Drennen IFPAC Summer Summit

Implementation of PAT in the Pharmaceutical Industry

James K. Drennen, IIIDuquesne University

James K. Drennen, III

2

Duquesne University Center for Pharmaceutical Technology (DCPT)

http://http://http://www.dcpt.duq.eduwww.dcpt.duq.eduwww.dcpt.duq.edu

James K. Drennen, III

3

What is PAT?

A system for:designing, analyzing, and controlling manufacturingtimely measurements (i.e., during processing)monitoring critical quality and performance attributes raw and in-process materialsprocess understanding

“Analyzing” includes:chemical, physical, microbiological, mathematical, and risk analysis

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Process Understanding

A process is well understood when:all critical sources of variability are identified and explainedvariability is managed by the processproduct quality attributes can be accurately and reliably predicted

Accurate and Reliable predictions reflect process understanding

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Key Elements of PAT Implementation

Risk AnalysisExperimental DesignControl Strategies

SensorsModel development/MaintenanceSPC

Process SamplingInformation Management

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Risk Analysis/Experimental Design

FBDrier

Milling

Blender Press

Coater

Sieve

Dispensary

Wet granulation

NIRNIR

NIRNIR

NIRNIR

NIRNIR

Direct Compression

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PAT for Solid Dosage Form

Feasibility/Risk AnalysisCalibration RangeProcess SignatureSamplingCalibration Maintenance/Transfer

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Real Time Release (RTR)

Quantitative calibrations required for RTR of Dosage Form

Projects involving established productsLab samples required to create necessary range for calibrationStudies performed to determine effect of combining laboratory- and production-scale samples in calibration

Parallel testing may lead to novel qualitative models for predicting product quality parameters

James K. Drennen, III

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Evaluation of Production Data

Evaluate the within-batch and between-batch spectral variability of production tablets manufactured over one year.

Samples chosen to provide maximum variabilityPotencyHardnessDissolutionDisintegration

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Evaluation of Production Data

Little inter-lot variability for production materials

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Quantitative Calibration from Production Samples only

Specified

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93.0 94 95 96 97 98 99 100 101 102 103 104104.693.7

96

98

100

102

104

106

108

110

112

114114.8

Estim

ated

SEC = 1.2 % w/w

SEP = 1.3 % w/w

R squared = 0.58

James K. Drennen, III

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Calibration including production and development samples

9071.0 75 80 85 95 100 105 110 115 120 125 130 134.366.5707580859095100105110115120125130135140

144.9

Specified

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xxxEs

timat

ed

SEP > 3.0

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Evidence of “Process Signature”

-0.185 -0.15 -0.10 -0.05 -0.00 0.05 0.10 0.150.169-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.189

PC 2 (22.95%)

PC 1

( 59 .

59%

)

x pt7069~1

x pt7069~3

x pt7069~9x pt706~14

x pt706~20

x pt706~23x pt706~25x pt706~27

x pt706~29

x pt706~31

x pt706~35

x pt706~39

x pt706~43

x pt706~45

x pt706~47

x pt706~53

x pt706~55

x pt706~57

x pt706~59

x pt706~62x pt706~67

x pt706~72x pt706~74x pt706~76 x pt706~78x pt706~81x pt706~85x pt706~87

x pt706~97x pt70~101

x pt70~109

x pt70~113x pt70~115x pt70~121

x pt70~127x pt70~131

x pt70~133x pt70~135

x pt70~139

x pt70~143

x pt70~146x pt70~149x pt70~153

x pt70~155

x pt70~159x pt70~165

x pt70~167

x pt70~172x pt70~176

x pt70~177x pt70~181

x pt70~185x pt70~191

x pt70~193 x pt70~199x pt70~201

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x pt70~265x pt70~267

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x pt70~349

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Using Laboratory Scale Samples to Expand Calibration Range

