28.06.10 1Lecture at VUT 1

PAT solution to the drug PAT solution to the drug release predictionrelease prediction

Semenov Institute of

Chemical PhysicsRussian

chemometricsociety

A.L. Pomerantsev,

O.Ye. Rodionova

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Generic PAT SolutionsGeneric PAT Solutions

Incoming inspection

Outcoming inspection

Processcontrol

Substance Process Product

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Stage Stage 2:2: Process control Process control

Goal: granulation monitoring

Control: In-line

Equipment: J&M

Method: NIR + PLS

Design: Russia + Germany

Implementation: in progress

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Objects: PelletsObjects: PelletsSugar +API

Coating: Acryl EZE

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ExperimentExperiment

t = 0t = 30t = 42t = 50

NIR Spectra Dissolution Profiles

t = 105

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Data overviewData overviewSamples

1 2 3 4 5 6 7 8 9

B a

t c h

13 25 44 62 82 99 117 136 154 171

14 22 37 52 67 81 98 110 124 142

15 18 30 41 52 62 73 85 97 114

16 19 36 52 67 83 98 114 129 137

17 17 28 38 50 55 64      

18 18 31 42 51 61 70 79 89 105

19 39 70 98 127 156 188 215 246 260

20 21 40 59 77 96 115 133 152 168

21 20 30 43 55 67 82 92 105 121

22 24 46 70 89 111 133 155 176 191

23 26 50 74 98 122 150 171 194 209

24 18 31 42 52 63 73 83 94 110

25 19 34 49 64 79 94 109 124 140

26 19 34 48 64 79 95 111 125 140

Process time, min

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PAT problemPAT problem

Prediction

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DataData

NIR Spectra Dissolution Data

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Evident approachEvident approachSpectra Dissolution time (min)

Wavelengths 0 15 30 45 60 90 120 180 240

Sa

mp

les

1

2

3

4

5

6

7

8

9

X YPLS2

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PLS2 resultsPLS2 results

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Kinetic approachKinetic approachk1 k2 ... kp

Sa

mp

les

1 3.56 0.10 ... 0.33

2 4.03 0.09 ... 0.66

3 4.99 0.10 ... 0.98

4 6.13 0.15 ... 1.28

5 6.84 0.20 ... 1.62

6 7.81 0.25 ... 1.93

7 8.66 0.30 ... 2.23

8 9.49 0.33 ... 2.54

9 9.82 0.35 ... 2.54

Features extraction Y

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AutocatalysisAutocatalysis

100)(;0)0(;1001

21

DDk

kDDk

dt

dD

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Fitter softwareFitter software

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Kinetics of dissolutionKinetics of dissolution

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Parameter Parameter kk11 is common within a batch is common within a batch

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Constant Constant kk1 1 and the layer gradeand the layer grade

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Constant Constant kk22 and the layer thickness and the layer thickness

Layer thickness

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A small wonder of scienceA small wonder of science

kinetic parameter k2 in the model

follows the layer thickness

kinetic parameter k2 in the model

follows the layer thickness

kinetic parameter k1 in the model

reflects the material grade

kinetic parameter k1 in the model

reflects the material grade

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Prediction of Prediction of kk22: NIR – PLS (White : NIR – PLS (White subset)subset)

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Prediction of Prediction of kk22: NIR – PLS (Yellow : NIR – PLS (Yellow subset)subset)

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Prediction techniquePrediction technique

NLRk1 :common for the grade

k21 , .... , k29 : for batch 20

k21 , .... , k29 : for batch 21

k21 , .... , k29 : for batch 22

k21 , .... , k29 : for batch 23

PL

S

PLSk2

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Prediction qualityPrediction quality

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ConclusionsConclusions

• We’ve obtained a very practical method of prediction

• There is something behind the autocatalytic model

• A lot of hidden information still could be dug up

• We’ve obtained a very practical method of prediction

• There is something behind the autocatalytic model

• A lot of hidden information still could be dug up

28.06.10 24Lecture at VUT

AcknowledgementsAcknowledgements

• GEA Pharma Systems AG, Bubendorf, Switzerland

• Michael Melichar and Anthony Wigmore for data acquisition

• Project PANOPOD-II sponsor Deutsche Bundesstiftung Umwelt (DBU)

• GEA Pharma Systems AG, Bubendorf, Switzerland

• Michael Melichar and Anthony Wigmore for data acquisition

• Project PANOPOD-II sponsor Deutsche Bundesstiftung Umwelt (DBU)