6/19/2019

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MVA and DOE: Throughout the Product Lifecycle

Dr. Charles E (Chuck) MillerCamo Analytics

• Background: • MVA and DOE: “The Tools” vs. “The Philosophies”• Synergies• Product Lifecycle

• Thesis: The tools and concepts of MVA and DOE are relevant throughout the product lifecycle

• Case studies:• On-line NIR Spectrometer: 30 years old!• Pharma RTRT Application• PAT Biotech Applications

Outline

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Multivariate Analysis (MVA)

• Multiple Linear Regression (MLR)

• Principal Components Analysis (PCA)

• Partial Least Squares (PLS) Regression

• Cluster Analysis• Linear Discriminant Analysis

(LDA)• Some Others…..

• Most relationships are seldom binary and linear

• Combine both “Statistical and Chemical” thinking (Martens, Naes 1989)

• Utilize Domain Knowledge whenever possible

• Accept, embrace and utilize increasing multivariate nature of data

• “All models are wrong, but some models are useful”

The Tools The Philosophy

• Experimental Design Schemes:

• Screening, Factorial, Composite, Mixture,….

• Confounding• ANOVA, Regression

Design of Experiments (DOE)

• Taguchi’s three concepts:• Design Quality into the Product• Achieve Quality by Minimizing

Deviation from the Target• Measure the Cost of Quality as a

Function of Deviation from the Standard (continuous improvement)

• Key Principles: Randomization, Replication, Blocking and Control

The Tools The Philosophy

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• Mixture DOEs for Multivariate Calibration of PAT analyzers

• Use MVA and DOE for candidate selection, optimization

• Use DOE to optimize MVA calibration model development process

• Use MVA to analyze multivariate responses generated from a DOE

• Multi-way MVA tool (PARAFAC), for multiplicative ANOVA (MANOVA)

• “Design Space” concept in QbD

The Many Synergies of MVA and DOE

Geir Rune Flåten, Frank Westad, Pat Whitcomb, Synergy of DoE and MVA, IFPAC 2019

• Generate data that sufficiently cover the range of PAT analyzer responses expected to be generated during real-time operation, and

• Generate data that can be used to sufficiently characterize any non-linear effects in the analyzer data.

DOE for Multivariate PAT Calibration: Objective(s)

FEED

REACTOR

PRODUCT

ANALYZER

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• Non-Linearities: X-X and X-Y• Modeling Math: Inverse modeling math (MLR,

PLS, PCR) vs. Direct modeling math (CLS, and extensions thereof).

• Interactions: Don’t need to model, but presence needs to be taken into account

• Y Variable Type: Compositional (ex. API concentration), or non-compositional (ex. dissolution rate, hardness).

• Uncontrolled Factors: Environmental factors that could influence the response (X) variables

• Limited Resources: ex. API (active ingredient) for generating standards, especially during small-scale process development.

DOE for Multivariate PAT Calibration: Key Considerations

• Exclusively multiplicative: expect factor interactions

• NIR Calibration Transfer Study• 4D data array containing model

prediction errors (RMSEPs) from transferred models

• Mode 1 = Standardization Method (4 types)

• Mode 2 = Preprocessing (7 types):• Mode 3 = Master/Slave instrument pair

(6 options)• Mode 4 = Analyte: 1 through 4 (4

options)

PARAFAC as a Multiplicative ANOVA Tool

C. Miller, IFPAC 2008

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PARAFAC Results, loadings

Metric = RMSEP

none GLS only PDS only PDS, then GLS0

2

4

6

8

10

12

14

none std-MSC std-SNV std-2D MSC-std SNV-std 2D-std0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

m5/mp5 m5/mp6 mp5/m5 mp5/mp6 mp6/m5 mp6/mp50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

moisture oil protein starch0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Mode 1 = Standardization Method Mode 2 = Preprocessing 

Mode 3 = Master/Slave instrument pair Mode 4 = Analyte: 

-1 -0.5 0 0.5 1 1.5 20

20

40

60

80

100

120

140

160

91.296% of the variance explained with one (4‐dimensional) PARAFAC component

Residual distribution

Must do X‐fer (GLS slightly better)

X‐fers between “mp” instruments most successful

2nd derivative preprocessing slightly better results

Oil is best‐predicted analyte

• Design Space: “The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality” (ICH Q8R2)

• Regulatory flexibility –Working within the design space is not considered as a change

QbD: “Design Space” Concept

S. Chatterjee, IFPAC 2012 0

2

4

6

02

46

0

2

4

6

8

10

B

x2

A

C

x1

x3

Linear algebra says: • Space (sub‐space) extends infinitely 

in all relevant dimensions• Region represents only a specific 

part of the space

DOE:1. establish relevant process variables (screening), 2. define the space in terms of manipulated process 

variables

MVA: used to handle multivariate outputs from DOE, and to enforce Design Space Compliance in real‐time

Should it be called “Design Region” instead?

