Real-Time Process Control for Healthy, High-Purity Water Systems

Transcription

1 An Executive Summary Real-Time Process Control for Healthy, High-Purity Water Systems Allison Scott, PhD Senior Principal Scientist Azbil North America Research and Development, Inc. Lisa Graham, PhD, PE CEO and Founder Alkemy Innovation, Inc. Michael J. Miller, PhD President Microbiology Consultants LLC Owner Rapidmicromethods.com Exploring the role of online water bioburden analyzers in the development and implementation of cost-effective water management strategies for pharmaceutical manufacturing. Overview Water is the single largest raw ingredient in pharmaceutical manufacturing. Usually drawn from a continuously fed distribution loop at the point of manufacture, its microbial cleanliness is crucial but conventional microbiological testing fails to provide timely or complete information. Consequently, water use is often based on the assessed contamination risk, with actual data only available post-manufacturing. Now, advances in real-time analysis using process analytical technology (PAT) enable both the determination of the water s microbial appropriateness at the time of manufacturing and real-time process control. This article discusses the implementation of a new class of instrumentation, online water bioburden analyzers (OWBAs), for determining and managing water system health. Microbiological Testing of High-Purity Water Conventional culture methods used in testing the microbiological cleanliness of water and water systems must contend with organisms that are stressed and/or naturally slowgrowing. Incubation times, therefore, tend to be lengthy (days) and variable. Definitive results are unlikely to be delivered until after a pharmaceutical production run has been completed, posing risks to product quality and manufacturing processes. The development of automated rapid microbiological methods (RMMs) is helping to overcome some of the testing challenges by delivering faster detection and quantification, and in some cases identification, often with improved sensitivity, accuracy, and reproducibility. High throughput, continuous processing, and enhanced data handling are all possible with these new methods. Yet despite such innovations, there are water testing applications for which even these RMMs are not fast enough. When using water as a raw material, there is a strong justification for systems that deliver near- or actual real-time results, especially if manufacturing is on a just-in-time basis. SPONSORED BY

2 REAL-TIME PROCESS CONTROL FOR HEALTHY, HIGH-PURITY WATER SYSTEMS The Regulatory Environment The use of RMMs in pharmaceutical manufacturing is subject to a range of regulatory guidance. A recent key document relating to the use of rapid methods associated with water is Questions and Answers on the Production of Water for Injections by Non-Distillation Methods: Reverse Osmosis and Biofilms and Control Strategies (1). This was published in 2017 by the European Medicines Agency s GMP/GDP Inspectors Working Group as an interim measure pending revision of Annex 1 (Manufacture of Sterile Medicinal Products) of the EU Guidelines to Good Manufacturing Practice Medicinal Products for Human and Veterinary Use (2). Several questions raised in the Q&A point toward the usefulness of new technologies. For example, when addressing the use of reverse osmosis (RO) for manufacturing water for injection (WFI), the microbiological quality of the water produced was identified as a concern. Alongside this, the working party highlighted the need for a control mechanism to eliminate or reduce risks associated with microbial proliferation throughout the water system such as the presence of endo- and exotoxins and viable organisms, which might not be easy to detect, and the latter of which might also result in the development of sterilization-resistant biofilms. Setting out what testing to employ during the initial qualification and routine operations sampling, the working group stated that RMMs should be considered as part of the control strategy to aid with rapid responses to any deterioration of the system. These should include a rapid endotoxin test as well as quantitative microbiological test methods in line with Ph. Eur Alternative Methods for Control of Microbiological Quality (3), with validation of the water system also in line with Ph. Eur Alternative and rapid methods are also recommended as part of an overall control strategy for a water system, with the establishment of appropriate alert levels based on trends and statistical analysis of the data. Routine review of the data with appropriate investigation of any adverse trends (even those within acceptable limits) is also needed to ensure effective running of the system. The revision of Annex 1 issued in December 2017 for public comment includes positive guidance on the use of rapid methods in sections covering environmental monitoring and quality control testing, both of which apply to water systems. So, what makes a good RMM? In the context of the applications identified above, the desired rapid method attributes will include: Real-time detection and quantitation of a wide range of water-borne organisms Sample volumes that are the industry norm given USP and Ph. Eur. water compendial limits, for example at least 100 ml for highly purified and WFI systems. Validated according to acceptable guidelines (Eur. Ph [2]; USP Ch.1223 [4]; or PDA Technical Report 33 [5]) Statistically non-inferior to the current method Continuous operation at temperatures relevant to distribution loops (hot to cold) Connects to water systems in- or online Robust data trending capabilities to quickly determine if alert or action levels are being reached. Introducing Online Water Bioburden Analysis Online water bioburden analyzers (OWBAs) are systems that examine pharmaceutical-grade water bioburden in real time using the technique of light-induced fluorescence (LIF) (regarded as an alternative method) to detect microorganisms. Demand for this type of instrumentation is being driven by the pharmaceutical industry as it moves from batch release toward parametric release or continuous manufacturing, supported by regulatory encouragement and a range of quality initiatives. Pharmaceutical water generally requires testing for the four specific quality attributes shown in Table 1. Of these, Table 1: USP<1231>, Water Quality Attributes Quality Parameter Purified Water Water for Injection Conductivity < C < C Total Organic Carbon (TOC) <500 ppb <500 ppb Endotoxin N/A <0.25 EU/mL Bioburden (action level) 100 CFU/mL 10 CFU/100 ml bioburden is the only attribute with no associated PAT instrumentation, reliant instead on grab samples and traditional culture methods. To address this, an OWBA work group was formed in 2013 comprising representatives of seven major pharmaceutical companies who provided guidance to instrument vendors on the development of a system intended to be acceptable to industry and regulators alike. The group has developed several key documents covering the requirement specification, testing protocol and business benefits and has published articles that describe systems and applications, and the benefits of enhanced process understanding, risk assessment and energy saving. Figure 1 compares the traditional water sampling and culture approach with that of an OWBA type method and illustrates the immediacy of the OWBA technique. OWBA does not, however, identify microorganisms and is therefore entirely complementary to traditional culture methods, offering additional real-time information. To further explore the technology, features, and capabilities of OWBA systems, and later to illustrate real-life testing applications, the descriptions that follow relate specifically to measurements made with the IMD-W system (Azbil North America BioVigilant div., Tucson, AZ). Essentially, this OWBA

