Process Monitoring Applying QbD Principles in a Biopharmaceutical Environment

Transcription

1 WCBP 205- CASS Conference Washington DC, July-20 to 2, 205 Process Monitoring Applying QbD Principles in a Biopharmaceutical Environment Michael Kraus PhD MBB Process Science & Technical Operations Baxalta US Inc. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page

2 Theme Why we should reconsider our standard approach towards process monitoring in a biopharmaceutical environment, and what it all has to do with QbD. Note: We call this internally Enhanced Process Control (EPC) Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 2

3 Presenter Michael Kraus, PhD MBB Process Science & Technical Operations Baxalta >25 years in Bioscience: Biochemist and former lecturer for Hemostaseology Product and Analytical Development Manufacturing Operations Quality Control & Product Release Process Improvement Techniques (Lean, Six Sigma, QbD) Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 3

4 Topics ) Why Shewhart Control Charts might not always be the right approach. 2) Statistics vs Process Understanding 3) Statistical vs Risk based Limits 4) Integration into CPV 5) Risk based management of monitoring Summary & Discussion Optional: Examples for Illustration Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 4

5 Why Shewart Control Charts might not always be the right approach. Breaking a paradigm in process monitoring

6 Why do we monitor? Variation is inherent and unavoidable to any system, leading to unexpected and/or undesired process outcomes. The risk of undesired process outcomes should already be minimized during process design PREVENTIVE process control However, there will always be a residual risk of undesired variation going through the process we need to know to counteract. REACTIVE process control Monitoring is to minimize residual risk for process failure by reacting to information from the process. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 6

7 Standard vs Biopharm Approach. Shewhart Control Charts Enhanced Process Control 2. Statistics Process Understanding 3. Techniques Operating Mechanisms 4. Statistical Limits Risk Based Limits 5. Signal Event 6. Data Monitoring Process Monitoring Monitoring in Biopharmaceutics requires some paradigm shifts to be efficient and effective. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 7

8 Shewhart Control Charts vs BioPharmaceutics What is a Control Chart? A. Something with red lines B. Something with red dots everybody becomes nervous about C. A model D. Least desirabe level of control (flagging) E. All of the above Individual Value Moving Range I-MR Chart of SA 3 37 Observation 3 37 Observation UC L=2069,5 _ X=948,4 LC L=827,3 UC L=48,8 MR=45,5 LC L=0 Note: Data have been treated mathematically to avoid conclusions on existing processes Within red lines everything is ok Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 8

9 Shewart Control Charts vs BioPharmaceutics What is a Control Chart? Foremost it is A mathematical model describing a mechanistic process An analytical test leading to decisions about actions stats data software process mathematic signal r e a c t i o n You have to understand the assumptions of the model to draw correct conclusions! Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 9

10 Shewart Control Charts vs BioPharmaceutics What is a Control Chart? Source: Wikipedia All models are wrong, but some might be useful Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 0

11 Shewart Control Charts vs BioPharmaceutics What is a Control Chart? The model assumption for Shewhart Control charts are: Randomness Normality Independency of observations Bernoulli experiments Coherent population representativeness Timely order Normal Distribution ±σ 68.3% ± 2σ 95.4% ± 3σ 99.7% z-value Source: Wikipedia Shewhart Control Charts highlight any process behaviour which is not following the Gaussian, i.e. Normal distribution. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page

12 Shewart Control Charts vs BioPharmaceutics Does Normality Exist? Yes, if Gage or Measurement variation exceeds process variation σ > σ 2 Measurement 2 total σ < σ 2 Measurement 2 total A Total Variation B Total Variation LSL USL LSL USL Measurement System Variation Measurement System Variation Where in the process will you more likely obtain failure in normal distribution testing? Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 2

13 Shewart Control Charts vs BioPharmaceutics Does Normality Exist? The extension of process monitoring upstream from final container testing requires enhanced approaches! A σ > σ 2 Measurement 2 total B σ < σ 2 Measurement 2 total QAs Fill/finish FC (QC testing) PPs Upstream IAs Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 3

14 Shewart Control Charts vs BioPharmaceutics Violations of Normality in BioPharm Controlled processes Natural boundaries Proportional variation in analytical tests External influences Rounded data Non-Linear processes Interactions Dynamic processes Turbulent processes Seasonal influences Limit of detection normality based models for processes in Biopharm are of limited value Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 4

15 Shewart Control Charts vs BioPharmaceutics Violations of Normality in BioPharm Or would you Controlled processes want to state that Natural boundaries you manufacture Proportional variation in analytical tests your product by External influences random chance? Rounded data Non-Linear processes Interactions Dynamic processes Turbulent processes Seasonal influences Limit of detection Control Random Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 5

