drennen idrc 2004

Implementation of PAT in the Pharmaceutical Industry

Duquesne UniversityCenter for Pharmaceutical Technology

James K. Drennen, III

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Duquesne University Center for Pharmaceutical Technology (DCPT)

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

James K. Drennen, III

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“No wind favors him who has no destined port.”

Montaigne

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Today’s Manufacturing ProcessesCharacterised by:

Large inefficient batch equipmentLow utilization 30 - 40 % on averageLow product yieldsExcessive amounts of product non-conformancesLong lead-times due to stage and final product testingCapital and labour intensiveHigh operating costsHigh inventories and excessive warehouse capacityResistant to innovationCycle time improvement perceived to be limited by regulatory constraints

CAMP Member Companies Presentation to FDA March 2002

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“The significant problems we face cannot be solved by the same level of thinking we were at when we created them.”

Albert Einstein

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Pharmaceutical Manufacturing in the Future

Guidance for IndustryPAT- A framework for Innovative Pharmaceutical Manufacturing and Quality Assurance

Scientific principles and tools supporting innovationPAT ToolsProcess Understanding Risk-Based Approach

Regulatory Strategy accommodating innovation PAT Team approach to Review and Inspection Joint training and certification of staff

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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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Sampling/SPC/Data Management

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Objectives (I)

Qualify capabilities of instrument and sampling systemEvaluate the potential effect of “process signature” on calibration developmentCompare the utility of reflection and transmission data

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Instrument Performance Testing-Performed On-Line, When Possible

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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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Sample Position- Reflection

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Sample Position- Transmission

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Process Signature

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Production Samples

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Compression Samples- Reflection

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Production Samples Projected onto Compression Model

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Conclusion (I)

The impact of positioning error on NIR reflection and transmission analysis can be mitigated using preprocessing techniques, automatic positioning system suitable for its intended useShielding not required for transmission measurements Spectra acquired from laboratory and production samples can be pooledDiffuse reflection spectra were less sensitive to sample positioning

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Objectives (II)

Calibration Development/Validation

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Truncated Spectra from API Content Calibration

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Robustness Index (RI) for selection of preprocessing

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Robustness Index

∫=

++= 3

0

2 CBA

1RI

NLNN LL

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Robustness Index

Where:RI = Robustness indexLN = Level of simulated noise added

= Quadratic fit of the noise augmented prediction error data

RI is inverse of the AUC defined by quadriaticfit of RMSE plotted as function of added spectral noise

CBA 2 ++ NN LL

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Cross-Validation and Robustness Testing for API Calibration

Preprocessing Treatment

PLS Factors

RMSECV ( mg )

RMSE ( mg ) r2

Robustness Index

Raw Data 5 2.36 1.96 0.886 0.18SNV 5 2.19 1.70 0.915 0.25SNV + 1st Deriv. 4 1.79 1.49 0.935 0.33SNV + 2nd Deriv. 3 1.93 1.64 0.921 0.29MSC 5 2.12 1.68 0.917 0.26MSC + 1st Deriv. 4 1.80 1.48 0.936 0.33MSC + 2nd Deriv. 3 1.89 1.61 0.923 0.291st Deriv. 4 1.80 1.48 0.936 0.322nd Deriv. 3 1.89 1.61 0.924 0.29

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Cross-Validation and Robustness Testing for Hardness Calibration

Preprocessing Treatment Model Type

Factors/ Terms RMSECV RMSE r2

Robustness Index

Raw Data PLS 3 10.11 8.88 0.907 0.042SNV PLS 3 10.96 9.75 0.888 0.040SNV 1st Deriv. PLS 3 12.04 10.86 0.861 0.038SNV 2nd Deriv. PLS 3 12.38 11.08 0.855 0.037MSC PLS 3 9.66 8.82 0.908 0.043MSC 1st Deriv. PLS 3 8.82 8.11 0.923 0.047MSC 2nd Deriv. PLS 3 8.21 7.48 0.934 0.0461st Deriv. PLS 2 9.11 8.22 0.920 0.0432nd Deriv. PLS 3 8.73 7.91 0.926 0.0451st Order Baseline Fit 2 NA 8.79 0.909 0.0361st Order Baseline Fit 1 NA 8.75 0.910 0.0382nd Order Baseline Fit 3 NA 8.01 0.924 0.0332nd Order Baseline Fit 2 NA 8.32 0.918 0.038

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Prediction Plot for API Content

