This document provides an outline of a presentation and is incomplete without the accompanying oral commentary and discussion. Conclusions and/ or potential strategies contained herein are NOT necessarily endorsed by Pfizer

management. Any implied strategy herein would be subject to management, regulatory and legal review and approval before implementation.

On the horizon: The integrated PAT control strategy for

PCMM (portable, continuous, miniature, modular)

- an agile development and manufacturing platform

Yang Angela Liu, Ph.D.

Pfizer Worldwide Research & Development

Groton CT, USA

Outline

• Introduce PCMM OSD

– Agile, small foot print, fast change over, knowledge accural

• The design sweet spot – integrated control strategy starting from the

design

– Engineering design

– Engineering model

– PAT

• Integrated attribute based monitoring system

• Maximize value in the early development life cycle to cover critical control

strategy components

– APC

• Stage appropriate approach in an accelerated development timeline

• Conclusion

│ 1

PCMM OSD: A Factory in a POD Continuous Processing Platform Technology for Solid

Oral Dosage Forms (HSWG and CDC)

Skids Pre-fabricated in Belgium

… and re-assembled into a grey space warehouse in Groton, CT

Integrated into a ‘Portable’ cGMP POD

6 Modules Pre-fabricated in College Station

Texas

Building 90

Mock-up

FAT in Belgium

Shipped to Groton

FAT in Texas

Shipped to Groton Equip.

Skids 1. Phillip R. Nixon, Changing the Pharmaceutical Industry

Paradigm: Portable, Continuous, Miniature & Modular

Development and Manufacturing, 2015 ISPE/FDA/PQRI

Quality Manufacturing Conference, June 1, 2015

PCMM OSD POD Layout

Raw

Materials

cGMP

Space

Airlo

ck &

Cle

an

ing

Tech

Space

Co

rrido

r/En

tran

ce

PO

D

│ 3

Future State PCM&M Platform Technology

Current State

Tech Transfer &

Process Scale Up

Tech Transfer &

Process Scale Up

x

x 100 to

1000 kg

<10 kg

<100 kg

Commercial Supplies

Phase III Clinical Supplies

Phase IIB Clinical Supplies

Drug Product

Quantities (dry granulation / roller compaction)

│ 4

Future State (dry blend / direct compaction)

several hours

Flexible

time

time

<1 hour

time

Future State PCM&M Platform Technology

100 to

1000 kg

<10 kg

<100 kg

Drug Product

Quantities Future State

(dry blend / direct compaction)

several hours

Flexible

time

time

<1 hour

time

│ 5

Engineering

Models

Process Analytical

Technology

Advanced Process

Control

Experiments

Reduced Time, $, Resources

Commercial

Supplies

Phase III Clinical

Supplies

Phase IIB Clinical

Supplies

Platform Technology

2. Daniel Blackwood, Portable, Continuous, Miniature and Modular (PCM&M)

Approach to Redefining the Development and Manufacturing Paradigm,

AAPS - Arden House Conference, Baltimore, MD, March 17, 2015

Ac

Continuous Knowledge Accrual Paradigm with PCMM F

utu

re

Product B

Within R&D Tech Transfer R&D to Commercial Tech Transfer

Knowledge from

Product A directly

informs Product B

Knowledge

Accrual

Knowledge

Accrual Knowledge

Accrual

Within Commercial Tech Transfer Within R&D Tech Transfer R&D to Commercial Tech Transfer

Within Commercial Tech Transfer

Product A

Process Knowledge Build

Process Knowledge Build

3 . Phillip Nixon, Broad Implementation of Continuous Manufacturing for Solid Oral Drug Products: What

Can the Future Look Like? AAPS - Arden House Conference, Baltimore, MD, 2015 March 17, 2015

