Webinar: How to Develop a Regulatory-compliant Continued Process Verification (CPV) and Process Monitoring of Bioprocesses
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The document discusses the regulatory-compliant development of continued process verification (CPV) and process monitoring within the bioprocessing sector by Merck KGaA, operating as MilliporeSigma in North America. It outlines various aspects of bioprocess complexity, emphasizes the need for effective monitoring strategies, and specifies the requirements for data collection and analysis as per regulatory guidelines from FDA and EMA. Key monitoring parameters, statistical process control, and strategies for ensuring consistent product quality are highlighted as integral components of the process validation framework.
![How to identify a trend? (Example : Nelsons’ Rules)Table 6 [2]
Rule Description Chart Example Problem Indicated
Rule 1
One point is more
than 3 standard
deviations from the
mean.
One sample (two shown
in this case) is grossly
out of control.
Rule 2
Nine (or more)
points in a row are
on the same side of
the mean.
Some
prolonged bias exists.
Rule 3
Six (or more) points
in a row are
continually
increasing (or
decreasing).
A trend exists.
Rule 4
Fourteen (or more)
points in a row
alternate in
direction,
increasing then
decreasing.
This much oscillation is
beyond noise.
This is directional and
the position of the mean
and size of the standard
deviation do not affect
this rule.](https://image.slidesharecdn.com/dec5biopharmaceuticalprocessmonitoringcpvwebinarfnl-191204170508/85/Webinar-How-to-Develop-a-Regulatory-compliant-Continued-Process-Verification-CPV-and-Process-Monitoring-of-Bioprocesses-20-320.jpg)
Webinar: How to Develop a Regulatory-compliant Continued Process Verification (CPV) and Process Monitoring of Bioprocesses
- 1. The life science business of Merck KGaA, Darmstadt, Germany operates as MilliporeSigma in the U.S. and Canada. How to develop a regulatory-compliant Continued Process Verification (CPV) and Process Monitoring of bioprocesses Anshuman Bansal December 5, 2019
- 2. The life science business of Merck KGaA, Darmstadt, Germany operates as MilliporeSigma in the U.S. and Canada
- 3. Agenda 1 Why 2 3 Monitoring Strategy 4 Statistical Process Control (CPV Basics) Examples5 Other Visual Monitoring/Investigational Tools
- 4. WHY
- 5. Bioprocess data complexity › Multi unit operations involving splits and combinations › Uncertainty due to biological processes (inherent variability) › Dynamic relationships shifted in time › Changing correlation structures CC1-Lot1 P1-LotA P2-LotA F1-Lot1 F2-Lot1 Cell Culture Purification Filtration CC1-Lot2 CC1-Lot3 CC2-Lot1 CC2-Lot2 P1-LotB P1-LotC P2-LotB F1-Lot2 F2-Lot2 *Reference: A. Bansal, J. Hans, A. Rathore; Knowledge Management and Process Monitoring of Pharmaceutical Processes in the Quality by Design Paradigm; Measurement, Monitoring, Modelling and Control of Bioprocesses; Advances in Biochemical Engineering/Biotechnology Volume 132, 2013, pp 217-247
- 6. Butterfly Effect: Unknown variability at process stages accumulates into product inconsistency Seed Train Cell Culture Clarification Capture Purification ProbabilityofError Process Step/time Formulation Heterogeneity of biotech products Variability in process and analytical methods Variability in quality of raw materials High variability in product quality Complexities associated with biotech processes and products Limited understanding of biotech processes
- 7. Regulatory Expectations (FDA, EMA) Quality and regulatory expectations for process monitoring Pharmaceutical companies should plan and execute a system for the monitoring of process performance and product quality to ensure a state of control is maintained -ICH Q10 An ongoing program to collect and analyze product and process data that relate to product quality must be established. The data collected should include relevant process trends and quality of incoming materials or components, in-process material, and finished products. -FDA Process Validation Guidance, Jan 2011
- 8. Regulatory Expectations (FDA) FDA Expectations for CPV Must have a system for detecting unplanned departures from the process • Evaluate the performance of the process • Identify Problems • Determine if corrective action is necessary • Anticipate and Prevent problems to ensure control An ongoing program for collecting and analysing product and process data that relate to product quality • Procedures for data collection and trending • Data collected should verify the quality attributes • Intra-batch and inter-batch variation • Data should be collected to evaluate process stability and capability • Data should be statistically trended • It is recommended that a statistician or person with adequate statistical training develop the data collection plans and methods for analysis Source: FDA Guidance for Industry: Process Validation: General Principles and Practices (Jan 2011)
- 9. Examples of some of the 483s issued by FDA
- 10. Monitoring Strategy What to routinely monitor and what to archive for investigational analysis?