16 Shewart Control Charts vs BioPharmaceutics st Paradigm Change Normal = ok! Distinguish between typical vs atypical process behaviour Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 6

17 It s the question! 2 Statistics vs Process Understanding

18 2 Statistics vs Process Understanding What is a Control Chart? Foremost it is A mathematical model describing a mechanistic process alpha risk false Alarm An analytical test leading to decisions about actions beta risk being Blind As for any test there is risk of raising false alarm or missing important signals. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 8

19 2 Statistics vs Process Understanding What is a Control Chart? Foremost it is A mathematical model describing a mechanistic process An analytical test leading to decisions about actions All models are wrong, but some might be useful alpha risk false Alarm beta risk being Blind The choice of the model determines the risk. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 9

20 2 Statistics vs Process Understanding How do we come to the best model? Apply QbD principles! Process understanding Risk assessment Risk Assessm ent QbD Meaningful data Data We hardly use process understanding when setting up control charting. Process Knowledge Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 20

21 2 Statistics vs Process Understanding Relevant Signals = low alpha & low beta risk We introduced this term in our EPC initiative as to fight the Measles syndrome The objective of monitoring is to obtain relevant signal ( events ) not statistical ones. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 2

22 2 Statistics vs Process Understanding Relevant Signals Means that signals ( red dots ) do have a meaning. The wrong mathematical model will Increase the number of useless investigations Hide true process changes which are deviating from the typical process behaviour The objective of monitoring is to obtain relevant signal ( events ) not statistical ones. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 22

23 2 Statistics vs Process Understanding 2nd Paradigm Change Model the process! NOT the data! Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 23

24 Link decisions to patient risk! 3 Statistical vs Risk based Limits

25 3 Statistical vs Risk based Limits Definitions Risk limits are taking into consideration the risk for the patient although still in spec. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 25

26 3 Statistical vs Risk based Limits Risk Limit Calculation Risk Limits are expressed as the distance from the spec limit (patient view) in multiples of standard deviations k A - expressing the risk that the new data point is indicative of a shift of the process and thus, within random variation other data points might exceed the specification limit by chance. UAL = USL = k A = Power (-β) UAL estimate of mean USL Acceptable failure rate - α upper action limit upper specification limit distance from spec limit in multiples of variation Input parameters Reaction Rate Power (-beta) beta error = false negative Power is - the probability to miss a signal (beta error). Typical value used in statistical handbooks and standard settings in software is 90%. Acceptance Failure Rate (alpha) alpha error = false positive The risk to react to a signal although it is due to pure chance. Typical values are 0.% for CQA, % for intermediate attributes A standard text book approach. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 26

27 3 Statistical vs Risk based Limits 3nd Paradigm Change Not every signal is RELEVANT for the patient! Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 27

28 An Interim Summary Mathematics PFMEA Statistical signal Risk based Limits Mathematical Model? Knowledge Process Understanding Once we come to relevant signals, WHAT are we doing with them? Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 28

29 4 Integration into CPV Capture the learnings!

30 4 Integration into CPV Stage I Design and Development Stage II Validation Stage III Continued Process Verification Research and Development Full Scale Production & Licensure Trending and Monitoring R&D is used to build process knowledge Scale up activities equate R&D work to full scale production CPV demonstrates consistency and sustained quality Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 30

31 4 Integration into CPV Core Procedural Documents Continuous Process Verification Procedure links control strategy to daily execution, feed into continuous improvement plan and feed back to QbD documents (RACT, PFMEA, ID cards etc) Global Division Procedure minimum requirements for the monitoring and control policy, roles and responsibilities of the tracking and trending program, notification process, escalation process, data review process Trending and Monitoring Guideline provides site guidance regarding how to setup a trending and monitoring program, including setting limits, configuring charts, creating process performance estimates, selecting and applying statistical trending rules. Site Process Monitoring Procedures capture the site level plan and local set up of monitoring program Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 3

32 4 Integration into CPV Procedure Trending and Monitoring Control Strategy Continuous improvement and feed back. Site Monitoring Plan Guideline Routine execution of process monitoring Selection and harmonization of parameters Technical implementation into Software With a worldwide procedure as the framework, the guidance is providing technical details and links to CPV. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 32

33 4 Integration into CPV What it is NOT! A Change Control system CAPA, OOS, CC A replacement of current OOS system A new CAPA procedure The new monitoring procedures are embedded within the current frame work of existing Quality procedures. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 33

