Learn how Quality By Design (QBD) principles can be applied to understand the critical processing and feed parameters affecting virus retention, allowing the development of a streamlined validation approach and robust process control strategy for virus clearance via filtration. In this webinar, you will learn: •How to simplify validation study design •Identify critical process and feed parameters affecting virus retention •How to compile a robust regulatory filing package Abstract: ICH Q8 defines Quality by Design (QbD) as “…a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on quality risk management.” Within the context of virus clearance for bioprocesses, QbD principles can be applied to understand the critical processing and feed parameters affecting virus retention, guiding the development of a streamlined validation approach and robust process control strategy for virus clearance unit operations. We will explore how QbD principles can be applied to downstream virus filtration of mAbs and recombinants, the application of these principles within the framework of the Viresolve® Pro Device (parvovirus retentive filter), the benefits through simplifying the validation strategy and increasing the robustness of your regulatory filing package.

1. The life science business of Merck KGaA,
Darmstadt, Germany operates as
MilliporeSigma in the U.S. and Canada.
Quality by Design (QBD)
for Downstream
Virus Filtration
Tathagata Ray
Nov 6, 2020

2. The life science business
of Merck KGaA, Darmstadt,
Germany operates as
MilliporeSigma in the U.S.
and Canada

3. Agenda
1
2
3
Quality by Design
Virus Filtration
QbD in Virus Filtration

4. Quality by
Design

5. Introduction
LOW
HIGH
Concept Design Testing Production
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

6. Quality Has Key Economic Attributes
Introduction
Costscausedbypoorquality
Characteristics critical to the customers' experience
Lower Limit Upper LimitTarget
Traditional
perspective
Realistic
perspective
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

7. Quality in Biopharmaceuticals
Introduction
1
100
10000
1000000
2σ 4σ 6σ
DPMO
Restaurant bills
Airline baggage arrival
Aviation industry
Nuclear industry
Quality to
patient
How do we follow “absolute” quality? By Testing!
Or, it has to be built in by design...
DPMO – Defects per million opportunities
Healthcare industry
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

8. Quality Can Be A Key Regulatory Concern
Introduction
Does the production process result in
product/residues that interfere with final
product strength or efficacy?
Does the production process result in
product/residues that interfere with final
product purity?
Does the production process result in
product/residues that are toxic to the
patient?
Suitability of either a drug substance or product for its intended use (ICH Q6A)
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

9. QbD: Overall Approach
Introduction
CQ
A 4
CQA
2CQA
1
CQ
A 3
PRODUCT DESIGN SPACECLINICAL DESIGN SPACE PROCESS DESIGN SPACE
TARGET PRODUCT PROFILE
CONCEPT > DESIGN > PRE-CLINICAL > CLINICAL > MASS PRODUCTION
Animal studies
PROCESS
PARAMETER
S
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

10. QbD: From Process to Step
Introduction
Quality target product
profile (Safety !)
Identify CQAs
(virus retention)
Design Process
Design formulation
Material attributes and Process parameters
CQAint
1
CQAint
2
Identify and control sources of variability
Monitor and update
Process Step
Characterization
based on risk
assessment /
control
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

11. Risk Management
 Minimizes risk by identifying high risk/impact
parameters and implementing a
control/monitoring strategy
 Results in a more robust/controlled/consistent
process
 Reduces chance of out of spec. batches,
deviations, etc.
Simplifies Process Development
 Holistic development of a design space to
accommodate multiple diverse molecules within
a pipeline
 Less repetitive “reinventing the wheel” studies
with each new molecule using a bracketing
approach
Simplifies Validation
 Differentiating critical vs. non-critical processing
parameters enables more compact validation
study designs
 Facilitates a modular validation approach for a
multi-molecule pipeline
 Faster validation time and lower associated costs
Stronger Regulatory Submission
 Demonstrates a proactive understanding of your
process to regulatory bodies
 Rationale for design space makes regulatory
submission package more defendable
Why Apply QbD?
Introduction
1
2
3
4
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