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Calibration/Validation API

Calibration Dataset VAL1 VAL2 VAL3

Samples ( n ) 500 350 40 38 Batches ( n ) 23 30 4 2

Maximum ( mg ) 65.32 66.06 50.31 65.53Mean ( mg ) 48.90 49.07 49.44 47.92

Minimum ( mg ) 32.66 33.63 47.70 32.43Standard Deviation ( mg ) 5.83 4.94 0.67 13.93

Model TypePreprocessing

Spectral Range ( nm )Latent Variables ( n )

RMSE ( mg )* 1.48 1.25 5.35 (1.04) 5.07 (3.76)RMSE ( %, nominal )* 2.96 2.50 10.7 (2.08) 10.1 (7.52)

r 0.967 0.972 0.441 0.974r2 0.936 0.944 0.194 0.948

RPD* 3.9 4.0 NA 2.7 (3.8)Bias ( mg )* 0.00 -0.22 -5.3 (0.71) -4.0 (2.04)

Full-spectrum PLS regression

* A prediction bias was identified for the VAL2 and VAL3 datasets. The corrected values are in parentheses.

MSC+1st Derivative(1300 - 2000), 2

4

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Hardness Prediction

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Calibration/Validation Hardness

Calibration Dataset

Validation Dataset

Samples ( n ) 437 152Batches ( n ) 22 8

Maximum ( N ) 140.0 145.0Mean ( N ) 61.7 58.1

Minimum ( N ) 16.0 13.0Standard Deviation ( N ) 29.2 30.9

Model TypePreprocessing

Spectral Range ( nm )Latent Variables ( n )

RMSE ( N )* 8.1 12.0 (8.5)r 0.961 0.961

r2 0.922 0.92344RPD* 3.6 2.6 (3.6)

Bias ( N )* 0.0 -8.0 (-0.01)

* A prediction bias was identified. Corrected values are in parentheses.

Full-spectrum PLS regressionMSC+1st Derivative

(1300 - 2000), 23

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Model Robustness

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High-Flux Noise Robustness Test

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Wavelength Accuracy Robustness

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Conclusion (II)

Calibration model form (for hardness) and preprocessing operations were selected based on RI analysis and cross-validationValidation of accuracy, precision, linearity, specificity and robustness, using independent datasetsRobustness demonstrated to variation in instrumental high-flux noise and wavelength shift

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Objectives (III)

Develop a system for continuous calibration monitoringFormulate a strategy for calibration transfer/update to support instrument maintenance and inter-instrument transferDetermine the required number (and evaluate stability) of instrument standardization “rescue” samples

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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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Instrument Performance Testing

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IPC using Pearson Correlation

21

21

22

111

)(Σ)n(Σ)(Σ)n(Σ

))(Σ(Σ)(n(Σ),r(

+λ+λλλ

+λλ+λλ+λλ

Χ−ΧΧ−Χ

ΧΧ−ΧΧ=ΧΧ

Xλ = Odd-numbered spectral data pointsXλ+1 = Even-numbered spectral data

points.

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IPC for Typical (blue) and Noisy (red) NIR Spectra

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Noise Factor Level (NFL)

Using this formula, a noise factor level (NFL) is estimated

NFL = f(1 – r(Xλ, Xλ+1))

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Histogram of historical NFL scores for API content calibration spectra

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Sample-Based AOTF Wavelength Uncertainty Test

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Calibration Monitoring

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Use of Q residual and T2 for Calibration Monitoring

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Use of Q and T2 for API CAL and VAL2

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Calibration Maintenance and Transfer

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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

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Master and Slave Instruments

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Exhibit 5

Potential investigation actions1. Review daily internal

performance test and internal performance test history

2. Run an internal performance test

3. Rescan tablet4. Review SPC of method

assessment5. Review SPC of tablet test

results6. Perform a parallel laboratory

test on tablet

Potential remediation actionsA. Instrument repair,

standardization, and external performance test

B. Calibration updateC. Address as an

out-of-specification (OOS) investigation

Instrumentevaluation and

corrective action(legend)

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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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Continuous Calibration Monitoring for Stability Samples

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Conclusion (III)

Key instrument performance parameters can be monitored using features of sample spectraHotelling’s T2 and Q residuals provide basis of predicting spectral deviationsCalibration transfer among multiple instruments can be achieved using baseline subtraction and as few as 15 transfer samples

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Conclusion (III)

Calibration transfer samples can be stored for at least one month without compromising calibration transfer performanceLong-term spectral database uniformity can be maintained using appropriate calibration transfer methods

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Acknowledgements

Carl AndersonRobert CogdillDavid MolseedMiriam Delgado

Robert ChisholmAli AfnanRaymond BoltonThorsten HerkertKen Leiper

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Duquesne University Center for Pharmaceutical Technology

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