ispe annual meeting 4-7 november 2007 - nhs wales by design - simon... · ispe annual meeting 4-7...

ISPE Annual Meeting4-7 November 2007

An introduction to Quality By Design

NHS Conference

27th September 2011

Simon HollandGlaxoSmithKline R&D, Ware, [email protected]

A little about my background

• Interested in Science at school• Studied “O” and “A” levels: first use of

calculators in maths “O” level (1978)• Studied Chemistry at

University• Married to Julie• Live in Hertford• Support Bolton

Wanderers FC 2

1980: Bradford – met love of my life

3

X☺

1983: Aston: more chemistry and more curry

4

1986: Beecham, Worthing

5

Formulation development of penicillins e.g.

Amoxil and Augmentin

1995 SBWG City

6

Formulation development of neuroscience molecules e.g. Cox-2

inhibitors

Process development of

respiratory molecules: Relovair

Process development of

respiratory molecules: Relovair

1997 SB & GSKHarlow

2008 GSK Ware


What is Quality by Design?

Pharmaceutical QbD

• “Quality should be built into a product with a thorough understanding of the product and process by which it is developed and manufactured along with a knowledge of the risks involved in manufacturing the product and how best to mitigate those risks” …… a bit of a mouthful

Pharmaceutical QbD

• “Quality should be built into a product with a thorough

understanding, of the product and process by which it is

developed and manufactured along with a knowledge of the risks involved in

manufacturing the product and how best to mitigate those risks”

• “Reducing product variation and Quality Risk Management in Development and Manufacturing” Moheb Nasr FDA 28FEB2007

Quality: old verses new concepts

• Old:– Quality through end product testing

– Processes scaled up and three large scale “Validation” batches made

• New:– Quality through risk assessment

considerations during development

– Processes mapped to demonstrate understanding of where batches can fail

Guidelines

Guidelines

• International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH)

• Q8 Pharmaceutical Development• Q9 Quality Risk Management• Q10 Pharmaceutical Quality Systems• (Q11 Development and manufacture of drug substances

under consultation - Drug Substance Quality Link to Drug Product )


Pharmaceutical Development (Q8)Pharmaceutical Development (Q8)

Past: data transfer / variable content

Present: knowledge transfer / consistent content

Pharmaceutical Quality Systems (Q10)Pharmaceutical Quality Systems (Q10)

Past: GMP checklist

Future: Quality Systems across product life cycle

Quality Risk Management (Q9)Quality Risk Management (Q9)

Past: inconsistent usage

Present: consistent use of structuredprocess thinking

Links between ICH Q9 with Q8 and Q10

Q8

Q9 Q

10

Q10Q8

Alignment of ICH Q8, Q9, and Q10 with Product Development

Process

Materials

Design

Manufacturing

Distribution

Patient

Facilities

Opportunities to mitigate risk using quality risk

management

G.- Claycamp, FDA, June 2006

Q9

ICH Q9: Two Main Principles of Quality Risk Management

The evaluation of the risk to quality should be based on scientific knowledgeand ultimately link to the protection of the patient.

The level of effort , formality and documentation of the quality risk management process should be commensurate with the level of risk.

ICH Q9

GeneralQuality Risk ManagementProcess

Risk Review

Ris

kC

om

mu

nic

a tio

n

Risk Assessment

Risk Evaluationunacceptable

Risk Control

Risk Analysis

Risk Reduction

Risk Identification

Review Events

Risk Acceptance

InitiateQuality Risk Management Process

Output / Result of theQuality Risk Management Process

Risk

Managem

entt ools

ICH Q9

FDA view (1) Moheb Nasr FDA 28FEB2007

http://www.aaps-ispe.org/

Concept of design spaceAnurag S. Rathore Biopharm International Vol 20 iss ue 4 April 2007


FDA view (2) Moheb Nasr FDA 28FEB2007

http://www.aaps-ispe.org/

CQA = critical quality attribute e.g. tablet hardness

Case Study

Magida ZeaiterGlaxoSmithKline R&DGlaxoSmithKline R&D

Monitoring Injection Moulding process of

Pharmaceutical Device components using

cavity sensor technology

Injection Moulding Process & Pressure Sensor Technology

Cavity Sensor

Ejector side of the machine

G&A Injection Moulding development machine - Peterborough

Injection Moulding Process - Fishbone

Melt Preparation

Mould Packing

Mould Filling

Mould Cooling

Mould Movement

A very complex process

Cavity Balance

Material Grade

� 4 temperature / pressure sensors fitted in a the front cover single cavity tool.