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• Non-DOE (Happenstance) data- to balance, avoid redundancies

• Even within a DOE, subset selection critical:

• Calibration set, Validation set, test set

Sample Selection in NIR

Næs, T. and Isaksson, T.(1989). Selection of samples for calibration in near-infrared spectroscopy. Part I. General principles illustrated by example. Appl. Spec. 43, 2, 328-335Isaksson, T. and Næs, T.(1990) Selection of samples for calibration in near-infrared spectroscopy, Part II. Selection based on spectral measurements. Appl. Spec., 44, 7, 1152-1158.

• Traditionally, DOE tools are relegated to the early (development) stages of the lifecycle

• MVA tools are gaining traction throughout the lifecycle (especially for PAT calibration), but are still relatively under-utilized, considering the increased data volumes being generated

Product Lifecycle

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• “PAT” = Process Analytical Technology

• All four value propositions depend on DOE and MVA

• PAT calibrations in development• PAT calibrations in supply• High-volume data screening/analysis• Scale-up process• MVA monitoring

•PAT = “catalyst” for more DOE, MVA usage!

Lifecycle: “PAT”-Centric View

Manoharan Ramasamy, Nathan Pixley, Bruce Thompson, Chuck Miller, Louis Obando, John Higgins, Mark Eickhoff, IFPAC 2015

Product Lifecycle TableLifecycle Phase  Development Marketable Product

Pharma phase Pre‐clinical, Phase I Phase II Phase III Launch Growth Maturity‐Decline

Business Activities Safety testing Patient testing‐

Efficacy, side 

effects

Patient testing‐

Efficacy, effectiveness, 

safety, PAI

Start manufacturing 

per filing

Manufacture per 

filing, possible filing 

updates

Generic competition

Technical Activities HTS, basic R&D, 

exploration

Clinical studies, 

MFG for studies, 

process scale‐up, 

control strategy

Clinical studies, MFG 

for studies, assess 

therapeutic effect, 

prepare filing, MFG & 

PAI readiness

Closely monitor 

process, QA systems

Scale up MFG to 

demand;

Ongoing process monitoring

DOE Activities Screening, 

development DOEs

Clinical trial 

designs, PAT 

mixture/cal

design; pilot 

plant designs

Clinical trial designs, 

PAT mixture/cal design; 

pilot plant designs

Designs for PAT 

method updates, 

process updates

Designs for PAT and 

Analytical Method 

transfers & method 

updates

Designs to support process 

improvements

MVA Activities Exploratory MVA; 

Lab Analytical 

development

PAT MV 

calibration 

models for R&D, 

Exploratory MVA

Scale‐up/down process 

modeling; PAT method 

optimization, filing 

preparations

“Hypercare” Process 

monitoring & fault 

detection; PAT 

method monitoring 

(ODs), PAT updates

PAT calibration 

transfer, monitoring 

(outlier diag), 

method 

maintenance; 

Retrospective 

process MVA

PAT calibration 

maintenance, MVA process 

investigations, deviation 

support; Retrospective 

process MVA

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• CASE 1: In-line NIR Reaction Monitoring: Since 1989• CASE 2: Pharma Real Time Release Testing (RTRT)

Application: Since 2005• CASE 3: Upstream Biotech PAT and MVA Applications:

Since 2008

Case Studies

Acknowledgements: DuPont, Merck

CASE1: In-line FTNIR Feed Monitoring

• Purpose: Reactor control in a continuous process

• Redundant with on-line GC, process model

• Complex composition space!• 4 production units across 2 plant sites• 100s of product grades• >10 constituents • 31 PLS models total

• 1989: Instruments and Sample System installed

• 1992: PLS models deployed

• Still running today!

DuPont Sabine River Works‐ Orange TX

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• Sampling: Slip stream transmittance, 5000 PSI, flammable fluid, with entrained “wax”

• Phase separation risk• T, P and flow sensors on sampling system

• 20 sec. analysis frequency• Instrument: Analect “wedge” FT-

NIR• NEMA enclosure, in “shaky” location

• 1996: High Pressure Calibrator: injected DOE standards for calibration development

• Custom real-time chemometrics

Case 1: In-line FTNIR Reaction Monitoring

FEED

REACTOR

PRODUCT

ANALYZER

C. Miller, et al,  “Multivariate Outlier Diagnostics: A Critical Component of  NIR/PAT Method QA”, IDRC2014

Product Lifecycle Table- CASE1Lifecycle Phase  Development Marketable Product

Pharma phase Pre‐clinical, Phase I Phase II Phase III Launch Growth Maturity‐Decline

