An industry perspective on qb d


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An industry perspective on qb d

  1. 1. B i o P r o c e s s TechnicalAn Industry Perspectiveon Quality By DesignRick Johnston, Jim Lambert, and Emily StumpT he US FDA’s quality by design The FDA provides a basic definition (QbD) initiative and associated in a current strategic guidance ICH Q8, Q9, and Q10 document: “QbD is understanding the guidance documents are manufacturing process and identifyingincreasingly embraced by the the key steps for obtaining andbiopharmaceutical manufacturing assuring a pre-defined productindustry for ensuring consistent quality” (3). Here we consider QbDproduct quality and lower costs of with ICH Q8, Q9, and Q10 togetherdevelopment and manufacturing. One as they form part of a broader industrycritical problem the industry faces perspective on risk-basedinvolves understanding how to manufacturing. The aim is to increaseimplement QbD and determine the process knowledge through a data collection and statistical analysis ofbenefit of such projects — which systematic understanding of how manufacturing process parameters torequire the work of many groups quality attributes are derived. detect changes in a process or in productacross quality, manufacturing sciences, Understanding how quality relates to quality. Figure 1 summarizes the mainand engineering departments. Here the manufacturing process is more themes and data flows in each of thesewe present the results from a survey of difficult in biotech than in many other two complementary approaches.biopharmaceutical manufacturing industries because of the sensitivity of The two cycles of processprofessionals undertaken to determine biologic processes to seemingly small development and manufacturingcurrent QbD understanding in the changes (both deliberate and improvement are related by way ofindustry. We outline key gaps and unintentional) in process parameters. ICH Q8, Q9, and Q10 throughshow how QbD has been implemented Although the effect on product quality process data. Although each cycle usesin practice. We focus on simple, may be understood, the cause of that data for different purposes (processpractical implementation of QbD effect is not as clear. The combinatorial development to improve currentprinciples and show the perceived effect of process variables on product designs and establish better processvalue of such investment by industry. quality traditionally has not been “platforms”; manufacturing sufficiently evaluated. Historically, that improvement to deliver quality productQbD and ICH: A Primer has encouraged a risk-adverse approach more reliably), process data are key toExtensive definitions of QbD and the to manufacturing process development both. As such, biomanufacturers areassociated ICH Q8–Q10 framework and improvement due to significant currently attempting to assess how tocan be found in the references (1, 2). costs associated with process change. • better collect process data Our belief, shared by many others in the (continuous time historians, better industry, is that this has discouraged laboratory information managementProduct Focus:  All biologics innovation and kept manufacturing systems, batch context)Process Focus:  Manufacturing costs comparatively high. • achieve direct control over The QbD approach has been processes (through means such asWho Should Read:  QA/QC, implemented in the biomanufacturing process analytical technologies)process development, validation, and industry in two distinct but related • perform more sophisticatedmanufacturing ways. First, QbD increases up-front analyses of collected data experimentation as part of process • determine ways to allow moreKeywords:  Risk management, data development to establish the operating groups in an organization to see andmanagement, statistics, PAT, ICH Q8, boundaries between where quality is analyze data in real time.ICH Q9, ICH Q10, process validation and isn’t affected. Second, QbD With traditional risk assessmentLevel:  Intermediate increases the quantity and fidelity of approaches, biomanufacturers are26 BioProcess International 10(3) M arch 2012
  2. 2. Figure 1:  Implementation of QbD for process development and manufacturing improvement Science Base Different unit operations • Equipment data Process • Operating characteristics Development Process Optimization ICH Q8 Tech Transfer Know your process • Design of Experiments • Optimized process, • Quantification of CPPs and CQAs operating ranges and • Quality target product quality controls profile (QTPP) • Alternatives evaluated Process Design Management ICH Q9 Risk Improvement Manufacturing Cycle Improvement Design Lessons Facility Process Data ICH Q8 Know your process Facility Model • Design or actual • Statistical process control • Process performance • Performance data on • Golden batch root or theoretical areas of improvement cause analysis • Analysis of predictive • Lessons learned ICH Q10 • Stochastic models power of models Risk Effectiveness