Transcript Perspectives in Chemometrics Experience form GlaxoSmithKline

Perspectives in Chemometrics
Experience form GlaxoSmithKline
Where are we starting from
• Fortunately, not from scratch
• ASTM E1655-97
– “Standard Practices for Infrared, Multivariate,
Quantitative Analysis”
• USP Chapter on the use of NIR
– scheduled for the Second Supplement
– issue data June 2002
“When provided with identical information,
statistical procedures achieve greater
empirical accuracy than do professional.
This remains true when one provides
professionals with information not available
to the statistical procedure, …”
Dawes, Faust and Meehl, Science, 243:1668-167, 1989
First things:
• We need a clear definition of what
chemometrics encompasses
– Does MLR constitute chemometrics?
– Is this strictly for higher order techniques such
as PLS, PCR, ….?
• Are we approaching this as a data
independent study?
– Do we need to consider the source of data?
General classes of chemometric
methods
• On-line determination of composition
• Pattern recognition and classification
techniques
• Multivariate statistical process control
ASTM E1655-97
• Arises from the petrochemical industry
• Specifically addresses issues around IR
though does mention NIR
• Defines the term “multivariate
mathematical technique” to be all inclusive
• Defines many terms that we can reference
What separates the ASTM document
from the needs of the pharma industry?
• ASTN document describes methods for processes
that run continuously
– Pharma companies typically run in batch mode
– Pharma companies often do not have the volume of
batches to meet this requirement
What separates the ASTM document
from the needs of the pharma industry?
• A large sample set is required that spans between 3
to 5 SD of all constituents
– Generating these OOS samples is difficult as they
should be prepared using the same equipment as used in
the process. For the pharma companies this represents
large $$$
– If PAT is to be used upon product launch, the amount of
active ingredient required may exceed the ROI
<1119> USP chapter:
Near-infrared Spectrophotometry
• In process of revision for a large number of years
• Defines terms for both reflectance and
transmittance
• Defines PQ/IQ frequency
• Wavelength standard (NIST SRM 1920) for
reflectance only
• Only refers to MSC, no mention of chemometric
techniques for data analysis
What technologies have been or may
be used for PAT?
• Focus on spectroscopic techniques
• Offers the advantage of bring the measurement
system to the sample
• UV/vis
– Well understood technology, USP guidance
– Spectra tend to be highly overlapped due to the broad
nature of the absorbance - low specificity
– UV/vis will rely heavily on chemometrics
– Commercial and validatable hardware/software
available
• IR
–
–
–
–
Well understood
Spectra have high specificity
Difficulties making truly 0n-line measurements
Commercial hardware available but software not
written to be validated
• Raman
–
–
–
–
Not well understood by manufacturing groups
Safety concerns
Spectra have high specificity
Commercial hardware available but software not
written to be validated
• NIR
– Reasonably well understood technology, USP guidance
soon
– Spectra tend to be somewhat overlapped
– NIR will rely on chemometrics
– Commercial and validatable hardware/software
available
– Technology over-sold to the industry, still facing “bad
taste”
So, what steps do we need to take to
ensure success?
• First, and foremost, we must ensure that we are
doing good science
– This will require that any candidate processes
for PAT/chemometrics be well understood
– This in turn will require a rigorous calibration
effort with real process samples and generation
of data from referee methods
– This will take a considerable amount of time
and effort - does the ROI exist?
... success? (cont.)
• Are we targeting existing processes or new
processes/product?
– The former has the advantage of being an
established, validated process
– The latter may be easier to generate required
sample sets
On-line or at-line determination
of composition issues
•
•
•
•
•
Calibration
Maintenance of calibration
Sampling issues
Software issues
Process control
Calibration
• Will require a large number of batches
– These will need to include out of specification
(OOS) batches to properly span the desired
range
– Who will generate these?
– Cost, especially if a new product?
– Will they be generated on the actual production
equipment? If not, are they valid?
– What group within the company performs the
validation?
Maintenance of calibration
• How often must the calibration be checked?
– Daily suitability performed with some reference
material?
– Does it depend on what type of measurement?
– If the method is fiber optic based, does the
probe need to be removed for this test?
