. Typical volumes range from 0.3–1.0 mL, with springs generating 20–80 N and injection times of 5–10 seconds. Early modeling integrates these factors to ensure safe, consistent, and comfortable administration. Understanding how formulation and device design affect injection performance informs the assembly process, where precise alignment and integration of components are critical to ensuring consistent function and patient safety.

1. Outer Cap 2. Needle Shield 3. Needle 4. PFS System 5. Plunger Stopper 6. Flange 7. Plunger Rod 8. Spring 9. Main Body 10. Activator / Trigger

Autoinjector assembly begins with automated machines feeding and aligning components such as housings, springs, plungers, and caps. Sensors and vision systems inspect parts for correct orientation and defects, ensuring only compliant components proceed, in line with ISO 13485,

 Prefilled syringes are loaded using robotic or vacuum systems designed to prevent damage and maintain sterility. Vision inspection confirms syringe quality and alignment per ISO 11040,

 while environmental controls follow 21 CFR Part 211.

Springs are inserted and compressed using automated stations, with sensors verifying proper force and placement according to safety and GAMP 5

 validation requirements. Internal components, including the syringe holder, plunger, and trigger, are assembled into the housing using precision fixtures and secured via snap-fit or welding. Alignment and design specifications are confirmed with sensors and measurements.

Completed autoinjectors are labeled and packaged, with vision systems checking accuracy and appearance. Serialization tracks each unit for full traceability, ensuring compliance with 21 CFR Part 820,

This process supports consistent quality, reliable device function, and regulatory compliance, forming the basis for factory acceptance testing of high-speed autoinjector equipment.

A 100,000-unit FAT may be conducted to verify autoinjector assembly, especially for high-value, complex lines. Large FAT runs are often required when the purchase agreement or User Required Specification (URS) mandates a throughput demonstration, when the system is first-of-a-kind or high-risk, or when post-installation troubleshooting would be costly or disruptive, such as installing directly in a cGMP environment or overseas. Very expensive equipment (often $5M+) also justifies extended FAT to confirm reliability, throughput, and reject rates. Standard projects typically use smaller FAT batches of 5,000–20,000 units, with a 100,000 run reserved for commissioning or performance qualification at the manufacturing site. High-speed systems record force curves, cycle data, and alarms in the HMI, enabling engineers to detect assembly or transport deviations.

 The following sections describe two critical autoinjector assembly stations and their sampling requirements.

This station is responsible for precisely inserting the PFS into the auto-injector housing.

 The syringe typically contains the drug product and includes key components such as the glass syringe barrel, plunger stopper, needle, and needle shield. A servo-controlled robot or linear axis performs the pick-and-place operation to transfer the syringe from a tray or feeder. The system usually uses a vacuum gripper or custom end-effector designed to handle the fragile glass syringe safely. The servomotor ensures accurate positioning, smooth motion, and correct alignment during insertion into the injector body or cartridge holder. Sensors verify syringe presence, orientation, and proper seating inside the housing before the assembly proceeds to the next station. The servo operates primarily in position control mode, emphasizing precision and repeatability. If the syringe is misaligned, not fully seated, or cracked, the injector may not function correctly. Problems at this stage can lead to needle deployment issues, incomplete dose delivery, or device activation failures during later functional or destructive testing (DT). Therefore, this station ensures that the drug container (syringe assembly) is correctly positioned within the auto-injector mechanism for reliable operation.

The press-fit station assembles critical autoinjector components, such as the outer housing, spring module, actuator parts, trigger mechanism, and syringe carrier, using a servo press system.

 The servo press applies precise force over a controlled distance to join each component correctly. For every part pressed, the machine records a force–displacement curve showing how the applied force changes as the component move into position. Each component has its own expected profile due to differing mechanical properties.

If the press force is too low, components like the spring module or trigger may not lock properly. If too high, parts such as the housing, syringe holder, or internal mechanisms can deform or be damaged. The system compares each curve against validated limits, flagging or rejecting devices that fall outside tolerance. Curves are stored in the HMI or quality database for traceability and process validation. Monitoring these curves helps engineers detect misaligned parts, incorrect tolerances, or damaged components, which could later cause trigger failure, incomplete needle extension, or improper drug delivery during destructive testing. By ensuring correct assembly and secure integration of mechanical and activation components, this station supports reliable autoinjector function and consistent drug delivery. To ensure the assembly processes at critical stations perform consistently across the production run, representative sampling strategies are applied.

