UC2013 Umetrics PetterMoree ApplicationsOfMultivariateDataAnalysis

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© Copyright 2013 OSIsoft, LLC.

Applications of Multivariate Data Analysis

Petter Möree & Jonas Elfving


•  Ability to be proactive rather than reactive to variation or poor quality.

•  Saving batches •  Reducing OOS •  Helping to optimize the

process

Solution Results and Benefits

Novartis values from MVDA for PAT & QbD

Business Challenge

•  Reduction of dimentionality è -  Conformity check -  Real time release testing - Trend analysis - Root cause analysis

Increase of process understanding –  Identification of influential process parameters –  Identification of correlation pattern among the process

parameters –  Generation of process signatures –  Relationship between process parameters and quality attributes

Increase of process control

–  Enabling on-line early fault detection –  Support for time resolved design space verification (real time

quality assurance) –  Predicting quality attributes based on process data –  Excellent tool for root cause, trending analysis and visualization

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Topics •  Introduction: MVDA in the context of pharmaceutical production •  Case studies for MVDA I

–  Process monitoring of a granulation process in pharmaceutical production •  Case studies for MVDA II

–  Statistical Process Control Biopharmaceutical Production for optimization •  Short real-time demonstration

Using PI Server, PI Batch and PI Event Frame & PI Interface for SIMCA-online

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Umetrics •  Part of ~1Billion conglomerate •  The market leader in software for multivariate analysis

(MVDA) & Design of Experiments (DOE)

•  25+ years in the market

•  Off line analysis tools •  On-Line process monitoring and fault detection •  700+ companies, 7,000+ users

•  Pharmaceutical, Biotech, Chemical, Food, Semiconductors and more

•  Worldwide Presence with MKS •  Close collaboration with universities in USA, Sweden,

UK and Canada

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Building a capable process

DOE

MVDA

QFD Quality Function Deployment

QRA: Quality Risk Assessment

DOE Analysis Design Space

Control Plan

MVPC Multivariate Process Control

•  DOE is a knowledge building tool for process development •  MVDA is used both for process understanding and process monitoring

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Our Customers’ Goals in Pharma •  The goal in Pharma production is to help take advantage of data

present in the development labs, and the production environments all the way from API to the final product. = ROI

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The Need for Multivariate I

The information is found in the correlation pattern - not in the individual variables!

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The Need for Multivariate I

Multivariate Control Limits

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The Need for Multivariate II •  Data explosion, more process measurements

than ever before, reduce false alarms

•  Spectrometers –  NIR, FTIR, RAMAN, UV, LLSD –  MS, GC, HPLC

•  Process Sensors –  Acoustic, Video –  P, T, Flow, pH –  pO2 pCO2

•  Require MVDA methods to visualise and extract reliable information from raw data

•  MVDA handles noise, missing data, correlation and visualize in graphs

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This control chart is familiar to you ?

SMI= x1*Novartis + x2*Roche + x3*Merck + x3*FB....

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So this control chart is easy to understand....

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tPS[

1]

$Time (normalized)

PO_WST3433_EXJADE_Drying_V01.M3:3Predicted Scores [comp. 1]

+3 Std.Devt[1] (Avg)-3 Std.DevtPS[1] (Batch S0058_A_854826)

SIMCA-P+ 11 - 01.08.2009 14:42:24

t1= x1*Temperature + x2*Pressure + x3*Agitation speed ....