Advanced

Process

Control

Engineering

Models

Process

Analytical

Technology

Equipment

Design

Integrated System

An Integrated Approach to a Platform Technology

│ 7

World-class

Materials

Science

& Formulation

Development

Practices

World-class

Commercial

Manufacturing

Particle

Engineering

for API and

Excipients

~500 MM tab/year

24/5 operation,

30% downtime

PCMM OSD Prototype Processing Equipment

Granule

Conditioning

Unit

Total

Elevation

~14.5 ft HSWG Wet

Granulation

Dryer

Continuous Mixing

& Direct Compression ConsiGmaTM WG

CMT

Mixer

Tablet

Press

Feeders

CMT

Mixer Feeders

Raw

Material

Dispensing

Continuous Mixing Technology Goals

• Powder mixing as close to dosage form creation

as possible

• Independent control of

– Powder Hold Up Mass,

– Mass Throughput, and

– Impeller RPM

• Residence Time Distribution

– Based on simple CSTR model

– Consistent RTDs over a wide range of

process conditions

• Integrated powder de-lumping capabilities

• Integrated PAT sensors

• Minimal/Zero Waste Start Up & Shutdown

Continuous Mixing Technology (CMT) Engineering Design

│ 9

Gravimetric

Feeders

In-Line

Mixer

Tablet

Press

2. Daniel Blackwood, Portable, Continuous, Miniature and Modular

(PCM&M) Approach to Redefining the Development and

Manufacturing Paradigm, AAPS - Arden House Conference,

Baltimore, MD, March 17, 2015

What Is PAT Expected To Achieve

• Fast development of “processing”

• Optimization of product processing

• Condition monitoring / fault diagnostics

– Know if something deviates from normal

• Check correctness of each unit operation

• Support for the use of Advanced Process Control

• “Fast Release” of product

• Mission statement - Maximize the value and knowledge

starting from the early design and development stage

10

4. Steve Hammond, Sonja Sekulic, Fast Release and Continuous Processing: A Vision of the Future, Going

Continuous - Advanced PAT and Real Time Release, Graz, Austria, September 16th, 2015

PCMM OSD PAT and Product Diversion

Continuous Mixing

& Direct Compression ConsiGmaTM WG

PAT 1 (NIR) Post CMT

Potency & Blend

Uniformity

PAT 2 (NIR) Post TSWG

Granule Formation PAT 3 (NIR)

Post Granule Sizing

Potency & Granule

Uniformity, Moisture PAT 4 (FBRM) Post Granule Sizing

PSD

PAT 5 (NIR) Feed Frame

Potency & Blend/Granule

Uniformity

Diversion 1

Post Fluid Bed

Drying

Diversion 2

Post Granule

Sizing

Diversion 3

Tablet Eject Chute

Combi Tester Weight Hardness

Thickness

The Integrated PAT Platform

• Analyzers: reliable, fast sensing, consistent, standardized.

• Sample interfaces :

– Representative sampling

– No probe fouling

– Minimal process intrusion

• Communication platform – PAT Real Time Manager system

– Supported by automation and IT infrastructure

– PAT devices, data and model are centrally control and managed

– Data exchange with process SCADA: acquisition triggering, alarming, diversion

– PAT data and process data are synchronized: trending and modeling

• Chemometrics /modeling

– Large, real time, multivariate data

– Robust and minimal calibration approaches

– Connection with APC

│ 12

Foundation of robust

real time monitoring

Intelligence Center

0 50 100 150 200 250 300-0.5

0

0.5

1

1.5

2

2.5

3

3.5x 10

-3

AP

I pote

ncy t

rend

Samples (elapsed time)

CMT trial DoE Run

Run #15

Mass throughput: 7.5kg/hr

Hold up mass: 460g

Upper impeller: 1000

Lower impeller: 1000

PAT1 – Mixing Dynamic Characterization

• PAT1 tracks the blend potency out of CMT.

• Important to characterize the CMT mixing dynamic and residence

time distribution

5.5% Tracer

Spike

Segmented dryer 100 200 300 400 500 600 700 800 900 1000

-4

-3

-2

-1

0

1

2

3x 10

-3

Sample

Sco

res

on P

C 1

(68

.04%

)

Samples/Scores Plot of pX

Liquid addition ramp: 15% to 18%

PAT2 – NIR at Twin Screw Wet Granulator Outlet

PAT2 - Granule Condition Monitoring

• Granules from different conditions showed in clusters based on their

overall spectral feature.

