The Smart Pharma Wiki

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!

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