Statistical Sampling for Critical Stations

For a FAT of 100,000 autoinjector units, stratified sampling ensures representative testing across the production run.

 The run is typically divided into five strata of ~20,000 units each, capturing variations at the start, middle, and end. Non-destructive testing (NDT) is performed on 1–2% of units per stratum, verifying assembly integrity, syringe placement, press-fit quality, and vision inspection without destroying the product. Force-displacement curves for pressed components are included to monitor mechanical consistency, and NDT samples are evenly distributed to detect drift or anomalies. DT is performed on 0.1–0.5% of units per stratum, focusing on critical functions such as trigger activation, spring release, needle deployment, and dose accuracy. DT samples are evenly distributed across all strata to ensure consistent functional performance. Together, NDT and DT provide statistically significant evidence that the line produces conforming devices.

 guidelines, balancing regulatory confidence with efficiency while avoiding full-batch destructive testing. Each stratum acts as a control segment to detect process drift or setup issues, and sample sizes align with ANSI/ASQ Z1.4

 functional testing requirements. For autoinjector assembly, five well-distributed strata are sufficient because the process is highly controlled, critical quality attributes are limited, and continuous monitoring reduces the risk of undetected variation. Adding more strata increases inspection effort with minimal additional benefit.

) analysis measures how well a process produces parts within specification by combining variability and centering of key parameters.

 In a 100,000-unit autoinjector run, NDT samples 1–2% of units per stratum and DT samples 0.1–0.5%, across five 20,000-unit strata are typically sufficient. Continuous monitoring via force-displacement curves, sensors, and vision systems supports both C

 calculations and functional assurance. Each stratum captures temporal process variation.

Autoinjector molds may have up to 16 cavities, so sampling represents all cavities to detect cavity-specific variation. NDT samples are distributed evenly across cavities, and DT includes units from each cavity to identify functional failures.

 Process stability is checked using control charts, ensuring no special causes of variation. C

 is calculated from the mean and standard deviation of mechanical parameters, either per stratum or pooled across strata and cavities. Comparing C

 across strata and cavities detects drift, outliers, or localized issues. This stratified, cavity-aware approach ensures C

 reflects both temporal and mold-related variation. Combined with DT results, C

 provides robust evidence that the autoinjector production run consistently meets quality and functional specifications.

A coordinated approach integrating peptide formulation, PFS design, high-speed autoinjector assembly, and lifecycle-aligned regulatory compliance ensure consistent product quality, dosing accuracy, and patient safety. Applying FAT, C

 analyses, and adherence to industry standards provides a robust framework for reliable commercial manufacturing and effective delivery of peptide therapies via autoinjectors.

1. 21 CFR Part 210 - Current Good Manufacturing Practice in Manufacturing, Processing, Packing, or Holding of Drugs; General; 2026, 

https://www.ecfr.gov/current/title-21/chapter-I/subchapter-C/part-210

2. 21 CFR Part 211 - Current Good Manufacturing Practice for Finished Pharmaceuticals, 2026, 

https://www.ecfr.gov/current/title-21/chapter-I/subchapter-C/part-211

3. ISO 14644-1, Cleanrooms and associated controlled environments - Part 1: Classification of air cleanliness by particle concentration, 2015, 

4. USP General Chapter <797> Pharmaceutical Compounding – Sterile Preparations, 2023, 

https://www.usp.org/compounding/general-chapter-797

5. USP General Chapter<85> Bacterial Endotoxins Test, 2024, 

https://www.usp.org/harmonization-standards/pdg/general-methods/bacterial-endotoxins

6. USP General Chapter<1228> Depyrogenation, 2017, 

https://doi.usp.org/USPNF/USPNF_M8097_03_01.html

7. USP General Chapter<1229> Sterilization of Compendial Articles, 2022, 

https://doi.usp.org/USPNF/USPNF_M7408_01_01.html

8.ICH Q2 Validation of Analytical Procedures, 2024, 

https://www.ema.europa.eu/en/ich-q2r2-validation-analytical-procedures-scientific-guideline

9. Process Validation: General Principles and Practices, 2011,

https://www.fda.gov/files/drugs/published/Process-Validation--General-Principles-and-Practices.pdf

