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CONTINUOUS QUALITY VERIFICATION (CQV) G.K.Raju, Ph.D. Pharmaceutical Manufacturing Initiative (PHARMI), MIT Program on the Pharmaceutical Industry, Massachusetts Institute of Technology July 2001 MIT Pharmaceutical Manufacturing Initiative Objective: To Describe the Opportunity to Improve

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slide1

CONTINUOUS QUALITY VERIFICATION(CQV) G.K.Raju, Ph.D.Pharmaceutical Manufacturing Initiative (PHARMI),MIT Program on the Pharmaceutical Industry,Massachusetts Institute of Technology

July 2001

slide2

MIT Pharmaceutical Manufacturing Initiative

Objective: To Describe the Opportunity to Improve

Pharmaceutical Manufacturing Performance

Research

Marketing

Development

Manufacturing

slide3

Pharmaceutical Manufacturing

Research

Marketing

Development

Manufacturing

Inbound

Logistics

Bulk

Active

Bulk

Formulation

Filling &

Finish

Outbound

Logistics

Packaging

slide4

Bulk

Formulation

Filling/

Tableting

Packaging/

Finishing

Bulk

Active

STUDYING PHARMACEUTICAL MFG:

VERTICAL VS. HORIZONTAL APPROACH

VERTICAL

APPROACH

HORIZONTAL

APPROACH

Plant A

Plant A

Bulk

Active

Bulk

Formulation

Filling/

Tableting

Packaging/

Finishing

Bulk

Active

Bulk

Formulation

Filling/

Tableting

Packaging/

Finishing

Plant B

Plant B

Bulk

Active

Bulk

Formulation

Filling/

Tableting

Packaging/

Finishing

slide5

Packaging/

Finishing

Filling/

Tableting/

etc.

Bulk

Active

Bulk

Formulation

PHARMACEUTICAL MANUFACTURING

THE HORIZONTAL APPROACH

Company A

Packaging/

Finishing

Filling/

Tableting/

etc.

Bulk

Active

Bulk

Formulation

Company B

Packaging/

Finishing

Filling/

Tableting/

etc.

Bulk

Active

Bulk

Formulation

Company C

which processes
WHICH PROCESSES?

PROCESS CYCLE TIMES

In Process Development

In Routine Manufacturing

Biggest Impact Here?Time-to-marketEnablingPotent Products

Place forValidation?Potent ProductsDifficult ProcessesHigh Volume ProductsProducts with Tough QC TestsGeneric Competition

process a with qc tests
PROCESS A WITH QC TESTS

BLEND

DRY MIX

STEP

FB DRY

WEIGHING

WET

GRANULATION

STEP

SIEVE

ENCAPSULATE

QC1

QC2

QC3

QC4

  • API
  • MICRO
  • LOD
  • Particle Size
  • Description
  • ID
  • Assay
  • CU
  • Impurity
  • Dissolution
  • MICRO
process a with cycle times
PROCESS A WITH CYCLE TIMES

< 3 DAYS

BLEND

DRY

MIX

Processing

FB DRY

SIEVE

STEP

WEIGH

WET

GRANULN

ENCAPSULATE

7 DAYS

13 DAYS

QC2

QC3

QC1

QC4

  • LOD
  • Particle
  • Size
  • API
  • MICRO
  • Description
  • ID
  • Assay
  • CU
  • Impurity
  • Dissolution
  • MICRO
process b with qc tests
PROCESS B WITH QC TESTS

CHEMICAL

WEIGHING

BLEND

FILL CAPSULES

BOTTLE

PACKAGING

QC1

QC2

  • API
  • OVI
  • Description
  • ID
  • Assay
  • CU
  • Impurity
  • Dissolution
process b with cycle times
PROCESS B WITH CYCLE TIMES

17 DAYS

CHEMICAL

WEIGHING

BLEND

FILL

CAPSULES

BOTTLE

PACKAGING

14 DAYS

7 DAYS

QC1

QC2

  • API
  • OVI
  • Description
  • ID
  • Assay
  • CU
  • Impurity
  • Dissolution
process c with qc tests
PROCESS C WITH QC TESTS

FILM

COATING

BOTTLE

PACKAGING

COMPRESS

GRANULATION

STEP

WEIGHING

FB DRY

BLEND

QC1

QC2

QC2

  • API
  • Particle Size
  • LOD
  • Description
  • ID
  • Assay
  • CU
  • Impurity
  • Dissolution
process c with cycle times
PROCESS C WITH CYCLE TIMES

