Introduction
In 2004, the U.S. Food and Drug Administration (FDA) introduced a visionary framework for pharmaceutical manufacturing modernization based on two foundational pillars: Quality by Design (QbD) and Process Analytical Technology (PAT) (1). These approaches were intended to drive a shift from empirical, end-point testing to real-time, science- and risk-based process control. The goals were clear: improve product quality, mitigate manufacturing risk, and reduce cost through enhanced process understanding.
Since this time, there have been several collaborations between formal regulatory agencies, industry consortiums, and universities advancing understanding and adoption forward (2). While the industry has acknowledged the benefits of QbD and PAT, implementation across companies has been inconsistent. Many large organizations have advanced robust programs, yet others, particularly mid-sized or resource-constrained firms, struggle to scale beyond pilots. This paper evaluates the current state of adoption, explores enabling technologies, and offers guidance for practical implementation in constrained environments.
As Joseph Juran once said, “Quality is fitness for use,” emphasizing the need for processes that meet both patient needs and operational efficiency (10). This principle underpins the adoption of PAT and QbD.
Current State of PAT and QbD Adoption
Industry Progress and Disparity
Over two decades after the FDA’s call to modernize, the pharmaceutical sector exhibits a broad spectrum of adoption maturity. Some companies, such as Novartis, GSK, Amgen, and Eli Lilly, have integrated PAT and QbD into their commercial operations, leveraging inline spectroscopy and real-time multivariate models to enable continuous manufacturing (6-9). For example, Lilly’s Kinsale, Ireland, facility has implemented continuous manufacturing with PAT for small-molecule drugs, using real-time NIR spectroscopy and UHPLC to monitor critical quality attributes (CQAs) and ensure process consistency. Similarly, GSK’s Singapore manufacturing site has adopted PAT for real-time blend uniformity testing, utilizing inline NIR and UHPLC, supported by advanced data analytics, to reduce batch release times.
Yet, in many organizations, PAT remains in the pilot phase, and QbD is relegated to regulatory documentation. This fragmented approach fails to capture the full benefit of a lifecycle management strategy and reflects several common barriers:
- Organizational resistance to change from traditional batch practices.
- Limited internal expertise in new and improved technologies, chemometrics, automation, specialized software (MVDA, Control), and analytics.
- Funding constraints without clear ROI.
- Disconnected functions across R&D, QA, IT, and manufacturing.
The Consequence: Uneven Adoption
This disparity is not only inter-company but intra-company—varying by product type, site maturity, and leadership commitment. How should QbD, PAT, and continuous manufacturing be approached? (Top-down with a passionately driven executive? Or a mid-level team that understands what QbD, PAT, and continuous manufacturing could mean to the business and patient?) A consistent, scalable adoption strategy remains elusive for many.
Ajaz Hussain has noted, “Quality by Design is not a regulatory burden but an opportunity to innovate,” highlighting the potential for QbD to drive progress even in constrained settings (10).
Enabling Technologies for Modern PAT
Technological innovation has made PAT implementation more feasible, cost-effective, and scalable.
Analytical Instrumentation
- Raman spectroscopy, NIR, and ultra-high performance liquid chromatography (UHPLC) now support real-time, non-destructive CQA monitoring (spectrophotometric).
- Mass spectrometry, especially for biologics, has advanced sensitivity and selectivity. An example of this is the work done using the Multi-Attribute Method, where industry leaders collaborated to demonstrate its significant improvement to the biologics testing suite.
- When used in concert with a sampling strategy, these tools typically form the analytical backbone of PAT systems (6).
Data Platforms and MVDA Tools
- Integration of manufacturing execution systems (MES) and distributed control systems (DCS) has become more seamless.
- Platforms such as SIMCA® (Sartorius), SynTQ® (Optimal Industrial Technologies Limited), and Empower® (Waters Technologies Corporation) enable real-time data visualization, multivariate analysis, and model lifecycle management (6).
- AI and machine learning (ML) enable pharmaceutical companies to extract insights from complex data, automate decision-making, and predict outcomes across the drug lifecycle—from discovery to manufacturing. The impact includes faster development timelines, improved product quality, reduced operational costs, and enhanced regulatory compliance through real-time, data-driven control.
Digital and AI Integration
- Machine learning applications support anomaly detection, predictive control, and process optimization. This capability is enhanced with broader process information.
- Cloud-based systems improve data access, while digital twins and process models aid in real-time decision-making (8). Together, these advancements lower the technical barriers to PAT and QbD implementation, especially when paired with targeted problem-solving.
Case Studies and Lessons Learned
Site-Led Implementation
- Vertex’s early success with Orkambi, one of the first drugs to utilize continuous manufacturing, demonstrates the value of site-led innovation in achieving regulatory and patient-focused outcomes.
- GlaxoSmithKline (GSK) and Eli Lilly have each become industry leaders in re-engineering their supply chains by moving from traditional batch to continuous manufacturing. GSK reformulated fluticasone propionate from a six-stage batch synthesis to a telescoped multistage flow process, transferring commercial production to its Jurong, Singapore site, where it now produces several tons annually with tighter quality control, faster startup, and lower costs. Eli Lilly transformed its Kinsale, Ireland site into a global center of excellence for CM, building the award-winning Small Volume Continuous (SVC) facility that uses modular plug-and-play skids and advanced PAT; this shift has cut cycle times roughly in half, reduced costs by over 30%, and positioned Lilly to rapidly scale high-demand therapies like GLP-1 drugs.