Business Activities Safety testing Patient testing‐

Efficacy, side 

effects

Patient testing‐

Efficacy, effectiveness, 

safety, PAI

Start manufacturing 

per filing

Manufacture per 

filing, possible filing 

updates

Generic competition

Technical Activities HTS, basic R&D, 

exploration

Clinical studies, 

MFG for studies, 

process scale‐up, 

control strategy

Clinical studies, MFG 

for studies, assess 

therapeutic effect, 

prepare filing, MFG & 

PAI readiness

Closely monitor 

process, QA systems

Scale up MFG to 

demand;

Ongoing process monitoring

DOE Activities Screening, 

development DOEs

Clinical trial 

designs, PAT 

mixture/cal

design; pilot 

plant designs

Clinical trial designs, 

PAT mixture/cal design; 

pilot plant designs

Designs for PAT 

method updates, 

process updates

Designs for PAT and 

Analytical Method 

transfers & method 

updates

Designs to support process 

improvements

MVA Activities Exploratory MVA; 

Lab Analytical 

development

PAT MV 

calibration 

models for R&D, 

Exploratory MVA

Scale‐up/down process 

modeling; PAT method 

optimization, filing 

preparations

“Hypercare” Process 

monitoring & fault 

detection; PAT 

method monitoring 

(ODs), PAT updates

PAT calibration 

transfer, monitoring 

(outlier diag), 

method 

maintenance; 

Retrospective 

process MVA

PAT calibration 

maintenance, MVA process 

investigations, deviation 

support; Retrospective 

process MVA

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• Relied on “happenstance” samples only

• X = online NIR analyzer during normal production

• Y = online GC or process model/lab result

• Concerns about method accuracy and precision

• Develop means to deliver synthetic mixture standards to the field analyzer

Initial MVA (PLS) Calibrations

REACTOR

PRODUCT

PROCESS MODEL

CALIBRATION DATA

• High-pressure calibrator apparatus:

• Mix and pressurize gas and liquid blends• 1.5 yrs of engineering, permits, etc!…

• “Fortify” existing data with higher-precision data from calibrator

• DOE Backbone: • Central Composite designs, within each

product family: same chemistry• Uncontrolled factors: C1, C2, T, P• Separate composition designs for gas and

liquids

DOE for Calibration Improvement

FEED

REACTOR

PRODUCT

ANALYZER

CALIBRATOR APPARATUS

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• Non-Linearities: expect these, from spectroscopy and sample physics

• Modeling Math: inverse (PLS) must span the NIR response space

• Interactions: Expect chemical/spectral interactions need multiple levels

• Y Variable Type: most are compositional • Uncontrolled Factors:

• T and P are supposed to be controlled, but allow narrow band of variation

• Low-level components (C1, C2): allow some variation (some confounding)

• Limited Resources: only have 15 days, on only one unit, hazardous environment

• Logistics: Design applies to materials (gas phase, liquid phase), and to experimental protocol

CASE 1: DOE Considerations

FEED

REACTOR

PRODUCT

ANALYZER

CALIBRATOR APPARATUS

• Selected subset of happenstance data + DOE-generated data

• More confident model validation, outlier filtering

• Simpler calibrations (fewer LVs)!

Model Updates, with Mixed Data

-3 -2 -1 -0 1

PCA score 1

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

PC

A s

core

2

synthetic standardsprocess samples

measured Y

pre

dic

ted

Y

synthesized standardsprocess samples

Component 1:

Component 2:

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• Improved precision!• From more accurate and precise

reference values, and “method customization”

• Extended analyte concentration ranges

• No consensus change in model fit parameters (RMSEC)

• BUT, the validation error as a % of range did decrease!

• Improvements from 2 to 70%!

• AND, operator feedback: “better long-term stability”

• This is VERY important!

