Bringing ICH Q10 to life – Knowledge Management for efficient life-cycle management in the context of QbD

1. The
Wiki
Bringing ICH Q10 to life – Knowledge Management for
efficient life-cycle management in the context of QbD
K1 Competence Center - Initiated
by the Federal Ministry of
Stefan Leitgeb, RCPE GmbH Economy, Family and Youth
(BMWFJ) and the Federal Ministry
Christoph Trattner, Know-Center GmbH of Transport, Innovation and
Technology (BMVIT). Funded by
FFG, Land Steiermark and
Steirische Wirtschaftsförderung
(SFG).
BPI Europe, Düsseldorf, 18.4.2013

2. 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

3. ICH Q10 Pharmaceutical Quality Systems
Management responsibilities
Pharmaceutical Technology Commercial Product
development transfer manufacturing discontinuation
GMP
Process performance & product quality monitoring system
PQS Corrective action / preventive action (CAPA) system
elements Change management system
Management review
Knowledge management
PQS
enablers
Quality risk management

4. 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”

5. From data to knowledge
• Reduce time for decisionmaking
• Reduced time-to-market
• Smart manufacturing  increase efficiency
• Competitive advantage
Creating value
Reasoning
Know- 35°C is too low for optimal growth of
ledge production strains  raise temperature
Putting data into context
e.g. Temperature in the fermenter is
Information at 35°C
Raw data  often unstructured
Data Data graveyards
e.g. 35°C

6. Knowledge Management
How to access knowledge at the right time
and place in the right form?
Knowledge management is the formation of
a framework and processes within an
organisation with focus on the production
factor „Knowledge“

7. State of the art
Importance of Knowledge
Management
100%
80% 75.00%
60%
40%
16.67%
20%
8.33%
0.00%
0%
Not at all Not too Somewhat Very important Obstacles for implementing
important important important Knowledge Management
100%
n = 12
80%
60%
40.00%
40% 35.00% 35.00%
25.00% 25.00%
20%
5.00%
0%

8. Knowledge management framework
Community
Culture level
Objective level
Feedback
Employees
Goals
Information
Knowledge Data level
level
Documentation
Employement
Learning
Operation level

9. 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.

10. 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

11. Knowledge management framework
Community
Culture level
Objective level
Feedback
Employees
Goals
Information
Knowledge Data level
level
Documentation
Employement
Learning Raw data
Operation level

12. 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

13. 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

14. How does the system look like?

15. Login
The access to the system
is limited. A valid user
name and password is
required.

16. 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.

17. How does the tool work...?

18. From unstructured to structured data
to knowledge...
Data
Data is collected automatically or semi-automatically
Product Life Cycle
Technology
Research Development Manufacturing Feedback
Transfer
Phase Phase Phase Phase
Phase

19. Ontology
Editor

20. Ontology
Underlying Knowledge base

21. Hierarchical Visualization of Concepts
In order to access the data, you can either search for it or you can
hierarchically browse

22. 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...

23. However, what is also supported
by the system (of course) is
manual data entry...

25. Process Ontology
Hierarchical illustration
Not only the hierarchy of the concepts, but also the hierarchy of the
processes can be browsed visually to gain an complete overview.

26. Use cases
Ok, now two use cases... ;)

27. 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
Process Life Cycle
Technology
Research Development Manufactoring Feedback
Transfer
Phase Phase Phase Phase
Phase

28. Trend Analysis

29. Use case 2
 In the second use case we focused on the issue of
recognizing outliers and correlations between parameters
Process Life Cycle
Technology
Research Development Manufactoring Feedback
Transfer
Phase Phase Phase Phase
Phase

30. Batch Analysis
Unfiltered
This graph
visualizes
all batches
to clearly
represent
important
parameters

31. ....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...
• Stefanie Lindstaedt
• Johannes Khinast • Elisabeth Lex
• Thomas Klein • Sebastian Dennerlein
• José Menezes • Dieter Theiler
• Daniela Kniebernig • Simon Walk
• Nicolas Weber

32. Take Home Message
Leading the way from isolated
applications to one common
knowledge platform:
smart Pharma Wiki!