This document discusses data analytics and software sensors for single-use bioprocessing. It provides an overview of Exputec, which offers solutions to improve biopharmaceutical development and production processes using data analytics. Software sensors are described as using models to monitor process variables like substrates and metabolites, complementing hardware sensors. Challenges of data analytics include data integration from collaborators and dealing with outliers. The document advocates for using robust multivariate statistics rather than removing outliers to avoid incorrect results from data analytics.
Data analytics and software sensors for single use bioprocessing
1. Data analytics and software sensors for
single use bioprocessing
Dr. Patrick Sagmeister
Exputec GmbH
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Exputec at a glance
“Exputec offers comprehensive solutions for
biopharmaceutical and industrial biotechnology
companies to improve and run better development and
production processes.”
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Data Analytics Goals
•Acceleration of
R&D stages
•Well-
characterized,
high-performing
bioprocesses
•BLA information
completeness
•Robust &
validated
production
•Reduction of
failed batches
•Quick resolution
of mfg issues
•Rapid scale-up of
processes
•Consistent
documentation
and qualification
•Qualified scale
models
•Complete &
consistent data
sets, full data
integrity
•Correct statistical
workflows
•Validation Report
completeness
Bioprocess
R&D
Scale-up &
Tech Transfer
Process
Validation
Manufacturing
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Collaborative Data Analytics
Manufacturing
Quality
External Labs
Large
Biotech
Orginator
Small
Biotech
Orginator
Other Sites
R&D
5. Get There Faster.Sagmeister P, Wechselberger P, Herwig C. PDA J Pharm Sci Technol, 66(6), 526-541 (2012).
Interactions of process
parameters and cell
physiology
Interactions of up- and
downstream unit operations
We need sensors environment of the cell, metabolites, impurities, identity
We need data analytics to ensure quality
Product & Impurtities
)_(
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)(_
stateCellfPurityCQAs
stateCellfCQAsIdentity
PPsfstateCell
Product Quality in Bioprocesses
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What is a Software Sensor?
The Measurement, Monitoring, Modelling and Control
(M3C), a working group of ESBES, explains the origin of
the name softsensors:
“The term combines the words “because the sensor signal
evaluation software”, models are usually implemented in
computer programs, and “sensors”, because these models
are delivering information similar to hardware sensors”
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8
Software Sensors Types
1. First principle soft sensors
calculation of volumetric
rates, specific rates and
yield coefficients
estimation of unknown
non-measured variables
2. Differential equation
models/ hybrid models
For cell culture processes,
ODE modelling preferred
model type
3. Data driven software
sensors
INPUT OUTPUT
Accumulation
and Conversion
States
(concentrations)
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Softsensor – What can I monitor?
Substrates
Galactose
Glucose
Glutamine
Glycerol
Pyrovate
Sychrose
Metabolites
Acetate
Ammonia
Formate
Lactate
Products
Acetate
Ammonia
Formate
Lactate
Biomass
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Case Study of Software Sensors in GMP
VCD, lactate, glutamate
CHO process
Implemented in GMP
environment, production
scale
EXPUTEC AND SIEMENS:
ACTIVATING THE DESIGN SPACE
FOR CGMP MANUFACTURING,
BIOINNOVATION LEADERS SUMMIT,
BERLIN, 2017
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How to Validate a Software Sensor?
Audit trails for models, access protection etc. to meet FDA
21 CFR Part 11 and EudraLex Annex 11 requirements.
equipment
historian
LIMs
BR / ELN
PQ for data interfaces and
models individually executed
resulting in fully validated
system
IQ / OQ /PQ for core
software available and
performed by 3rd party
Validation
Partners
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What is data analytics?
“Analytics is the discovery, interpretation, and
communication of meaningful patterns in data”
“Process Validation is defined as the
collection and evaluation of data, from the
process design stage throughout
production, which establishes scientific
evidence that a process is capable
of consistently delivering quality
product FDA process validation guideline”
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Data Analytics - Challenges
Data mining
How to get data from collaborators?
Compliance
Data integrity
Compliant data analytics
Lots of data – little time
Multivariate data analytics for every
scientist and engineer
15. How to deal with Outliers in Data Analytics ?
Outliers can crush your data analytics and lead to
wrong results
Two Options
1) Remove them;
2) Use robust multivariate statistics
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Robust Multivariate Data Analytics
Paradigm shift: Multivariate statistics not for experts but for
every scientist and engineer
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Take Home Messages
Software sensor or
softsensor
Complement existing sensors
Decrease IPC samples
Validated
Custom made
Data analytics
Collaborative!
Duty of Engineers &
Scientists
18. Thank you! Dr. Patrick Sagmeister, EXPUTEC GmbH
Mariahilferstraße 147/2/2D | A-1150 Wien |
T: +43 1 9972849 | M: +43 680 400 500 7
E-mail: patrick.sagmeister@exputec.com |
www.exputec.com
Editor's Notes
2
Money lost in iterative characterization / validation cycles / lost batches ?
Huge optimization potential
„these guys know what my problem is“ should be the reaction
References on losses to the above
For detailed pitch deck, case studies (win doesn‘t have to be in money, but can also be time or batches)
Only mention the value chain and move on to what we do specifically
Options for quick wins / pricing ? Pilots? Project based? Enteprise projects? verbal
On-premise and SaaS
Single point of truth for data and workflows – Share Data with External Partners
Selectively share data with internal and external collaborators
Biopharmaceuticals are defined as “A protein or nucleic acid based pharmaceutical substance used for therapeutic or in vivo diagnostic purposes, which is produced by means other than direct extraction from a native (non-engineered) biological source” [5].