Results

The Bottom Line

• Increased usage of analyzer for automatic control

• Faster product transitions• Less waste

• On the order of “$100k’s” savings per year, over ~10 years

• 24

“A”‐analyzer

“A”‐lab

“A” via slower, redundantanalyzer

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• In service since 2006• Uses Bruker MPA NIR Diffuse

Transmittance• Three dosages (A, B, C)

• To date: billions of tablets manufactured, 100,000’s tablets analyzed

• Original Models based on mixture of DOE + normal manufacture

• On-going verification strategy supported by:

• Multivariate outlier diagnostics• Comparison to reference method (LC)

CASE 2: Pharma Real Time Release Testing (RTRT) Application

John Higgins, Zhihao Lin, Charles E. Miller, Nathan Pixley, Manoharan Ramasamy, George Zhou, and Niya Bowers‐ IFPAC 2014

Product Lifecycle Table- CASE 2Lifecycle Phase  Development Marketable Product

Pharma phase Pre‐clinical, Phase I Phase II Phase III Launch Growth Maturity‐Decline

Business Activities Safety testing Patient testing‐

Efficacy, side 

effects

Patient testing‐

Efficacy, effectiveness, 

safety, PAI

Start manufacturing 

per filing

Manufacture per 

filing, possible filing 

updates

Generic competition

Technical Activities HTS, basic R&D, 

exploration

Clinical studies, 

MFG for studies, 

process scale‐up, 

control strategy

Clinical studies, MFG 

for studies, assess 

therapeutic effect, 

prepare filing, MFG & 

PAI readiness

Closely monitor 

process, QA systems

Scale up MFG to 

demand;

Ongoing process monitoring

DOE Activities Screening, 

development DOEs

Clinical trial 

designs, PAT 

mixture/cal

design; pilot 

plant designs

Clinical trial designs, 

PAT mixture/cal

design; pilot plant 

designs

Designs for PAT 

method updates, 

process updates

Designs for PAT and 

Analytical Method 

transfers & method 

updates

Designs to support process 

improvements

MVA Activities Exploratory MVA; 

Lab Analytical 

development

PAT MV 

calibration 

models for R&D, 

Exploratory MVA

Scale‐up/down process 

modeling; PAT method 

optimization, filing 

preparations

“Hypercare” Process 

monitoring & fault 

detection; PAT 

method monitoring 

(ODs), PAT updates

PAT calibration 

transfer, 

monitoring (outlier 

diag), method 

maintenance; 

Retrospective 

process MVA

PAT calibration 

maintenance, MVA process 

investigations, deviation 

support; Retrospective 

process MVA

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CASE 2: NIR Model Strategy

Design Variables:• %LC: 60 ‐140!!• particle size• hardness• weight

DOE data

Process data

1) A “mixture” of Calibration Standards:• DOE data: provides robustness• Process data: provides relevance

2) Allow infrequent, science‐based model updates to improve relevance, as needed3) Only one production plant (thus far..)

74

39

Calibration Standards for product A model:

Mtd. Valid. (3.06)

Initial method (11.6.06)

N=127

N=167

14

2006 production from pilot site

74

39

14

402006 production from plant A

Method updated (2011)

N=167

50

77

40

9 more recent plant A batches (2010‐11)

Redundant DOE samples removedCa. 5 

years….

1/1/2006 1/1/2007 1/1/2008 12/31/2008 12/31/2009 12/31/2010

25mg

50mg

100mg

Plant A PAT Method Events

MAR, JUL 06: 3 NIR models put into service

10.3.2010 (A): 132 MD alarms over 8 batches! All negatives!

Investigation of outlier metric limits (too “tight”???)

18.3.2011 (all): new MD limits set using 95% CL

30.6.2011: A model updateB and C model 

updates

29.7.2009 (B): 6 MD alarms over 2 weeks, all negatives

B :3 MD alarms, all confirmed positives!

2 4 6 8 10

x 104

0

500

1000

1500

Sample

F v

alue

F value

F above limit

F below limit

27.1.2009 (A): 2 F alarms, confirmed positives!

•A model generated useful metrics right away; B and C models did so after a model update• Since 2006, only 7 confirmed positive tablets (out of >120k!)

• All were flagged by the outlier detector!• None resulted in tablet quality issues!

• However, 132 false alarms for model A in 2010

B model update effect on F value

12.2.2007 (A): 2 F alarms, confirmed positives!

XRCT confirmed “lumps” in tablet!

A strengthB strengthC strength

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Model A Outlier metrics: 2006-2011

29

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

50

100

150

M-Distance Value

freq

uenc

y

CALIBRATION

VALIDATION

0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.160

10

20

30

40

50

M-Distance value

freq

uenc

y

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.080

1000

2000

3000

4000

5000

6000

M-Distance Value

freq

uenc

y

Calibration (N=167) and Validation (N=123)

Routine Analysis (N=101310)

132 MD‐based false alarms 

M‐distance

F‐value

0 20 40 60 80 100 120 140 160 1800

50

100

150

200

250

F-Value

freq

uenc

y

CALIBRATION

VALIDATION

0 1 2 3 4 5 60

20

40

60

80

100

120

F-Value

freq

uenc

y

0 1 2 3 4 5 6 70

1000

2000

3000

4000

5000

6000

7000

8000

9000

F-Value

freq

uenc

y

• MD and F metrics of routine samples follow F‐distribution “fairly well”

• A few high‐F outliers in the calibration set, caused some “inflation” in the F‐value limit

• All 132 false alarms were caused by MD (not F value)

Evidence to support increase in CL for MD limits

Model A Update 2011

MD INDEX

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1 50 99 148

197

246

295

344

393

442

491

540

589

638

687

736

785

834

883

932

981

1030

1079

1128

1177

Spectra

MD

I

2006 Model

2011 Model

Model update evaluation includes studying the behavior of outlier metrics!