10. ISO 8362-2, Injection containers and accessories - Part 2: Closures for injection vials, 2024, 

https://www.iso.org/obp/ui/es/#iso:std:iso:8362:-2:ed-4:v1:en

11. USP General Chapter<381> Elastomeric Components in Injectable Pharmaceutical Product Packaging/Delivery Systems, 2024, 

https://doi.usp.org/USPNF/USPNF_M99140_60201_01.html

12. USP General Chapter<382> Elastomeric Component Functional Suitability in Parenteral Product Packaging/Delivery Systems, 2025, 

https://doi.usp.org/USPNF/USPNF_M11355_04_01.html

13. USP General Chapter<1207> Package Integrity Evaluation—Sterile Products, 2017, 

https://doi.usp.org/USPNF/USPNF_M99926_01_01.html

14. USP General Chapter<788> Subvisible Particulate Matter in Injections, 2026,

https://www.usp.org/harmonization-standards/pdg/general-methods/particulate-contamination

15. USP General Chapter<790> Visible Particulates in Injections, 2017, 

https://doi.usp.org/USPNF/USPNF_M7197_01_01.html

16. EU GMP Annex 1, Manufacture of Sterile Medicinal Products, 2022, 

https://health.ec.europa.eu/system/files/2022-08/20220825_gmp-an1_en_0.pdf

17. ISO 14698-1, Cleanrooms and associated controlled environments, 2003, 

https://www.iso.org/obp/ui/#iso:std:iso:14698:-1:ed-1:v1:en

18. USP General Chapter<71> Sterility Tests, 2016,

https://www.usp.org/harmonization-standards/pdg/general-methods/sterility-test

19. ISO 13485, Medical Devices - Quality Management Systems, 2016, 

20. ISO 11608, Needle-based Injection Systems for Medical Use - Requirements and Test Methods 2022, 

21. ISO 10993, Biological evaluation of medical devices, 2025, 

22. ICH Q1A Stability Testing of New Drug Substances and Drug Products, 2003,

https://www.ema.europa.eu/en/ich-q1a-r2-stability-testing-new-drug-substances-drug-products-scientific-guideline

23. ICH Q6B Specifications: Test Procedures and Acceptance Criteria for Biotechnological/Biological Products, 1999, 

https://www.ema.europa.eu/en/ich-q6b-specifications-test-procedures-acceptance-criteria-biotechnological-biological-products-scientific-guideline

24. ICH Q8 Pharmaceutical Development, 2006, 

https://www.ema.europa.eu/en/ich-q8-r2-pharmaceutical-development-scientific-guideline

25. ICH Q9 Quality Risk Management, 2023, 

https://www.ema.europa.eu/en/ich-q9-quality-risk-management-scientific-guideline

26. ICH Q10 Pharmaceutical Quality System, 2008, 

https://www.ema.europa.eu/en/ich-q10-pharmaceutical-quality-system-scientific-guideline

27. ICH Q5C Stability Testing of Biotechnological/Biological Products, 1996, 

https://www.ema.europa.eu/en/ich-q5c-stability-testing-biotechnological-biological-products-scientific-guideline

28. USP General Chapter <787> Subvisible Particulate Matter in Therapeutic Protein Injections, 2020, 

https://doi.usp.org/USPNF/USPNF_M6497_02_01.html

29. USP General Chapter<1663> Assessment of Extractables Associated with Pharmaceutical Packaging/Delivery Systems, 2020, 

https://doi.usp.org/USPNF/USPNF_M7126_03_01.html

30. USP General Chapter<1664> Assessment of Drug Product Leachables Associated with Pharmaceutical Packaging/Delivery Systems, 2024, 

https://doi.usp.org/USPNF/USPNF_M7127_03_01.html

31. Shire, S.J; Shahrokh, Z.; and Liu, J. Challenges in the development of high protein concentration formulations,

https://pubmed.ncbi.nlm.nih.gov/15124199/

32. ISO 11040-8, Prefilled Syringes: Requirements and Test Methods for Finished Prefilled Syringes, 2026, 

33. ISO 14971 Medical devices - Application of Risk Management to Medical Devices, 2019, 

34. 21 CFR Part 820, Quality Management System Regulation, 2026, 

https://www.ecfr.gov/current/title-21/chapter-I/subchapter-H/part-820

35. ISPE GAMP 5: A Risk-Based Approach to Compliant GxP Computerized Systems, 2

https://guidance-docs.ispe.org/doi/book/10.1002/9781946964571

36. 21 CFR Part 11, Electronic Records; Electronic Signatures, 2026, 

https://www.ecfr.gov/current/title-21/chapter-I/subchapter-A/part-11

37. J. Sprovieri, Servo Presses Aid Assembly of Autoinjectors, 2022, Assembly Magazine, 

https://www.assemblymag.com/articles/97398-servo-presses-aid-assembly-of-autoinjectors