12. Poll Question

13. Virus
filtration in
DSP

14. A Key Component of Viral Safety Assurance
Virus Filtration
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

15. Virus Removal via Size Exclusion
Virus Filtration
Membrane Pore Size Distribution
 All membrane pore paths < virus size: expect complete retention
 Reality: membrane morphology is complex and has an inherit size distribution
 Other parameters may impact retention in extreme cases
− E.g. very high flux decay => preferential plugging of smaller pores leaving larger
pores more accessible to virus passage
Vendor Controls: Viresolve® Pro Device
 Recurring (every lot)
− Membrane pore size distribution measurement and control via liquid – liquid
porometry
− Membrane and finished goods device binary gas and air/water diffusion testing
− Selective layering process to increase performance consistency
 Control factors can be incorporated into the FMEA
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

16. How Could Plugging/ Flux Decay be a CPP?
Virus Filtration
Breakthrough at High Flux Decay / Plugging
 Membrane has an inherent pore size distribution
 Pores on the upper end of the distribution could be non-retentive
 Preferential plugging of smaller pores first
− Redirects more flow through the larger pores
− At higher plugged states, virus breakthrough is possible
 Different filters exhibit different behaviors!
− Membrane pore size distribution ultimately dictates retention performance
− Process endpoints of V90 or more are possible without significant loss of retention
(Viresolve® Pro Device)
Breakthrough in
Large
(Non-Retentive)
Pores
Functional
(Retentive)
Pores
1 nm 100 nm10 nm
MVM
(20–25 nm)
fX-174
(25–30 nm)
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

17. Risk Assessment: Process Parameter Inputs
Introduction
Feed Stream Characteristics
 pH, conductivity, titer, aggregate loading, buffer matrix, hold time, temperature, etc.
 Consider for both inter and intra lot
 Consider molecule to molecule across entire pipeline for a template approach
Virus Filter Operating Parameters
 Pressure, volumetric/mass loading, process interruptions, flux decay endpoint, etc.
Virus Filter Characteristics
 Filter brand/model, scale, integrity test result, lot to lot variability, etc.
Goal
 Understand which are the critical processing parameters which might affect virus retention
 Document the sensitivity/insensitivity of retention to each parameter within your expected
operational window
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

18. Risk Assessment: FMEA
Introduction
Too Many Variables!
 Limit parameters to those rationally significant (i.e. operator, feed turbidity not likely critical)
FMEA Approach Example (not prescriptive)
 Rank each parameter based on:
− Severity (impact on LRV): 1 – 10 (low – high)
− Probability of occurrence: 1 – 10 (low – high)
− Detectability via controls: 1 – 10 (detectable – undetectable)
− Risk priority number (RPN): Severity x Probability x Detectability => prioritize and work top down
Estimating Severity
 How would you know if flux decay, process interruptions, etc. would impact retention?
 Leverage internal data sets for previously tested molecules
 Leverage vendor supplied / industry publication data sets
 The goal is to minimize the exploratory testing required
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

19. QbD Approach for Virus Filtration
Introduction
1
2
3
4
Risk Assessment
 What are the theoretical processing parameters
that could impact viral retention performance?
 Not all parameters are critical or equal…
 Use an FMEA approach
 How can we determine the relative risk
(severity/probability/detectability)?
Characterization Studies
 Which processing parameters actually affect retention
and to what extent?
 Every commercially available filter behaves differently!
 How can we determine the effect?
 Univariate or multivariate analysis?
 In house and/or vendor supplied data
 Update FMEA based on findings
Design Space Establishment
 Understanding of what to expect for retention at
the boundaries of your multidimensional design
space
 Establishment of allowable ranges of processing
parameters to ensure CQA is met
 Can be established for single or multiple molecules
Control Strategy
 Develop control/monitoring strategy for CPPs to
ensure operation within the established design
space validated during spiking studies
 Focus top down on high risk items from FMEA
Virus Filtration: CQA is achievement of a predetermined LRV of virus from drug product
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

