EBE Satellite Session on Biotech Process Validation;

CASSS CMC Forum EU, Prague - May 6, 2013

BWP workshop 2013:

Industry Presentations and Perspectives on

Process Validation for Biotech Products

Markus Goese, F. Hoffmann-La Roche Ltd, Basel;

on behalf of EBE

Presentation Outline

• Overview BWP Validation Workshop

• Highlights of topics from industry presentations:

- Parameters and Indicators

- Biological and other raw materials

- LIVCA, EOP

- Single-use equipment

- Multi-facility production

- Sampling and Testing frequency

- Reprocessing

- Scale-down models

- Enhanced approach/ Continuous Process Verification

• The expert teams

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Overview BWP Biotech Validation Workshop

April 9, 2013

• One-day stakeholder workshop on Biotech API manufacturing

process validation held at EMA offices in London on April 9, 2013:

to address key questions concerning specific MAA evaluation/ validation data

required to confirm reproducibility and robustness of the manuf. process steps

assist BWP in drafting the Guideline on process validation for the manufacture of

biotechnology-derived active substances

• Workshop consisted of main session focused on the tools and strategy

followed in a “traditional” validation approach (upstream/ downstream)

and second session dealing with the “enhanced/ QbD” approach

• Preparation of industry presentations was a joint effort of EBE,

EuropaBio, and EGA

• Approx. 100 participants: 40 representatives from EU nat. regulatory

agencies, EMA, PMDA, Swissmedic, and 65 participants from biotech

industry

• All slides presented are available for download: http://www.ema.europa.eu/ema/index.jsp?curl=pages/news_and_events/events/2013/01/event_detail_000693.jsp

&mid=WC0b01ac058004d5c3

• For full video recording of the workshop please see: http://www.youtube.com/playlist?list=PL7K5dNgKnawb9d8XcxTld7Qgo2psguegy

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BWP Biotech Validation Workshop 2013

• Personal take-home message(s):

- Terminology matters (CPV...)!

- Non-CPPs: how to handle?

- How to take harmonization forward?

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Highlights of topics discussed Process Parameters and Performance Indicators (I)

• Process Parameters: Defines the input variable that can be

directly controlled in the process

• Performance indicators: Defines calculated or measured

process output.

Using prior knowledge, development information and risk

assessments, parameters are classified into:

- Critical with impact on product quality (CQA),

- Non-critical with no impact to product quality

• Examples:

- Process Parameters: Temperature, Starting Cell Density, Raw material

attributes

- Performance Indicators: Seed train parameters/ final cell density, Cell

concentration and/or Viable cell count at harvest, Product concentration/ titer

at harvest

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Highlights of topics discussed Process Parameters and Performance Indicators (II)

• Definition of the Parameters is confirmed during Process characterization and

control strategy is developed based on understanding of risks to product quality

• Control strategy is confirmed under manufacturing conditions during Process

Performance Qualification or Process Validation

• A Process Verification plan is developed using the control strategy

Dossier should contain information related to critical parameters as part of

the control strategy

Filing of limits for non-critical parameters should not be required (limits

could change with continuous learning and process understanding)

Process performance indicators and associated control strategy, that are

important to understand process performance and consistency are

described in MAA but are not considered as regulatory commitments; they

are handled internally via the company quality system

Material attributes that are not part of the control strategy (e.g. binding

capacity of an ion binder) should not be submitted in the MAA but are

maintained under the review of the company’s quality system

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Highlights of topics discussed Biological and other raw materials – upstream considerations

• Extent of understanding:

- Complex and undefined raw materials, such as biological raw materials, often

require small scale model testing, based on impact to product quality

Prior experience from other processes and scale down data should

serve as the foundation of understanding

- There should be an effort to characterize the extent of variability due to the raw

material

Different lots to ensure adequate process robustness, by monitoring at

large scale using as many lots as possible during development/ clinical

production

Data for MAA-filing:

- Data on impact of variability based on multi-lots, small scale model testing

- If variability is known to be high, risks have to be mitigated via the control strategy

- Risk mitigation could include qualification of a second source of supplier; use of

small scale model/ pilot scale studies coupled with data from legacy processes/

platform knowledge could provide added assurance

- It is not feasible to use all potential suppliers during process validation, (e.g.

soy hydrolysate) - this may not be viewed as a requirement.

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Highlights of topics discussed Biological and other raw materials – downstream considerations

• A risk based approach should be used: - If variability of raw material is determined to be a critical process input (impact on

CQA), which cannot be adequately controlled, e.g. by incoming material testing, it should be investigated during Process Validation

• Resin reuse: - Number of resin re-use cycles is established through prospective small-scale

studies

- Resin performance is confirmed at manufacturing scale during process verification (for limited number of cycles); testing is continued post process verification under validation protocol to max. number of cycles

For MAA dossier: - data from small scale re-use studies and commercial scale process verification

should be included;

- further commercial scale data for resin re-use should be addressed by companies continued verification program and not be part of the dossier.