Implementation Challenges
Successful programs share:
- Strong cross-functional teams (QA, Ops, Regulatory, IT, R&D), such as equipment funding and support, project timing, workflow details, and coordinated implementation, will lead to smooth implementation.
- Clear business objectives tied to yield, compliance, or throughput – cycle time and improvement in factory utilization.
- Early operator involvement and training.
- Solid data governance and traceability. Common pitfalls include treating PAT as a regulatory checkbox, underestimating the complexity of integration, and neglecting effective change management. Leadership explains why it’s being done, as well as the overall business impact and improvement of safety and efficacy to the patient.
W. Edwards Deming emphasized, “Quality is everyone’s responsibility,” underscoring the need for cross-functional collaboration in PAT and QbD initiatives (12).
Practical Strategies for Resource-Constrained Environments
Start with Process Pain Points
Often, there is a goal to advance PAT at the departmental level, leading to proof of benefit in advance of formally funding a project or larger initiative.
Targeting the business impact of improvements in yield and reducing or eliminating known bottlenecks—such as high variability or long release cycles—rather than chasing high-tech solutions, can lead to progress.
Leverage Existing Tools and Data
Historical batch data from HPLC or UV/Vis systems can support basic multivariate modeling or SPC before investing in inline sensors. This can help set areas of focus and benchmark initial feedback.
Implement Low-Risk, Scalable Pilots
Optimizing the process and having a more thorough understanding of the CQAs, minimal workflow disruption, and technologies familiar to the site. Demonstrate small wins to build internal momentum. Lessons learned from reaction/process challenges and what is the impact, but knowing and correcting sooner and saving the product, showing the worthwhile investment in the latest PAT tools.
Collaborate Across Functions
IT and QA involvement ensures proper data integration and regulatory alignment. Shared goals improve buy-in.
Build a Case for Investment Over Time
By documenting and communicating incremental gains, such as reduced deviations and faster release, can help justify continued and increased PAT funding.
Recommendations for Scaling PAT Adoption
These actions convert isolated successes into enterprise-wide analytics programs.
Conclusion
While some companies have made significant strides, many still face challenges implementing PAT and QbD at scale—especially in resource-limited environments. Fortunately, strategic approaches grounded in problem-solving, incremental deployment, and cross-functional collaboration can unlock substantial value.
Rather than being a compliance obligation, PAT is a critical enabler of Pharma 4.0—a transition toward more agile, informed, and responsive manufacturing. For companies willing to act incrementally and align efforts with business priorities, PAT can transform operations and create lasting competitive advantage.
Figure 1. A Graphical representation of the implementation of QbD and PAT leading to Continuous Manufacturing. As Technology continues to evolve, providing greater sensitivity, resolution, and specificity, it has led to increased knowledge of raw materials and critical quality attributes, yielding higher-quality drugs faster.
References and notes
- U.S. FDA. (2004). Guidance for Industry: PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance. FDA PAT Guidance.
- A. National Academies of Sciences, Engineering, and Medicine. Continuous Manufacturing for the Modernization of Pharmaceutical Production: Proceedings of Workshop. Washington, DC: The National Academies Press, 2019. https://nap.nationalacademies.org/read/25340/chapter/2#6..
- ICH Q8(R2). (2009). Pharmaceutical Development.
- ICH Q9(R1). (2023). Quality Risk Management.
- ICH Q10. (2008). Pharmaceutical Quality System.
- Bakeev, K. A. (Ed.). (2020). Process Analytical Technology: Spectroscopic Tools and Implementation Strategies for the Chemical and Pharmaceutical Industries (2nd ed.). Wiley.
- Narang, A. S., & Badawy, S. (2013). Pharmaceutical Process Scale-Up. CRC Press.
- Simon, L. L., et al. (2015). “Assessment of recent process analytical technology (PAT) trends: A multiauthor review.” Organic Process Research & Development, 19(1), 3–62. DOI: 10.1021/op500261y.
- ISPE. (2017). PAT and Lifecycle Control Strategy. A. National Academies of Sciences, Engineering, and Medicine. Continuous Manufacturing for the Modernization of Pharmaceutical Production: Proceedings of a Workshop. Washington, DC: The National Academies Press, 2019. https://nap.nationalacademies.org/read/25340/chapter/2#6.
- Juran, J. M. (1988). Juran on Planning for Quality. New York: Free Press.
- Hussain, A. S. (2005). “Quality by Design: A New Paradigm for Pharmaceutical Development.” Journal of Pharmaceutical Innovation, 1(1), 12-18.
- Deming, W. E. (1986). Out of the Crisis. Cambridge, MA: MIT Press.
ABOUT THE AUTHOR
Bill Foley is a seasoned industry leader with proven expertise in strategic planning, product management, and marketing, driving cross-functional teams to achieve disruptive goals. As an independent consultant, he empowers smaller companies to implement innovative, transformative changes.
After 39 years at Waters, Ernie Hillier retired and founded EJH Consulting to advance Process Analytical Technology and Continuous Manufacturing. His career spanned HPLC development, detector marketing, and technical product management, culminating as Principal Systems Product Manager for PATROL, Alliance, and Breeze. With a BS in Chemistry and Biology from Northeastern, he serves on IPFAC’s Scientific Board, chairs the Advanced Separations Session, and advises Chemistry Today. He continues supporting QbD and PAT by organizing and moderating technical sessions.