F-Probability

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 50 99 148

197

246

295

344

393

442

491

540

589

638

687

736

785

834

883

932

981

1030

1079

1128

1177

Spectra

FP

2006 Model

2011 Model

MEAN DIFFERENCE

0.0

0.5

1.0

1.5

2.0

2.5

1070108

1070137

1070257

1070436

1070444

1070510

1070696

1070698

1071027

1080184

1080265

1080570

1080812

1080930

1081082

1081223

1090025

1090207

1090264

1090451

1090452

1090455

1090456

1090567

1090757

1090933

1090964

1090976

1091138

1091391

1091463

1100057

1100181

1100183

1100234

1100237

1100238

1100239

1100244

1100245

1100246

1100247

1100437

1100581

1100749

1101010

1101182

1101287

1101288

1110034

1110039

1110040

1110041

1110043

1110075

1110086

1110150

1110156

2006 Model

2011 Model

RMSEP

0.0

0.5

1.0

1.5

2.0

2.5

1070 108

1070 137

1070 257

1070 436

1070 444

1070 510

1070 696

1070 698

1071 027

1080 184

1080 265

1080 570

1080 812

1080 930

1081 082

1081 223

1090 025

1090 207

1090 264

1090 451

1090 452

1090 455

1090 456

1090 567

1090 757

1090 933

1090 964

1090 976

1091 138

1091 391

1091 463

1100 057

1100 181

1100 183

1100 234

1100 237

1100 238

1100 239

1100 244

1100 245

1100 246

1100 247

1100 437

1100 581

1100 749

1101 010

1101 182

1101 287

1101 288

1110 034

1110 039

1110 040

1110 041

1110 043

1110 075

1110 086

1110 150

1110 156

2006 Model

2011 Model

2006 Model had very good prediction resultshard to further improve!!

Both outlier metrics had improved behavior M‐distance. in particular

Prediction 

Perform

ance

Outlier 

Perform

ance

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Site 1

Site 3

Life Cycle of RTRT NIR method

NIR 1a

Site 2Dose ADose BDose C

NIR 2a

Dose ADose BDose CDose DDose E

Dose ADose BDose CDose DDose E

NIR 2b

NIR 2b

Transfer

Transfer

Inter-instrumentTransfer

Inter-instrumentTransfer

NIR 3a

NIR 3b

Inter-instrumentTransfer

NIR 2c

Intra-site transfer

Inter-sitetransfer

Inter-site transfer?

Manoharan Ramasamy, Nathan Pixley, Bruce Thompson, Chuck Miller, Louis Obando, John Higgins, Mark Eickhoff, IFPAC 2015

RTRT still in service today‐ at three sites!

• Exploratory MVA models• Multi-scale MVA models• Pilot Plant MVA

monitoring• In-line Raman (PAT)

CASE 3: MVA Upstream Biotech Applications

Charles E. Miller, Louis Obando, John P. Higgins, Gert Thurau, AAPS 2012 Annual Meeting, Chicago IL, 10/17/12 

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Product Lifecycle Table- CASE 3Lifecycle Phase  Development Marketable Product

Pharma phase Pre‐clinical, Phase I Phase II Phase III Launch Growth Maturity‐Decline

Business Activities Safety testing Patient testing‐

Efficacy, side 

effects

Patient testing‐

Efficacy, effectiveness, 

safety, PAI

Start manufacturing 

per filing

Manufacture per 

filing, possible filing 

updates

Generic competition

Technical Activities HTS, basic R&D, 

exploration

Clinical studies, 

MFG for studies, 

process scale‐up, 

control strategy

Clinical studies, MFG 

for studies, assess 

therapeutic effect, 

prepare filing, MFG & 

PAI readiness

Closely monitor 

process, QA systems

Scale up MFG to 

demand;

Ongoing process monitoring

DOE Activities Screening, 

development DOEs

Clinical trial 

designs, PAT 

mixture/cal

design; pilot 

plant designs

Clinical trial designs, 

PAT mixture/cal design; 

pilot plant designs

Designs for PAT 

method updates, 

process updates

Designs for PAT and 

Analytical Method 

transfers & method 

updates

Designs to support process 

improvements

MVA Activities Exploratory MVA; 

