PQRI

Process Drift Workshop

Case Study

Denise Rivkees, R.Ph., Ph.D.

Pfizer Global Manufacturing

The Setting:

Product Quality Lifecycle Implementation

Purpose of this PQRI Presentation

• To demonstrate the use and advantages of Product Quality Lifecycle Management

• To demonstrate the use and advantages of new technology

• To demonstrate the use these tools in multiple collaborator environments over departments and organizations

• To demonstrate how the paradigm allows progress to go forward even in light of unexpected results

• To demonstrate what the data “look like” and demonstrate software differences and effects

• To demonstrate the challenges that occur

Notes:

• The presentation represents a live work-in-process that will continue after this presentation.

• The model process was out-of-trend, but no result was outside of a specification that would impact patient care.

Situation Description

• An excipient supplier changed the milling step for a raw material used in an extrusion process. Risk analysis revealed little expectation of impact to the critical quality attributes, and a straightforward like-for-like validation plan was put in place.

• During Offsite Stability Testing of the validation lots, the average 30 minute dissolution results began to drift while site retains at 30 minutes complied with the L2 dissolution criteria.

• FMEA followed the traditional investigation route to special causes:

Was it the excipient?

Was it the offsite lab?

Was it something about packaging?

Was it The dissolution method?

But the new paradigm of lifecycle management proved the root cause was none of these.

Filter Settings

- Accept: (True)

Initially, multiple studies were performed as part of an FMEA,

but the results showed nothing out of the ordinary. For example, fill weight

studies concerning the fill of individual raw materials were conducted.

Results of fill weight study:

Dissolution method

Guage R&R Studies of dissolution across sites

FOUND

All sites and methods were WITHIN NORMAL VARIATION

None of these aspects explained the change!

Example: Multiple aspects concerning the dissolution test, including the

test method and variance across multiple sites, were examined.

Ivelisse Colon-Rivera, PGRD

Process Drift Case Study Overview

Occurred during a material Change Control

A drift in Critical Quality Attribute was noted

The drift was visualized through Process Capability

Data Mining was used to identify Latent Variables

Data used for Risk Assessment

Control Strategy established

Restart Manufacturing in relatively short time period

Quality By Design DOE for Process Understanding

Identification of new latent variable

Latent variable used for Continuous Improvement

Process Capability Phase

Low variability within a lot observed from 2005

Variability within a lot increased from 2007

DATA TRENDS: IMR Chart

Validation batch

for Excipient #2

process change

at manufacturerExcipient #1

Slight change

associated

With small

variance

Compared

to spec

20092009200920082007200720052005200520052003

8

6

4

2

0

Mfg Date

In

div

idu

al

Va

lue

_X=2.22

UC L=5.76

LC L=-1.32

20092009200920082007200720052005200520052003

8

6

4

2

0

Mfg Date

Mo

vin

g R

an

ge

__MR=1.331

UC L=4.347

LC L=0

1

1

1

111

1

1

11

I-MR Chart of 30min Stdev by Mfg Date

Trending by

Israel Cotto, PGM

What is it? Matrix Composed of API in Excipient

Confocal Raman Imaging by

Don Clark, PASG

How is it made?

Three raw materials charged to blender via mill

Blend for 45 minutes

Blend charged to downstream process equipment

Extruder

Five additional major downstream processes

Process Drift Case Study Overview

Occurred during a material Change Control

A drift in Critical Quality Attribute was noted

The drift was visualized through Process Capability

Data Mining was used to identify Latent Variables

Data used for Risk Assessment

Control Strategy established

Restart Manufacturing in relatively short time period

Quality By Design DOE for Process Understanding

Identification of new latent variable

Latent variable used for Continuous Improvement

*

CONTROL STRATEGY PHASE

-5 0 5-4

-3

-2

-1

0

1

2

3

4

t3

t 4

Scatter Plot: t4 vs t

3

V090762

V09076318818V

V09037317178V

v

1.2

UNK

OOS

Data Mining Shows Outliers

Model Developed showing inverse relationship between API particle size

and excipient critical quality attribute.

Restriction of particle size and excipient attribute implemented.

Manufacturing to proceed with temporary Control Strategy.

