pat and applications richard g brereton centre for chemometrics university of bristol
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PAT AND APPLICATIONS Richard G Brereton Centre for Chemometrics University of Bristol [email protected] Phone +44-117-9287658. Chemometrics and PAT PAT tools Some potential applications and their solutions. Origins of tablets as determined by pyrolysis GCMS. - PowerPoint PPT PresentationTRANSCRIPT
PAT AND APPLICATIONS
Richard G Brereton
Centre for Chemometrics
University of Bristol
Phone +44-117-9287658
Chemometrics and PAT
PAT tools
Some potential applications and their solutions.
Origins of tablets as determined by pyrolysis GCMS.
Characterisation of route specific impurities by LCMS.
On-line reaction monitoring.
Material Analysis.
Chromatographic Pattern Recognition.
CHEMOMETRICS AND PAT
Guidance for Industry PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance
U.S. Department of Health and Human Services Food and Drug Administration
Pharmaceutical CGMPs September 2004
http://www.fda.gov/cder/guidance/6419nl.pdf
Other countries are following, for example European pharmaceutical companies.
Chemometrics and PAT
PAT tools
Some potential applications and their solutions
Origins of tablets as determined by pyrolysis GCMS.
Characterisation of route specific impurities by LCMS.
On-line reaction monitoring.
Material Analysis.
Chromatographic Pattern Recognition.
PAT TOOLS
1. Multivariate tools for design, data acquisition and analysis
2. Process analyzers
3. Process control tools
4. Continuous improvement and knowledge management tools
1. MULTIVARIATE TOOLS
Achieved through the use of multivariate mathematical approaches, such as
•statistical design of experiments,
•response surface methodologies,
•process simulation, and
•pattern recognition tools,
in conjunction with knowledge management systems.
The applicability and reliability of knowledge in the form of mathematical relationships and models can be assessed by statistical evaluation of model predictions.
When used appropriately, these tools enable the identification and evaluation of product and process variables that may be critical to product quality and performance.
The tools may also identify potential failure modes and mechanisms and quantify their effects on product quality.
2. PROCESS ANALYZERS
Multivariate methodologies are often necessary to extract critical process knowledge for real time control and quality assurance.
Comprehensive statistical and risk analyses of the process are generally necessary.
Sensor-based measurements can provide a useful process signature.
3. PROCESS CONTROL TOOLS
Develop mathematical relationships between product quality attributes and measurements of critical material and process attributes
4. CONTINUOUS IMPROVEMENT AND KNOWLEDGE MANAGEMENT
Continuous learning through data collection and analysis over the life cycle of a product is important.
Scientific understanding of the relevant multi-factorial relationships (e.g., between formulation, process, and quality attributes).
Many areas of PAT where chemometrics methods can be useful.
Important to have an overall grasp of the potential of chemometrics methods.
Many levels. How can it help? Then turn to the specialist.
Hierarchy of users and developers of chemometrics methods.
PAT applications
This takes advantage of many well established areas of chemometrics, especially in process monitoring and control.
Over 20 years “theoretical” development, e.g. CPAC in Washington.
After many years FDA have recognised this area.
Classical chemometrics is being used to advantage.
On-line spectroscopy has an important role in catalysing the need for chemometrics methods.
Chemometrics and PAT
PAT tools
Some potential applications and their solutions
Origins of tablets as determined by pyrolysis GCMS.
Characterisation of route specific impurities by LCMS.
On-line reaction monitoring.
Material Analysis.
Chromatographic Pattern Recognition.
SOME POTENTIAL APPLICATIONS AND THEIR SOLUTIONS
Origins of tablets as determined by pyrolysis GCMS.
Characterisation of route specific impurities by LCMS.
On-line reaction monitoring.
Material Analysis.
Chromatographic Pattern Recognition.
Chemometrics and PAT
PAT tools
Some potential applications and their solutions
Origins of tablets as determined by pyrolysis GCMS.
Characterisation of route specific impurities by LCMS.
On-line reaction monitoring.
Material Analysis.
Chromatographic Pattern Recognition.