- 12. Bench-top Instrument Data Quality Control Results Batch Record Data Real-time Sensor Data % DO pH Temperature %CO2 N2 RPM Air FLow O2 Flow CO2 Flow Media Quantity Media Blend Ratio Harvest Age Harvest Volume Feed Age Temperature shift Age Feed Flow Feed Quantity Generation No. Hold Times Integrated Viable Cell Density Inoculation Density Titer RVLP Mycoplasma MMV Bioburden Endotoxin Cell Count %Viability pH(offline) Glucose Glutamate Glutamine Lactate Ammonia HCO3 Osmolality Galactose pCO2 pO2 Sodium Potassium Example: Mammalian Cell Culture
- 13. Upstream Process (Production Bioreactor) Cell growth Culture Environment Product Formation Contamination Control Cell Density Viabilities Harvest (Viab, Age) CGN Glucose Lactate ~Oxygen Flow ~DO ~pH ~Temp Titer (Daily, Harvest) CGN Glycans IVCC Peak (VCD, Viab, Age) Harvest (VCD) GN Feed (VCD, Viab, Age) TempShift (VCD, Viab, Age) Media (pH, Osmo, Endo) ~Air Flow ~Off Gas (OUR) ~CO2 Flow Metabolites trend Specific Productivity Passage No Events (Feed, Antifoam, Base etc) Media Lots (Culture, Feed) Total Base Total Antifoam Total Feed Total Flow (O2, CO2, Air) Specific metabolite consumption pH Probe (ID, cycles) DO Probe (ID, cycles) CO2 Probe (ID, cycles) ~Agitation Speed ~Reactor Volume ~Overlay (Air flow) ~Pressure Sterile Filters Media Hold ~SIP trends (temp, pr) CIP pre/post (pH, Conductivity) Deviations, Root Cause, Change Control, MFR, Experiment ID, Equipment ID, Process Time, Change Over The Elements InvestigationSecondary MonitoringPrimary Monitoring Example: Mammalian Cell Culture
- 14. Routinely Monitor % DO pH Temperature Harvest Age Harvest Volume Integrated Viable Cell Density Titer Bioburden Endotoxin Cell Count %Viability pH(offline) Glucose Lactate Osmolality Collect and Archive %CO2 N2 RPM Air FLow O2 Flow CO2 Flow Feed Age Temperature shift Age Feed Flow Feed Quantity Generation No. Hold Times Inoculation Density RVLP Mycoplasma Glutamate Glutamine Ammonia HCO3 Galactose pCO2 pO2 Sodium Potassium Media Quantity Media Blend Ratio Example: Mammalian Cell Culture
- 15. Poll question 1
- 17. Parameter Type Abbreviation Description Routine Monitoring Critical Process Parameter CPP A performance or input parameter that directly impacts product identity, purity, quality or safety. Must Key Process Parameter KPP A performance or input parameter that directly impacts CCPs or used to measure the consistency of the process step Must Monitored Parameter MP A performance or input parameter that may or may not impact KPPs and is used to measure the consistency of the process step or routinely trended for troubleshooting purposes Not All, Case By Case Basis What to SPC?
