Data Science Tools for Successful Technology Transfer · 2019-12-10 · Unicorn Report Run - Formblatt Multiple spreadsheets . Projekt. QC. Email, Powerpoint, Word, Analyseauftrag

Data Science Tools for Successful Technology TransferSpeakers: Thomas Zahel & Christoph Herwig

11 December 2019

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Connecting Pharmaceutical Knowledge ISPE.org

Speaker

Christoph Herwig, PhDProfessorVienna University of Technology

Christoph Herwig, a bioprocess engineer from RWTH Aachen, worked in industry in the design and commissioning of large chemical facilities prior to enter his interdisciplinary PhD studies at EPFL, Switzerland in bioprocess identification. Subsequently he positioned himself at the interface between bioprocess development and facility design in biopharmaceutical industry.

Since 2008, he is full professor for biochemical engineering at the Vienna University of Technology. The research area focuses on the development of data science methods for integrated and efficient bioprocess development along PAT and QbD principles for biopharmaceuticals. In 2013 he founded the company Exputec addressing data science solutions for the biopharma life cycle.

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Speaker

Thomas Zahel, PhDHead of InnovationExputec GmbH

Thomas Zahel is head of innovation at Exputec, with outstanding experience in statistics, algorithm development and turning complex challenges into easy concepts for our clients. His background is in bioprocess engineering and he holds a PhD in applied statistics for process development and validation. He is most passionate about developing new statistical methods that turn data into knowledge. He has successfully helped top-ranked biopharmaceutical companies to develop, improve and validate their processes according to regulatory compliance.

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BackgroundRegulatory Context of Technology Transfer

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Connecting Pharmaceutical Knowledge ISPE.org6

Product Lifecycle

Process Development

Process Validation• Stage 1: Control Strategy• Stage 2: PPQ• Stage 3: CPV

Commercial Production

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Product Lifecycle

“…establish scientific evidence that a process is capable of consistently delivering quality product.“

FDA, 2011

Process Development



QRMPQS, ICH guideline Q10

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Product Lifecycle

Process Development



Tech Transfer

Tech Transfer

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Product Lifecycle

Process Development



Tech Transfer

Tech Transfer

“The goal of technology transfer activities is to transfer product and process knowledge between development and manufacturing, and within or between manufacturing sites to achieve product realisation. This knowledge forms the basis for the manufacturing process, control strategy, process validation approach and ongoing continual improvement” ICHQ8

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Present Data Science Toolswhich can be applied to the different tasks along

technology transfer activities

Presentation Goal

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From Data to KnowledgeA data science route to platform based technology transfer

using

process mechanistic and platform knowledge

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Data • Data

Management• Data

Contextualization• Data Integrity

Analysis

Information• Feature

extraction• Scale

independent measures

• Soft-sensors

Knowledge• Statistical

comparability analysis

• Mitigation of differences

PV/CPV

Success Criteria of Technology Transfer

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Data Fusion & Data Integrity

… features from images

… data from PAT equipment… data from Sensors

Process Development & Production

… data from quality

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We Need Structured Data: As-IsÄK-01 Äkta

Explorer

ÄK-02 Äkta Pilot chromatography

skid

ÄK-03 Äkta Explorer 100 Amersham

LC-01 Liquid Chromatography System Äkta pure

150 L

LC-02 Liquid Chromatography System Äkta pure

150 M

LC-03 Liquid Chromatography

System Äkta avant 150

Projektzip

Automated Backup

Äkta Chromatography -

Devices

Backup, zum Packen

ausgelagert

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ÄK-05 Äkta Process

Scan

Report

CF-03 Crossflow System Äkta Flux

S

USB

Run - Formblatt

UFDF-Sheet („Template“)Raw Data

Projekt

TF-01 AllegroTM Single Use TFF /

PALL

PALL Analytical Tool

USBProjekt

.chart, .sqliteDerzeit nicht ausgewertet

Auswertung am Computer

Report

Chromatography

Column Packing

UFDF

NF

Run - Formblatt

Projekt

Buffer Prep

Run - Formblatt

Projekt

Scan

HarvestProjekt

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We Need Structured Data: To-Be

DB+ Analytics

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From Data to Scale Independent Information