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Highlights of topics discussed Limit of In Vitro Cell Age (LIVCA), End of Production (EOP) Cells

• ICH Q5B: “The limit for in-vitro cell age for production should be based on data derived from production cells expanded under pilot (plant) or full scale conditions to the proposed in-vitro cell age or beyond”:

- LIVCA is performed at a representative scale/ during development and scale-up; not necessarily part of process validation

• End of Production (EOP) cells from expansion of WCB to pilot

and full scale:

- Integrity of expression construct in EOPs determined once for MCB at full scale

- Where direct comparison with MCB is not possible surrogate markers can be

used (e.g. nutrient consumption rates).

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Highlights of topics discussed Single use equipment

• For product contact single use material (e.g. cell bags) – the treatment is similar to critical raw materials:

- Risk assessments should capture risks from leachables and extractables, primary and secondary sources of manufacturers of product contact materials, etc.

• Detectability of problems is higher in upstream processes than in downstream (example: abnormal cell growth in bags that have quality issues)

• Equipment and facilities process validation considerations should be similar to process validation in traditional equipment

The difference from multi use material is that single use material do not need cleaning validation and SIP, however, suitability for use must be demonstrated

Same principles apply for downstream

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Highlights of topics discussed Multi-facility production/ site-transfer

Documentation transfer

Process and Product information from Donor

Translation into site specific process

description by receiving site

Gap Analysis and Change control

Activity: GAP-Analysis

Activity: Risk assesment on differences

Activity: Including cleaning evaluation into risk

assesment

Activity: Facility and process changes

Output: Risk management report

Output: Change records

Output: Process validation plans and protocols

Analytical transfer

Transfer of analytical methods

Production of batches at new site

Activity: Validation batches

Output: PV Report

Output: Comparability

Reports and change control authorization

Activity: Evaluation of acceptance criteria

Activity: Close change records

Outputs: Transfer summary report

Grand of Changes

Example of a Transfer Process

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Diversification by process changes can be prevented by:

• Change management

• Meaningful specification of raw materials (material

attributes) and raw material testing

• Continued/ periodic process monitoring at each site

Impact!

Highlights of topics discussed Sampling and testing frequency

• The testing (sampling, frequency, tests) for process validation as well as for

continued process verification should be defined as part of the overall control

strategy considering:

- complexity of the process

- level of process variability

- available process understanding (e.g. development data)

- continued verification program and data should be subject to inspection

Depending on the level of process and product understanding, a

science- and risk-based testing strategy within the overall control strategy

will allow for meaningful and efficient control:

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Highlights of topics discussed Reprocessing

Reprocessing

(ICH Q7A) Operator or

technical failure? Non-conformance of

the process

Representative small

Scale-model available? Prior knowledge?

Yes

Perform studies and present

results in the dossier

Discussion: • If validated process delivers non-conformance, e.g. a filter-integrity test failure, due to

reasons that are not indicative for a lack of process understanding or control, this can

typically be foreseen and be supported, e.g. by (small-scale) re-filtration studies.

• If validated process delivers an unexpected result, this is an indicator of a lack of

process understanding and control, and requires a deeper investigation and an update

of the process controls.

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• By definition, a scale-down model (SDM) is an incomplete

representation of a more complicated, expensive and/or physically

larger system.

• But scale-down models must be used because of the limitations

to conduct experimental studies with the at-scale equipment.

2 L Bioreactors

10 K Production Facility, Penzberg

8,000 x smaller

Highlights of topics discussed Scale-down models (I)

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• Inputs: raw materials and components, feedstock/ cell source,

environmental conditions

• Design: selection of scaling principle(s), equipment limitations, on-

and off-line analytical instruments

- Use of sound scientific and engineering principles for scaling

- Important to meet the same operating window for SDMs as for the at-scale

process, if possible (window can be process and cell line specific):

• Outputs:

- performance metrics (eg., product titer, cell

density, substrate concentration);

Note: Dissimilar behavior may indicate a

problem that can be very valuable for

troubleshooting and model improvement

- product quality metrics (eg., charge

heterogeneity, glycosylation pattern)

- plus sample handling/storage, analytical

methods

Highlights of topics discussed Scale-down models (II)

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SDM-justification in the dossier:

• Documented evidence a model is suitable for evaluating the

effect of input material and parameter variation on process

performance and product quality outputs:

- Same change in inputs results in a substantially similar change in outputs

- Adequate description that the design provides the data it is intended to deliver

• Match full-scale as much as possible and feasible

- Understand and/or control for differences between scale-down and full-scale

(e.g., materials of construction, use of different assays etc.)