– For example: NIR for octane in motor fuel need daily check with verification from lab
testing also
Maintenance of calibration (cont.)
– What if the check reveals an OOS result?
• Does this shut the process down?
• Does it bring into question the previous results?
– Again, who is responsible for the check?
Pattern recognition / classification
techniques
• Identify & assess quality of raw materials & products
– Develop a library of spectra for acceptable lots
– Develop a multivariate statistical model of the library
– Compare future samples to predict identity & quality
– Demonstrate sensitivity to known / expected impurities,
degradation products and foreign materials.
– Up-front investment in calibration is avoided
– On-going calibration maintenance costs are avoided.
Multivariate statistical process control
• Develop a statistical model of an existing process
– Use rapid, low-cost on-line or in-situ spectroscopic
measurements.
– Uses multivariate statistics / chemometrics to
characterize the processes from relevant, sensitive
measurements.
– Control limits derived from sound statistical practice
and historical database of measurements.
– Up-front investment in calibration is avoided
– On-going calibration maintenance costs are avoided.
Multivariate statistical process control (cont)
• Statistical characterization of a process is superior to
unaided human judgment
– Multivarite statistics are extremely effective tools for
detecting correlation amidst significant noise.
– Probabilistic relationships are more readily obtained
and verifiable than causal understanding
– Methodical “mechanical” approach is more thorough
compared to heuristics and intuition
• Potential issues
– still in a research state
– volume: does pharma have the number of batches to do
this
Sampling issues
• How is the sample measured?
– Is the process sample collected the same way
the validation data was collected
– If it is a fiber based measurement, what if the
probe/fiber break?
– Are there issues of probe fouling?
• Are there issues of sample presentation
– This could be an issue for solids or turbid
samples
– Is particle size an issue
Sampling issues (cont.)
• Are there environmental issues that need to
be considered?
– Summer/winter? Dry/humid?
– Source of raw materials?
Software issues
• Who does the burden of validation fall on?
– Vendor: can provide a validation package but is
this enough
– End user: What degree of testing is required?
• Do we need to ensure 21CFR 11 compliance
• Vendors are aware of these issue and some have
begun to address it
– Bomem: process FT-NIR software Enablir
– SpectrAllaince: process UV/vis software
NovaPack
Software issues (cont.)
• What of some current software packages
– GRAMS/IQ: expecting release 8
– Matlab: doubtful that it could be validated but
useful for development
– LabView: doubtful that it could be validated but
useful for development
Process control
• Now that we have all of these tools in place,
what can we do with the information?
– Can we make process variations based on the
data from the PAT?
– These are validated processes
– If a change is warranted, does this imply that
the process was out of control?
– Or, do we use this information to trigger a
manual sampling?
For example
• Dryer monitoring
– Measuring the effluent from an oven
– Looking at solvent vapors coming off product
– Reasonable clean sample stream
• Saw slight deposition of material on optics
– Using PLS to model multiple gasses when
appropriate
– Data used to signal manual sampling and offline testing
What was learned?
• Not going to be used as final release of material
– Manufacturing is conservative
• Using chemometrics requires training local staff
– Manufacturing sites often do not have the technical
expertise
– Anything beyond linear regression was initially
confusing
• Calibrations generated off-site were not accepted
– Assurance at local site of validity of calibration
– Methodology for generating calibration was used
What was learned? (cont.)
• Need to access instrument manufacturer support
world-wide
– This includes software support
• Validation not required as not used for release
What can ease this in the future?
• Advanced training of staff
– Internal/external options
• Easier to use software
– Reliance on vendors to provide this
• Validation of software (vendor)
– 21CFR11 compliance
• Guideline for chemometrics
– What analysis technique is appropriate
– How to chose the correct number of factors
What are other issues/approaches?
• Can pattern recognition be used
– Based on historical data can the process be monitored
– Need enough history to account for all possible
conditions
• Can consortia help with some of these issues
– CPAC, MCEC, CPACT
• Regulatory approval of new approaches
– current is causal - understand every aspect via
conventional mean/techniques
– probabilistic - compare good batches to in-situ
measurements to develop history