Comparison of three scales of compression

-0.0003

-0.0002

-0.0001

0.0000

0.0001

0.0002

0.0003

-0.0015 -0.0010 -0.0005 0.0000 0.0005 0.0010 0.0015

P C1

single punch compressor small scale rotary comp. full scale compressor

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Sampling/SPC/Data Management

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Test of Sample Positioning System

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Test of Sample Positioning System

Two sides of tablet provide identical spectra

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Test of Sample Positioning System

Early positioning studies led to improvements in conveyor and trigger system

X-position study for 2nd Deriv. Intensity vs. Position along the belt

-0.002

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

40.0 42.0 44.0 46.0 48.0 50.0 52.0 54.0 56.0 58.0

P o sit io n alo ng the belt (1 / 64 in)

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Example API Calibration

~500 calibration/150 validation samplesMultiple independent lots

30

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45

50

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60

65

70

30 35 40 45 50 55 60 65 70

Reference Assay (mg)

Pred

icte

d A

ssay

(mg)

Elements=599Slope=0.963129Intercept=1.803031Correlation=0.981368RMSEP=1.247676Bias=-0.002690

James K. Drennen, III

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Example Hardness Calibration

~500 calibration/150 validation samplesMultiple independent lots

0

20

40

60

80

100

120

140

160

180

0 20 40 60 80 100 120 140 160

Reference Hardness

Pred

icte

d H

ardn

ess

Elements=588Slope=0.93Intercept=4.36Correlation=0.9629RMSEP=7.99Bias=-0.0143

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Calibration Maintenance/Transfer

Why are transfer/update protocols necessary?Need for a calibrated backup instrumentEventual expansion to further linesTransfer-in-time of knowledge from earlier experiments

James K. Drennen, III

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Calibration Maintenance/Transfer

Variability in Product/ProcessVariability in Instrument/Sensor

Methods must be developed prior to implementation

Calibration updateCalibration transferIntegration with existing SPC and OOS procedures

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Possible Outcomes of NIR Prediction of Assay Results

In S

pec

Out

of S

pec

ValidNIR Result

Not-ValidNIR Result

1 2

4 3

NIR

Tes

t Res

ult f

or T

able

t:

Evaluation of NIR Test:1. Accepted result - pass2. Investigation required

(NIR result not acceptable)3. Investigation required

(NIR result not acceptable)4. Accepted result – fail

orDuring initial period –Investigation recommended

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Calibration Update

Definition:The enhancement of an existing calibration model through the inclusion of additional calibration samples

Calibration update samples are more representative of the current test samples than the original calibration samples

Calibration update performed following change in product (eg: new supplier)

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Continuum of Calibration Transfer Types

Preprocessing Methods (scatter correction, derivatives, etc.)

Instrument Matching (univariatelinear, etc.)

Complex, black-box Method-Method: PDS, ANN

Orthogonal Projection Methods

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Failure Detection

NIRInstrument Sample

NIRData

Pre -treatment

Model

Result(Prediction) Final

Result

Is prediction valid?(Qres and T 2)

InstrumentStandardization Instrument

Evaluation andcorrective action

Potential errors ( A) due to:-New instrument-Changed instrument response

Potential errors ( B) due to:-Raw material change-Process change

Result is valid

Result requires investigationInstrumentMatching

Calibration TransferProcesses NIR Prediction Prediction Validity

Historicaldata and

actionthreshold

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Example: Master and slave instruments

1300 1400 1500 1600 1700 1800 1900 20000.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

Wavelength ( nm )

Ref

lect

ance

Rat

io

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Bias Correction Needed

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Bias Correction Applied

0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 1.05

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

New Instrument Absorbance @ 1440 nm

Ref

eren

ce In

st. A

bsor

banc

e @

144

0 nm

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Calibration transfer model

1300 1400 1500 1600 1700 1800 1900 2000-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Wavelength ( nm )

Arb

itrar

y U

nits

Multiplicative Constant

Additive Coefficients

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Prediction Following Instrument-Instrument Transfer