0 5 10 15 20 25 30-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

ProcessTime (days)

grow

th ra

te (1

/hou

rs)

0 5 10 15 20 25 30-0.5

0

0.5

1

1.5

2

2.5

3x 10

7

ProcessTime (days)to

tal c

ell c

onc

(cel

ls/m

l)

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54321

Make Information Ready forStatistical Analysis: Feature Extraction

4 52.5 2.51 0.5

t

𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚()

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Make Information Ready forStatistical Analysis: Unfolding

Mercier, Sarah M., Bas Diepenbroek, Marcella C.F. Dalm, Rene H. Wijffels, and Mathieu Streefland. “Multivariate Data Analysis as a PAT Tool for Early Bioprocess Development Data.” Journal of Biotechnology, 2013. doi:10.1016/j.jbiotec.2013.07.006.

Features = Information!

Featuresmean, median, max, min, derivative

Yields , qs!

unfolding

MVIAMVDA

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POLL #1How do you contextualize your different data sources for doing tech transfer data analysis?

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Manual alignment via Excel

Historian data from Scada/DCS and compare process data trajectories only

Automated process phase detection in time series and alignment of features with ELN, MES, and LIMS data

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Multivariate Data Integrity Analysis (1)

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Multivariate Data Integrity Analysis (2)

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Comparability AssessmentThe potential and practical limitations of the working horse equivalence testing

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Comparability Assessment

Scales Sites

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What do we want to verify?

CQ

A

pCPPtarget

Site BSite A

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Comparability Analysis

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A

C D

B

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26CQ

A

pCPP

CQA

pCPPtarget

target

CQA

pCPPCQ

A

pCPPtarget

target

Site B

Site AOff-setNo off-set

Equal effect

Different effect

A

C D

B

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27CQ

A

pCPP

CQA

pCPPtarget

target

CQA

pCPPCQ

A

pCPPtarget

target

Off-setNo off-set

Equal effect

Different effect

A

C D

BSite B

Site A

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Possible outcomes of comparability study for SDM

Equivalence Test

All responses equivalent Some responses are not equivalent

Mitigate risk of detecteddifferences (e.g. adapt

control strategy)A is more likely than C

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Visual Comparison

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Statistical Comparability Analysis General Workflow

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2. Define general aim(non-inferiority, equivalence)

1. Define responses for comparison (e.g. CQAs)

3. Define measure of similarity(e.g. difference in means, ratio of variances, multivariate measure)

4. Set EAC for each measure of similarity

5. Calculate required sample size

6. Perform study and conduct Statistical test on equivalence

(e.g. TOST, F test)

“Reflection paper on statistical methodology for the 5 comparative assessment of quality attributes in drug substance development”, EMA 2017“Comparability Protocols for Human Drugs and Biologics: Chemistry, Manufacturing, and Controls Information Guidance for Industry”, FDA 2017

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Statistical Comparability Analysis General Workflow

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2. Define general aim(non-inferiority, equivalence)

1. Define responses for comparison (e.g. CQAs)

3. Define measure of similarity(e.g. difference in means, ratio of variances, multivariate measure)

4. Set EAC for each measure of similarity

5. Calculate required sample size

6. Perform study and conduct Statistical test on equivalence

(e.g. TOST, F test)

Use KPIs and CQAs

Equivalence

Set Equivalence Acceptance Criteria for eachparameter difference to be accepted

Determine sample size for LS and SDM runs

Perform equivalence test (i.e. TOST) foreach CQA and state practical equivalence

Difference in means, (ratio of variances, multivariate approaches might apply)

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TOST: Golden Standard for Equivalence Testing

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Is 95 % of my data (difference of means) within a predefined equivalence margin?

