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Industry Perspective on Models forReal-Time Release Testing (RTRT)
Dr. Gert Thurau
Hoffmann-LaRoche, Basel (Switzerland)
Outline
• Real-time release testing approaches – the opportunity
• Types of real-time release testing approaches
• RTRT for new products or in-line products?
• Development and implementation aspects of real-time release testing approaches
• Regulatory aspects– Initial registration– Life cycle management
2
Real-time release testing approaches (RTRT)–the opportunity
There are many benefits to real-time release testing approaches– Typically enhanced process development => more process understanding to
harvest from during life cycle– Typically more analytical and process data analyzed or monitored =>
Potential/need for integrated data structures across unit operations => Great starting point for Quality Assurance and continuous process verification
– Resource savings across life time of product• Production and testing time• Different skill sets/reduction in resources
3
The realization of Real-Time Release Testing approaches typically require more upfront resources than for conventional release approaches.
Types of Real-Time Release Testing approaches using multivariate models*
4
Mostly analyzer-based testing schemesi.e. “test replacement”, in-process and lab tests replaced 1:1 with PAT
IFPAC Cortona meeting presentation Nathan Pixley, with friendly permission of Merck & Co., Inc.
Process model/MVDA-heavy schemesi.e. including prediction of non-real-time measurable data
Process, material, PAT type data
Graphics Ref: European Pharmaceutical Review 2011,Volume 16 Issue 6, with friendly permission of Novartis
* Multivariate models are only one type of models which can be used in Real-Time Release Testing approaches. Many other types of models can be useful.
Development of models – better to target a new or an in-line product?
Several factors to consider– Process understanding/history
• to be generated for new products• Existing for in-line products
– Registration status of in-line product• incl. global harmonization challenges• Parallel RTRT and conventional products marketed
– Economic aspects• Business case• Life cycle of in-line product
– Technical/Scientific aspects
5
Models for new or in-line products – Technical Challenge
6
We ideally want both types of data:
Target
Target
Intended model validity range
Middle-of-the-road data =
typical production
Away from the middle but not
impossible/ not a product quality
concern
7
Models for new or in-line products –Technical Challenge
…but you typically have these:
Existing products Lot’s of “at target” values =>limited value for correlation/calibration
Very little desire to create off-target value (product cost/risk)
No desire to generate additional variation in processing conditions
New products Often significant variations (intentional, sometimes
unintentional) at various scales (pilot and full scale) = good to use for robustness challenges
Limited development runs at target values = fine-tuning of model at target can be challenging
Model Data for new products – The Timeline Challenge
8
The textbook scenario – a perfect string of activities
Jan 2015 June 2015 Jan 2016 June 2016 Jan 2017
Take some calibration data
Finalize Model
Take some internal validation data
Analyze, Confirm Model, write external validation protocol
Take external validation data
• Validate model
• Write report
• Register
Int. validation data set (not
used in calibration)
Calibration data set
External validation data set
Model Data for new products – The Timeline Challenge, cont.
9
The reality of pharmaceutical development – leaps and bounces
Jan 2015 June 2015 Jan 2016 June 2016 Jan 2017
Pilot scale batches
Feasibilty, model dev.
Pilot scale batces
Model dev. Full scale batches
• Finalize model
• Validate
• Write report
• Register
Full scale batches
External Validation data set
Calibration data set
Int. validation
data set (?)
Development and implementation aspects of models in real-time release testing approaches- Development phase• Technical/data requirements
– IT and Quality Systems to handle large data sets and modeling execution
• Implementation challenges– Including quality system adaption– “Culture” changes for roles and responsibilities (QC lab. vs Operations
area)
• Knowledge management– Initial knowledge transfer– Ongoing knowledge maintenance
10
Registration aspects of real-time release testing approaches - all RTRT models will be considered high impact*
• Initial registration – how to convey enough information in dossier
– Without writing a Ph.D. thesis in each dossier– What is the difference between scientific description
(to facilitate review) and regulatory commitment (for life cycle management)
11*High impact as per ICH Implementation Working Group “Points to Consider Document”
** picture reference swcenter.fortlewis.edu/dreamstime.com
• Life cycle management– For (multivariate) models changes will be reality in their life
time• Risk posture for pharmaceutical companies to manage
these models as part of routine control strategy– How to handle from registration perspective
• Expl. EMA NIR guidance and variation definitions leads to significant change notifications during life cycle
Regulatory Life Cycle Management Example RTRT NIR assay methods transfer across 3 sites, 7 analyzers*
12* used with friendly permission of Merck & Co., Inc.
Variation registration impact:
Under previous interpretation:
• 4 initial registrations
• No variations
• ~ 6 successful on-site inspections
Applying EMA NIR guidance:
• 4 initial registrations
• 4 comparability protocol submission
• ~30 variations
Summary/Starting Point of Discussions
• Use and registration of models as part of innovative control strategies has increased
– Many examples of multivariate spectroscopic models for PAT-analyzer based approaches
– Some examples of multivariate process models (many more non-RTRT, non-registered!)
• Development and implementation challenges can be overcome with holistic approach and cross-functional planning
• Registration aspects have seen good solutions, however the field is still evolving. The global harmonization of regulatory oversight is still under debate
13
Doing now what patients need next