Lab Analytical 

development

PAT MV 

calibration 

models for R&D, 

Exploratory MVA

Scale‐up/down 

process modeling; PAT 

method optimization, 

filing preparations

“Hypercare” Process 

monitoring & fault 

detection; PAT 

method monitoring 

(ODs), PAT updates

PAT calibration 

transfer, monitoring 

(outlier diag), 

method 

maintenance; 

Retrospective 

process MVA

PAT calibration 

maintenance, MVA process 

investigations, deviation 

support; Retrospective 

process MVA

Upstream Biotech: Data Types

day

OFAV

batch

batch

SV

time

QV

In‐line Sensors:Ex: P, T, flow, DO, OUR, CERHigh frequency (~1/min)More Relevant to Operations

Off‐line Analytical :Ex: viability, Glu, LacLow frequency (~1/day)Relevant to Operations and Quality

Post‐Batch Analytical:Ex: IEX, N‐glycan %sOnce per batchRelevant to Quality

batch

Modeling Goals:

On‐line monitoring, exploratory (multi‐scale/site)

Quality‐predictive models, CPP/KOP “verification”

On‐line monitoring, exploratory (multi‐scale/site, CPV), CQA/CPP “verification”

6/19/2019

18

Multiscale Model

20 off‐line cell culture variables

• 8 batches, covering 3 scales: 3L, 500L and 2000L

• All run at target conditions (no DOE data)• PLS Observation‐level model to batch 

maturity

Variable na me 3L 500L 2kL

DO D D N

pH online 1 PI PI PI

pH online 2 PI PI PI

pH offline D D D

pCO2 (mm Hg) D D D

pO2 (mm Hg) D D D

Gluc (g/L) D D D

Lac (g/L) D D D

Gln (mg/L) D D DC

Glu (mg/L) D D DC

NH4 (mmol/L) D D DC

Osmo (mOsmo/kg) D D DC

Viable Density (x10^5 

cells/mL) D D D

Total Density (x10^5 

cells/mL) D D D

Viability (%) D D D

RP‐HPLC Titer (g/L) <D <DC <DC

Mini‐Neph Titer (g/L) <D <D N

CVC D D C

Volume (L) D D

NaOH Addition (Kg) E D E

% NaOH added E D E, C

Component%X variance explained- Component

%X variance explained- Cumulative

%Y variance explained- Component

%Y variance explained- Cumulative

1 0.422 0.422 0.913 0.913

2 0.256 0.678 0.0229 0.936

3 0.0793 0.758 0.0197 0.956

LEGEND:

D = daily

<D = less than daily

E = collected and analyzed end of batch (EOB)

C = calculated quantity

DC = collected daily, but analyzed EOB

<DC = collected less than daily,  analyzed EOB

N = not measured

PI = measured online

Multiscale Model

Scores: 

3L and 500L batches behave similarly

2000L batches behave differently,  but HOW?....

Loadings: Variable correlations

w*c[2

]

t[2]

6/19/2019

19

Multiscale Model

2000L batches have more variation in pH, Lac and pCO2 than smaller‐scale batches

XVar

(pH

onlin

e 2)

XVar

(pCO

2 (m

m H

g))

XVar

(Lac (

g/L))

Exploratory MVA

• Problem statement: FVF potency values from recent lots have average potencies of ~30% less than the 3-year average”

• GOAL: Identify root cause for the low potency and implement associated CAPAs in order to return FVF to average historical potency values

• 6/19/2019

• 33 unique observations (FVF, HVF and CELL_EXP lot combinations)

• All are normal production runs

• 565 X‐variables (460 continuous, 105 discrete)

• Y = potency

FVF_LOT HVF_LOT

CELL_EXP

_LOT

1 101 201

2 102 202

3 103 203

4 104 204

5 105 205

6 106 206

7 107 207

8 108 208

9 109 209

10 110 210

11 111 211

12 112 212

13 113 213

14 114 213

15 115 215

16 116 216

17 117 217

18 118 218

19 119 219

20 120 220

21 121 220

22 122 222

23 123 222

24 124 224

25 125 224

26 126 226

27 127 226

28 128 228

29 129 228

30 130 230

31 131 230

32 132 232

33 133 232

Note: Mapping/Genealogy of lots

6/19/2019

20

Exploratory MVA

“OK” cross‐validation results, using only 2 PLS latent variables

2 lots‐ potential outliersSpecific RM lots are among top 

predictors

FIT

Component%X variance explained- Component

%X variance explained- Cumulative

%Y variance explained- Component

%Y variance explained- Cumulative

%Y variance CV- Component

%Y variance CV- Cumulative

1 0.0971 0.0971 0.78 0.78 0.503 0.503

2 0.0719 0.169 0.145 0.924 0.128 0.566

YVar

(C2_

FVF_

POTE

NCY_

VALU

E_)