38. Gerresheimer’s, Adapter Aids Positioning of Syringes in Autoinjectors, Pharmaceutical Technology, 2014, 

https://www.pharmtech.com/view/adapter-aids-positioning-syringes-autoinjectors

39. ASTM E2910-12, Standard Guide for Preferred Methods for Acceptance of Product, 2018, ASTM International, 

https://www.ema.europa.eu/en/human-regulatory-overview/marketing-authorisation/compliance-marketing-authorisation/sampling-testing

41. ANSI/ASQ Z1.4, Quality Standards, 2003, 

https://asq.org/quality-resources/z14-z19?srsltid=AfmBOookYlqMIDtOTau_oZbzycHPOl7a1VyxcIUfGLZ8qw01ryll5iw1

42. ISO 2859-1, Sampling Procedures for Inspection by Attributes, 2026, 

43. Engineering Statistics Handbook: Process Capability Indices, Chapter 6, NIST, 2012, 

https://www.itl.nist.gov/div898/handbook/

44. ISO 20457 Plastics Moulded Parts - Tolerances and Acceptance Conditions, 2018, 

Articles

Peptide/protein drug products in prefilled syringes require tightly controlled aseptic manufacture (ISO 5), validated fill–finish, and comprehensive QC for sterility, endotoxin, particulates, dose accuracy, and container-closure integrity. Formulation attributes—especially viscosity, pH/buffer selection, surfactants, and lubrication—directly determine autoinjector injection force, delivery time, and robustness to handling. High-speed assembly relies on automated alignment, sensor/vision inspection, servo-press force–displacement profiling, and end-to-end traceability. Large-scale factory acceptance testing, stratified sampling, and CpK analyses provide evidence of sustained throughput and functional reliability.

Peptide autoinjector manufacturing demands precise formulation-device coordination, rigorous Cpk-driven quality control, and FAT validation for reliable, compliant dosing.

A Lifecycle Approach to Peptide Formulation in Prefilled Syringes for High-Speed Autoinjector Assembly

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CAI's Jon Thompson explains how digital twins cut cleanroom rework by up to 50% and pressure excursion investigation time by up to 70%.

Manufacturing Intelligence Episode 4: Cleanroom Digital Twins

Videos

, co-hosts Chris Cole and Richard Jaenisch explore the emerging role of digital twins in cleanroom design and qualification in pharmaceutical manufacturing with guest Jonathon Thompson, global director of the digital center of excellence at CAI.

To view the full fascinating, practical, engaging conversation, click here!

Digital twins of cleanrooms are offering measurable reductions in commissioning time, rework, and pressure excursion investigation, but only when paired with rigorous real-time data verification, explains Thompson. He adds that the technology's core value lies not in replacing IQ/OQ/PQ, but in catching design flaws before construction locks them in.

The traditional cleanroom qualification workflow exposes problems late, Thompson notes, often after equipment is already installed and early-phase processes are running. Digital twins, fed with 3D building information modeling (BIM) models, HVAC specifications, personnel flow data, and physics-based fluid dynamics simulations, allow manufacturers to stress-test facility designs upstream. Thompson points to tangible returns: 10–30% reductions in commissioning time and resource hours, 20–50% reductions in rework, and 30–70% faster root cause identification during pressure excursions.

Regulatory acceptance, Thompson argues, depends on how the twin is used. "If you're using this digital twin as your single source of truth without verifying that data in real time with what's actually happening out in the field," he warns, "they're going to start to question it." Continuous process verification—cross-referencing the model against live environmental monitoring data, particle counts, and pressure differentials—is what earns FDA and EMA confidence in the approach.

For CDMO and modular facilities, where equipment configurations and product mixes change frequently, the stakes around model maintenance are especially high. Thompson observes that "we've got to change the mindset" to treat the BIM model as a living asset subject to change management, not a static deliverable from the capital project phase. As generative AI and light detection and ranging-based reality capture mature, automated model updating may eventually reduce that burden, making the digital twin a true operational constant rather than a commissioning tool.