1300 1400 1500 1600 1700 1800 1900 2000-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

Variable

PC

1 (

59.9

9%

), P

C 2

(19.1

4%

)

Variables/Loadings Plot for pX

-10 -8 -6 -4 -2 0 2 4

x 10-3

-6

-5

-4

-3

-2

-1

0

1

2

3

4x 10

-3

Scores on PC 1 (59.91%)

Score

s o

n P

C 2

(18.0

7%

)

Samples/Scores Plot of pX

Low moisture,

high potency

PAT5 - Feed Frame Monitoring

• Innovated sample interface design to

achieve robust spectral signal.

│ 16

• Location advantage:

– Characterize the blend right before tablet

compression: potency and the extent of

lubrication.

– Real time characterize process dynamic.

– Enable diversion, feed back/forward

control.

– Platform technology: no impact from tablet

size, commercial image

NIR probe

Micrometer

Modified paddle wheel

6% (120% potency)

4% (80% potency) *assume 5% as target

potency (100%)

Sensitivity for low dosage product demonstrated

PAT and Process Variables For Process Dynamic

Understanding

API% setpoint

change

Potency in feed

frame change

onset

CSTR

Impeller set

point change

Hold up mass

sudden drop

Potency decrease

observed by PAT 5

Product Tracking and Condition Monitoring

│ 18

Feeders

-60 -40 -20 0 20 40 60 800

5

10

15

20

25

Time/Min

AP

I% Feeders Set Point

Step Change

PAT 1 (NIR) Post CMT

Time 0:00

Feeders Set Point

Step Change

-60 -40 -20 0 20 40 60 800

5

10

15

20

25

Time/Min

AP

I% PAT1, Post CMT

~75sec

Time 1:15

Blends Post CMT

PAT 3 (NIR)

Post Granule Sizing

-80 -60 -40 -20 0 20 40 60 800

5

10

15

20

25

Time/Min

AP

I%

11min

PAT3, GCU

Time 11:00

Milled Granules in

GCU

PAT 5 (NIR) Feed Frame

-80 -60 -40 -20 0 20 40 60 800

5

10

15

20

25

Time/Min

AP

I%

22min

PAT5, Feed Frame

Time 22:00

In feed frame

immediate before

tableting

-80 -60 -40 -20 0 20 40 60 800

5

10

15

20

25

Time/Min

AP

I%

Set Point

PAT1 Post CMT

PAT3 GCU

PAT5 FeedFrame

22min 11min

75sec Case study:

Results are for the

specific operating

conditions of the

trial

Value of PAT For Control Strategy and Quality

Assurance

• Fast, real time monitoring

• Map out critical control strategy components at early design

stage by leveraging effective experiment design

– Characterize process dynamic

– Product tracking

– Condition monitoring/fault detection: Besides potency, potentially

on other components and physical properties

│ 19

Advanced Process Control

• PCMM is equipped with APC platform capability

• POC demonstrated on prototype equipment

– Model predictive control on critical process parameters

– Feed back control with PAT and soft sensor

• Ongoing effort on:

– MSPC and fault detection

– Hybrid soft sensors

– End-to-end APC system

│ 20

Stage Appropriate Control Strategy: A Development

Continuum

The platform is equipped with monitoring, modeling and control capability to

ensure product quality – higher level control with combination of different

components.

To realize the vision of an adaptable and agile manufacturing paradigm, there

is a need to balance the online and offline analytics to the stage of

development..

Low

Medium

High

Process Control: Comprehensive

Closed loop control

strategy

Release: Online (RTRt)

Process Control: MSPC/SQC

(parameters &

attributes), MPC on

CPP

Release: Combination of

Online and Offline Process

Monitoring/Control: SPC/MSPC/SQC,

(parameters & attributes)

Release: Offline

Imp

ac

t

Stage 1

Clinical Stage 2

Co-Development

Stage 3

Commercial

Development Stage

Variable Input

& Limited Understanding

Managing Variable Inputs: Part of the Journey to Six Sigma

Current State

Variable Input & Advanced

Understanding

Preferred State

Variable Output

(Optimized) Constant Process

Variable Process (APC)

Constant Output (Robust Process)

Feed Forward Feed Back

Stage-appropriate!