30 35 40 45 50 55 60 65 7030

35

40

45

50

55

60

65

70

Measured Content ( mg )

Pre

dict

ed C

onte

nt (

mg,

bia

s co

rrec

ted)

N = 78RMSEP = 2.25%RSD = 23.2 %r = 0.973r2 = 0.946

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Example: Lamp change

1300 1400 1500 1600 1700 1800 1900 20000.7

0.8

0.9

1

1.1

1.2

1.3

1.4

Wavelength ( nm )

Ref

lect

ance

Rat

io

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Justification for baseline subtraction method

46 46.5 47 47.5 48 48.5 49 49.5 5046

46.5

47

47.5

48

48.5

49

49.5

50

Prior to Lamp Change, ( mg )

Follo

win

g La

mp

Cha

nge,

Unc

orre

cted

( m

g )

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Calibration transfer model for correcting lamp change

1300 1400 1500 1600 1700 1800 1900 2000-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

Wavelength ( nm )

Ref

lect

ance

Rat

io

Additive Calibration Transfer Coefficients

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Prediction quality with calibration transfer

46 46.5 47 47.5 48 48.5 49 49.5 5046

46.5

47

47.5

48

48.5

49

49.5

50

Prior to Lamp Change, ( mg )

Follo

win

g La

mp

Cha

nge,

Tra

nsfe

r Cor

rect

ed (

mg

)

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How Many Transfer Samples?

5 10 15 20 25 30

1

Number of Transfer Samples ( n )

Rel

ativ

e Er

ror (

mul

tiple

)

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Stability of transfer standards

0 5 10 15 20 25 30 352

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4

Time ( days )

Tran

sfer

SE

P (

mg

)

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Process Understanding- Blending

Optimization of PAT methodTechnologyMethodologySampling

Improved Process Control

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Advantage of PAT Method

RL

1 23 4

5

0

5

10

15

20

25

30

35

40

1 2 3 4 5Location

% O

utlie

rs

BA

Figure 2. Outlier pattern of thief probe collected samples. A) V-blender showing UV sampling positions. B) Percent of outliers as a function of sample location

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Inter- vs. Intra-shell MixingFront

L R

Back

R L

Top (n=4)Middle (n=4)

Bottom (n=4)

Left shell (n=6)

Right shell (n=6)

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V-BlenderTop

y = 88.231e-0.1197x

R2 = 0.9945

Middley = 77.703e-0.1201x

R2 = 0.9684

Bottomy = 79.212e-0.1307x

R2 = 0.9711

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Time (min)

%R

SD

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V-Blender

Left army = -9.2466x + 63.825

R2 = 0.9995

Right army = -1.9808x + 13.321

R2 = 0.9979

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

50.00

0 1 2 3 4 5 6 7

Time (min)

%R

SD

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V-Blender

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

0 5 10 15 20 25 30 35 40 45 50

Time (min)

%R

SD

Left armRight armTopMiddleBottom

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Optimization of Sensor Position

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Effect of Blender RPM

0.700.750.800.850.900.951.001.05

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56Time (min)

Cor

rela

tion

Coe

ffic

ient

IX

0.70

0.75

0.80

0.850.90

0.951.00

1.05

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56Time (min)

Cor

rela

tion

Coe

ffic

ient

XIII

Figure 3B. MBEST correlation coefficient-time profiles of high humidity 11% SA blends using single model

Higher RPM

Lower RPM

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Conclusion

Risk Analysis and Statistical experimental design requiredChemical, physical and performance parameters can be predicted, but understand the chemistry/physicsSampling issues are criticalData management system necessaryControl systems integration necessary- SPC, OOS, 21 CFR Part 11

James K. Drennen, III

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Duquesne University Center for Pharmaceutical Technology

http://http://http://www.dcpt.duq.eduwww.dcpt.duq.eduwww.dcpt.duq.edu