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MSPC Observation Level •  Example of a drying step

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tPS[

1]

$Time (normalized)

PO_WST3433_EXJADE_Drying_V01.M3:3Predicted Scores [comp. 1]

+3 Std.Devt[1] (Avg)-3 Std.DevtPS[1] (Batch S0058_A_854826)

SIMCA-P+ 11 - 01.08.2009 14:42:24

Control limits

Average (signature) of all batches

New batch assessed by the model

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Statistical Process Control BATCH CONTROL CHART

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t[1]

$Time (smoothed)

Model Data Ciclo - Oct 2010 v5.M2:16Scores [comp. 1] (Aligned)

+3 Std.Devt[1] (Avg)-3 Std.Devt[1] (Aligned): 635

SIMCA-P+ 11 - 14.03.2011 17:53:19

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8400 8500 8600 8700 8800 8900 9000 9100 9200 9300 9400 9500 9600 9700 9800 9900 10000 10100

Num

Variable, Batch: 617, Phase: 16-15.4122 * 543071TT--607 - 15.4122-2.84936 * 543071TT--600 - 2.849360.00281207 * 543071PT--644 + 0.00767695-1.10938 * 543071TT--602 - 1.109380.00826856 * 543071-Acetone_43x10e13

SIMCA-P+ 11 - 09.03.2011 19:17:38

Batch Process

Signature

average of all batches control limits (± 3σ from avg.)

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t[2]

t[1]

Model Data Ciclo - Oct 2010 v5 - batch level (scores).M2 (PCA-X)t[Comp. 1]/t[Comp. 2]

R2X[1] = 0.873728 R2X[2] = 0.0564179 Ellipse: Hotelling T2 (0.95)

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SIMCA-P+ 11 - 10.03.2011 19:27:42

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Work and Data flow For Method Development

All Process Parameters

Observation Level

Batch Level

Final Model

Individual Probes

Individual Probes …

Reduction of Dimensionality

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Num

Recorded Process Parameter during granulationObsID(Obs ID ($PhaseID))Mixer Power rate of change precss variable0.01 * Mixer torqute process variable0.1 * Mixer speed process variable0.1 * Product temperature process variableMixer power process variavle (electrical)

Aims: - Creation of batch signature - Identify correlation patterns

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Work and Data flow For Routine Use in Production

Identification of responsible Parameter(s)

Observation Level

Batch Level

SIMCA-online

Investigation on process data …

Aims: -  Conformity check -  Real time release testing - Trend analysis - Root cause analysis -2

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0

1

2

Flo

w li

quid

feed

Mix

er

Pow

er

Rate

Mix

er

pow

er

Mix

Pow

er

(calc

ula

ted)

Mix

er

Torq

ue

Liq

uid

feed p

um

p s

peed

Bow

l Pre

ssure

Mix

er

Speed

Chopper

Speed

Pro

duct T

em

pera

ture

Bow

l Tem

pera

ture

Tota

l Liq

uid

Added

Score

Contr

ibP

S(S

0007_B

_854825:1

5.7

895 -

Avg:1

5.7

895),

Weig

ht=

p1

Var ID (Primary)

PO_WST10332_EXJADE_GRAN_Steintraining.M2:7, PS-ComplementoryScore Contrib PS(S0007_B_854825:15.7895 - Avg:15.7895), Weight=p[1]

Mis

sin

g

SIMCA-P+ 11 - 08.02.2009 17:17:00

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$Time (normalized)

PO_WST10332_EXJADE_GRAN_Steintraining.M2:7Predicted Liquid feed pump speed

+3 Std.DevXVar(Liquid feed pump speed) (Aligned) (Avg)-3 Std.DevXVarPS(Liquid feed pump speed) (Batch S0007_B_854825)

SIMCA-P+ 11 - 08.02.2009 17:17:46

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tPS

[2]

tPS[1]

PO_WST10332_EXJADE_GRAN_Steintraining - batch level (scores).M1 (PCA-X), All Batches, PS-ComplementorytPS[Comp. 1]/tPS[Comp. 2]

R2X[1] = 0.402627 R2X[2] = 0.341738 Ellipse: Hotelling T2PS (0.95)