21 DAYS

BOTTLE

PACKAGING

GRANULATION

STEP

COMPRESS

FB DRY

BLEND

FILM

COATING

14 DAYS

WEIGHING

6 DAYS

QC1

QC2

  • Description
  • ID
  • Assay
  • CU
  • Impurity
  • Dissolution
  • API
process d with qc tests
PROCESS D WITH QC TESTS

FILM

COATING

GRANULATION

STEP

CHEMICAL

WEIGHING

Processing

BLEND 1:

BLEND 2:

PRE-

BLEND

FINAL

BLEND

COMPRESS

BOTTLE

PACKAGING

QC1

QC2

QC3

  • API
  • Particle Size
  • LOD
  • Description
  • ID
  • Assay
  • CU
  • Impurity
  • Dissolution
process d with cycle times

FILM

COATING

BOTTLE

PACKAGING

PROCESS D WITH CYCLE TIMES

20 DAYS

15 DAYS

BLEND 2:

PRE-BLEND

GRANULATION

STEP

CHEMICAL

WEIGHING

PROCESSING

BLEND 1:

COMPRESS

FINAL

BLEND

10 DAYS

15 DAYS

QC1

QC2

QC3

  • API
  • Particle Size
  • LOD
  • Description
  • ID
  • Assay
  • CU
  • Impurity
  • Dissolution

60 DAYS

process d with qc tests cycle times including bulk active

FILM

COATING

BOTTLE

PACKAGING

PROCESS D WITH QC TESTS:Cycle Times includingBULK ACTIVE

20 DAYS

15 DAYS

BLEND 2:

PRE-BLEND

GRANULATION

STEP

CHEMICAL

WEIGHING

BLEND 1:

COMPRESS

FINAL

BLEND

PROCESSING

10 DAYS

15 DAYS

QC1

QC3

QC2

60 DAYS

21-90 DAYS

process d with qc tests cycle times

20

15

FILM

COATING

Actual

10

Target

20 DAYS

Potential

15 DAYS

5

BOTTLE

PACKAGING

0

BLEND 2:

PRE-BLEND

QC1

PFD

QC3

Release

GRANULATION

STEP

CHEMICAL

WEIGHING

PROCESSING

BLEND 1:

COMPRESS

FINAL

BLEND

10 DAYS

15 DAYS

QC1

QC3

QC2

60 DAYS

PROCESS D WITH QC TESTSCycle Times
what drives the qc testing times

20

15

Actual

10

Target

Potential

5

0

QC1

PFD

QC3

Release

WHAT DRIVES THE QC TESTING TIMES?

2%

  • Sampling
  • Batching
  • Other Products
  • Waiting
  • Coordinating

TEST

  • Other Products
  • Other Paperwork
  • Waiting
  • Coordinating
which processes19
WHICH PROCESSES?

PROCESS CYCLE TIMES

In Process Development

In Routine Manufacturing

Biggest Impact Here?Time-to-marketEnablingPotent Products

Place forValidation?Potent ProductsDifficult ProcessesHigh Volume ProductsProducts with Tough QC TestsGeneric Competition

process e with qc test points

LOD

PROCESS E WITH QC TEST POINTS

ACTIVE

INITIAL GRANULATION STAGE

MILL

QC1

WEIGH

DRY MIX

WET

GRANULN

WET GRANULN

FL BED DRY

MILL

SECOND GRANULATION STAGE

COATING STAGE

LOD

LOD

COAT

MILL

MILL

QC4

QC2

QC3

WET GRAN

DRY

SIFT

STORE

MIX

MIX

DRY

STORE

SIFT&BLEND STAGE

BLEND&FILL STAGE

STORE

SIFT

BLEND

BLEND

BLEND

BLEND

STORE

BOTTLE FILL

LOD

MIX

MIX

GRANUL

DRY

MILL

EXCEPIENT PREPARATION STAGE

process e with qc test times
PROCESS E WITH QC TEST TIMES

7 days

3 days

7 days

7 days

QC1

QC2

QC3

QC4

ACTIVE

< 1

day

< 1

day

1-2

days

< 1

day

< 1

day

< 1

day

SECOND

GRAN

SIFT&

BLEND

BLEND

FILL

FIRST

GRAN

COAT

which processes22
WHICH PROCESSES?