�Cavity sensor data collected via DATAFlow (Kistler Instruments).

Cavity Sensor Location on the ‘front cover’ componen t

Kistler Cavity Sensor(Pressure and Temperature)

Integration of sensor in the Mould Cavity Tool

Sensor positions (A,B,C,D) on the front cover component

AA

BB

CC

DDGate

Process Understanding tools

(1) Design of Experimentdata analysis


Injection Moulding Process Sensor Source

Screw force Output from Sumitomo machine

Screw position Output from Sumitomo machine

Barrel temp 1 Output from Sumitomo machine





Hopper temp Output from Sumitomo machine

Material intake temp Output from Sumitomo machine

Nozzle melt temp Output from Sumitomo machine

Nozzle melt pressure Output from Sumitomo machine

Cavity pressure 1 New sensor added

Cavity temp 1 New sensor added







Mould temp fixed New sensor added

Mould temp moving New sensor added

Injection Moulding Process Sensor Source

Velocity Output from Sumitomo machine

Melt temperature Output from Sumitomo machine

Melt pressure Output from Sumitomo machine

Overview of all the Process DATA collected during the DOE experiments

VariablesAcrylonitrile Butadiene Styrene (ABS) Lustran

DOE Process Parameter Range

Barrel temp 230 - 240 - 250 0C

Mould temp 60 - 70 - 80 0C

Holding pressure 300 - 500 - 700 Bar

Holding time 3.0 - 5.0-7.0 Secs

Cooling time 4.5 - 5.5 - 7.5 Secs

Half fractional factorial design 25-1 with 16 DOE run

Data sources and variables

� 6 dimensions

Quality / Metrology measurements

� Weight

� Visual Inspections

After every experimental run:

• 10 components manufactured for each run, each measured for weight and visual inspection

• 4 randomly chosen components were taken for full dimensional check

Device component measurements

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

Series (Settings for Quality)

MissingEjector marks - part deformation on ejectionGoodMinor flash on split line of internal cut outsSevere flash on split line of interal cut outsSink marks on ribs - No flash

G(32)H(10)I(32)

SolutionNew TG

ABS8

ABS15

ABS12

ABS6

F(32)J(32)

ABS5ABS2

ABS7

ABS10

ABS11ABS16ABS9

ABS3

ABS4ABS1

ABS13

ABS14

SIMCA-P+ 11 - 25/02/2009 17:32:43

Principal Component Analysis

Sink Marks

Severe Flash

Minor Flash

Good

Direction explaining variation due to Holding Pressure

Dire

ctio

n ex

plai

ning

var

iatio

n d

ue

to H

oldi

ng a

nd C

oolin

g tim

e

Ejector

Marks

Design-Expert® Software

Desirability1

0

X1 = A: Holding TImeX2 = C: Holding pressure

Actual FactorsB: Cooling Time = 5.41D: Mould Temperature = 68.98E: Barrel Temperature = 246.20F: Raw Material = ABS

3.00 4.00 5.00 6.00 7.00

300.00

400.00

500.00

600.00

700.00Desirability

A: Holding TIme

C:

Ho

ldin

g p

res

su

re

0.700 0.7190.752

0.785 0.818

0.8510.901

Design-Expert® Software

Overlay Plot

WeightDim1Dim2Dim3Dim4Dim5Dim6

X1 = A: Holding T ImeX2 = C: Holding pressure

Actual FactorsB: Cooling T ime = 5.41D: Mould Temperature = 68.83E: Barrel Temperature = 244.89F: Raw Materia l = ABS

3.00 4.00 5.00 6.00 7.00

300.00

400.00

500.00

600.00

700.00Overlay Plot

A: Holding TIme

C:

Ho

ldin

g p

res

su

re

Weight: 10.2584

Weight: 10.6584

Dim2: 66.98

Dim2: 67.04

Dim3: 75.64

Dim3: 75.67

Dim4: 14.98

Dim4: 15.05

Dim5: 32.78

Dim5: 32.83

Dim6: 32.35

Dim6: 32.4

Establish Manufacturing Operation Space

Conclusion

• Quality by Design approach using Cavity sensor technology provide real-time monitoring of the components critical quality during injection moulding process (more information in back up slides)

• This approach of using cavity sensor helps to reduce/eliminate:– Sampling frequency – Measurement time (Metrology

measurements)– Manufacturing Waste, hence the cost.

QbD: a VisionVision of the Future Old Approach New Approach

General Concept

Quality decisions about the manufacturing process are separate from risk evaluation

Quality decisions are based on process understanding and risk management

QualityQuality is confirmed by end product testing only. Process validation is “complete”when discrete validation batches are made

Process control is focussed on process parameters that impact the product performance. Process validation is continuous

Systems

Quality systems are designed to inhibit process changes

Quality systems support process changes by allowing flexibility in areas that are not critical to patient safety and efficacy

RegulatoryFocus on compliance to the registered manufacturing process

Flexibility to change the process without prior approval from the Regulatory Agency

Based on EFPIA, PAT Topic Group, 2005


The Product, Process and Patient are all interlinked

Drug Substance

Robust Manufacture

Predictable Performance

Stable Products

QbD Any questions?

Back up slides (2) Multivariate Modelling of the Cavity sensor data

Cavity Pressure Sensor: Data from the different sensor ocations

AABB

CCDD

Pre

ssur

e (b

ar)

Comparison of correlation

0.000.030.060.090.120.150.180.210.240.270.300.330.360.390.420.450.480.510.540.570.600.630.660.690.720.750.780.810.840.870.900.930.960.99

R2 Error R2 Error R2 Error R2 Error

All curve All curve All curve All curve

Pressure A PressureB Pressure C Pressure D

Weight

Dim1

Dim2

Dim3

Dim4

Dim5

Dim6

Multivariate Analysis – Dimensional Correlation using Injection Moulding Cavity Pressure Sensor Data

Components Quality

attributes

Pressure A Pressure B Pressure C Pressure DAll curve All curve All curve All curve

R2 Error R2 Error R2 Error R2 ErrorWeight 1.00 0.0129 1.00 0.0157 1.00 0.0102 1.00 0.0093Dim1 0.73 0.06 0.72 0.06 0.54 0.06 0.73 0.05Dim2 0.96 0.04 0.97 0.02 0.97 0.02 0.97 0.03Dim3 0.94 0.02 0.93 0.02 0.91 0.02 0.91 0.02Dim4 0.94 0.04 0.92 0.04 0.93 0.03 0.90 0.05Dim5 0.73 0.03 0.85 0.02 0.78 0.03 0.76 0.03Dim6 0.97 0.03 0.98 0.02 0.98 0.02 0.98 0.02

Using cavity data pressure curves give optimal correlationfor all the dimensions and weight measurements from the Pressure sensor location B

AA BB CC DD


Correlation of Weight and Dimensions using cavity pressure curves with metrology measurement

10.2000

10.3000

10.4000

10.5000

10.6000

10.20 10.30 10.40 10.50 10.60

YV

arPS

(Wei

ght)

YPredPS[6](Weight)

Pressure Curve Prediction.M7 (PLS), PB_all_Y, PS-Pressure Curve PredictionYPredPS[Comp. 6](Weight)/YVarPS(Weight)Observations are colored if they are available in WS or PS

RMSEP = 0.0157429

WorksetTest Set

y=1.038*x-0.3997R2=0.9969

ABS4_PB1_2

ABS5_PB1_1ABS5_PB1_4


ABS15_PB1_1ABS15_PB1_2ABS15_PB1_3ABS15_PB1_4

J_PB1_1J_PB1_2J_PB1_3J_PB1_4

ABS4_PB1_1ABS4_PB1_3ABS4_PB1_4

F_PB1_1F_PB1_2F_PB1_3F_PB1_4



SIMCA-P+ 11 - 28/04/2009 14:28:20

Wei

ght

mea

sure

d (

g)