34 4 Integration into CPV ID Card ID cards are living documents containing the platform knowledge and process experience to provide adequate guidance to reactions once a signal is perceived. It captures the questions to the parameter, the knowledge and justification how it is set up. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 34

35 4 Integration into CPV ID Card - Details Structure of an ID card. Description of the CQA (BDS/FF) and related CPPs:. Data source 2. Data frequency 3. Definition of recurrence 4. Reference period used for data analysis and reason for its choice 5. Data type, distribution, & comment on distribution 6. Capability 7. Tool used to trend parameter 8. Conditions triggering the initiation of a preliminary investigation 9. Control limits 0. Specifications (for CPPs only) Structure of an ID card 2. Description of the measurement system:. SOP 2. Laboratory owner 3. Equipment used 4. Analysis conditions 5. Purpose 6. Description of the test 7. Measurement structure 3. Description of the measurement critical parameters to be checked during investigation 4. Description of manufacturing critical parameters to be checked during investigation 5. Desciption of typical elements and failure modes used for fast track investigations using platform knowledge and continuous process experience. ID Cards capitalize our process knowledge through a regular review process as part of CPV. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 35

36 4 Integration into CPV ID Card - Details Snapshot from an interim IT solution. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 36

37 An escalation system on relevance! 5 Risk based management of monitoring

38 5 Risk based management of monitoring Escalation System A multi-step process for evaluating signals and, if appropriate escalating them into the appropriate quality systems. Level 0 investigations should be pushed as close as possible to the point of occurence involving shop floor personnel and QC Cross-functional teams are used to integrate expertise. The process is laid out in the Site Monitoring Plan. Decisions are documented. Only true events shall be escalated into formal investigations. Investigations for learning purposes are always possible. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 38

39 5 Risk based management of monitoring QIT Level 0 Trending and Monitoring Team STEP Data check STEP 2 Verify trend Initial trend review will identify potential trends First, verify the data integrity transcription errors for manually entered data measurement system changes (ie standards, instrument) If a trend is real, the next step will be to escalate QIT Level QIT Level Subject Matter Experts Work with subject matter experts to determine explainable causes of variation Document explainable causes Present unexplained trends to senior management to determine need for further escalation or additional action to be taken Is this trend real? Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 39

40 5 Risk based management of monitoring STEP 3 Risk Prioritization QIT Level 2 Senior Management Cross functional review by senior management to review confirmed potential trends identified by QIT Level team Senior management will determine what if any response This is based on risk and relevance to the process Three outcomes No action required Continued off-line assessment, additional information needed to close trend investigation of escalate Escalate to formal investigation in data base Document all decisions Is the trend actionable? Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 40

41 5 Risk based management of monitoring QIT Level Subject Matter Experts Investigational Teams STEP 4 Data check Preventative Action event category in database Capture results in monitoring report Investigations will generate process knowledge Any substantial data review will create process understanding regardless of outcome QIT Level 0 Trending and Monitoring Team / Subject Matter Experts STEP 5 Continuous Improvement Capture all process knowledge gained monitoring reports Activities, both why and results Decisions and rationale Investigational results Update monitoring plan if needed Update Process Knowledge Databases (e.g. PFMEA, ID Card) Investigate and learn Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 4

42 Summing Up

43 Proposed Monitoring Using QbD Principles Knowledge Mathematics PFMEA Statistical signal Risk based Limits Mathematical Model Process Understanding Escalation System considering patient risk (relevance) process understanding (plausibility) Capture knowledge (RACT, PFMEA, ID Cards, CIP, ) Process Monitoring using Process Knowledge, Risk Evaluation and Statistics for Continuous Improvement. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 43

44 Two Key Messages. Process Monitoring is a mathematical description ( model ) of our processes to distinguish between typical and atypical behaviour. Models need to describe the process and not the data! 2. In the spirit of QbD, process understanding and patient risk need to be integrated when choosing limits for reaction, run rules, and escalation mechanisms. Judge on signals by relevance and not by statistics! Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 44

45 DISCUSSON / QUESTIONS?. Shewhart Control Charts Enhanced Process Control 2. Statistics Process Understanding 3. Techniques Operating Mechanisms 4. Statistical Risk Based Limits 5. Signal Event 6. Data Monitoring Process Monitoring Standard vs Biopharm Approach - should we go for an augmentation of current practises? Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 45

46 Non-linear process behavior chromatography yield Long-term vs short-term variation monitoring of raw material driven variation Enhanced Process Control - Examples