- 18. Limit Name Abbreviations Description Limits Source Applicable to Parameter type Specification Limits USL, LSL These limits are defined based on process characterization limits. Any excursion from these limits will cause OOS and batch rejection. Process Characterization, Process Development CPP Action Limits UAL, LAL These limits are process validation ranges. Any excursion from these limits will cause major process deviation or discrepancy. Process Validation CPP, KPP Alert Limits or Statistical Control Limits UCL, LCL These are monitoring ranges derived from historical runs for out of trend detection and measurement of process consistency. Statistical: Process History >15 commercial batches CPP, KPP, MP Target CL The target (or centerline) is derived again from historical runs as a measure to keep the process consistent and proactively alert if process is deviating from set target. Statistical: Process History >15 commercial batches All What SPC limits to apply?
- 19. How to estimate statistical control limits? Distribution Sample Graph Description and Examples Typical Limits applied Normal Most of the process parameters will follow this distribution of a normal/Gaussian bell shaped curve. Non-Normal (Beta or Gamma) Some parameters will not follow a normal distribution pattern and follow a skewed distribution. For example most of the data related to process impurities will be skewed towards the lower bound (approaching a value of 0). Some parameters like cell viabilities would be skewed towards the upper bound (approaching a value of 100%). CL Average UCL Average + 3 SD LCL Average - 3 SD CL Median UCL 99.865th Percentile LCL 0.135th Percentile
- 20. How to identify a trend? (Example : Nelsons’ Rules)Table 6 [2] Rule Description Chart Example Problem Indicated Rule 1 One point is more than 3 standard deviations from the mean. One sample (two shown in this case) is grossly out of control. Rule 2 Nine (or more) points in a row are on the same side of the mean. Some prolonged bias exists. Rule 3 Six (or more) points in a row are continually increasing (or decreasing). A trend exists. Rule 4 Fourteen (or more) points in a row alternate in direction, increasing then decreasing. This much oscillation is beyond noise. This is directional and the position of the mean and size of the standard deviation do not affect this rule.
- 21. Determining Process Capability and Process Performance A. Estimating Process Capability for Normally Distributed Data Process capability (Ppk, Cpk) for a normally distributed monitoring process parameter will be calculated using the following: Ppk = 𝒎𝒊𝒏{ 𝑼𝑺𝑳−𝑨𝒗𝒈 𝟑𝝈 , 𝑨𝒗𝒈−𝑳𝑺𝑳 𝟑𝝈 }, Cpk = 𝒎𝒊𝒏{ 𝑼𝑺𝑳−𝑨𝒗𝒈 𝟑𝝈 𝑴𝑹 , 𝑨𝒗𝒈−𝑳𝑺𝑳 𝟑𝝈 𝑴𝑹 } Where USL = Upper Specification Limit (for CPP) or Upper Action Limit (for KPP) LSL = Lower Specification Limit (for CPP) or Upper Action Limit (for KPP) Avg = Average or mean of the population under analysis 𝜎 = Standard deviation of the population under analysis 𝜎 𝑀𝑅 = Moving Range Standard Deviation B. Estimating Process Capability for Non-normally Distributed Data Since the average and standard deviations will not represent the non-normally distributed data correctly, process capability (Cpk) cannot be estimated for non-normal data. Instead Process performance (Ppk) will be evaluated based on all of data points in terms of percentile ranges. For a non-normally distributed monitoring process parameter Ppk will be calculated using the following: Ppk = 𝒎𝒊𝒏{ 𝑼𝑺𝑳−𝑿 𝟎.𝟓𝟎 𝑿 𝟎.𝟗𝟗𝟖𝟔𝟓− 𝑿 𝟎.𝟓𝟎 , 𝑿 𝟎.𝟓𝟎−𝑳𝑺𝑳 𝑿 𝟎.𝟓𝟎−𝑿 𝟎.𝟎𝟎𝟏𝟑𝟓 } Where USL = Upper Specification Limit (for CPP) or Upper Action Limit (for KPP) LSL = Lower Specification Limit (for CPP) or Upper Action Limit (for KPP) X0.50 = Median of the population under analysis X0.99865 = 99.865th Percentile of the population under analysis X0.00135 = 0.135th Percentile of the population under analysis