20. Poll Question

21. Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

22. QbD Applied to CQAs and Process Performance
Case Study
CPP identification
(parameter ranges
omitted here)
Risk estimation
based on knowledge
gaps
Parameter
understood => no
study needed
Knowledge gap =>
medium risk
Product quality Process performance
Process Parameter
Aggregate
content
(% by HPSEC)
Viral
clearance
Yield
Average
flow flux
Volume
throughput
Protein concentration
(mg/ml)
Medium Medium No study Medium Medium
Conductivity (mS/cm) No study Medium No study No study No study
Temperature (oC) No study Medium No study No study No study
pH Medium Medium No study Medium Medium
Feed hold time No study No study No study No study No study
Pressure during filtration No study Medium No study No study No study
Process time No study No study No study No study No study
% flow decay No study Medium No study No study No study
Volumetric & mass
throughput
Medium Medium Medium Medium Medium
Recovery flush Medium Medium Medium Medium Medium
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

23. QbD Applied to CQAs and Process Performance
Case Study
Vendor supplied
data sets
addressed most
knowledge gaps
Constituted a
significant
reduction
of study effort
needed
to implement QbD
Product quality Process performance
Process Parameter
Aggregate
content
(% by HPSEC)
Viral
clearance
Yield
Average
flow
flux
Volume
throughput
Protein concentration
(mg/ml)
Medium Medium No study Medium Medium
Conductivity (mS/cm) No study No study No study No study No study
Temperature (oC) No study Medium No study No study No study
pH Medium Medium No study Medium Medium
Feed hold time No study No study No study No study No study
Pressure during filtration No study Medium No study No study No study
Process time No study No study No study No study No study
% flow decay No study Medium No study No study No study
Volumetric & mass
throughput
Medium Medium Medium Medium Medium
Recovery flush Medium Medium Medium Medium Medium
Vendor
supplied
data
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

24. Hypothetical Example
Effect of Feed/ Process/ Filter Parameter on Retention
Category Process Parameter
Example Molecule Mfg.
Range
Example Template Mfg.
Range
Variation Causes
FeedCharacteristics
Protein concentration 5-10 g/L 0.5-25 g/L Titer, flushes, CEX cuts
Conductivity 8-10 mS/cm 0.5-25 mS/cm
Buffer variability, dilution, batch to batch, in-line pH
adjustments
Temperature 22-24°C 10-30°C Environmental controls, cleaning water temperature
pH 4.9-5.1 5.4-8.0 Feed variability, in-line pH adjustments
Hold
time/temperature
1-4 hr @ 23°C 0.5-24 hr @ 23°C Scheduling, buffer temp.
Aggregates < 1% <2% Hold time
Prefilter Shield None/Shield/Shield H/VPF Batch variability
Protein pI 8.5 6-9 Amino acid content
Protein molecular
weight
150 kD 50-150 kD Amino acid content
Buffer species Acetate
Acetate, phosphate, MES,
citrate, histidine
Protein specific
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

25. Hypothetical Example
Effect of Feed/ Process/ Filter Parameter on Retention
Category Process Parameter Example Molecule Mfg. Range Example Template Mfg. Range Variation CausesVirusFilterOperatingParameters
Constant flow or pressure operation Constant pressure Constant pressure Plant preference
Pressure 25-35 psig 20-50 psig Gauge error
Recovery flush 8-12 L/m2 0-100L/m2 Batch variability
% flow decay (plugging) 0-30% 0-50% Hold time
L/m2 Volumetric Throughput 700-900L/m2 400-2000L/m2 Batch variability
Kg/m2 Mass throughput 3.5-6.5 kg/m2 1-10 kg/m2 Batch variability
Interruption hold durations <10 min 0-30 min Scheduling
Process time 0.5-2 hrs 0.5-6 hrs Scheduling
Pre-use caustic flush 0 L/m2 0-50 L/m2 Plant decision
VirusFilter
Char.
Shelf time 0-1 yr 0-1 yr Scheduling & demand
Device lots Magnus 2.2 lot Magnus 2.2 lot Batch variability
Integrity test value 0.5-0.784 sccm/m2-psi 0.5-0.784 sccm/m2-psi
Batch variability,
temperature, wetting
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

