Efficient shutdown recovery: leveraging RMM to unlock value and ensure environmental quality

Navarro, Victoria; Salvas, Joanny; Bellón, Esther

article

Efficient shutdown recovery: leveraging RMM to unlock value and ensure environmental quality

By Esther Bellón (Pfizer), Joanny Salvas (Pfizer), Victoria Navarro (Pfizer)

24 June 2022

New technologies are awaiting implementation by the pharmaceutical industry to improve and optimise processes, but as they are not compendial and may require lengthy approval processes, their adoption as a replacement for a compendial method is slow, if at all possible. Though the industry is still contemplating how to unlock the full benefits of these new methods, they can be used to generate value. In this article, Pfizer colleagues Victoria Navarro, Joanny Salvas and Esther Bellón share an example that employs new technologies alongside the compendial methods to ensure quality within a process common across pharmaceutical plants: post-shutdown recovery and room release for manufacturing.

ASEPTIC MANUFACTURING requires regular, planned – and sometimes unplanned – shutdowns to perform line improvements, area modifications, and/or preventative activities such as maintenance and qualifications. During these interventions, aseptic environmental conditions could be compromised. Subsequently, environmental area recovery must be demonstrated and documented before resuming operations. However, compendial environmental monitoring (EM) typically takes five to seven days to return results, meaning that the areas are not used for seven days. Such loss of capacity on a line is significant, even when the final EM results are within specification. In cases where the EM data indicates further assessment is warranted, this creates even longer delays to the remediation and complicates the investigation process.

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To improve this situation at Pfizer, a multidisciplinary, innovative team was formed and, after thorough process mapping and risk assessment, recommended the use of bio‑fluorescent particle counting (BFPC) technology to obtain environmental quality data in real-time to support area release at risk with confidence. This created extra line capacity and the capability to react to an issue immediately if something was wrong.

The BFPC technology measures airborne biological particles as auto-fluorescent units (AFUs). It does not rely on the capture, growth and count of organisms, such as the colony-forming units (CFU) offered by traditional plate count methods. Rather, BFPC uses laser excitation to detect bio-fluorescence of biological compounds such as nicotinamide adenine dinucleotide (NADH), riboflavin and picolinic acid, which are universally found in all microorganisms. This allows detection of particles in a similar way to other particle counters, and also provides a biological count – the AFU – if fluorescence is measured.¹ This information is available instantaneously to the user, enabling dynamic use of the tool to inspect an area, and very useful in various situations; notably, for ad hoc purposes such as investigation support^1,2 and unplanned shutdown recovery support.³

Initiating the process

The aim of this project was to assess whether post-shutdown re-start times may be reduced without compromising area environmental quality. The first step was to perform an in-depth process mapping, highlighting the need to improve long waiting times, as illustrated in Figure 1.

Internal experts in rapid microbiological methods (RMM) formed part of the team, helping to decide the right methodology to assess the area quality and conditionally release it for use.

The complete methodology employed to bring the project to fruition began by performing a baseline assessment to understand the AFU load of areas in a known state of control and demonstrate method capabilities. This data would then be used to establish the method for any future data collection as well as acceptance criteria. Finally, the recommendations were implemented in the plant processes; first as a pilot and then as an official process.

Figure 1: Process map before project. An average of 15 days were needed to endorse environmental area quality and documentation for an approval.

Baseline assessment

Overview

The baseline study was carried out following the scheme presented in Figure 2.

The goal for the data collection was to:

Ensure the quality and reliability of data
Establish a baseline against which future room data collection could be compared
Develop criteria and acceptance limits for implementation.

This activity was documented in a protocol. The rooms for the data collection were defined in advance with sampling points placed on or near the routine sampling point, as those locations were already shown to be representative. The protocol showed these positions on a map for clarity.

Figure 2: Process to develop the area at rest baseline.

Colleagues were trained in the use of the BFPC equipment. Good practices included: testing the equipment before using it, using slow movements when measuring, avoiding standing close to the sensor, etc. The following considerations were also defined in the protocol:

Rooms must be clean and at-rest during data collection
Intentional disturbances were introduced while measuring to obtain dynamic data that was used to quantify room recovery time. It is vital to track any disturbance using accurate notes
Data collection times were maintained for 15 to 25 minutes
Sample points measurement repetition was carried out to ensure method repeatability
Active air sampling was used at the same points to assure that the rooms were compliant during data collection.

Results

The data collected for analysis were visualised as graphs for easy interpretation. Data were compared for similar rooms and area classifications to determine eventual groupings for limit setting. Results reported are detailed as follows:

Total particles: sum of all particles in the measurement point (all sizes, inert and biological particles)
Biological particles: any size particle that shows intrinsic fluorescence
Large particles: particles >5.0mm, including inert and biological particles.

European standards were considered (European Commission [EC] Guide to Good Manufacturing Practice Annex 1) in the analysis, however they provide no criteria for airborne biological particles measured as AFUs – criteria are set specifically only for cultivated colonies collected on settle plates (CFUs). These CFUs are not equivalent to the AFUs monitored with the BFPC, since they do not rely on the same detection principle. This was part of the challenge to establish acceptance criteria and why it was important to establish the baseline of the areas using the BFPC.

Limits setting

Data collection was compiled by grade, taking data from the stable parts of the acquired profiles and discarding disturbances. Statistical calculations were performed, establishing the average +3SDs (standard deviations) as the control limit. Illustrations are provided in Figure 3.

Figure 3: Examples of measurements showing disturbances, recovery time and determined limits.