Var ID (Primary) M3.VIP[2] CommentC34_SPI_TR 2.98716 TRYPSIN-EDTA LOT# T1, during split 1C5_PLANT_M 2.59824C23_SPI_ME 2.59824C72_SPI_DA 2.59824C81_SPII_M 2.59824C129_SPII_ 2.59824C138_SPIII 2.59824C186_SPIII 2.59824C4_SPIV_ME 2.59824C118_SPIV_ 2.59824C58_INFECT 2.59824C43_SPIV_T 2.59824C95_SPII_T 2.58938 Time b/w SPI Day 3 Refeed Incubation and Removal for SPIIC149_SPIII 2.56881 TRYPSIN-EDTA LOT# T1, during split 3C5_SPIV_ME 2.42436 TRYPSIN-EDTA LOT# T2, during split 4C51_INFECT 2.36898 2.5% Tryspin-NaCl Lot #TN1, during infection

C77_SPI_DA 2.32368SPI Day 3 Refeed - Total Time b/w SPI Incubation and Removal

for SPI Day 3 Refeed

FBS Lot# F1, at various times during process

Diluted Trypsin-NaCl Bottle Filter Lot# BF1

Top predictors

Exploratory MVA

C34_SPI_TRYPSIN_EDTA_MEDIA_PRE Lot# T1, VIP=2.96

C95_SPII_T

XVar

(C34

_SPI_

Lot T1

Lots other than T1

XVar

(C34

_SPI_

6/19/2019

21

On-line Monitoring: In-line Sensors

• 9 batches, all on 2000L fermenter• 4 Phases Monitored: SIP, FVSIP, BATCH and 

FED_BATCH• 14 sensor variables

• PLS Observation‐level model for each phase• Executed every 2 min for BATCH, FED_BATCH 

operations

BATCH phase variables

OUR

CER

DO#1

DO#2

pH#1

pH#2

Agitator Speed

Tank Pressure

Temperature

Air Flow

RQ

O2 Feed Flow

Tank Weight CUSUM by Phase

Acid Tank Wt CUSUM

w*c[2

]

Loadings: BATCH phase Loadings: FED_BATCH phase

w*c[2

]

On-line Monitoring: In-line Sensors

(x 23)

DMod

X Co

ntrib

ution

, Weig

ht =

RXC

um

OUR

CER

DO#1

DO#2

pH#1

pH#2

Agita

tor S

peed

Tank

Pres

sure

Temp

eratu

re

Air F

low RQ

O2 Fe

ed Fl

ow

Tank

Weig

ht C

USUM

by P

hase

Acid

Tank

Wt C

USUM

Air Flow

EK_B64_MK1293_PRODFERM_FE3610_v1 - Air FlowBATCH

Air F

low

2,000

2,200

2,400

2,600

2,800

3,000

3,200

3,400

3,600

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5Process Phase Time (hr) (shifted)

Air Flow

High outlier metric observed at 09:38

Engineer: “Air flow had been lowered to 2100 slpm before inoculation, but it did not get re-set to 3360 slpm for BATCH phase”

PPQ2 Batch, 21 June 2013:

6/19/2019

22

Pilot Plant MVA Monitoring

Model DevelopmentOn-Line Monitoring

• “Wider” model space: includes DOE process states• Analytics platform auto-integrates process data for later modeling work• Training opportunities for upcoming full-scale process validation, launch

-1.1

-1.0

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6

p[2]

p[1]

MK1293_PP_PROD_041012.M1:STERILIZE, STERILIZEp[Comp. 1]/p[Comp. 2]

R2X[1] = 0.632248 R2X[2] = 0.243824

pH PV Tank Pressure PV

Agitator PV

Sterilization Temperature

SIMCA-P+ 12.0.1 - 2012-08-02 16:50:57 (UTC-5)

-0.3

-0.2

-0.1

-0.0

0.1

0.2

0.3

0.4

0.5

0.6

-0.4 -0.3 -0.2 -0.1 -0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

p[2]

p[1]

MK1293_PP_PROD_041012.M2:INOCULATE, INOCULATEp[Comp. 1]/p[Comp. 2]

R2X[1] = 0.187375 R2X[2] = 0.191238

Dissolved O2 PV

Air Flow PV

pH PV

Tank Pressure PV

Agitator PV

Batch Temperature PVSterilization Temperature

SIMCA-P+ 12.0.1 - 2012-08-02 16:58:22 (UTC-5)