TOWARDS PARAMETRIC RELEASE

In Process Development

In Routine Manufacturing

Biggest Impact Here?Time-to-marketEnablingPotent Products

Place forValidation?Potent ProductsDifficult ProcessesHigh Volume ProductsProducts with Tough QC TestsGeneric Competition

process f liquid line
PROCESS F: LIQUID LINE
  • ENVIRO. MONITORING
  • WFI TESTING
  • Endotoxin
  • TOC
  • QC Check

WASH

AUTOCLAVE

WFI

STOPPERS

  • QC Check
  • QC Check

WEIGH

WASH

DEPYROGEN

SEALS

BUFFER

VIALS

TERMINAL

STERILIZATION

WEIGH

  • ID

pH ADJ

COMPOUND

FILL

STOPPER

CAP

WEIGH

FILTER

LABEL/PKG

  • Appearance
  • ID
  • Assay
  • Impurity
  • Fill Vol, Osmolarity, Partic.
  • Endotoxin
  • STERILITY TESTING
  • ID
  • pH
  • BIOBURDEN
  • Wt Check
  • Visual Check
process f liquid line with cycle times
PROCESS F: LIQUID LINE WITH CYCLE TIMES
  • ENVIRONMENTAL MONITORING

7 days

10 days

  • WFI TESTING

3-4 days

3-4 months

  • STERILITY TESTING

17-20 days

7 days

  • BIOBURDEN TESTING
on line technology impacts dominant cycle times
ON-LINE TECHNOLOGY IMPACTSDOMINANT CYCLE TIMES

On-line LIF, NIR, Pattern Recognition, etc.

slide28

CQV OPPORTUNITY IN ROUTINE MANUFACTURING

SUMMARY

  • Quality Monitoring is Discontinuous
  • QC testing times are approximately = 1 month
  • Factor of 20-25 opportunity in cycle time: Process
  • Factor of 20-25 opportunity in cycle time: QC
  • QC Cycle Times >= Process Cycle Times
  • Time is driven by off-line nature of test
  • Exception is MICRO test
slide30

Company A

Bulk

Active

Bulk

Formulation

Filling &

Finish

Packaging

Company B

Bulk

Active

Bulk

Formulation

Filling &

Finish

Packaging

Company C

Bulk

Active

Bulk

Formulation

Filling &

Finish

Packaging

THE VERTICAL APPROACH

Blending

Drying

Granulation

Flow

Tableting

Transport

Rapid

Microbial

Detection

Fermentation

slide31

VERTICAL ANALYSIS I:

BLENDING UNIT OPERATION

Company A

Bulk

Active

Bulk

Formulation

Filling &

Finish

Packaging

Company B

Bulk

Active

Bulk

Formulation

Filling &

Finish

Packaging

Company C

Bulk

Active

Bulk

Formulation

Filling &

Finish

Packaging

Eg. Blending

which processes32
WHICH PROCESSES?

PROCESS CYCLE TIMES

In Process Development

In Routine Manufacturing

Biggest Impact Here?Time-to-marketEnablingPotent Products

Place forValidation?Potent ProductsDifficult ProcessesHigh Volume ProductsProducts with Tough QC TestsGeneric Competition

focus
FOCUS

MIT Pharmaceutical Manufacturing Initiative

Explore the Potential Impact of On-line Monitoring Technology on Blending Process Development

slide34

8

8

8

Active ingredient

Excipients

Raw material load

Mixing

Sampling

Homogeneity test

OK?

Analysis

Transporting

Results & Decision Making

Blending Operation Model

Undermixed

mix-longer

Blender

cleaning

Homogeneous

Next batch

Discarded

Next batch

slide35

PHARMACEUTICAL MANUFACTURING:

LIF FOR ON-LINE MONITORING OF BLENDING

LIGHT INDUCED FLUORESCENCE SYSTEM

FOR THE DETERMINATION OF THE

HOMOGENEITY OF DRY POWDER BLENDING

slide36

LIF VERIFICATION STUDIES

Established a correlation between LIF assessment of

homogeneity and thief-sampling with off-line analysis

process d blending process development
PROCESS D: BLENDING PROCESS DEVELOPMENT

FILM

COATING

GRANULATION

STEP

CHEMICAL

WEIGHING

Processing

BLEND 1:

BLEND 2:

PRE-

BLEND

FINAL

BLEND

COMPRESS

BOTTLE

PACKAGING

  • OFF LINE QC TEST
  • ON LINE SENSOR
slide38

Blending Operation: Two Technologies, Two Approaches

Process Development, Validation and Manufacturing

Transport

Raw Materials

Blending

Sampling

Analysis

Process

knowledge

Results &

Decision making

Waiting Stock

Information Flow

Materials Flow

R/D/W

a- Process Development

b- manufacturing

Discarded

Reprocessed

Well Blended

Well Blended

Blending

Raw Materials

Analysis &

Decision making

Discarded

On-line

Information Feedback

OFF LINE

ON LINE

slide39

Low

Medium

High

Cleaning time (min)

20

250

480

Loading time (min)

10

35

60

Discharge time (min)

6

18

30

Sampling time (min)

60

90

120

Transport time (min)

6

30

60

QC Testing time (min)

20

250

480

2

25

48

QC Holding time (min)

Blending Data Collected from CAMP Companies

Operation Characteristics

slide40

Results

Blending Performance

Process Development and Validation

6% no wait between blends

1 Blend

2 Blends

3 Blends

Best

2.32

0.36

4.96

0.68

8.45

1.07

Med.