26. Feed: Protein Concentration
Effect of Parameter on Retention with Viresolve® Pro Device
Conclusion
No significant impact of protein
concentration on LRV
(2 – 25 g/L)
ϕX-174 LRV (grab samples) vs. mAb1 concentration
Impact of feed concentration and flow decay on MVM pool LRV
MVM retention in triplicate devices for two mAb
concentrations at processing endpoints of V75 and
V90
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

27. Feed: Conductivity and pH
Effect of Parameter on Retention with Viresolve® Pro Device
Conclusion
No significant impact of pH (5-7)and
Conductivity (8-25 mS/cm) on LRV
MVM LRV including pH, Conductivity and % Flow Decay.
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

28. Feed: Temperature
Effect of Parameter on Retention with Viresolve® Pro Device
conclusion
No significant impact of Temperature
(5-21 degree C) on LRV
ϕX-174 LRV vs. filtration temperature at different operating pressures
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

29. Operation: Operating Pressure
Effect of Parameter on Retention with Viresolve® Pro Device
conclusion
No significant impact of Pressure
(10 – 50 psi) on LRV
MVM @ V75
ϕX-174 @ V50
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

30. Operation: Processing Endpoint: % Flow Decay, Throughput
(L/m2) or Mass Loading (kg/m2)
Effect of Parameter on Retention with Viresolve® Pro Device
conclusion
No significant impact
of mass loading (2-25
kg/m2), Throughput
(10 – 2200 L/m2) and
% Flow decay (25% -
90%) on LRV
MVM @ V75
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

31. Operation: Process Interruption – Recovery Flush and Operating
Process Hold
Effect of Parameter on Retention with Viresolve® Pro Device
conclusion
No significant impact of process
interruption (0-300 min) / Flush
volume (0-50 L/m2) on LRV
No impact from membrane lot No impact from molecule type or concentration
• No impact from pause intervals
• No impact from pause duration
• No impact from flush volume
• No impact from pressure (up-down)
• No impact on single membrane lot
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

32. Operation: Pre-use Caustic Flush
Effect of Parameter on Retention with Viresolve® Pro Device
conclusion
No significant impact
of pre-post use
caustic flush (0-17
hrs; 0-0.5N NaOH;
room temperature) on
LRV
Viresolve® Pro devices (Micro, Modus and Magnus) were caustic sanitized with 0.5N NaOH (for 1h dynamic
and 16 hours static) then challenged with ϕX-174 in 24 g/L BSA in FA buffer pH 7.2 at 30 psig. Filtrate grab
samples were collected at V75 and LRVs were compared to LRVs from non-caustic treated devices.
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

33. Device: Consistency of Membrane and Device Performance
Effect of Parameter on Retention with Viresolve® Pro Device
conclusion
No significant impact of
Device (0.00031-1.53 m2)
and Membrane Lot on LRV
At least 4.0 logs of virus retention was
consistently achieved across device scales
and membrane lots.
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

34. Parameter Test Range Summary
Effect of Parameter on Retention with Viresolve® Pro Device
conclusion
No significant impact
with Viresolve® Pro
Device
• An internal database containing results of 498 small viral clearance evaluations using Viresolve® Pro
devices was analyzed using a similar approach to published reports.
• These clearance evaluations were not systematic controlled studies of the filtration design space, but rather
represent a composite of many filtration conditions executed by multiple biomanufacturers at different
testing laboratories using virus preparations prepared using a range of purification methodologies.
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

35. Feed Titer and Operational Pressure
Effect of Parameter on Retention with Viresolve® Pro Device
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
0
5
10
15
20
25
30
35
40
45
VirusLogReductionValue
Drug Product Concentration (g/L)
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
0
10
20
30
40
50
VirusLogReductionValue
Feed Pressure(psi)
O = non-detects (assay limit) + = detects
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

36. Download Here
Reference
Quality by Design (QBD) for Downstream Virus Filtration | 6 November 2020

37. Poll Question

38. Q&A

39. Welcome
Sterile Filtration of Complex Injectables | 20 October 2020
Tathagata Ray
Head of Technology
Management
Indian Subcontinent and
South-East Asia

40. The vibrant M, Viresolve® Pro 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.
© 2020 Merck KGaA, Darmstadt, Germany and/or its affiliates. All Rights Reserved.