Using these tools, the stability samples of interest led us to other lots that

were out of trend according to the model. Those lots were withheld based

on the prediction. DataMining by

Sal Garcia, PGRD

DataMining showed that the biggest contributors to

variability were particle size and excipient value

The site used the model developed from the site

data to match API and Excipient to achieve

desired dissolution

as a continuous improvement

Control Strategy

Business Factor Came Into Play:

Request to study new API source,

so we added it in and it revealed

information that we would not

have gathered had it not been in

the study

Process Drift Case Study Overview

Occurred during a material Change Control

A drift in Critical Quality Attribute was noted

The drift was visualized through Process Capability

Data Mining was used to identify Latent Variables

Data used for Risk Assessment

Control Strategy established

Restart Manufacturing in relatively short time period

Quality By Design DOE for Process Understanding

Identification of new latent variable

Latent variable used for Continuous Improvement*

Process Understanding Phase

1. Investigate a relevant design space

2. Acquire the raw materials

3. Run a pilot study

4. Based on Pilot Results, Design the DOE

5. Run the DOE

6. Analyze the data

Understand the Process!

1: Original Design Space Study Proposal

Statistical Support from

Anthony Carella, PGRD

Factorial Design 2X3

High and Low Excipient Values

High, Med, and Low API Size

Three Center Points

Eleven runs

Plus New API Source

2: API Milling Design of Experiments

Mill Study by

Al Pichieri, GMS

Jose R. Rivera, Site

Adjustable D6 Fitz Mill

Mill Settings: Low, Med, High

Produced: Low, Med, and High Particle Size

This study showed how we could manipulate the

particle size using a different mill. Useful for

continuous improvement of the API process.

3. Pilot Study: Dissolution and Water Content

*There was not enough water for success

**Routine finding for water content

Water added normally to process so we looked at three levels

In order to see the effect on the online NIR

Lot

number

Water

injected

(%)

Water

content

(%)

Dissoluti

on

At 30

minutes

0286-017 1 2.3 NA*

0286-013 2 3.0** 42

0286-015 3 3.8 39

Productio

n

Control

NA NA 61

Surprise!

Low results compared

to Production Control.

This meant we had to

expend DOE runs to

investigate equipment

differences at the pilot

study contractor.

No root cause was

Identified, but the team

agreed that the particle

size and excipient attribute

would show up in a smaller

DOE anyway, so we

Proceeded.

3 Pilot Blends

Appeared Identical by NIR: So far, so good

2nd derivative

SG 9pt.

2nd derivative SG 9pt.

Offline NIR (Bruker MPA) – 3 Lots

Appear Identical by NIR

Margot Cortese, Ron Beyerinck, Bend Research Offline NIR (ePAT601)

NIR (ABB) Extruder: 3 Lots - Comparable by NIR, non water

peaks overlapped

2nd derivative SG 9pt.

Zoom of first overtone region

H2OCH2

Zoom of second overtone region

H2O

Effect of water

variation

4. Design the study based on

successful pilot

API - Hi API- Lo

Excipient

Hi

Hi- Hi Lo-Hi

Excipient

Low

Hi- Low Low-Low

New Study Proposal

PLUS NEW API Source to examine effects

Statistical Support from

Anthony Carella, PGRD

Run

API

D (4,3)

µ

Excipient

Value

KF Water

%

Added

KF

%

Final

30 min

Dissolution

81 40±11 Low 2.15 3.4 45

82 40±11 High 2.20 3.5 44

83 89±10 Low 2.06 3.4 41

8489±10

High 1.94 3.4 43

85 52 High 3.6 3.6 48

Control NA NA NA NA 61

Study Results:

Smaller Particle Size Led to Faster Dissolution

KF data shown here for illustrative purposes because water typically

has an influence, but was not a major factor in this studySlight trend for the old API,

but something was different

about the new API.

Top-line Statistical Summary for Bend

DoE• Relevant data on the two parameters (API PS and Excipient

Value AV) . . .

• Mitigating factors

– Slower (& harder to impact) dissolution profiles using 27-mm extruder at

Bend

– Small DoE (due to resource constraints) led to weak statistical power

– Weak statistical conclusions, due to weak statistical power and to robust

manufacturing process (with respect to changes in dissolution)

• Statistical conclusions (over the parameter ranges studied) . . .

– Indication that increasing API PS decreased % dissolved at 30 minutes

– No indication that Excipient Value affected % dissolved at 30 minutes

– Indication that lot # BREC-0286-095 differed from other 4 lots

5. Analyze the Results

PAT is a major enabler! Even with weak statistical power, online trends revealed were known to beassociated with other findings throughoutall of the studies - and were therefore considered scientifically important

Online Blender NIR

Signals for alkyl groups move

Note: The NIR stopped working after

10 minutes on the first run, but the

effect of blending was already in place

after 5 minutes, as shown in trend

analysis below.

Note: When the new API batch was made,

the engineers called to say the API was more

cohesive and handled differently than ever

before. We knew at that minute why it might

yield different results. See PCAs on later

slides

1 Excipient CH2

2 Excipient CH3

3 API CH2

4 API CH3

5 API bound H20

6 Excipient CH2

These trends show powder

relfectance/absorbance

and are associated with density

and particle size.