ORIGINS OF TABLETS AS DETERMINED BY PYROLYSIS GCMS
Pharmaceutical tablets
Can we distinguish origin?
Patent protection law – illegal manufacturing.
Wet granulation
Direct compression
Dampening the powder with liquid adhesives and converting the mixture to a free flowing particles to achieve desired size distribution, shape and hardness density ratio.
Forming a slug from the powder that is then compressed.
0 400 800 1200 1600 2000 2400 2800
Time (s)
300 400 500 600 700
Pyrolysis GCMS
•Identification of peaks
•Determine peak areas
•Alignment
•Selection of common peaks
•Pattern recognition to determine class of unknown
•Validation to determine how well the method works
Samples
Peaks Group
21 data sets with known preparation(WG and DC)
Automatic deconvolution
Delete specific variables(21×21)
Construct a matrix by comparingspectra of different samples
(21×636)
Outlier detection using PC plot(20×18)
First k score vectors from PCA(20×k) (k=1,2,..6)
Fisher discriminant analysis
New feature matrix(20×1)
Fuzzy c-means classification Mahalanobis distance and QDA
k:=k+1 k:=k+1
Preprocessing
Classification by a method called “Mahalanobis distance”
Cross-validation importantSample Process Autoprediction Cross-validated
Predicted (4PC) Predicted (6PC) Predicted (4PC) Predicted (6PC)
1 DC DC DC DC DC
3 DC DC WG DC DC
4 DC DC DC DC WG
5 DC DC DC DC DC
6 DC DC DC DC DC
7 DC DC DC DC DC
8 DC DC DC DC DC
9 DC DC DC DC DC
10 DC DC DC DC DC
11 DC DC DC DC DC
12 WG DC WG DC DC
13 WG WG WG WG WG
14 WG DC DC DC DC
15 WG WG WG WG WG
16 WG WG WG WG WG
17 WG WG WG DC DC
18 WG WG WG WG WG
19 WG WG WG WG WG
20 WG DC DC DC DC
21 WG WG WG DC DC
22 WG WG WG WG WG
Misclassified - 3 3 5 7
• Prediction of DC is good.
•Prediction of WG slightly less good.
•Cross-validation is like a “blind test”, two methods are compared, the method witrh 4 PCs gives 5 erroneous results, all in WG
•Good as an exploratory method
•Normally this is useful for checking rogue samples, then invest more time and money in a second confirmatory phase.
SUPPORT VECTOR MACHINES : A NEW APPROACH
Py-GC-MSanalysis
Pre-processing
Mass selection and sample matrix composition
Feature extraction
Principal components analysis
Feature selection
Stepwise discriminant analysis
Classification
Support Vector Machines
MULTIVARIATE ANALYSIS
•These are methods that have been used a lot in biology and economics, but much less in chemistry.
•The models are non-linear. This is common in many applications, one cannot necessarily darw a straight line between two classes.
•Compare to conventional methods.
AUTOPREDICTION
LEGEND:WG =●, DC = ●, support vectors = ○-○
% samples correctly classified
0
20
40
60
80
100
Stepwise DA SVM dot
product
SVM radial
basis
SVM
polynomial
Auto-prediction
Cross-validation
0
20
40
60
80
100
Stepwise DA SVM dot
product
SVM radial
basis
SVM
polynomial
DATASET 1
DATASET 2
COMPARISON OF METHODS
What does this mean?
Samples can be classified from their Pyrolysis GCMS fingerprint.
So there is a unique underlying “signal” that allows classification.
Different methods can be compared.
Chemometrics and PAT
PAT tools
Some potential applications and their solutions
Origins of tablets as determined by pyrolysis GCMS.
Characterisation of route specific impurities by LCMS.
On-line reaction monitoring.
Material Analysis.
Chromatographic Pattern Recognition.
Characterisation of route specific impurities by LCMS.
Tabletsamples
Manufacturer & Distributor G
Manufacturer & Distributor I
Manufacturer & Distributor C
Is it possible to distinguish between the three sources G, I and C ?
Is it possible to characterise each source ?