Conclusion

• The PCMM control strategy roots from its integrated

design concept.

• The sweet spot of engineering design, engineering

models, PAT and APC ensures quality and maximizes the

continuous process benefit.

• The state appropriate control strategy enables agile

product development and manufacturing.

│ 23

References

1. Phillip R. Nixon, Changing the Pharmaceutical Industry Paradigm: Portable, Continuous, Miniature & Modular

Development and Manufacturing, 2015 ISPE/FDA/PQRI Quality Manufacturing Conference, June 1, 2015

2. Daniel Blackwood, Portable, Continuous, Miniature and Modular (PCM&M) Approach to Redefining the Development

and Manufacturing Paradigm, AAPS - Arden House Conference, Baltimore, MD, March 17, 2015

3. Phillip Nixon, Broad Implementation of Continuous Manufacturing for Solid Oral Drug Products: What Can the Future

Look Like? AAPS - Arden House Conference, Baltimore, MD, 2015 March 17,

4. Steve Hammond, Sonja Sekulic, Fast Release and Continuous Processing: A Vision of the Future, Going Continuous

- Advanced PAT and Real Time Release, Graz, Austria, September 16th, 2015

5. Yang (Angela) Liu, PAT and multivariate condition monitoring for drug product continuous process, SciX, Providence, RI,

September 29, 2015

6. Yang (Angela) Liu, Daniel Blackwood, Jeffrey Moriarty, NIR In-Line Monitoring for Drug Product Continuous Process: From

Understanding to Control, BioPharma Asia, June 16th, 2015

7. Koji Muteki, Daniel O. Blackwood, Brent Maranzano, Yong Zhou, Yang A. Liu, Kyle R. Leeman, and George L. Reid, Mixture

component prediction using iterative optimization technology (calibration-free/minimum approach), Industrial & Engineering

Chemistry Research, 52(35), 12258–12268

8. Yang (Angela) Liu, Koji Muteki, Daniel O. Blackwood, Online Feed Frame Monitoring of Blends/Granules During Tablet

Compression, IFPAC, Baltimore, Jan 22-25,2013

9. Howard W. Ward, Daniel O. Blackwood, Mark Polizzi, Hugh Clarke , Monitoring Blend Potency in A Tablet Press Feed Frame

Using Near Infrared Spectroscopy, Journal of Pharmaceutical and Biomedical Analysis, 80 (2013), 18– 23

10. Yang Liu, Daniel O. Blackwood, Sample Presentation in Rotary Tablet Press Feed Frame Monitoring by Near Infrared

Spectroscopy, American Pharmaceutical Review, May 2012

│ 24

Acknowledgement

• PCMM OSD Prototype Team

• PCMM Implementation Team

• PCMM PAT Team

• PCMM Analytical support team

• Perceptive Engineering

• Colleagues from GEA and G-Con

│ 25

• Slides and references courtesy

• Daniel Blackwood

• Phil Nixon

• Steve Hammond

• Sonja Sekulic

Questions?

│ 26

Backup

│ 27

A PCMM campaign as an example of large data mining

• 5 days. 1 day of DC integrated run, 1 day of WG integrated run.

• DC integrated run:

– 6 hours of run time, 19 DoE runs on process parameters.

– 1 PAT (PAT5) with ~60 process parameters simultaneously tracked in every 4 sec interval.

– ~110Mb data

• WG integrated run:

– 6 hours of run time, 18 DoE runs on process parameters.

– 5 PATs, 200+ process parameters simultaneously tracked.

– ~500Mb data

• Rich process understanding info.

• Great opportunity for utilizing data mining and modeling to gain process understanding for development and control.