47 An Example: Chromatography Yield I-MR Chart of Activity Individual Value UC L=85,75 _ X=67,05 50 LC L=48, Observation illustration Moving Range UC L=22,97 MR=7,03 0 LC L= Observation A standard control chart by a mouse-click Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 47

48 An Example: Chromatography Yield The normality test fails, i.e. you use the wrong mathematical model! Better to understand the process Probability Plot of Activity Normal Percent 99, Mean 67,05 StDev 9,490 N 24 AD,4 P-Value 0,005 0, Activity Normal may not mean typical Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 48

49 An Example: Chromatography Yield Use Process Understanding There is a tailing during the separation of the active ingredient, i.e. a non-linear elution profile. UV Elution profile (scheme) Collection Area Variation in the process (e.g. protein concentration, flow rates, conductivity, etc) is transmitted into a non-linear variation of the total amount collected. σ Activity distribution (scheme) time Process data should be transformed! Here: /x is proposed Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 49

50 An Example: Chromatography Yield Use Process Understanding Percent 99, , 0,000 0,025 Probability Plot of /Activity Normal 0,050 0,075 /Activity 0,0200 0,0225 0,0250 Mean 0,0526 StDev 0,00228 N 25 AD 0,306 P-Value 0,562 Individual Value Moving Range 0,020 0,05 0,00 0,008 0,006 0,004 0, I-MR Chart of /Activity Trend? Observation UC L=0,0968 _ X=0,0526 LC L=0,0083 UC L=0, MR=0, ,000 LC L= Observation Data transformation using process knowledge provides deeper insight. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 50

51 An Example: Chromatography Yield What is the question we answer? Is there an atypical amount of activity collected? Violations may be caused by Different gel behavior/elution profile Too short/long collection time frame Too low/high load UV Elution profile (scheme) σ Collection Area Activity distribution (scheme) time If we understand the question, we may draw conclusions for faster issue resolution! Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 5

52 An Example: Material Driven Shifts A standard control chart separated by changes in raw material number Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 52

53 An Example: Material Driven Shifts Processes in BioScience are highly influenced by external variables (aka environmental factors ). These are mainly materials such as Media components (yeast, detergents) Reaction components (heparin, chromatography gels, enzymes, absorbents) Contact surfaces (filters, skids) Analytical test components (reagents, standards) But also our own biological materials add natural variation Starting materials (eggs, cell inoculum, plasma source, viral strains) Intermediates External & natural sources of variation are a typical component of total process variation in BioScience! Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 53

54 An Example: Material Driven Shifts It s the question!. Is my manufacturing process stable? 2. Is my material to material variation stable? 3. Is there a risk for the customer? To answer #2 requires more than a simple mouse-click. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 54

55 An Example: Material Driven Shifts Use Process Knowledge! I/O diagram: the noise or environmental factors are on the top. Noise (uncontrollable Factors) Input (Controllable Factors) X varied X2 varied X3 varied X4 kept const X5 kept const X6 kept const START: START: PROCESS END: Output(s) (Responses) Y desired Y2 desired Y3 desired Y4 undesired Y5 undesired Y6 undesired Ideally this information should be found in the PFMEA, which is nothing else than tabulated format of an Input / Output diagram. Robustness studies are a special setup of experiments (DOE) to identify the impact of this upfront. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 55

56 Guideline Decision tree from our internal guideline and training material (~600 pages) Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 56

57 An Example: Material Driven Shifts. Is my manufacturing process stable? within process variation is fixed and the mean may float by material using a segmented ImR chart by campaign The true picture of process consistency irrespective from raw material driven shifts. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 57

58 An Example: Material Driven Shifts 2. Is my material to material variation stable? X-bar/R chart using campaign for sub-group size displays the long-term (raw material lot to lot) variation. This provides information on the consistency of your Raw Material Batch raw material affecting the process. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 58

59 An Example: Material Driven Shifts 3. Is there a risk for the patient? Based on the overall process variation from step, raw material lot to lot differences are excluded. Any shift in process location may now be assessed for criticality as to lead to a potential violation of the specification limit (potential OOS) A simple calculation also provides information on the allowed long-term tolerance for incoming materials. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 59

60 An Example: Material Driven Shifts 3. Is there a risk for the patient? ) One campaign was at risk to consistently violate the risk limit (potential OOS) 2) Few out of 3s range signals per campaign are not critical 3) Violation of risk limit should be investigated! 4) Early campaigns showed occasionally unusual variation. Much clearer and deeper insight in the process. Baxalta US Inc. Process Monitoring Applying QbD Principles Michael Kraus Page 60