S0006_A_85S0007_A_85S0007_B_85

S0006_A_85S0006_B_85

S0007_A_85S0007_B_85S0008_A_85

S0008-B_85S0009_A_85S0009_B_85

S0010_A_85S0010_B_85S0011_A_85S0011_B_85

S0012_A_85S0012_B_85S0014_A_85S0014_B_85S0018_A_85S0018_B_85S0019_A_85S0019_B_85S0021_A_85S0021-B_85

S0022_A_85S0022_B_85S0023-A_85S0023-B_85S0024-A_85S0006_B_85S0025_A_85S0025_B_85S0026_A_85S0026_B_85S0027_A_85S0027_B_85S0028_A_85

S0028-B_85

S0029-A_85S0029-B_85S0030-A_85S0030_TEILS0031_A_85S0031_B_85S0032_A_85S0032_B_85S0033_A_85S0033-B_85S0034_A_85S0034_B_85S0035_A_85S0035_B_85S0037_A_85S0037_B_85S0039_A_85S0039_B_85

S0040_A_85S0040_B_85S0041_A_85S0041_B_85S0042_A_85S0042_B_85S0043_A_85

S0044_A_85S0044_B_85S0045_A_85S0045_B_85S0046_A_85S0046_B_85S0047_A_85S0047_B_85S0048_A_85S0048_B_85

SIMCA-P+ 11 - 08.02.2009 17:15:01

Increase of level of detail 17


MVDA applied to granulation Example for a qualitative model used for MSPC

•  High Shear Granulation (Production Scale) •  Four phases

–  Dry Mixing –  Wet mixing –  Water addition –  Granulation

•  Variables –  Power consumption and torque –  Product temperatures –  Granulator and chopper speed –  Pump properties and flow parameters

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Key consideration for method development •  Which observations should be included ?

–  Sufficient number of batches to cover natural variability –  DoE Data for special cause variations –  Exclusion of anomalous, unsteady, discontinuous data (spikes)

•  Which variables should be included ? –  Exclusion of variables with no impact and low reliability –  Weighting and transforming of variables –  How many scores should be considered

•  Data alignment and synchronization –  Definition of start/stop conditions and phases –  Merger of variables with different acquisition rates –  Normalization of time based maturity variables vs. absolute time

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Granulation Process Data Setup

Granulation process Variable initial conditions : • Amount of granulation liquid • Granulation time time

1.  Power consumption 2.  Power consumption rate 3.  Torque 4.  Product Temperature 5.  Mixer Speed 6.  Chopper Speed 7.  Water addition rate 8.  Flow Liquid Speed 9.  Liquid Speed Pump

IPC results: • LOD • PSD

Input: Initial conditions

Output: Quality Attributes

X0

X(t)

Y

Dry Mixing Water Addition Kneading

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Example Granulation Results of DoE investigation

§  Different phases during granulation are monitored §  Process variability are reflected by the red lines §  Clustering of DoE batches can be visualised §  Common cause vs. special cause variation

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t[2]

t[1]


S0010-B

S0011-AS0011-B

S0012-AS0012-B

S0013-AS0013_BS0014_A

S0014_B

S0015-A

S0015_B

S0016-AS0016-B

S0009A_

S0009B_S0010A_

SIMCA-P+ 11 - 03.08.2008 17:12:10

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Loadings Identification of process parameters contributing to process variability

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p[2]

p[1]

Loadings of Granulation Models

R2X[1] = 0.401621 R2X[2] = 0.318985

Flow Liquid Feed

Mixer Power Rate

Mixer Power

Mixer Power (calculaMixer Torque

Liquid Pump Speed

Bowl Pressure

Mixer SpeedChopper Speed

Product TemperatureBowl Temperature

Liquid Added

SIMCA-P+ 11 - 04.01.2010 18:07:36

§  Which parameters are most influential? §  How do the variables correlate to each other?