13.19

1.31

23.45

1.93

30.65

2.41

Time (days)

Worst

25.82

2.57

43.40

3.56

56.17

4.46

slide41

Blending Performance

Process Development and Validation

approach to learning

Current Approach

Proposed Approach

1a

2a

Process Development

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

1b

2b

Commercial Production

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

APPROACH TO LEARNING:

Consequences on Process Development & Commercial Production

slide43

CQV Opportunity in Process Development

SUMMARY

  • Process development can be on the critical path
  • Factor of 10-15 reduction in cycle time in blend
  • process development (maybe more..)
  • Variability reduction in blend process dev. time
    • independence of organization/product -> predictability…
  • Benefits not restricted to use of new on-line sensors
    • improvement data analysis of existing sensor data
    • use of this for experimental design
consortium for the advancement of manufacturing in pharmaceuticals camp
Consortium for the Advancement of Manufacturing in Pharmaceuticals (CAMP)

Pharmaceutical Companies

  • Hoffmann-La Roche
  • Glaxo SmithKline
  • Wyeth-Ayerst
  • Abbott
  • Aventis
  • Bristol-Myers Squibb
  • Johnson & Johnson

FDA

CAMP

Vendors

MIT

Purdue

process a with current qc tests and new possibilities
PROCESS A WITH CURRENT QC TESTS AND NEW POSSIBILITIES

BLEND

DRY MIX

STEP

FB DRY

WEIGHING

WET

GRANULATION

STEP

SIEVE

ENCAPSULATE

QC1

QC2

QC3

QC4

  • API
  • MICRO
  • LOD
  • Particle Size
  • Description
  • ID
  • Assay
  • CU
  • Impurity
  • Dissolution
  • MICRO
manufacturing information management has hardware and software components

10%

5%

1%

0.5%

0.1%

Manufacturing Information Management: Has Hardware and Software Components
  • Fast Response
  • On-Line
  • Real-Time
  • Accurate
  • Robust
  • Rapid Rate of Learning
  • Short Cycle Times
  • Benchmarking
  • Modeling
  • Continuous Problem Solving
slide49

HORIZONTAL AND VERTICAL APPROACHES

Company A

High Vol

Bulk

Active

Bulk

Formulation

Filling &

Finish

Packaging

Company B

Variable

Bulk

Active

Bulk

Formulation

Filling &

Finish

Packaging

Company C

Bulk

Active

Bulk

Formulation

Filling &

Finish

Liquids

Packaging

Blending

Drying

Granulation

Flow

Tableting

Transport

Rapid

Microbial

Detection

Fermentation

cqv benefits
CQV: BENEFITS
  • Data Mining of Process Data
  • Data -> Information -> Knowledge
  • Rationale for New Sensors
  • Variable Categorization: PCCPs, etc.
  • Basis for Specifications, Batch Record Design
  • Basis for Experimental Design, Etc. …
learning curve cycle times
Learning Curve: Cycle Times

Accelerated Learning Curve Facilitated By

Continuous Quality Verification

slide52

CQV: SO WHAT?

  • On-line sensors doing the same thing will have
    • only incremental impact
  • This impact will still be only incremental
  • even if there is an MES/EBR system
  • Data Warehousing focused on exceptions can
  • have a large impact
  • On-line sensors + EBR + Data Warehousing
  • can fundamentally change pharma. mfg.
product life cycle opportunities

Reduction of

manufacturing cost

Reduction of

time-to-market

PRODUCT LIFE CYCLE: OPPORTUNITIES
pharmaceutical manufacturing opportunity areas
Pharmaceutical Manufacturing:Opportunity Areas
  • Manufacturing:Cost --> Profit
  • Organizational focus:Functional --> Process
  • Optimization:Local --> Supply chain
  • Inventory Management:JIC --> JIT
  • Cost of Quality:Inspection --> Prevention

KEY TECHNOLOGY OPPORTUNITY:

On-line Sensors+EBR+Data Warehousing!

acknowledgements
ACKNOWLEDGEMENTS
  • Professor Charles Cooney (MIT)
  • Professor Stephen Byrn (Purdue)
  • CAMP
note on context
NOTE ON CONTEXT
  • This presentation does not necessarily represent the views of MIT, Purdue or CAMP
  • Some data have been disguised for reasons of sensitivity and confidentiality