- 22. Significance of Process Capability Ideally : (USL-LSL) >> 6σ
- 23. Goal of CPV Process Evolution with time
- 24. ControlLimits Specificationor ActionLimits A Process Control Chart 2 3 3 2 Process Capability Trend rules violation Centerline
- 25. Setting up a SPC based monitoring program Identify CPP, KPP and MP from performance and operating parameters from process characterization/validatio n documents 1 Evaluate the root cause of each rule violation and is impact on product and process. Initiate appropriate CAPA if needed. 8 Publish quarterly process summary reports (can feed APRs). Will have recommendations for process improvements and limit updates 9 Identify parameters that need to be trended periodically 2 Get validation ranges and specification limits (if any) for these parameters respectively 3 Establish a technique for statistical control limits and frequency to update the control limits 4 Monitor parameters against set limits 7 Publish an in-process control and monitoring document for each commercial process 6 Establish trending rules (Nelson Rules) 5 Update control limits in the system upon every limit update via a change control 11Update Control Limit 10
- 26. CPV related procedures and documentation . CPVDocumentation 1 2 3
- 27. Statistical Process Control – SPC Phase SPC Phase Gather Data for future trending. Monitor only with action or spec limits. Set Alert limits (UCL, LCL and Target) based on stats of gathered history (and excluding any special cause outliers). Setting Alert Limits After setting Alert Limits (UCL, LCL and Target) from data gathering phase, routine process monitoring is done in this SPC phase and all future batches are compared against these set limits. Preliminary Process Monitoring – PPM Phase (Data gathering Phase) How it shows up in real life PPM Phase
- 28. Poll question 2
- 29. Other Process Monitoring and Troubleshooting Aids Investigation Analysis Toolset
- 30. Timelines Readymade process execution times Lot Genealogy On the fly generate full genealogy from vial (cell line) to vial (drug product) Profiles Overlay batch profiles Correlations Find correlations across Parameters SQC Control charting for batch trends and six sigma analysis Multivariate Understand interactions and discover new correlations in ever dynamic relationships Compare groups Compare groups of data within batch or across batches to examine differences Records Access to raw execution records data for context and reference When What How Why Is there precedence? What’s the benchmark? How far from target? TheQuestions TheAnswers Investigation Toolset
- 31. SS0101 Spinner Stage SF0101 Seed Fermentation PF0101 Fermentation DS0101-1 HIC-Chromatography SF0102 Seed Fermentation DS0101-2 HIC-Chromatography P0101 BDS SS0102 Spinner Stage RM0301 RM0302 RM0303 RM0102 RM0101 RM0103 RM0201 RM0102 RM0101 RM0101 RM0101 RM0401 RM0402 RM0501 RM0502 RM0601 RM0602 RM0603 RM0701 RM0702 RM0703 Lot Genealogy
- 34. Correlations: Upstream vs Downstream
- 35. Grouping batches for comparability
- 37. Example stories How the toolset is applied for troubleshooting
- 38. YieldVariability Note: All Charts except indicated were generated in ProcessPad
- 39. Roundinganddistribution Analyst Used JMP for this analysis * Analyst Used JMP for this analysis Note: All Charts except indicated were generated in ProcessPad
- 40. InstrumentMaintenance Note: All Charts except indicated were generated in ProcessPad
- 41. Poll question 3
- 42. T Thank you!
- 43. The vibrant M, and Millipore are trademarks of Merck KGaA, Darmstadt, Germany or its affiliates. All other trademarks are the property of their respective owners. Detailed information on trademarks is available via publicly accessible resources. © 2019 Merck KGaA, Darmstadt, Germany and/or its affiliates. All Rights Reserved.