Monitoring Management ICH Q9 Manufacturing Risk Improvement Cycle Production Economics Process • Lower failure rates Improvements • Improved productivity • Risk-based focus • Better monitoring • Better measurement • Faster lot release ICH Q10 and control systems Risk Effectiveness Monitoringattempting to lower their failure rates is made of data collection, statistical The FDA on Datato improve quality and productivity analysis, and the need to operationalize “Managers should use ongoingand justify implementation of analysis methods rather than perform programs to collect and analyzemonitoring efforts based on economic them infrequently and offline. product and process data to evaluatevalue. Increased data accessibility However, it is far from clear what such the state of control of the process.”during process development provides guidance actually means in practice for “We recommend that a statistician orinformation that allows future process organizations wanting to implement person with adequate training indesigners to properly focus on a robust approaches that look more closely at statistical process control techniquescontrol strategy and those parts of process–quality relationships. develop the data collection plan.”each process with the greatest risk “We recommend continued monitoringdensity. Together, the process and Industry Perspective and sampling of process parametersmanufacturing improvement cycles We assessed the state of the industry and quality attributes … Processprovide concrete ways to design and through a recent survey conducted variability should be periodicallyoperate better biopharmaceutical with BioProcess International (survey assessed.”production processes, which deliver on details available on request). The goals From CDER/CBER/CVM. Guidance forthe promise of higher quality and of our survey were to determine the Industry: Process Validation — Generallower compliance costs. level of implementation of QbD, key Principles and Practices. US Food and Drug Administration: Rockville, MD, January 2011; Recent FDA (and other) guidance advantages companies perceived in the focused on quantitative aspects of approach, and challenges encountered drugsguidancecomplianceregulatoryQbD by way of data collection (see the in justifying implementation of QbD information/guidances/ucm070336.pdf.“FDA on Data” box). Explicit mention and ICH Q8–Q10 guidelines.28 BioProcess International 10(3) M arch 2012
  3. 3. Figure 2:  Two ranged questions (participants asked to rank each on a Figure 3:  Ranged answers (1–5) regarding implementation of ICH Q8, scale of 1–5) compared the perceived importance of QbD and its actual Q9, and Q10 principles implementation How extensive is your organization’s implementation of ICH Q8, Q9, and Q10 principles? How would you rate QbD’s perceived importance to your organization? 0% 5% 10% 15% 20% 25% 30% 35% 0% 5% 10% 15% 20% 25% 30% None or never heard of them 1 Not important or no assigned budget 1 2 Average: We have 2 implemented Q8 standards 3 but have not achieved Important: Assigned budget continuous quality systems. 3 and dedicated headcount 4 Excellent: We have been 4 using continuous quality verification systems Q8−Q10 5 Critical: Mandated by for 3 years or more. management that all 5 filings include QbD turn, leads to increased flexibility in process design and an How would you rate QbD implementation in your organization? ability to make changes after regulatory filing as well as an 0% 5% 10% 15% 20% 25% 30% Low: We dont have any increased understanding of key business risks. kind of activities, but we 1 Although the potential benefits of QbD are well known, have a team looking at it. biomanufacturers experience road blocks in its actual Medium: We are trialing QbD 2 execution. Difficulty with implementing QbD relates to and have made some two main themes, both based in a lack of information. postapproval regulatory 3 filing changes as a result First, knowledge is limited regarding business drivers and of these efforts. 4 return on investment (RoI) associated with implementing High: We use QbD for all QbD (Figure 5). Evidence available suggests that once such existing and new product 5 systems are in place, they do have significant benefits filings and day-to-day analysis at all of our sites. across manufacturing, quality, and process development. Biomanufacturers implementing such systems can expect to see productivity improve as resources are focused on a small Figure 2 illustrates a disconnect between the current, number of key variables. Closer monitoring of that reducedperceived importance of QbD and its implementation state set can justify faster batch release and dramatically decreasein the industry. Most companies have some assigned the time required to perform root-cause investigations. Butbudget and dedicated staff associated with QbD — with we need to report on success stories in the industry to help>50% of survey respondents identifying it as “important.” justify the up-front costs associated with