Raw material verification with AssureID: problems and solutions Dr. Yaroslav Sokovikov Yuri Shishkin SchelTec AG

Page 2 Introduction  NIR Spectroscopy is a useful measurement throughout various stages of the manufacturing process, however it is particularly useful for raw materials checking and verification. If the materials to be identified are spectroscopically dissimilar, it is often only necessary to use a simple distance measure such as a spectral difference. If the spectra are similar, it may be necessary to utilize more sophisticated techniques which take into consideration both the variability of the spectra of interest and the differences between the spectra.  The PerkinElmer Spectrum AssureID materials checking system provides pharmaceutical QA functions with a rapid, unambiguous means of verifying the identity and quality of a production material, confirming suitability for use in the manufacturing process. For the first time QA can implement a measurement system that integrates easily into the manufacturing process and return business benefits shortly after delivery.

Page 3 AssureID: key benefits  Simple, proven spectroscopic technique with minimal sample preparation  Choice of MIR or NIR systems  Powerful chemometric engine eliminates the need for a chemometrician  Designed to work the way QA works  Minimal training requirements and up-keep  21 CFR Part 11 technical compliance  Powerful trending and plotting tools

Page 4 AssureID for NIR: what is?  PerkinElmer FT-NIR Spectrometer Spectrum 100N or  FT-MIR/NIR Spectrometer Spectrum  Plug-and-play accessory  NIRA (NIR reflectance accessory)  Fiber optic accessory  Tablet checker  Transmittance module +  Special software packed AssureID  Method Explorer & Editor  Analyzer  Result Browser

Page 5 Problems  Analysis of raw materials has show some problems in classification  Has been received very low a level on the parameter “Classification Rate”  Probably the reason of it in use of different parties of the same raw material  The given presentation is devoted to methods of the decision of this problem

Page 6 Calibration conditions  Model calibration conditions  Approximate number of samples is 350  Each sample from a batch has three replicates scanned from different places of a same can by a fiber probe accessory  Each scan was performed through two layers of PE  No sample direct “hit” was allowed  Spectrometer scanning parameters:  Range: 10000…4100 cm-1  Resolution: 16 cm-1  Apodization: strong  Accessory type: fiber probe  Model pre-process settings

Page 7 Raw spectral data  All the raw spectral data  Visual analysis determines deviations in spectrum collecting process  There were decided to separate all the data into, at least, two groups  Main criteria: baseline drift  Drifting and sloping baselines determinedBATCH ONE  “stable” baseline determined BATCH TWO  BATCH ONE and TWO are the “subsamples” of the same material

Page 8 Batch 1: feature  Batch one  the current subgroup mainly consists of the samples which were suffered from the different pressure applied during probe scan  the weak probe pressure affects the baseline nature  SIMCA compensation is expected from the model  “baseline suffer” samples included into the model are considered to protect the user from further sampling issues

Page 9 Batch 2: feature  Batch two  the current subgroup mainly consists of the samples which were registered in the right way – another user probably  similar probe pressure provides the baseline nature stability  SIMCA compensation - in case of the other user - is expected from the model  “right baseline” samples included into the model are considered to protect the user from further sampling issues

Page 10 Pre-processing data  Pre-processing data – a certain option provided by the software  based on further calculations there were distinguished, that “baseline slope” and “9 points 1 st derivate” provide the best calibration results  the best recognition and rejection parameters are aquired by “slope” and “derivate” pre- processing  MSC was selected as the most appropriate baseline normalization (SNV ended up to be a bit worth as the sample nature was a powder)

Page 11 Baseline slope  processed data by “baseline slope” – all spectrums are divided in two kinds of a same “material”  samples subgroup spread is visually evident (blue vs green)

Page 12 Two submaterials: models  Calibrated variance space for the two “submaterials” of the same substance – “baseline slope”  spectrum overlap is visually evident  Green – is eventually a “first group”  Blue – is eventually a “second group”