TOST test:1. Define equivalence

boundaries 𝜃𝜃 = EAC2. Calculate a

confidence interval for difference in means

3. Check if interval is completely within upper and lower boundary.

CI = �𝑦𝑦 ± 𝑡𝑡 1−𝛂𝛂,𝑑𝑑𝑑𝑑𝑠𝑠𝑛𝑛

�𝑦𝑦 .. Difference in meanst .. Students T distribution𝛼𝛼 .. Desired alpha error of the test (1-confidence)𝑠𝑠 .. Standard deviation of difference of meansn .. Sample size

1 − 𝛼𝛼 makes CI broader

n makes CI more narrow

𝑦𝑦1 + 𝑦𝑦2 ± 𝑡𝑡 1−𝛼𝛼,𝑑𝑑𝑑𝑑𝑠𝑠1²𝑚𝑚1

+𝑠𝑠2²𝑚𝑚2©2

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Equivalence Test Result

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Possible outcomes of comparability study for SDM

Equivalence Test

All responses equivalent Some responses are not equivalent

Mitigate risk of detecteddifferences (e.g. adapt

control strategy)A is more likely than C

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POLL #2For comparability tests between scales and sites I use?

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Visual comparison

Golden batch comparison of process data trajectories using fixed standard deviations

Endpoints of quality data from LIMS

Univariate TOST approaches

Multivariate analysis, e.g. Hotelling’s T² test or PCA

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Mitigate Differences in Technology Transfer using Integrated Process ModelsA holistic approach to control strategy

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How to Set a Control Strategy using an IPM?

UO 1 UO 2 UO 3 UO 4 UO 5 UO 6 UO 7 DS

3.2 4

DSDS

CQA

Elution pH

Load Density

14 24

Upper PAR

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POLL #3For testing success tech transfer and valid control strategies of the full process chain, I use:

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Comparison of single unit operations only

Multitude of linkage studies to demonstrate elasticity of the process along perturbations onside of the control strategy

Integrated modelling approach to focus on holistically critical process parameters and limit the number of experiments

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• From mess to structure• Sound implementation of data management tools is key to data analytics• Tools exist that leverage all possible kind of data (timeseries, pictures, scalar values, …)

• From data to information• Feature extraction based on process understanding enables robust multivariate

information analysis• From information to knowledge

• Statistical comparability analysis forms risk awareness about similarities and differences• Integrated process modelling is essential to derive a control strategy based on holistic

process performance

Conclusions:Successful tech transfer using data science tools

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Q&A

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Contact Information

Christoph Herwig, PhDProfessor, Vienna University of [email protected]

Thomas Zahel, PhDHead of Innovation, Exputec [email protected]

Upcoming Webinars• 23 January - One Size Does Not Fit All:

Strategies for Bringing Advanced Therapy Medicinal Products to Market

Topic Ideas or Feedback? Send to [email protected]

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mailto:[email protected]:[email protected]

Data Science Tools for Successful Technology TransferSpeaker��Christoph Herwig, PhD�Professor�Vienna University of TechnologySpeaker��Thomas Zahel, PhD�Head of Innovation�Exputec GmbHSlide Number 4BackgroundProduct LifecycleProduct LifecycleProduct LifecycleProduct LifecyclePresentation GoalFrom Data to KnowledgeSuccess Criteria of Technology TransferData Fusion & Data IntegrityWe Need Structured Data: As-IsWe Need Structured Data: To-BeFrom Data to Scale Independent InformationMake Information Ready for�Statistical Analysis: Feature ExtractionMake Information Ready for�Statistical Analysis: UnfoldingPOLL #1Multivariate Data Integrity Analysis (1)Multivariate Data Integrity Analysis (2)Comparability AssessmentComparability AssessmentWhat do we want to verify?Comparability AnalysisComparability AnalysisComparability AnalysisPossible outcomes of comparability study for SDMVisual ComparisonStatistical Comparability Analysis General WorkflowStatistical Comparability Analysis General WorkflowTOST: Golden Standard for Equivalence TestingEquivalence Test ResultPossible outcomes of comparability study for SDMPOLL #2Mitigate Differences in Technology Transfer using Integrated Process ModelsData SourcesData SourcesData SourcesData SourcesDigital TwinWhat Does it Do?How Does it Work?How Does it Work?What Does it Look Like?Holistic Risk AssessmentHow to Set a Control Strategy using an IPM?POLL #3Conclusions:�Successful tech transfer using data science toolsQ&A Q&A

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Data Science Tools for Successful Technology Transfer · 2019-12-10 · Unicorn Report Run - Formblatt Multiple spreadsheets . Projekt. QC. Email, Powerpoint, Word, Analyseauftrag