QbD.However, despite this level of interest, few companies use The second issue slowing implementation is a lack ofQbD today in regular filings. Most current approaches are practical knowledge related to putting QbD into practice.based on limited piloting of QbD. Figure 5 also shows that biomanufacturers are unaware of Similar analysis applies to implementation of ICH what steps to take in applying QbD to their processes andprinciples (Figure 3). ICH Q8 calls for increased organizations. For example, statistical process controlexperimentation to determine the importance of variables and (SPC) techniques are currently used in other industriespotential interactions that affect product quality. Although (such as semiconductor manufacturing), but those cannotQ8 has been implemented at many companies, few have be applied “ad hoc” to biotech because of the inherentlyestablished a comprehensive model for continuous quality high levels of biologic variability, and sequentiallyverification, which is a central goal of ICH guidelines. dependent results (autocorrelation) (4). The nascent state of the industry in this regard presentsa number of questions: To what extent should QbD be A Roadmap to Continuous Quality Verificationembraced by an organization? What concrete steps should Whether biomanufacturers choose to increasebe taken to change manufacturing systems? And should experimentation and incorporate QbD-related concepts inQbD be focused primarily around development or their regulatory filings, the operations community has comemanufacturing — or both? to broad consensus regarding the importance of increasing process knowledge. Most biomanufacturers collect large dataPerceived Benefits and Roadblocks sets about their processes. However, the challenge is to ensureThe overwhelming majority of survey respondents that what’s collected is in the correct format and to adoptidentified the primary benefit of QbD as a better tools that use the information in the best way to gain insightsunderstanding of manufacturing processes leading to into processes. Reuters reported in December 2010 that Erichhigher product quality. Figure 4 shows top reasons for Hunziker (head of IT at Roche) called dealing with this ever-implementing QbD. They are all tightly aligned with FDA growing steam of data one of his biggest concerns (5).goals for QbD as well as the overall ICH Q8–Q10 goals: a Biopharmaceutical manufacturing organizations have (to abetter understanding of process fundamentals. This, in greater or lesser level of sophistication) adopted the following30 BioProcess International 10(3) M arch 2012
  4. 4. Figure 4:  Primary benefits of QbD and top reasons for investment in QbDfour steps in this regard: Collect theright kind of data; perform biotech- In theory, what do you view as the primary benefit of QbD? 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%informed SPC and statistical qualitycontrol (SQC); correlate process Better understanding of the relationship between processparameters to quality attributes; and parameters and quality outcomescreate a comprehensive stochastic model. Improved product quality Below are some successfulapproaches to those steps that have Better understanding of businessbeen applied in the industry. risks (e.g. higher inventories, low yields, and high discards) SPC and SQC seem to be the firstsignificant QbD-related changes to Increased flexibility/ability to tweak the processbiomanufacturing. SQC isretrospective (e.g., testing quality Shortened quality release timesprocesses into a manufacturingprocess), whereas SPC is prospective Lower compliance costs(e.g., real-time process variablemonitoring). These tool sets have theability to provide insight by focusing What is the primary reason you think would justify further QbD investment in your organization?on variation of a process from a “mean” 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%or reference batch. Variability is bothsignificant in biotech and typically the Process Understanding: to drive better understanding of the corecause of quality variations. So it makes processes and technologies supportingsense to establish SPC controls that them to drive long-term changecan more accurately detect and control Lower Compliance Risk: tothose variations. understand variability and prevent product withdrawal, Figure 6 shows survey results contamination, or other adverse eventsrelating to adoption and sophisticationof SPC/SQC approaches within Business Drivers: tobiopharmaceutical manufacturing. increase yields, lower reported deviations, decrease discard ratesData indicate that SPC is in use by~50% of manufacturing companies, butits use is mostly offline and constrainedto performing analyses that support Figure 5:  QbD implementation challengesexperimental design rather than What are the challenges to implementing QbD in your organization?activities on the “shop floor.” 