Page 13 Two submaterials: validation  Validation results  Summary “baseline slope”  the substance was divided into two different “submaterials”  the separation was taken into account by processed data examination  the validation process was performed on 49 independent samples  the results by “baseline slope” correction are:  Class 86%  Rejection 96%

Page 14 Preprocessed data – 9 points 1 st derivate  processed data – “9 points 1 st derivate” – all spectrums are divided in two subkinds of a same “material”  spectrum overlap is visually evident

Page 15 9 points 1 st derivate: model  spectrum overlap is visually evident  Calibrated variance space for the two different “submaterials” of the same samples – “9 points 1 st derivate”  Green – is eventually a “first group”  Blue – is eventually a “second group”

Page 16 9 points 1 st derivate: validation  Validation results  Summary “9 points 1 st derivate”  the substance was divided into two different “submaterials”  the separation was taken into account by processed data examination  the validation process was performed on 49 independent samples  the results by “9 points 1 st derivate” are:  Class 78%  Rejection 96%

Page 17 Validation results  Validation results: short summary  “baseline slope” vs “9 points 1 st derivate”  “baseline slope” and “9 points 1sr derivate” assures good calibration and validation results  “baseline slope” provides more accurate prediction - up to 10% better  sample spread is more evident under “baseline slope” pre-process  “BS” is selected as the best pre-process parameter for the further model configuration  “9 points 1 st derivate”  “baseline slope”

Page 18 Second calculation: samples transfer  spectrum overlap is visually evident  All the “agglutinated” or the “similar nature” samples were decided to move into the “second group”  Green – is eventually a “first group”  Blue – is eventually a “second group”

Page 19 Second calculation: transfer and extreme samples  All the “red circled” samples were decided to be separated into several subgroups; “blue circled” samples were revised as an extreme group  spectrum overlap is visually evident

Page 20  Separation is complete: black sphere means to be a variance space of the extreme samples which evidently overlap all the other spaces  spectrum overlap is visually evident Second calculation: extreme samples

Page 21  Validation and Comments  SIMCA model validation results are relatively poor corresponding to the “Classification rate”  any other samples’ pre-processes do not help the result  Solution  exclude all the samples from the extreme - “black” - group Second calculation: extreme samples

Page 22  spectrum overlap is visually evident  Extreme samples removal is done - all the data is now spread in a quite efficient manner  overlapping is occurred in “pink” and “yellow” zones  overlapping is occurred in “green” and “blue” zones  However Second calculation: extreme samples

Page 23  Validation and Comments  SIMCA model validation results are much better comparing to the previous calibration  any other samples’ pre-processes and remodeling do not help the result  Solution  slight overlapping in pink and yellow zones is not crucial as the samples are of the same material  separate green and blue zones by checking the extreme samples in both spaces Second calculation: extreme samples

Page 24 Second calculation: working on “blue” zone  blue space is now being “adjusted” by local extremes exclusion  Processing data…

Page 25  Processing data… Second calculation: working on “blue” and “green” zones  green space is now being “adjusted” by local extremes exclusion  exclusion is suggested by the interactive module of the software  each sample is excluded manually though  blue space is now being “adjusted” by local extremes exclusion  exclusion is suggested by the interactive module of the software  each sample is excluded manually though

Page 26  Developing and Validating  no any spectrum overlap is visually observed nor detected by the software Second calculation: working on…

Page 27  Discussion Results  Interactive SIMCA by AssureID software:  visual analyses in the beginning of the calibration helps to manage the samples  software options provide RAW and PROCCESSED data acquisition on demand  Assure ID helps the user interactively “adjust” the model parameters on any step of the calibration or the whole calculation may be completely automated  integrated math database provides a brief course of SIMCA  Overall performance:  first sample subgroups spread increased classification and rejection parameter  second sample subgroups spread decreased “class” and “rejection” but further data processing assures better results  model validation approves that all the pressure differences applied to the samples during data collection were compensated  User satisfaction:  fast and easy way of model creation  interactive and user-friendly interface of the software doesn’t require a user to be a high grade specialist in SIMCA calculation