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Bringing ICH Q10 to life – Knowledge Management for efficient life-cycle management in the context of QbD
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K1 Competence Center - Initiated
by the Federal Ministry of
Economy, Family and Youth
(BMWFJ) and the Federal Ministry
of Transport, Innovation and
Technology (BMVIT). Funded by
FFG, Land Steiermark and
Steirische Wirtschaftsförderung
(SFG).
Bringing ICH Q10 to life – Knowledge Management for
efficient life-cycle management in the context of QbD
Stefan Leitgeb, RCPE GmbH
Christoph Trattner, Know-Center GmbH
BPI Europe, Düsseldorf, 18.4.2013
Wiki
The
Quality by Design
A systematic approach to
development that begins with
predefined objectives and
emphasizes product and process
understanding and process
control, based on sound science
and quality risk management.
M. Nasr, FDA
ICH Q10 Pharmaceutical Quality Systems
Quality risk management
Knowledge management
GMP
Pharmaceutical
development
Technology
transfer
Commercial
manufacturing
Product
discontinuation
Management responsibilities
Process performance & product quality monitoring system
Corrective action / preventive action (CAPA) system
Change management system
Management review
PQS
elements
PQS
enablers
Motivation
• Increasing need for knowledge management in
pharmaceutical industries
• Reasons:
• regulatory guidelines (ICH)
• increasing economic pressure
• increasing use of “platform technologies”, e.g.
monoclonal antibodies (lessons learned)
• increasing complexity of systems and data
Pool strengths of competence centers “Research
Center Pharmaceutical Engineering GmbH” and
“Know-Center GmbH”
Reasoning
35°C is too low for optimal growth of
production strains raise temperature
From data to knowledge
Data
Information
Know-
ledge
Creating value
Raw data often unstructured
Data graveyards
e.g. 35°C
Putting data into context
e.g. Temperature in the fermenter is
at 35°C
• Reduce time for decisionmaking
• Reduced time-to-market
• Smart manufacturing increase efficiency
• Competitive advantage
Knowledge Management
Knowledge management is the formation of
a framework and processes within an
organisation with focus on the production
factor „Knowledge“
How to access knowledge at the right time
and place in the right form?
State of the art
8.33%
0.00%
16.67%
75.00%
0%
20%
40%
60%
80%
100%
Not at all important
Not too important
Somewhat important
Very important
Importance of Knowledge Management
25.00%
40.00%35.00% 35.00%
25.00%
5.00%
0%
20%
40%
60%
80%
100%
Obstacles for implementing Knowledge Management
n = 12
Knowledge management framework
Knowledge
level
Documentation
Information
Objective level
GoalsFeedback
Operation level
EmployementLearning
Culture level
Em
plo
ye
es
Community
Data level
Knowledge management in ICH Q10
Product and process knowledge should be managed from
development through the commercial life of the product up
to and including product discontinuation. For
example, development activities using scientific
approaches provide knowledge for product and process
understanding. Knowledge management is a systematic
approach to acquiring, analysing, storing and
disseminating information related to
products, manufacturing processes and components.
Sources of knowledge include, but are not limited to prior
knowledge (public domain or internally documented);
pharmaceutical development studies; technology transfer
activities; process validation studies over the product
lifecycle; manufacturing experience; innovation; continual
improvement; and change management activities.
Gaps in Knowledge Management
Huge gaps in knowledge
management throughout life-cycle: Data graveyards
Access to prior knowledge and experts
Availability of data and information at the
right time and place in the right format
Dissemination of knowledge
Knowledge transfer between development
phases
Knowledge transfer between unit
operations
Lessions learned
Structuring of data and information
Inhomogeneity of data
ISPE global PAT COP Data Management Task Team, "Concept Paper -
Implementing Knowledge Management in Bioprocesses: A QbD Driven Approach
Turning Data into Knowledge in Reference to the CMC A-Mab Case Study," 2012
Knowledge management framework
Knowledge
level
Data level
Documentation
Information
Objective level
GoalsFeedback
Operation level
EmployementLearning
Culture level
Em
plo
ye
es
Community
Raw data
Aim of the study
A single system for Knowledge Management, no
isolated applications, to eventually be used as
platform for pharma companies including
functionalities such as:
Life-cycle management
Deal with heterogeneous data
Data mining, knowledge extraction and visualization
Management of platform technologies (lessions
learned)
Customizable
Web-based (multi-site companies)
Interfaces to already existing systems
Easy to use (no expert system)
Traceability
Access control
Solution
smart Pharma Wiki
Our Solution:
The smart Pharma Wiki
What is it?
Put simple: A System which enables a user or a
company to mine, store and visualize unstructured and
structured data, that is produced during the product life
cycle.
Based on MediaWiki technology Collaborative knowledge creation
Version Control
Access Control
Accessible from various devices
Use of semantic technologies
How does the system look like?
Login
The access to the system
is limited. A valid user
name and password is
required.
Main Page & Sidebar
After logging in the user is
redirected to the main page. This
site provides him/ her with an
overview of all available
functionalities and areas, available
in the software. Certain areas and
functionalities can easily be
restricted to different user groups
such as researchers or managers.
All important functionalities and
areas are also accessible by the
sidebar independent of the
currently opened page. Therefore
an overview in the system is
provided at all times.
How does the tool work...?
From unstructured to structured data
to knowledge...
Data
Research
PhaseDevelopment
Phase
Technology
Transfer
Phase
Manufacturing
Phase
Feedback
Phase
Product Life Cycle
Data is collected automatically or semi-automatically
OntologyEditor
OntologyUnderlying Knowledge base
Hierarchical Visualization of Concepts
In order to access the data, you can either search for it or you can
hierarchically browse
What is also supported by the system are methods of
visual analytics of structured and unstructured data
sources, such as for instance plain-textor PDFs...
However, what is also supported
by the system (of course) is
manual data entry...
Process OntologyHierarchical illustration
Not only the hierarchy of the concepts, but also the hierarchy of the
processes can be browsed visually to gain an complete overview.
Use cases
Ok, now two use cases... ;)
Use case 1
Problem: A monoclonal antibody shows loss of efficiency
during the freeze-thaw cycles
Is there already data available (internal / external)?
What has been done bevor with similar products to solve problem?
how can already existing knowledge be extracted and
used for intelligent product design
Research
PhaseDevelopment
Phase
Technology
Transfer
Phase
Manufactoring
Phase
Feedback
Phase
Process Life Cycle
Trend Analysis
Use case 2
In the second use case we focused on the issue of
recognizing outliers and correlations between parameters
Research
PhaseDevelopment
Phase
Technology
Transfer
Phase
Manufactoring
Phase
Feedback
Phase
Process Life Cycle
Batch AnalysisUnfiltered
This graph
visualizes
all batches
to clearly
represent
important
parameters
....ok, that is more or less the end of this presentation...
...and just short overview of our system...
Of course, we can do many more things
Thanks to...
• Johannes Khinast
• Thomas Klein
• José Menezes
• Daniela Kniebernig
• Stefanie Lindstaedt
• Elisabeth Lex
• Sebastian Dennerlein
• Dieter Theiler
• Simon Walk
• Nicolas Weber
Leading the way from isolatedapplications to one commonknowledge platform:
smart Pharma Wiki!
Take Home Message