• Comparison of at-target performance:

- “Ideal Scenario”: Model is compared against full-scale at-target and off-target to

verify the scale-down model is fully representative under various process

parameter conditions

Not practical: Means multiple additional runs, may also require sufficient

replication at off-target points for statistical confidence; Full-scale runs are

prohibitively expensive

Generic qualification should be possible, depending on understanding of

scale-effects & control strategy

Highlights of topics discussed Scale-down models (III)

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Highlights of topics discussed Scale-down models (IV)

• Traditional applications of SDM from upstream

perspective:

- Cell line selection

- Process and media development

- Investigation of Raw Material Variability

- Characterization/ Validation of cell age effects

- Characterization/ Validation of process parameter

excursions

- Determination of PARs for process parameters

- Supporting consistency claim when few at-scale

batches are available

Validation/ MAA relevant data 17

Highlights of topics discussed Scale-down models (V)

• Traditional applications of SDM from downstream perspective:

Full model: miniaturized versions of the manufacturing scale process (-step). Example: Chromatography models employed under manufacturing target conditions. Partial model: aspects of the at scale system are modeled, typically to isolate or exaggerate a condition. Example: Intermediate hold time study models – vessel surface area to volume ratio, temperature, and time may be exaggerated at small scale beyond the manufacturing conditions to evaluate a challenge condition.

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Highlights of topics discussed Scale-down models (VI) – benefits for “enhanced approach”

• 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

• (Easy) replication for statistical validity possible

• (Complex) Interactions and quadratic effects can be identified

• “Categorical variables” (like raw material lots) can be investigated

Data rich-process knowledge

Challenge: Extrapolation of rich database of knowledge to full-

scale

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Highlights of topics discussed The enhanced approach/ continuous process verification (I)

• Extensive process (& product) knowledge

• Better prediction of scale effects

• Leverage process knowledge into control strategy via

continuous process verification

• Process Validation:

- establishing by objective evidence that a process consistently

produces a result or product meeting its predetermined

specifications

- Evolving landscape with greater focus on lifecycle approach

- PV approach likely to be a continuum from ‘traditional’ to

‘enhanced’

- ‘Enhanced’ PD does not always provide for ‘Enhanced’ PV and

‘Enhanced’ PV incorporating continuous process verification can

be conducted with varying amounts of process understanding;

Control strategy is the enabler 20

• Culture duration

• Culture conditions

• (VCD as output)

Examples:

- HCP

- HMW

DOWNSTREAM «select and protect»

BIOREACTOR «make right product»

Formulation & Fill «preserve»

• Column operating parameters

• Column lifetime

• (IPC for HCP as output)

• Culture conditions

• Culture conditions

• Raw material

• Chromatography selectivity

• Bioburden control

• (Control Temp/Conductivity)

• Chromatography selectivity

• Control of generation

• In-process testing

• Formulation process

• Filling process

• Storage

• Final product testing

- Glycan

• Control strategy: Fundamentally exists to describe and

manage the influence of CPPs on CQAs

Highlights of topics discussed The enhanced approach/ continuous process verification (II)

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Highlights of topics discussed The enhanced approach/ continuous process verification (III)

• Continued Process Verification:

Demonstrating the maintenance

of the validated state

• Part of ongoing manufacturing

and lifecycle management

• Can include some or all of the

data sources used to

demonstrate Continuous

Process Verification

“Continuous”: “Continued”:

• Continuous Process Verification:

An alternative approach to

process validation in which

manufacturing process

performance is continuously

monitored and evaluated.

• Demonstration that the process is

validated (under specified control)

• Based on control strategy and

process knowledge

• Applied at various scales and

stages

• Composite of data from lab and

various scale manufacturing

• Can include multiple data sources

(IPC, batch, in-line/at-line/off-line)

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• Filing requirements:

- supporting data for Continuous Process Verification will be in

the MAA

- Continued Process Verification (as part of continuous PV) is a

prospective proposal and design basis may be described in the

filing but the data are in the GMP system

• Location of these descriptions in the filing: tbd

• Important linkage between review and inspectorate

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Highlights of topics discussed The enhanced approach/ continuous process verification (IV)

BWP Biotech Validation Workshop 2013

• Recap take-home messages:

- Terminology matters (CPV...)!

- Non-CPPs: how to handle?