Different synthetic routes
generate different impurities
may be at very low level
may be not persistent
Characterise by diagnostic ions
Distinguish by CWIA
The main peaks in the GCMS are from the drug and the excipient, but the peaks that distinguish routes are very minor.
A consequence of the manufacturing route.
•Can we distinguish the routes?
•Can we find the ions that are significant?
Remove main peaks
Select best masses
The Component Detection Algorithm (CODA) The CODA algorithm assigns a quality index to each m/z value. Why necessary? Because only certain mass ions are useful.
MCQ is higher when the profile is similar to its smoothed and mean-subtracted version.
Spikes dissimilarly to smoothed version
Background dissimilarity to mean-substracted version
M/z = 955MCQ = 0.231
M/z = 252MCQ = 0.437
M/z = 207MCQ = 0.844
Usually a cut-off for MCQ is selected and those chromatograms above (of better quality) are retained Usually a cut-off for MCQ is selected and those chromatograms above (of better quality) are retained Usually a cut-off for MCQ is selected and those chromatograms above (of better quality) are retained
The Component Detection Weighted Index of Analogy Flaws:
A different number and type of chromatograms can be selected for each sample, if many samples this can be problematic
It may be to difficult to find an optimal cut-off
Relevant information can still reside in the portion left out.
Alternative:
Take all M/z into account but with an exponential weight.
Alternative:
Take all M/z into account but with an exponential weight. Hence all masses in the chromatogram are used, but some are better than others.
CWIA for clustering
q index CWIA
Similarity of pairs
Similarity matrix
Clustering
Tablets cluster according to origin
Replicates cluster at the earliest stages
Some heterogeneity still present (e.g. I1-I2).
Improvements of CWIA
CWIA for determining characteristic ionsCWIA can be applied on the entire dataset considering a single ion each time
Ions can be ranked according on how much they resemble a target similarity matrix
This application is slightly different to the first one.
•When products of different quality (in this case forgeries) are manufactured, very small differences in manufacturing process.
•This are indicated by very minor peaks in LCMS, the main peaks are the excipient and the drug.
•Use this information to detect samples that come from different origins.
•Find information about which m/z values are diagnostic for samples from different sources.
Chemometrics and PAT
PAT tools
Some potential applications and their solutions
Origins of tablets as determined by pyrolysis GCMS.
Characterisation of route specific impurities by LCMS.
On-line reaction monitoring.
Material Analysis.
Chromatographic Pattern Recognition.
ON-LINE REACTION MONITORING
Obtaining information in real-time.
Important aspect of PAT: on-line processes and probes. Can obtain spectra as the reaction progresses, need to develop software and then can study processes, e.g. drying, when reactions reach end-points etc.
BORIS – reaction monitoring software
Developed for Glaxo Smith Kline
Aims :
To develop software that can: -Read in data from a variety of sourcesPre-process this dataApply various chemometric methods to the dataBe extended or expanded at a later date
and…Do all this in real-time
UV Data source
Variableselection
standardisation
PCA
Graphical output
Graphical output
Curve resolution
Save results to file
MIR Data
source
row-scaling
Graphicaloutput
MLR
Join datasets
Very sophisticated control. Multi-instrument.
0 20
40
60
80
100
time / min
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
Scores plot shows build up of product and then crystallisation, can monitor process in real-time.
On-line software allows real-time process monitoring using spectroscopic probes.
• When has a reaction gone to completion? Is a process being run for too long?
•Monitoring of drying.
•Monitoring crystallisation.
•Are impurities or side reactions building up?
•Best to obtain results in “real-time”, i.e. when the reaction is running, rather than later.
•Costs of destroying batches.
•Problems of validation and compliancy.
•Problems of factory operators who do not understand chemometrics.
Chemometrics and PAT
PAT tools
Some potential applications and their solutions
Origins of tablets as determined by pyrolysis GCMS.
Characterisation of route specific impurities by LCMS.
On-line reaction monitoring.
Material Analysis.
Chromatographic Pattern Recognition.
MATERIALS ANALYSIS
Co-operation Triton Technology Ltd
•Develop a low-cost polymer test and identification instrument – the Plastics Analyser.
•Test and analyse commercial samples results using chemometrics techniques.
