pat and applications richard g brereton centre for chemometrics university of bristol

PAT AND APPLICATIONS

Richard G Brereton

Centre for Chemometrics

University of Bristol

r.g.brereton@bris.ac.uk

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)