CAI's Jon Thompson details how digital twins cut cleanroom commissioning time by up to 30% and accelerate GMP deviation investigations.

Manufacturing Intelligence: How Digital Twins Are Changing Cleanroom Validation

In a rapidly shifting global marketplace characterized by post-pandemic onshoring and an increasingly complex tariff environment, the pharmaceutical manufacturing industry is searching for stability and speed, according to Delaware Governor Matt Meyer. At the 

 session on June 2, 2026, Meyer detailed his state’s aggressive strategy to become a premier hub for biopharmaceutical innovation. While his remarks focused on Delaware, the principles of collaboration, workforce pipeline development, and regulatory speed provide a vital roadmap for pharma manufacturing across the United States and the globe.

For many in pharmaceutical manufacturing, the relationship with government is often defined by regulatory friction. However, the Delaware model suggests that local governments must evolve into active partners to move technology forward. Governor Meyer emphasized that a state’s role should be to collaborate on research, help secure funding, and reduce the friction between development and market entry.

For professionals globally, this highlights the growing importance of public-private ecosystems. In Delaware, this is anchored by institutions like the 

National Institute for Innovation in Manufacturing Biopharmaceuticals

 (NIIMBL), where global partners collaborate to improve how medicines are made, tested, and delivered.

Scaling Innovation Through Specialized Infrastructure

The transition from a breakthrough molecule to a scalable product is the industry's greatest challenge, noted Meyer. As such, the Delaware vision leverages a deep-rooted culture of precision material sciences and large-scale manufacturing that dates back to the founding of the DuPont company in 1802.

Current advancements that manufacturing professionals should note include:

Continuous Manufacturing: Moving away from batch processing to increase efficiency.

Specialty Materials: Developing polymers for sterile and biologic products.

Next-Gen Therapies: Focusing on cell and gene therapy manufacturing and protein engineering.

These technologies are being advanced at the 

 and through partnerships with companies like 

, illustrating that the future of manufacturing lies in integrating basic chemistry with advanced analytical capabilities.

A recurring theme for global manufacturers is the scarcity of specialized talent. Governor Meyer argued that workforce development must begin long before college. By implementing biosynthetic engineering programs—such as 

Delaware is training 10th and 11th graders in CRISPR gene-editing technology

partnered with Thomas Jefferson University to create Delaware's first medical school

, ensuring a steady stream of clinical and scientific professionals. Meyer’s takeaway here is that securing the workforce of tomorrow requires direct involvement in early-stage education and training pipelines.

In an industry in which a day of delay can impact both patient lives and the bottom line, speed is a critical metric. Delaware has addressed this through specific policy interventions that might view as a "gold standard":

: An executive order designed to reduce permit wait times from months to minutes using modern technology.

—a rarity in the U.S.—to support high-growth entrepreneurs.

Proximity and Cost: Utilizing a low cost of living and low tax burden to attract talent, while maintaining proximity to major consumer markets and regulatory hubs like Washington, DC.

Ultimately, the push for manufacturing innovation is not just about regional economic growth; it is a matter of national security, explained Meyer, adding that ensuring that life-changing therapies are both created and scaled within stable, efficient environments is paramount for public health. The goal for any manufacturing hub, he said, should be to provide a low-friction environment in which innovators can "build, grow, and stay." Whether in Delaware or abroad, the path forward for biopharma manufacturing lies in these strategic partnerships between industry leaders and the governments that support them.

Delaware is positioning itself as a biopharmaceutical manufacturing hub amid onshoring pressures and tariff uncertainty by reframing government as an enabling partner rather than a source of regulatory drag. The strategy emphasizes public–private ecosystems anchored by NIIMBL, specialized infrastructure to bridge molecule-to-scale challenges, and integrated capabilities spanning precision materials science, analytics, and next-generation modalities. A long-horizon workforce plan starts in high school with synthetic biology training and extends through a new in-state medical school. Competitive advantage is framed around permitting speed, refundable R&D credits, and cost-effective proximity to major markets.

At CPHI Americas, Delaware governor Matt Meyer outlined the state's biopharma vision: fostering workforce talent, industry collaboration, and regulatory speed to market.

A Pharma Manufacturing Blueprint from Delaware’s Innovation Vision 

Stuart Tindal, Sartorius, and David Chau, Thermo Fisher

connected with Stuart Tindal, portfolio manager, Intensified Downstream Systems, Sartorius; and David Chau, global technical product specialist, Thermo Fisher—both Bio-Process Systems Alliance committee leaders—discuss automation, single-use technology challenges, and smarter process control in bioprocessing.