•Build a material library with all the data acquired.
Change in phase as heated.
•Thermal analyser.
•Different patterns for different plastics.
•Cost effective mass market product. £5,000 total kit.
Chemometrics has been slow to take off in rheology and materials analysis – new application area.
dynamic mechanical analysis
Characteristic graph of physical properties (e.g. force / displacement) against temperature as materials changes state
Dynamic Properties vs Temperature
0.00E+00
1.00E+08
2.00E+08
3.00E+08
4.00E+08
5.00E+08
6.00E+08
0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0
Temperature (C)
Mo
du
lus
(P
a)
0
0.5
1
1.5
2
2.5
3
3.5
4
Tan
D
Loss Modulus1.
deriv E'
Tan Delta1.
Two aims
1. To determine whether an unknown plastic comes from one of twelve groups from a library.
2. To determine the grade of a plastic, QC.
Three different grades of polypropylene, use PCA on thermal profiles.
Scores PLOT
rd9rd8rd7
rd6rd5
rd4
hl9
hl8
hl7
hl6hl5
hl4
hl3
hl2
hl10
5079
5078
5077
5076
50755074
50735072
50710
RD10
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
-0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3
PC1
PC
2
Chemometrics methods can be used for
• Distinguishing polymers
• Grade distinction
• QC of a manufacturing process.
Need software that the less experienced user can employ.
Levels of software
1. Operator
2. Manager
3. Method developer
Chemometrics and PAT
PAT tools
Some potential applications and their solutions
Origins of tablets as determined by pyrolysis GCMS.
Characterisation of route specific impurities by LCMS.
On-line reaction monitoring.
Material Analysis.
Chromatographic Pattern Recognition.
CHROMATOGRAPHIC PATTERN RECOGNITION
Chromatographic columns and tests
Industrial importance as new columns arrive
•Different columns (8)
•Different test compounds (9)
•Different pHs (2)
•Different mobile phases (3)
•Different peakshape parameters (4)
A lot of experimental work.
Aims
•Determine the relationship between the columns.
•Determine the relationship between the test parameters – which measure similar properties so can the number of test compounds or chromatographic peakshape parameters be reduced.
•Compare directly the results of using different conditions e.g. different pHs or different mobile phases.
•Compare directly the results using the full set of parameters and a subset – hence what information is lost by reducing the number of tests
Scores and loadings plots
Inertsil ODS
Inertsil ODS-2
Inertsil ODS-3
Kromasil C-18
Kromasil C8
Symmetry C18
Supelco ABZ+
Purospher
-4
-3
-2
-1
0
1
2
3
4
5
6
-4 -2 0 2 4 6 8 10
Comparing conditions
Procrustes analysis
Methanol and acetonitrile ; methanol and THF
Inertsil ODS
Inertsil ODS-2
Inertsil ODS-3
Kromasil C-18
Kromasil C8
Symmetry C18
Supelco ABZ+
Purospher
-4
-3
-2
-1
0
1
2
3
4
5
6
-6 -4 -2 0 2 4 6 8 10
Inertsil ODS-2
Kromasil C-18
Inertsil ODS
Inertsil ODS-3
Kromasil C8
Symmetry C18
Supelco ABZ+
Purospher
-6
-4
-2
0
2
4
6
8
10
-8 -6 -4 -2 0 2 4 6 8 10
ACKNOWLEDGEMENTS
Origins of tablets as determined by pyrolysis GCMS.
Hailin Shen, Jim Carter, Simeone Zomer (BRISTOL),Christine Eckers (GSK)
Characterisation of route specific impurities by LCMS.
Simeone Zomer (BRISTOL), Jean-Claude Wolff, Christian Airiau, Caroline Smallwood (GSK)
On-line reaction monitoring.
Tom Thurston, Antonio Carvalho, Lifeng Zhu (BRISTOL), Richard Escott, Duncan Thompson, Christian Airiau (GSK)
Material Analysis.
Bozena Lukasiak (BRISTOL), Rita Faria, John Duncan (Triton)
Chromatographic Pattern Recognition.
David McCalley (University of West of England, Bristol)