It's Not Just You, Everyone's Talking About OPV
A conversation with DÃdac Garcia Mancebo and Miquel Romero Obon, Almirall
A changing view of quality, looking beyond compliance activities and more toward strategic opportunity, has reinvigorated attention toward ongoing process verification or OPV.
The approach has been part of regulatory guidance for years, and now its practical adoption is accelerating thanks to digital transformation, evolving compliance expectations, and the growing need for agility in manufacturing.
Almirall’s Dídac Garcia Mancebo and Miquel Romero Obon will speak about this evolution at the International Society for Pharmaceutical Engineering’s 2025 ISPE Pharma 4.0 Conference.
Ahead of their talk, they offered to give us a preview of the ideas they’ll be sharing and why OPV is gaining traction across the industry.
Setting the stage, continuous process verification seems to be popular now, but it's an established concept. What's changed to make it especially feasible or valuable now?
OPV has been part of regulatory guidance for over a decade, but its practical adoption is accelerating now due to key technological and strategic shifts. The most significant enabler is digitalization. Modern manufacturing systems now support real-time data capture, integration, and analysis across the product life cycle. This allows for continuous monitoring of critical parameters, something that was far more limited in the past.
In parallel, regulatory expectations have evolved. Agencies increasingly promote life cycle approaches to validation and quality assurance, where OPV plays a central role. Instead of relying solely on traditional revalidation cycles, companies can now demonstrate ongoing control and product quality through live data. This aligns with ICH Q10 principles and supports a more dynamic and risk-based approach to compliance.
Finally, business needs have changed. The demand for agility has made OPV a strategic asset. It enables faster scale-up and better supply chain resilience and supports continuous manufacturing models. Combined with the rise of AI and predictive analytics, OPV is no longer just feasible, it’s a competitive advantage. It empowers pharmaceutical companies to ensure quality, reduce risk, and respond quickly to market and operational changes.
What's the difference between a modernized OPV system and a traditional one?
Traditional OPV systems were largely manual and reactive. They relied on periodic reviews of batch data, often months after production. Data was typically siloed across systems, making it hard to detect trends or deviations in real time. The focus was on compliance through documentation rather than proactive process control. These systems were resource-intensive and slow to respond to variability.
Modernized OPV systems, by contrast, are data-driven, integrated, and real-time. They leverage digital platforms, automated data capture, and advanced analytics to continuously monitor critical process parameters. This enables early detection of trends, faster root cause analysis, and predictive insights. Instead of waiting for deviations to occur, modern OPV supports proactive decision-making and continuous improvement.
The key difference lies in agility and intelligence. Modern OPV systems are not just tools for compliance, they’re strategic enablers of quality, efficiency, and risk management. They align with regulatory expectations for life cycle validation and support adaptive manufacturing environments, including continuous and hybrid models.
How does Almirall integrate data from different systems like manufacturing, QC, LIMS, and MES into a unified OPV framework?
The first step is to implement a centralized data platform that can ingest structured and unstructured data from various sources. Middleware solutions or data integration tools (e.g., OPC UA, ISA-95-compliant connectors, or ETL pipelines) are used to extract data from manufacturing equipment and company systems.
Once data is integrated, it is contextualized linking batch records, test results, and process parameters to specific products and units of operation. Advanced analytics platforms then apply statistical models, control charts, and machine learning to detect trends, deviations, or risks in real time. This enables proactive decision-making and supports continuous improvement.
Last, but not least, integration must comply with data integrity standards and systems should support audit trails, user access controls, and validation. Cross-functional collaboration between IT, OT, QA, and manufacturing is essential to maintain data quality and regulatory alignment.
How do you ensure that continuous monitoring doesn’t lead to data overload or false alarms that waste resources?
The key is to focus on critical quality attributes (CQAs) and critical process parameters (CPPs). By applying risk-based approaches during development, we identify which signals truly matter. Data is then contextualized to specific batches, equipment, and conditions so that only relevant deviations trigger alerts.
Modern OPV systems apply statistical process control to distinguish between normal variability and true out-of-specification trends. Dynamic control limits, trend analysis, and predictive models help reduce false positives. Instead of reacting to every fluctuation, the system learns what constitutes meaningful change.
Technology is powerful, but human judgment remains essential. Cross-functional teams always involve QA, regularly review alert logic, refine thresholds, and audit system performance. This ensures that alerts are actionable and aligned with operational priorities, avoiding unnecessary investigations or resource drain.
Can you share an example where continuous data visibility led to catching a problem before it occurred?
With the implemented tools, we are able to detect differences in performance of new raw materials, assess the effectiveness of corrective and preventive actions (CAPA), and evaluate the effect of changes.
Trends are easier to observe and correlate to potential causes with higher speed and more robust rationales based on continuous knowledge acquisition.
Additionally, risk is dynamically assessed, leading to adaptative control strategies, creating a suitable risk control that prevents future failures.
How do you balance automation and human judgment — especially when data trends suggest an issue that operators don’t see on the floor?
Balancing automation and human judgment is essential to making continuous monitoring truly effective.
Automation excels at detecting patterns, trends, and subtle shifts that may not be visible on the shop floor. But it’s not infallible. That’s why we design systems where automation flags potential issues, and human experts validate and interpret those signals. Human teams bring contextual knowledge, like recent maintenance, environmental changes, or atypical but acceptable conditions that algorithms may not fully grasp.
When data trends suggest an issue that operators don’t observe directly, it’s important to explain the rationale behind the alert. Training teams on how the system works, what parameters it monitors, and how thresholds are set helps build confidence. Over time, as teams see that early alerts prevent real problems, trust in the system grows. Modern OPV is not about technology, it's about human beings using technology.
About The Experts:
Dídac Garcia Mancebo is a quality assurance specialist at Almirall where he analyzes the critical quality attributes of products and processes. He is also an active ISPE member. He received his master’s degree from IQS Barcelona and completed postgraduate work at the University of Barcelona.
Miquel Romero Obon is a quality assurance director at Almirall and an active ISPE member. He has led strategic initiatives to modernize QA frameworks, including the implementation of Pharma 4.0 principles, digital quality systems, and risk-based approaches to compliance. He is a co-leader of the Pharma 4.0 ISPE group in Spain and Portugal and a member of the Steering Committee of the international ISPE Pharma 4.0 Community of Practice.