3 REAL-TIME PROCESS CONTROL FOR HEALTHY, HIGH-PURITY WATER SYSTEMS S P E C I A L S P O N S O R E D S E C T I O N detects particles and determines biological status simultaneously using native water samples that require no preparation and delivering real-time results on a per second basis. It is 21 CFR Part 11 compliant, has touch screen operation, and has a flexible communications interface for networking and external control through common industrial systems such as SCADA and PLC. Measurement Principles As shown in Figure 2, 405 nm laser light is passed through the water flow being sampled by the OWBA. Any particle present will scatter the light. If the particle is biological, then it will both scatter light and emit a fluorescence signal. Particle detection is based on Mie theory, which describes light scattering by particles whose size is on the same order of magnitude as the incident light wavelength, and which is the model recommended in ISO for particles below 50 microns in size. Scattering intensity depends on particle size and shape and the difference in refractive index (n) between the medium and the particle. Figure 1: Traditional vs. OWBA-type method. Tra9i:;nal Water Sampling Periodically and manually collect grab sample from water system Process sample (i.e., pour plate or membrane bltraqon) Incubate hrs (minimum), C Count the plate, record data, enter into LIMS LimitaQons of culture method Any response is retrospecqfe OWBA-Type Water Sampling Hnline (conqnuous) sampling or At-Line grab sample " real-qme result Light-Induced Fluorescence (LIF) Simultaneous biological parqcle and total parqcle counqng [lectronic records automaqcally createdm networked with LIMS Disk assessment and miqgaqon can occur immediately Azbil North America, Inc., BioVigilant Div. Figure 2: Scientific principle of LIF technology. Laser light, commonly 405 nm, intersects water flow IM present, parqcles will proeuce scaber ane biologic parqcles will also proeuce autofluorescence Wie scaber ane laser-ineucee intrinsic fluorescence EetecQon are usee to monitor pharmaceuqcal water biobureen in real Qme Azbil North America, Inc., BioVigilant Div.