0% 10% 20% 30% 40% 50% SPC has been very successful in Additional upfront costsother industries (e.g., automotive andsemiconductor manufacturing) for Poor understanding of the benefitsdriving costs lower and quality higher. of QbD and its return on investmentHowever one issue with itsimplementation in biotech is that No in-house experience with QbDbiological processes are both highlysensitive and inherently variable. That Unsure how to actually implement QbD principles in practicecan lead to false positives — SPCreadings that appear to be trending No business drivers for implementingout of control but are actually not — QbD for existing/legacy productsand those need to be carefully Perceived longer new productaccounted for. Poor application of introduction timesSPC theory to a process, low Management hostile or don’tunderstanding of the fundamental understand or support QbDdrivers of a process, and poor ICH Q8effort to determine critical processparameters can all cause problems. Figure 7’s sample X-bar chart from statistical methods. Here, the red star distinct from a control limit, which isBio-G’s Crosswalk software shows identifies one. The colored regions are defined according to regulatory filingdeviations from a sample mean. calculated from sample data and used documents as a deviation from theCertain trends (e.g., the upward trend to calculate whether a process is in or process important enough to requireto the right) can be detected using out of statistical control. (This is special investigation.)32 BioProcess International 10(3) M arch 2012
  5. 5. Figure 6:  Adoption and sophistication of SPC within biopharmaceutical Figure 7:  X-bar chart for a process showing sigma bands and statistical manufacturing (participants were asked to answer with 1–5 rankings) control limits How extensive is your organization’s use of statistical process controls? 0% 5% 10% 15% 20% 25% 30% 35% 190 Low: Limited to a few 1 process experts Sample Mean 180 2 Medium: Used regularly 170 by manufacturing sciences 3 and quality for root-cause analysis 160 4 High: Used hourly by 150 operators and 5 supervisors on the floor January February March April May June How sophisticated is your organization’s Date (grouped by all, 2007) use of statistical process controls? 0% 5% 10% 15% 20% 25% 30% 35% 40% Low: Use offline, Minitab or other manual toolsets 1 Figure 8:  “Golden batch” measurements for a chromatogram showing deviation from a reference batch 2 Medium: Use enterprise 350 toolsets that do multifactor 3 300 DOE but are not real time 250 4 200 High: Real-time multifactor tool sets with the ability to 150 incorporate stochastic 5 multivariate models 100 Values 50 0 Graphs such as those in Figures 7 and 8 are important for −50two reasons. First, they provide a means to extract patterns −100 −150from single data sources and identify points at which corrective −200action needs to be taken independent of a specification limit. −250That provides a more sensitive manner of detecting issues than 0.0 0.2 0.4 0.6 0.8 Normalized Measurementsalarms that are at best lagging indicators that lack texturedinformation because they offer only a “binomial view” of theprocess (within or outside specification). Second, these graphs those untrained in complex stochastic methods. Users canare easy for operators to understand. So they can be used in exclude >90% of the possible set of interactions and focusreal time on the shop floor (rather than offline with on those remaining, for which interactions are important.applications such as Minitab software). This moves adverse This offers multiple benefits in relation to the ICH guides.event identification (and root-cause analysis) closer to where It reinforces process knowledge (ICH Q8) and supplementsproblems occur — rather than being performed after the fact risk-based assessments performed as part of ICH Q9by statistical process experts with less operational knowledge of activities (by evaluating probability of occurrence andaffected manufacturing systems. detection in near real time). And it can be continual to Correlation of Process Parameters to Quality Attributes: promote risk-effectiveness monitoring (ICH Q10).Another aspect of QbD that is currently under examination Stochastic Control Tool Sets: The biopharmaceuticalis how to relate process parameters to adverse quality events. industry’s approach to stochastic control tool sets isThis was identified in our survey results as one of the most evolving. In mapping current tool sets, it becomes clear thatimportant potential benefits for QbD (Figure 4), but the more advanced use of QbD will rely on our ability tobiotech community is debating how best to approach it. One analyze collections of data sets that are highly complex,option is multivariate SPC; however, the sheer number of highly variable, and autocorrelated. Figure 10 shows somepossible interactions between process parameters and quality tool sets available today.outcomes complicates this approach. The industry is currently leaning toward multivariate Another strategy that has met with success is stochastic process tool sets. They go beyond simple correlationperforming