32.30

32.40

32.50

32.60

32.30 32.40 32.50 32.60

YV

arP

S(D

im6

)

YPredPS[6](Dim6)

Pressure Curve Prediction.M7 (PLS), PB_all_Y, PS-Pressure Curve Predic tionYPredPS[Comp. 6](Dim6)/YVarPS(Dim6)Observations are colored if they are available in WS or PS

RMSEP = 0.0215534

WorksetTest Set

ABS4_PB1_2

ABS5_PB1_1

ABS5_PB1_4

ABS11_PB1_ABS11_PB1_

ABS15_PB1_ABS15_PB1_ABS15_PB1_ABS15_PB1_

J_PB1_1

J_PB1_2

J_PB1_3J_PB1_4

ABS4_PB1_1ABS4_PB1_3ABS4_PB1_4

F_PB1_1F_PB1_2

F_PB1_3

F_PB1_4

y=1.053*x-1.715R2=0.9766


ABS11_PB1_ABS11_PB1_

SIMCA-P+ 11 - 27/04/2009 11:58:12

Dim6 measured from Pressure curve B (mm)

Dim

6 m

easu

red

(mm

)

14.90

15.00

15.10

15.20

14.90 15.00 15.10 15.20

YV

arP

S(D

im4)

YPredPS[6](Dim4)

Pressure Curve Prediction.M7 (PLS), PB_all_Y, PS-Pressure Curve PredictionYPredPS[Comp. 6](Dim4)/YVarPS(Dim4)Observations are colored if they are available in WS or PS

RMSEP = 0.0394849

WorksetTest Set

y=1.064*x-0.9536R2=0.921

ABS4_PB1_2



ABS15_PB1_1

ABS15_PB1_2


J_PB1_1

J_PB1_2J_PB1_3

J_PB1_4

ABS4_PB1_1

ABS4_PB1_3

ABS4_PB1_4

F_PB1_1F_PB1_2

F_PB1_3

F_PB1_4



SIMCA-P+ 11 - 28/04/2009 14:30:20

75.70

75.80

75.70 75.80

YV

arP

S(D

im3)

YPredPS[6](Dim3)

Pressure Curve Prediction.M7 (PLS), PB_all_Y, PS-Pressure Curve Predict ionYPredPS[Comp. 6](Dim3)/YVarPS(Dim3)Observations are colored if they are available in WS or PS

RMSEP = 0.0221612

WorksetTest Set

y=1.124*x-9.383R2=0.9327

ABS4_PB1_2ABS5_PB1_1 ABS5_PB1_4

ABS11_PB1_2

ABS11_PB1_4



J_PB1_1

J_PB1_2J_PB1_3J_PB1_4


ABS4_PB1_4F_PB1_1

F_PB1_2

F_PB1_3F_PB1_4



SIMCA-P+ 11 - 28/ 04/2009 14:29:52

Weight Dimension 3

Dimension 4 Dimension 6

Monitoring of the injection moulding process using the cavity sensor and the

developed multivariate model

-Development scale –Example of 2 batches

with different process settings

Online prediction of Weight on 2 batches

Batch 1

Batch 2 : Different process settings

Online prediction of Dimensions of Batch 1

Dim 1

Dim 2 Dim 5

Dim 3

Online prediction of Dimensions of Batch 2

Using the Multivariate regression model on Cavity Pressure Sensor data can monitor in real-time the Weight and dimensions of the manufactured components

Dim 1

Dim 2 Dim 5

Dim 3

Reference

M Zeaiter, W Knight and S J Holland, Multivariate regression modelling for monitoring quality of injection

moulding components using cavity sensor technology: Application to the manufacturing of pharmaceutical device

componentsJ Process Control 21 (1) 2011, 137-150

http://dx.doi.org/10.1016/j.jprocont.2010.10.018

ispe annual meeting 4-7 november 2007 - nhs wales by design - simon... · ispe annual meeting 4-7...

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