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

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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

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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*

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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

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Models for new or in-line products – Technical Challenge

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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

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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

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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.

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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

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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

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Doing now what patients need next