- How to take harmonization forward?:

EMA: BWP<>QWP

EMA<>FDA

PANEL DISCUSSION

The Industry Teams

The “Traditional”/Upstream Team

• Arie van Oorschot Uniqure

• Kristopher A Barnthouse Janssen (J&J)

• Vijay Chiruvolu Amgen

• Ranjit Deshmukh MedImmune

• Ray Field MedImmune

• Jason Gale Pfizer

• Christian Hakemeyer Roche

• David Kirke UCB

• Li Malmberg Abbvie

• Karin Sewerin Consultant for MedImmune (lead)

• Juergen Wieland Ratiopharm

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The “Traditional”/Downstream Team

• Kristopher Barnthouse Janssen (J&J)

• Jürgen Bongs Sanofi-Aventis

• Richard Turner MedImmune

• Marco Strohmeier Roche

• Ciaran Tobin Pfizer

• David Kirke UCB

• Ronald Imhoff Janssen Biologics (lead)

• Thomas Stangler Sandoz

• Vijay Chiruvolu Amgen

• Norbert Hentschel Boehringer-Ingelheim

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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 (lead)

The “Enhanced Approach” Team

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Back-Ups

Highlights of topics discussed Single use equipment – downstream considerations

• Qualification (DQ, IQ, OQ, PQ; independent from potential

product) and process validation (related to dedicated process in a

qualified environment ) working package acc. to ICH Q7A guideline

• Additional effort to be considered in MAA using single-use equipment:

Leachables/ Extractables studies

Elements in the MAA:

- List of all disposable materials used at different steps

- Duration of product/ intermediate contact with disposable

material incl. worst case assumptions

- Risk assessement regarding impact on QTPP

- Design and result of L/E-studies (final report)

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Highlights of topics discussed Multi-facility production/ site-transfer

Process Validation elements of Site 1

Bio Purification

Scale down Model qualification

Stability of intermendiates

Buffer stability

Extractables and Leachables

Mixing/Homogenisation

Impurity removal and carry over

Validation of Process parameter (Chromatography, Filtration,

Ultrafiltration parameter)

Cycle no. of Media/Membranes

Regeneration and desinfection

Media/Membrane storage

Site 1 Site 2

Process Validation elements of Site 2

Bio Purification

Scale down Model qualification

Stability of intermendiates

Buffer stability

Extractables and Leachables

Mixing/Homogenisation

Impurity removal and carry over

Validation of Process parameter (Chromatography, Filtration,

Ultrafiltration parameter)

Cycle no. of Media/Membranes

Regeneration and desinfection

Media/Membrane storage

Transfer

Only for identical sites the validation results of site 1 are applicable also

for site 2

BUT Sites are rarely identical

Thus

Differences have to be assessed

with

Subsequent verification of validation status and

comparability

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Highlights of topics discussed Hold time(s)/-studies

• Hold time studies (to investigate product quality over a pre-defined time period under defined conditions) can have different scopes:

- Long time storage of process intermediates/ Short time storage between process steps

• Often, storage of intermediates is impractical at commercial scale: - Typically stability indicating tests done at small scale

- Bioburden testing at small scale does not represent commercial equipment. Control should be demonstrated by, e.g. routine bioburden testing of samples pre-filtration in the commercial facility combined with validation studies demonstrating effective filtration & container sterilization routine bioburden testing post storage can be eliminated.

• Depending on scope it can make sense to do cumulative studies or not, e.g.: - If a final drug substance bulk can be stored for more than just weeks, the impact on drug

product stability should be assessed

- If a final drug substance can be stored for extended (longer) time periods, prospective cumulative study may be unreasonable, may take years.

- Short time storage between process steps typically used to allow for production flexibility, storage of intermediates between process steps for the max. allowable time typically does not occur cumulative studies do not add value for commercial process.

Hold time data in the dossier: - Usually hold conditions and time and the resulting product quality data/ acceptance criteria from

the stability indicating assays are filed.

- In case of hold time changes, preferable to describe the hold validation program including acceptance criteria in the dossier to reduce efforts for reviewer and industry.

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Enhanced approach/

Pre-requisites for Process Validation

Product knowledge

Process knowledge

Control Strategy • Criticality assessment • Structure function studies • Prior knowledge

• Univariate and multivariate analyses • Prior knowledge (platform) • Scale down and model studies

• Parametric and attribute control • On-line/at-line/off-line • Settings to detect in-control/out-of control and trending • Actively managed as part of production , batch disposition and continuous improvement

Enhanced approach/

Confirmation at scale

• Limited number of runs

at full-scale • Focus on confirmation of control strategy at scale • Limited ranges explored • Selection of set-points and testing to maximise value of at-scale-data • Cannot directly test edges of Design Space at scale

• Extensive evidence of

process performance • Examination of performance at multiple parameter set points Forms the basis for Continuous Process Verification

• Multiple runs • Information density • Interaction data

• Limited number runs • At-scale data for all Unit Ops • Key stage in confirmation of PV

Enhanced approach/

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

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Enhanced approach/

SDM Qualification - Process Model Extrapolation

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

qu

iva

len

ce

ma

rgin

Will be adressed by

continued process

verification

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