This was carried out by room classification (Grades A to D), leading to four different sets of limits. Each set of limits includes acceptance criteria for total particles, AFUs and large particles separately. All three must comply for the room to be deemed acceptable for conditional release.

In order to apply the limits, data collection must be defined. For instance, ‘what volume of air will this be applied to?’. Since the instrument reads continuously, it is important to bind the data collection in time (or volume). If left unlimited, one could collect data until the room passes, effectively limiting the capability of the instrument to truly assess quality.

The baseline data was used to establish the typical recovery period for a given area (by grade), after a disturbance. This information was accounted for to establish the duration of future data collection: it must be sufficiently long to enable the room to return to normal conditions after the disturbance created by the sampler entering the room. Our baseline data indicated very rapid recovery – two to five minutes – thus the maximum data collection time was set as 15 minutes. If the measurement of AFU per pre‑defined volume of air is above the established alert limit after a maximum of 15 minutes, the room is deemed to have failed the test.

With this new method, when the alert limit criteria are met, it means the room conditions are equivalent to that of the baseline, thus the area environment is under control and there is no anticipated additional risk in using the room to manufacture product. The official room release remains based on the results from compendial testing.

Result for repeatability

To demonstrate method repeatability, baseline data were collected in duplicates for several rooms, covering all grades. Disturbances were nearly impossible to replicate, as they are transient events each with different particularities; however, static conditions should be repeatable and data from different collection sessions should be equivalent. Without this, how can one know that future measurements are appropriate to measure quality?

In our case, and overall, repeatability of the method was appropriate for the intended purpose since all duplicate readings in at-rest rooms showed consistent results. Examples are provided in Figure 4 for Grade C. We can see that spikes are different because they are due to different disturbances, but that the stable “static” period between spikes is indeed equivalent. Formal statistics were applied to the data to assess this equivalence.

Figure 4: Examples of measurements showing repeatability of method.

Implementation of the method for use

The general steps for using BFPC in post-shutdown activities is illustrated in Figure 5. It shows that data collection with BFPC is carried out after cleaning and prior to the collection of the compendial data for release. If the room is compliant to the BFPC criteria, it is released for next steps before manufacturing, notably to collect the compendial EM data. If it is found to be out of trend when compared to the baseline, immediate remedial action can be taken.

Figure 5: Proposed process after pilot phase.

BFPCs are not approved compendial methods, so the final release for batches still requires compendial results within specification, hence the process is not complete until these results are available, but the gains are still enabled, including the ability to be more proactive.

The process was first implemented as a pilot, whereupon its successful completion enabled the process of resuming operations in the area (change control, plant documentation, procedures, etc) to be updated accordingly. Since then, the process has been in routine use. A comparison of the new and previous process is provided in Figure 6, illustrating the efficiency gains that created additional batch capacity after a shutdown.

What are the main benefits of this process? Besides enabling proactive action in case of issues, tangible value was generated. For each shutdown, up to seven calendar days are now freed for manufacturing use.

Figure 6: Process optimisation result.

Lessons learned

One of the most important learnings is that using innovative thinking is the best way to change things; keep an open mind for new technologies, identify different ways of doing things, and be ready to embrace them.

In this project, adopting a holistic approach was key to success: looking for quality, capacity, simplest operations and financial results. Equally, if not more important is to involve the right people at the right time; this saves significant time and effort. Empower the process leaders to own the initiative from the start and to propose and evaluate all potential changes that could lead to improvement. This grows the project but also ultimately delivers greater results.

About the authors

Victoria Navarro is an Engineering Quality & Validation manager at Pfizer, and an expert in process optimisation and problem-solving. She is a Pfizer representative in BioPhorum, Alternative and Rapid Micro Methods workstream (ARMM) and also a member of the Pfizer internal ARMM steering committee. Part of her role includes helping Pfizer sites to bring innovative solutions by providing strategic, proactive and hands-on support across the network. Victoria’s background is in manufacturing, engineering and quality engineering, with operational excellence and innovative projects as core areas of expertise, for more than 20 years.

Joanny Salvas is a senior manager at Pfizer. She has been developing and implementing process analytical technologies and rapid microbial methods for more than 10 years. Her background in engineering and process control has enabled her to support many successful implementations of advanced control strategies in the Pfizer network. She leads the Pfizer Rapid Micro Method steering team responsible for establishing the strategy and the evaluation and implementation of future microbiology analytics. She is also a member of the BioPhorum Alternative and Rapid Micro Methods workstream (ARMM) workstream. Joanny has a passion to learn and drive innovation forward, striving to use the best science in her projects.

Esther Bellón is a senior manager of Drug Product Manufacturing at Pfizer. She is involved in the Alternative and Rapid Micro Methods group in Pfizer (ARMM) for the evaluation and implementation of alternative microbiology methods. Esther’s background is in microbiology, with more than twenty years in the microbiology lab.

References

Prasad A, Villari P, Henry RB, et al. Practical applications of bio-fluorescent particle counting in Environmental Monitoring investigations. PDA Journal of Pharmaceutical Science and Technology,
Montenegro-Alvarado J-M. Leveraging Rapid Microbiological Methodology in Forensic Evaluation to Identify Elusive Root Cause. American Pharmaceutical Review, July August 2018.
Montenegro-Alvarado J-M, Salvas J, Weber J, et al. Pharmaceutical Online; July 2018.

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Efficient shutdown recovery: leveraging RMM to unlock value and ensure environmental quality

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