large-scale correlations of process parameters and SPC analysis of raw data. These tool sets combine real-with one another and with quality attributes (or measurable time data with a model that can be used to understand thesurrogates for them). Figure 9 shows one such correlation. relationship between a process parameter in one unit operationEach cell in the “heat map” correlates that process and its effect on quality throughout the manufacturingparameter with associated quality attributes. Hot-colored process. This is a necessary precursor to implementing truecells indicate either high positive or negative correlation real-time release and “release by exception” methods byand cool colors little or none. The advantage of such a allowing companies to look at how a batch evolves throughtechnique is that it is rapid. It provides visual references to many processing steps will map against the design space.34 BioProcess International 10(3) M arch 2012
  6. 6. Figure 9:  Massive-scale correlations of process parameters and quality outcomesTime to Get Started High correlationQuality by design and associated ICHguidelines are becoming increasingly Correlation Process Parametersimportant in the biotherapeutics Scaleindustry, which is currently focused 1.0 0.8around the initial stages: data Quality Outcomes 0.6collection, SPC/SQC pilots, and 0.4mostly offline analysis. Clearly there 0.2are significant capital drivers for 0.0implementing such projects. These −0.2 −0.4benefits come in −0.6 • improved productivity (focusing −0.8resources on what is often a small −1.0number of key variables) Low correlation • fewer variables allowing real-timemonitoring and timely release ofproduct (a closer observation of key improvements to quality. Such an Corresponding author Dr. Rick Johnston isprocess parameters and quality approach will also lead to development executive director of the University ofoutcomes) of processes and technologies that are California at Berkeley’s CELDi Center for • improved control strategies and increasingly flexible and adaptable to Research in Biopharmaceutical Operations, an adjunct professor at Keck Graduaterobust risk assessments the changing needs of the market. Institute (Claremont Colleges), and founder • a valuable knowledge and CEO of Bioproduction Group, 1250management system (people no longer References Addison Street, Suite 107 Berkeley, CA 94702;spend substantial periods performing 1 McCormick K. Introduction to ICH: 1-510-704-1803, fax 1-510-704-0569; rick@ Essential Background to PQLI. Pharmaceut.root-cause analysis), James Eng. May–June 2008; 1–3. • lower rates of production failure Lambert ( is 2 Berridge J. PQLI: What Is It?and scrap. Pharmaceut. Eng. May–June 2009: 33–39. director of quality engineering at Figure 11 shows some of the next 3 US Food and Drug Administration. Genentech’s Hillsboro, OR fill–finish project.steps. Biotech companies will need to Advancing Regulatory Science at FDA. US Dept. And Emily Stump (Emily.Stump@CAgents.get the QbD basics right. Forming a of Health and Human Services: Rockville, com) is a validation scientist forteam to look more closely at QbD and MD, August 2011; Commissioning Agents, Inc. This article ScienceResearch/SpecialTopics/ references no real data fromdata collection was cited most RegulatoryScience/UCM268225.pdf. biomanufacturers other than 193 responsesfrequently in our survey results (by obtained in the survey conducted through 4 O’Neill, J. Continued Process61% of respondents). Verification for Biological Processes. 16th BioProcess International in August 2011. It is clear from our survey that Symposium on the Interface of Regulatory and Details survey are available on request.biomanufacturers believe in the Analytical Sciences for Biotechnology Healthimportance of QbD and associated ICH Products, 23-25 January 2012. To order reprints of this article, contactguidelines. Although a significant 5 Hirschler B. Roche Fears Drug Industry Rhonda Brown ( Drowning in “Spam” Data. Reuters 1 Decembernumber of roadblocks to implementation 1-800-382-0808. Download a low-resolution roche-data-idUSLDE6B01XF20101201. • 2010;, biomanufacturers are looking at PDF online at as a method for increasing processunderstanding and driving fundamentalFigure 10:  Tool set mapping against process complexity and variability Figure 11:  Top “next steps” for QbD implementation in the biopharmaceutical industry High Minitab What do you think your organization’s Multivariate next step toward is likely to be? SPC/SQC Process Variability Stochastic Process 0% 10% 20% 30% 40% 50% 60% tools Toolsets Form a team to look Crystal Ball Monte Carlo more closely at QbD and collect more data. JMP Add sensors and process control Excel to manual systems (PAT). ERP/MRP systems DoE toolsets Implement a real-time Low statistical process control Low Process Complexity High (SPC) package. Implement a modeling framework that uses SPC data to perform multiunit operation stochastic modeling. M arch 2012 10(3) BioProcess International 35