4 REAL-TIME PROCESS CONTROL FOR HEALTHY, HIGH-PURITY WATER SYSTEMS Biological detection relies on the excitation of internal fluorophores, such as NADH and riboflavin, when biological particles are exposed to 405 nm laser light. This intrinsic fluorescence is orders of magnitude less than that derived from fluorescence staining, so OWBA systems must be highly sensitive to these low levels. Reporting of particle and biologic counts takes place in real time, alongside information on system and water status. Effective separation of microbial and interferent particles is critical to the utility of OWBA systems. Their profiles have different characteristics and Figure 3 shows how IMD-W OWBA systems make use of these to discriminate between common water interferents, such as Teflon, Kalrez, and silicone rubber, and waterborne microbes, in this case Brevundimonas diminuta and Pseudomonas putida. Installation and Real-World Application While common installation sites include the water loop Figure 3: Enhanced discrimination with IMD-W OWBA systems. 1 Fluorescence Detector 1 Detection Range Raman Band Fluorescence Detector 2 Detection Range Normalized Fluorescence Intensity Brevundimonas diminuta Pseudomonas putida PTFE (Teflon) Kalrez (FFKM) Silicone rubber Wavelength (nm) Pypical microbial ane intermerent fluorescence probles hafe Eiferent characterisqcs Apectral Eiferences can be elploitee to malimize EiscriminaQon Rith mulqple signals None scaber p tro fluorescence channels) A mulq-eimensional EiscriminaQon map om biologic ane intermerent space can be createe ane appliee to measuree parqcles on a real-qme basis Azbil North America, Inc., BioVigilant Div. Figure 4: Real-time view of water system health and operation IMD-W Counts / 100mL Particle counts Biologic counts IMD-W Counts / 100mL Particle Counts Biologic Counts Sample Time (Hours) Old purified water loop (~20 years old) Higher counts, trend with tank fill and use Sample Time (Hours) New purified water loop (<2 years old) Lower counts, stable biologic counts

5 REAL-TIME PROCESS CONTROL FOR HEALTHY, HIGH-PURITY WATER SYSTEMS S P E C I A L S P O N S O R E D S E C T I O N return, locations after pre-treatment and at point of use, OWBA systems can be placed anywhere there is a TOC system on the loop. Each water system is unique, and the OWBA-generated data will reflect this, indicating the loop s current state. Since the OWBA approach does not rely on the growth of microorganisms, biologic counts will likely be higher than those typically seen when using culture methods. However, the abundant data generated by the OWBA enable characterization, monitoring, and real-time process control of the water system, thus providing a new view of its health and operation as illustrated in Figure 4. On the left is data from a 20-year-old purified water loop that shows an increase in counts approximately every 12 hours. This count further increases at night because of particle shedding and decreases during the day as new filtered water is added to replace water used. So, a trend of tank fill and use is seen on this loop. On the right is data from a purified water loop that is less than two years old. Counts are plotted on the same scale as before and are significantly lower than for the old loop. Comparisons such as these illustrate the uniqueness of each loop and demonstrate the ability to correlate physical particle and biologic counts to what is happening in practice. The availability of particle and biologic count alongside other sensor information provides further detail. Plotting particle count against flow pressure, for example, can illuminate what is happening in the system whenever a pressure valve is opened. With access to such comprehensive data, examining the impact of a range of actions and matching cause and effect becomes easier, enabling better characterization of each unique water loop, enhancing process understanding, and producing real-time actionable information. Data-Driven Decision-Making Good decision-making is the foundation for water-system optimization and requires access to appropriate data at the right time. Real-time measurements should be used in ways that enable observation of transient changes impacting performance; identify repeating patterns that impact the water loop; show trending of particle and microbial counts over time; and provide process monitoring and continuous improvement. In support, real-time particle data from the OWBA can be used alone or in conjunction with the full water system data via a data analytics application. The practical illustrations that follow refer to continuous monitoring of a single water loop with the IMD-W installed, where there is access to both the OWBA dataset and, via the use of data historians, to other operational data including valve positions, flow rate, temperature, and cycle information. Certain external variables such as shift changes and environmental data are also available. Example 1: The value of real-time data analysis. The focus here is on the key metric of particle count and shows how the per second data provides a level of insight likely to be missed using only infrequent off-line analysis. The OWBA particle count and system metrics were mapped on a per second basis using a data analytics software application from Seeq. The time window can be moved to quickly check historical data and look for any changes in the system and Figure 5 zooms in on a portion of the output to investigate an observed change. It shows that a change in flow pressure is followed by an immediate increase in particle count. Without the per second data, this would be missed, either because there is no sampling in this period, or particles get diluted out of the sample. While the increase in particles may or may Figure 5: Data Analytics Example 1 Real-time data analysis. [nabling scienqsts and engineers to have valuable insight through access to per second data which would otherwise be missed when only gesng inmreauent, ofline, analysis. Flow Rate (ml/min) ParQcle Counts/Aecond Flow Pressure (psig)