First experience in production Preventive maintenance

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t[2]

t[1]

PO_WST10332_EXJADE_GRAN_V1 - Batch Level scores: t[1]/t[2]

Ellipse: TCrit (95%) = x²/19.9118² + y²/17.1288² = 1

S0075_B_854826

S0064_A_854826

S0075_A_854826S0075_B_854826

S0063_B_854826

S0068_B_854826

S0072_A_854826S0069_B_854826S0072_B_854826

S0080_A_854826

S0073_A_854826

S0083_B_854826

S0067_B_854826

S0066_B_854826

S0065_A_854826

S0073_B_854826

S0063_B_854826

S0071_B_854826

S0071_A_854826

S0064_B_854826S0066_A_854826

S0074_A_854826S0068_A_854826

S0084_A_854826

S0067_A_854826

SIMCA-Batch On-Line View 2.2 - 01.08.2009 16:58:57

Last campaign This campaign Last batch after liquid feed tube change

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Root cause analysis Liquid feed pump speed trajectory during the latest 2 campaigns

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Root Cause: Worn out liquid feed tube

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By replacing the tube # batches were saved. $ XXXXXXX saved


Cell cultivation process •  Motivation: established process, not fully characterized,

most of process understanding based on experience •  Modeling: > 80 DS batches, fully meeting release

specifications but some variability in main fermentation yield is observed

•  Defined reference (golden) batches, i.e. batches which provided the highest total amount of antibody during cell culture

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S[1]

DaySIMCA-P+ 12.0.1 - 2011-03-18 12:30:07 (UTC+1)

REFERENCE (i.e. avg of golden batches)

±3 sd from ref.

Statistical Process Control for cell cultivation process

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YVar(Day) (smoothed and shifted)

Batch: 41m, Phase: main

-0.025 * XVar(VCD (cells/mL)) - 0.025 -0.38843 * XVar(Glucose (mg/L)) - 0.38843-0.233716 * XVar(Lactate (mg/L)) - 0.233716 -0.0251397 * XVar(Titer (mg/L)) - 0.0251397-0.0526316 * XVar(Luft (U/L)) - 0.0526316 -0.150825 * XVar(Spec Feed (mL/cells)) - 0.150825

SIMCA-P+ 12.0.1 - 2010-04-09 09:13:16 (UTC+1)

Perfusion Start

All the variables for each batch are summarized

into one quantity (carrier of the information) –

process signature

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t[2]

t[1]

R2X[1] = 0.430057 R2X[2] = 0.145144 Ellipse: Hotelling T2 (0.95) SIMCA-P+ 12.0.1 - 2011-05-21 15:42:07 (UTC+1)

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Improve Process Understanding Compared the other batches (“non-golden“) against the “golden” to establish which are the variables responsible for the observed differences

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tPS[

1]

DaySIMCA-P+ 12.0.1 - 2011-03-18 12:22:40 (UTC+1)

-2.0-1.5-1.0-0.50.00.51.01.52.0

VCD

(1E5

cells

/mL)

Viab

ility (

%)

Med

ium

Fee

ding

Rat

e (K

g/24

h)

Wei

ght (

kg)

D M

ediu

m F

eed

Exch

ange

rate

(1/2

4h)

Gluc

ose

Con

c (m

g/L)

Lacta

te C

onc (

mg/

L)

NH4+

Con

c (m

g/L)

LDH

Conc

(U/L

)

pH

Scor

e Co

ntrib

utio

n

Var ID (Primary)SIMCA-P+ 12.0.1 - 2011-04-29 18:06:16 (UTC+1)

3 sd from ref.

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MVDA learning •  Generated useful process knowledge

–  Enhanced process understanding supported by data –  Improved process consistency

•  Established key parameters for cell cultivation –  medium feeding rate –  inoculation cell density –  cells aeration

•  No correlation between cell behavior and DS quality attributes

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tPS[2]

tPS[1]


golden non-golden

SIMCA-P+ 12.0.1 - 2011-05-21 15:45:20 (UTC+1)

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Petter Moree [email protected] Director, Global Product Manager Umetrics

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