Watch the 2-part video interview with Tindal and Chau:
Part 1: 

How Predictive Control Is Reshaping Continuous Bioprocessing

Stuart Tindal and David Chau on Sensors, Standards, and Shared Progress

PT: How Should Companies Think About Integrating Automation into Continuous Bioprocessing?

 Automation isn't optional anymore, it's an integral part of any continuous or advanced bioprocessing operation. The industry has moved decisively away from purely manual operations. As processes have become more data-driven and connectivity has increased, the need for robust control and automation has become paramount. It's no longer a nice-to-have; it's a must-have for maintaining consistency and productivity.

 The foundation must be process understanding. Before you can automate anything, you need to know what you're trying to make continuous. You must consider what the inputs and outputs are, what parameters matter, how you'll measure them through sensors or equipment. People entering continuous bioprocessing right now can feel overwhelmed, whether they're building a process from scratch or retrofitting an existing one. Going back to the fundamentals of your process is often the most valuable first step.

How Do Single-Use Technologies Fit in with the Demands of Continuous Manufacturing?

 It's a real tension. Single-use technology has earned its place in the industry with faster turnaround times, flexibility, speed at the R&D and pilot scale. But those same benefits can become concerns when you're talking about commercial-scale continuous manufacturing, where processes run for weeks or even months. Single-use was designed to be limited. As an industry, vendors and end users must come together to honestly assess the limitations and innovate around them.

 It comes down to the materials of construction. When something is designed for a single day of use and you're asking it to perform for weeks, critical failure points emerge. Are the sensors drifting over time? Are the actuators, the pumps and valves moving plastic components, going to withstand prolonged friction and wear? The work being done now is about understanding how far these materials can be pushed and what alternatives or design changes can support longer continuous runs.

What Does the Future of Process Control Look Like in Continuous Bioprocessing?

 We're moving beyond conventional PID controllers toward predictive and model-based control. In a continuous process, your operating window becomes more of a tunnel, you need to steer the process along that tunnel in real time, being adaptive and anticipatory. That requires a very fast control and response loop. For more complex or labile molecules with tighter critical quality attributes, advanced predictive control isn't a luxury, it's what enables you to maintain the process trajectory from start to finish.

 Continuous manufacturing generates enormous amounts of data, and the question becomes what we do with it. Model-based approaches make a lot of sense in this context; with enough data, you can build predictive models that guide processes rather than simply hold set points. The shift is from reactive control to genuinely steering and predicting where your process is headed. That's where the industry is going, and it's an exciting direction.

Automation is now foundational to continuous and advanced bioprocessing, driven by increased connectivity and data-centric operations requiring robust control to maintain consistency and productivity. Effective implementation starts with deep process understanding—defining critical inputs, outputs, and measurable parameters—especially when retrofitting legacy operations. Single-use systems offer flexibility and rapid changeover, yet commercial continuous runs expose durability limits in materials, sensors, pumps, and valves. Process control is shifting from PID to predictive, model-based strategies that use high-frequency feedback and large datasets to steer processes within tight operating windows.

Stuart Tindal, Sartorius, and David Chau, Thermo Fisher, discuss automation, single-use technology limits, and predictive control shaping continuous bioprocessing's future.

Q&A: How Continuous Bioprocessing Is Evolving With Smarter Automation

sat down with Bryan Poltilove, strategic advisor and independent board director focused on bioprocessing and advanced therapy manufacturing, to discuss how just-in-time, point-of-care manufacturing is revolutionizing biopharma without compromising quality.

Watch the 2-part video interview with Poltilove:
Part 1: 

Validation and GMP rigor remain non-negotiable in bioprocessing, but manufacturing paradigms are shifting toward distributed, point-of-care/place-of-care models that prioritize flexibility, resilience, and sustainability alongside cost. This represents a just-in-time mindset change analogous to automotive manufacturing’s 1980s evolution, requiring inventory reduction without sacrificing reproducibility or quality. Cell therapy is the clearest near-term proving ground, enabled by modular clean rooms and hospital-executable unit operations. Powder-based, on-demand media manufacturing could materially reduce logistics burden, waste, and end-to-end carbon footprint.

Bryan Poltilove discusses how distributed, point-of-care manufacturing is reshaping biopharma supply chains and cutting carbon footprints.