6 REAL-TIME PROCESS CONTROL FOR HEALTHY, HIGH-PURITY WATER SYSTEMS not be a cause of concern, observing patterns and knowing what is happening helps to establish a baseline for further process control. Example 2: The value of pattern searching. Such a process change may be the prompt to look further into historical data for any patterns in performance. This approach is illustrated in Figure 6, which shows a change in the flow pressure signal (purple trace) and a repeating pattern. Drilling through the data helps explore an obvious change in system performance, allowing detailed analysis of the patterns: Are there similar ones? How often do they occur? Do they correlate with other changes in the system? In the example shown a selected pattern is seen to repeat approximately every 29 minutes. The analytics application can then seek similar patterns and determine any correlation with other changes in the system or the operating environment. The main point is that on noticing something of interest, it can be quickly highlighted and investigated. Example 3: The value of real-time particle counts. Increases in particle counts for short periods may have no material impact on the water system, but particle counts totaled per standard volume provide a baseline for an early warning that something has changed. These can be displayed as total particle counts or can be broken down Figure 6: Data Analytics Example 2 Pattern searching. Choose a Different PaBern of Interest IdenQfy DepeaQng PaBerns (Demove the Qme between them in chain view ) Hverlay the PaBerns (Capsules) (Assess Metrics of Similarity) % Similar Flow Pressure (psig)u Notec DuraQon q29 min 8 PaBerns (Capsules) Hv Figure 7: Data Analytics Example 3 Real-time particle counts. Potal ParQcle CountsX100ml Flow Pressure (psig) Potal ParQcle CountXAecond As shown prefiously, Qme periods Mor the raw Sow pressure pabern are regular. Potal ParQcle CountsX100ml Daw signal, parqcle counts per second increase with each pabern, and appear to grow in total amount ofer Qmec Flow Pressure (psig) Potal ParQcle CountXAecond

7 REAL-TIME PROCESS CONTROL FOR HEALTHY, HIGH-PURITY WATER SYSTEMS S P E C I A L S P O N S O R E D S E C T I O N Figure 8: Data Analytics Example 4 Process monitoring and continuous improvement. into bio- and non-bio particles. In our example system, it is already clear that time periods for the flow pressure pattern are regular. Closer examination of the associated raw particle counts, and particle counts totaled per 100 ml (Figure 7) indicates a trend of increasing numbers of particles with each flow pressure change, suggestive perhaps of altered filter performance or the health of the UV system. Example 4: Process monitoring and continuous improvement. Pulling together data from the OWBA, other system sensors and the data historian enables detailed understanding of the water system and the development of predictive models and real-time control systems. The resulting definition of operating boundaries permits the establishment of action alerts that take account of different operational modes, as illustrated in Figure 8. Users can then detect changes as they occur, making monitoring more proactive and supporting continuous improvement. Summary Online water bioburden analyzers are a new class of instrumentation that deliver real-time information to provide a more meaningful view of water system health than conventional off-line techniques. Online bioburden data can be used in concert with data from elsewhere about the water loop to construct a holistic assessment. Implementation of OWBAs on high-purity water systems for pharmaceutical manufacturing enables better characterization of each water loop and promotes enhanced process understanding for the development of predictive models and improved process control. Their use accords with the industry s PAT initiatives and moves toward continuous manufacturing. References Questions and Answers on the production of water for injections by non-distillation methods GMP/GDP Inspectors Working Group. EMA /INS/GMP/443117/ Other/2017/08/WC pdf (Accessed 25 March 2018) EU Guidelines to Good Manufacturing Practice Medicinal Products for Human and Vete r inar y Use. A nnex 1 Manufacture of Sterile Medicinal Products vol-4/2008_11_25_gmp-an1_en.pdf (Accessed 25 March 2018) Chapter Alternative Methods for Control of Microbiological Quality, European Directorate for the Quality of Medicines & HealthCare. Supplement 9.2: <1223> Validation of Alternative Microbiological Methods. United States Pharmacopeial Convention. USP 40/NF35: Technical Report No. 33. Evaluation, Validation and Implementation of Alternative and Rapid Microbiological Methods. United States Pharmacopeial Convention. USP 40/NF35: BioVigilant, a division of Azbil North America, Inc., is the inventor of instantaneous microbial detection for air and water monitoring in pharmaceutical environments.