Scale down models - ema.europa.eu · Benefits of using SDM • SDM can be extremely useful even if...
Transcript of Scale down models - ema.europa.eu · Benefits of using SDM • SDM can be extremely useful even if...
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EMA Expert Workshop on Validation of Manufacturing for Biological Medicinal Products
Tuesday 9th April 2013
Process Validation-Enhanced Approach
Scale down models Frank Zettl
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Key elements enhanced approach
• Extensive and intensive process knowledge
• Better prediction of scale effects • Leverage process knowledge into control
strategy via continuous process verification
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Scale Down Model (SDM) Lifecycle
Design
• Within development • Continuous improvement
Qualify
• Compare outputs • Assess suitability
Maintain
• Facility changes • New CQAs
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SDM Design
• SDM useful during design phase - Process development and characterization - Process validation (e.g. Virus removal)
• Design Options - Whole unit operation models - Cover specific aspects of a unit operation - Worst case model
• Relevant outputs are defined (e.g. CQA) • Based scientific and engineering principles • Inputs and environment are considered
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Typical Model Systems Purification
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Robotic system
Lab system Production Column volume ~ 0.2 – 0.6 ml
Column volume ~ 15 – 25 ml
Column volume ~ 150 – 400 L
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Typical Model Systems – Cell culture
Process time
Viab
le c
ell d
ensi
ty V
CD
2 L Lab system
10 – 15 mL scale
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Benefits of using SDM
• SDM can be extremely useful even if they do not exactly match large scale performance, provided the differences are understood
• A large number of process parameters can be explored in large ranges
• Several process parameter can be varied independently in a systematic manner
• Interactions and quadratic effects can be identified
• “Categorical variables” (like raw material lots) can be investigated
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Qualification of SDM
• Statistical approach is gold standard • But effort may vary based on
- Availability of manufacturing scale batches - Applied control strategy - Predictions that are made from SDM
• A generic qualification should be possible - Depending on understanding of scale effects - Depending on control strategy
• Concurrent (re-)validation should be possible
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Equivalence Testing (TOST)
• Contains information about - Observed offset between
scales - Observed variability
• Equivalence margin is defined based on scientific considerations
• SDM containing non-equivalent results may still be suitable
Difference in means between scales Confidence interval of difference
Equivalence margin
Line of zero difference 9
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Suitability of Scale Down Models
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• Even if not statistical equivalent • Depend on intended use • Offsets may be applied
• Scientifically explained • Verified with independent data
• Observed variability can be de-risked • Worst case studies • Control strategy (including in-process testing,
specification testing, stability etc.)
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Process Models
• Mathematical description of input/output relationship • Result from univariate and multivariate
experimentation • Can cover interactions and quadratic effects • Are assessed with regard to their quality
- Coverage of data - Prediction quality
• Estimate value of process outputs and the confidence of prediction
• Process models cannot be verified over the entire range at scale
• But can be assessed within monitoring program 11
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The Process Modeling Approach
Input Parameter
Output attribut
Control space
SDM Qual
Input Parameter
Input Parameter
Output attribut
Input Parameter
Process model extrapolation
Scale down model Manufacturing scale E
quivalence m
argin
Will be adressed by continued process verification
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Process Models - Limitations
• In many cases not all parameters can be investigated in a single study
• Categorical variables are difficult (if not impossible) to model
• Continued Process Verification and control strategy will overcome potential issues related to this
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At scale verification - Limitations
• Statistical verification is not achievable
• Example IEC- HPLC Peak • SD (@ scale) = 5% • Delta model prediction = 2% => 199/2 batches needed
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Error
Std Dev
0.8
Power
0.050
Alpha
0
50
10
15
20
Sam
ple
Siz
e
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Difference
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Deborah Baly Bayer Bob Kuhn Amgen Norbert Hentschel Boehringer Ingelheim Brendan Hughes BMS Enda Moran Pfizer Luis Maranga BMS Frank Zettl Roche Karl-Heinz Schneider Bayer Kris Barnthouse Janssen (J&J) Gilles Borrelly Sanofi Camilla Kornbeck Novo Nordisk Markus Goese Roche
The Enhanced Approach Team
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• THANK YOU
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