-0.2

-0.1

-0.0

0.1

0.2

0.3

0.4

0.5

0.6

-0.30 -0.20 -0.10 -0.00 0.10 0.20 0.30 0.40 0.50

p[2

]

p[1]

MK1293_PP_PROD_041012.M3:PRODUCTION, PRODUCTIONp[Comp. 1]/p[Comp. 2]

R2X[1] = 0.374154 R2X[2] = 0.0892702

Dissolved O2 PV

Air Flow PV

CER

OUR

RQ

pH PV

Tank Pressure PV

Glycerol Feed Pump PV

Agitator PV

Batch Temperature PV

SIMCA-P+ 12.0.1 - 2012-08-02 17:00:05 (UTC-5)

(x 23)

(x 23)

• 2012: Proof of concept• FMEA DOE at pilot scale,

for Calibration development• Platform roll-out to 2 in-line

products and 2 pipeline products

• Across 2 sites• Bioreactor units• Cell culture operations

• In-scope for new development and manufacturing facilities

In-line Raman (PAT)

11/15/12 11/19/12 11/23/12 11/27/12 12/01/12 12/05/12 12/09/120.9

1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

Date/Time

Glu

cose

(g

/L)

Glucose Time Series

Raman-Predicted Glucose (g/L)Raman prediction flagged as OUTLIERBioprofile Glucose (g/L)

Charles E. Miller , John P. Higgins, Louis Obando, Jorge Vazquez, IFPAC 2018

6/19/2019

23

• MVA and DOE have strong synergy• Driven by PAT applications

• Tools and concepts apply throughout the product lifecycle

Summary

Case 3: PAT Method Events-Original Site

46

• C model generated useful metrics right away; A and B models did so after a model update

• Since 2006, only 6 confirmed “positives” (out of >120k!)• all of which were flagged by the outlier detector!• all of which had no product quality issues!

• However, 132 false alarms for C model in 2010 Re-evaluate outlier metric limits (too conservative?...)

6/19/2019

24

• In service since 2006• Uses Bruker MPA NIR Diffuse

Transmittance• Three dosages (A, B, C)

• To date: billions of tablets manufactured, 100,000’s tablets analyzed

• Original Models based on mixture of DOE + normal manufacture

• On-going verification strategy supported by:

• Multivariate outlier diagnostics • Comparison to reference (LC)

CASE 2: Pharma Real Time Release Testing (RTRT) Application

John Higgins, Zhihao Lin, Charles E. Miller, Nathan Pixley, Manoharan Ramasamy, George Zhou, and NiyaBowers‐ IFPAC 2014

• “In-space” metric (M-distance)• Expresses “distance from model center”• Generally, describes structured variance in the data• For some calibration data, do NOT expect random distribution!

• Especially for Pavia’s [“DOE” + process] calibration data!

• “Out-of-space” metric (F-value)• Describes less-structured variance (“noise”) in spectral space• Therefore, expect more randomly distributed metric values

The Two Outlier Metrics: Different Expectations!

0 0.2 0.4 0.6 0.8 10

2

4

6

8

Distance from Model Center

Fre

qu

en

cy

6/19/2019

25

• 23 15L vessels• Two 1000L vessels• One product in development• Two processes: seed and production fermentations

• Each has three process phases

• Observation- AND batch-level models• >500 batches• 11 process variables • 25 SBOL models running concurrently, since Dec 2011

Pilot Plant example

49

T09

T10

T11

T12

T13

T14

T15T16

T17

T18

T19

T20

T21

T22

T23

T24

T25

T26

T27

T28

T29

T30

MKxxxx SEED calibration

dataset

MKxxxx PRODUCTION

calibration dataset

MKxxxx PRODUCTION

validation dataset

MKxxxx SEED validation

dataset

MKxxxx SEED model

MKxxxx PROD. model

Validated MKxxxx SEED model

Validated MKxxxx PROD. model

T09 SEEDT10 SEED

T11 SEED

T12 SEED

T13 SEED

T14 SEED

T16 SEED

T13 PROD

T14 PROD

T15 PROD

T16 PROD

T17 PROD

T18 PROD

T19 PRODT20 PROD

T21 PROD

T22 PROD

T23 PROD

T24 PROD

T25 PROD

T26 PROD

T27 PRODT28 PRODT29 PROD

T30 PROD

T09

T10

T11

T12

T13

T14

T15

T16

T17

T18

T19T20

T21

T22

T23

T24

T25

T26

T27T28T29

T30

Mapping of model inputs to specific PI

tags of different

tanks

OCT 2010

to OCT 2011

NOV 2011

to FEB 2012