One would expect the

middle particle size group,

the new API, to be in the

center --it was here

but not on dissolution!

Extruder Online NIR

API

Variance 1

Variance 2

Variance 3

Note: Second loading (Variance) almost a straight line after melting-

does this mean something??

DIFFERENT

SOFTWARE

NIR Peaks Assigned for API and Excipient

Zoom on API Peaks Zoom on Excipient Peaks

API+ H2O Peaks

Compritol PeaksExcipient

These two peaks directly demonstrate the trade off between

particle size and excipient value, VALIDATING the Data Mining

model

Trending with Excipient Peak at 1732 nm

(Second derivative)

Lot 81 Lot

82

Lot 81 Lot

82TREND

Visualization of raw data

producing this trend

Note large movement

In Excipient Peak

But small movement

in API peak

What changed between the two? The excipient value: and it was reflected in the

movement of the excipient alkyl peak AND the API particle size (or granule size?) as shown

In thereflectance that produces the trend line. This shows the interaction.

Trending with API Peak at 1694 nm

Lot 83Lot 84

Lot 84

Lot 83

TREND

Note less movement in

Excipient signal than last slide

Trending with Excipient Peak at 1732 nm

Lot 85

87 40 0.96 197 45

89 40 2.56 199 44

91 89 0.96 201 41

93 89 2.56 201 43

95 52 2.56 194 48

MSC

#

API

D(4,3)

Exc

AV

Sphere

D(4,3)

Disso

30min

I

N

C

R

E

A

S

I

N

G

R

E

F

L

E

C

T

A

N

C

E

The new

API absorbance

was in trend with

the other sizes,

but dissolution

was different-

meaning that

something

else

about the

new API

was different

TREND ANALYSIS

NIR Peaks Assigned for API and Excipient

Zoom in API Peaks Zoom in Excipient Peaks

API+ H2O Peaks

Compritol PeaksExcipient

Trending with API Peak at 1694 nm:

40 micron API does not produce a stable process

Lot 81

Lot 81

Lot 82

Lot 82

Regardless of mechanistics, this is not a stable process Regardless of mechanistics, this is not a stable process

Note difference induced by excipient value

Trending with API Peak at 1694 nm: Larger API Particle

Size Produces a More Stable Process

Lot 83

Lot 84

Lot 84

Lot 83

Stable Stable

Note:

Less

Variation

Than 81 and 82

Trending with API Peak at 1694 nm

Lot 85

Lot 85

52 micron (average)

Material still does not

produce a flat trend

Different Trend Analysis:

Average of last 10 Spectra of Each Blend

Lot 81, 82, 83, 84 and 85

Bend Ref 81

Bend Ref 82

Bend Ref 83

Bend Ref 84

Bend Ref 85

Note crossovers:

Why would this happen?

Look at the raw data or second derivatives for clues,

then trend those points as we did in the previous slides

PCA Score #1 Vs. Score #2

If one agrees than some small particles might aggregate and act

as larger particles, then the first PCA (measures variance) trends

with particle size this way-

PCA Score #1 Vs. Score #2

But the dissolution appears to

trend on a different axis- because it is

Influenced by a different variable:

Something to do with the API surface

Particle Size Quantitative Model Calibration

Results

97% Correlation

NIR Spectra Correlate to %Dissolution (30 Mins)

Based on PLS

98.5% Correlation!

Dissolution

Correlated

With

Online NIR Spectra

by

98.5%

NEXT:

Determine and use the new latent variable identity

to

correlate with dissolution and online NIR

In order to steer the knowledge gathering

and

CONTINUOUS IMPROVEMENT

Process Understanding!

SUMMARY

• Traditional Batch Record Control Strategy initially

• Drift

• Temporary New Control Strategy using Data Mining

• Designed Experiment for Process Understanding

• Latent Variables Identified: The latent variable was not the original excipient that precipitated the study

• Permanent Control Strategy Determined

• Continuous Improvement

Product Quality Lifecycle Implementation

Team

Jose Mercado

Vionette Padovani

Israel Cotto

Victor Ruiz

Alex Montanez

Amaryllis Roman

Denise Sanchez

Elvira Alvarez

Jose Rivera

Bend Research:

Margot Cortese

Ron Beyerinck

Greenridge Consultants:

Leah Appel

Josh Shockey

Matt Shaffer

Alan Phillips

Chi-Shi Chen

Ke Hong

Don Clark

Shailesh Hiremath

Al Pichieri

Anthony Carella

Ivelisse Colon-

Rivera

Sal Garcia-Munoz

Julian Lo

Avi Thombre