rchapter near infrared spectroscopy in drug discovery …iapc-obp.com/assets/files/632340chapter...

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NEARINFRAREDSPECTROSCOPYINDRUGDISCOVERYANDDEVELOPMENTPROCESSES

YvesRoggo,KlaraDégardinandMichelUlmschneider

Contents

9.1. INTRODUCTION......................................................................................................................................433

9.2. NIRSANDIMAGING:THEORYANDINSTRUMENTATION...................................................434 9.2.1. Generalintroduction...............................................................................................................434 9.2.2. CharacteristicsofNIRS..........................................................................................................435 9.2.3. NIRinstrumentation...............................................................................................................436

9.3. CHEMOMETRICSANDVALIDATIONOFNIRMETHODS......................................................437 9.3.1. DevelopmentandvalidationofNIRmethods..............................................................437 9.3.2. Mathematicalpreprocessingofspectroscopicdata..................................................441 9.3.3. Principalcomponentanalysis(PCA)...............................................................................442 9.3.4. Patternrecognition..................................................................................................................443 9.3.5. Regressionmethods................................................................................................................445

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9.4. QUALITATIVEANALYSESBYNIRS................................................................................................446 9.4.1. Identificationandqualification..........................................................................................446 9.4.2. Polymorphism...........................................................................................................................448 9.4.3. Otherapplications...................................................................................................................448

9.5. QUANTITATIVEANALYSESBYNIRS............................................................................................449 9.5.1. Physicalparameters...............................................................................................................449 9.5.2. Polymorphsdetermination.................................................................................................450 9.5.3. Moisturedetermination........................................................................................................450 9.5.4. Contentdetermination..........................................................................................................451

9.6. ON‐LINECONTROLBYMEANSOFNIRS.....................................................................................454 9.6.1. PowderBlending......................................................................................................................454 9.6.2. Granulation.................................................................................................................................456 9.6.3. Drying............................................................................................................................................457 9.6.4. Crystallinityandpolymorphism.......................................................................................457 9.6.5. Coating..........................................................................................................................................458 9.6.6. Biotechnology............................................................................................................................458

9.7. APPLICATIONSOFNIRSPECTRALIMAGING............................................................................460 9.7.1. GeneraluseofNIRchemicalimagingforpharmaceuticalapplications..........460 9.7.2. TabletcompositionanalysiswithNIRimaging..........................................................460 9.7.3. ApplicationofNIRimagingtoprocessoptimization...............................................461

9.8. CONCLUSION...........................................................................................................................................463

REFERENCES......................................................................................................................................................463

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9.1. INTRODUCTION

Process analytical technology (PAT) has existed for many years in variousindustries. Itwasone of theobjectives contained in thePharmaceutical cGMPsfor the 21st Century initiative launched in 2002 by the Food and DrugAdministration(FDA).TheFDAguidelinedefinedPATasasystemfordesigning,analyzing and controlling pharmaceutical manufacturing through themeasurementofcriticalqualityandperformanceparameters.Themeasurementsperformedonrawandin‐processmaterialsorprocessparameterswereintendedto enhance final product quality. Both the FDA and the industry anticipatedbenefitsoverconventionalmanufacturingpractices:higherfinalproductquality,increased production efficiency, decreased operating costs, better processcapacity,andfewerrejects[1].

Building quality into a pharmaceutical product has to be considered from themoment of a product’s conception. PAT provides a motivating framework. Ifproductqualityrequirementsareunderstoodandimplementedfromtheoutset,process failure after scale‐up to commercial manufacturing will be much lesslikely. PAT greatly enhances process understanding. It continuously improvesproduct quality, extends the acquired knowledge base for new projects, andshortens time to market. Trends in PAT were reviewed in 2011 [2], whileHerdling and Lochmann have described and discussed PAT implementation insoliddosageformproduction[3].PATdrawsontherelevantbasicsciencesandacomplexmultiplicityofengineeringandcontroltechnologies.

Near infrared spectroscopy (NIRS) is an important tool for PAT implementa‐tion[4],asitisincreasinglyusedinpharmaceuticalresearchanddevelopment.

In 1800, William Herschel discovered radiation beyond visible red light.However,itwasmanyyearsbeforetheNIRregionwasusedforspectroscopy[5].Awideoverlapwasobservedinitsbands,makingthemdifficulttointerpret.KarlNorris, an engineer at the U.S. Department of Agriculture, demonstrated thepotentialvalueoftheNIRregionforquantitativeworkbymakingmeasurementsof agricultural products in the 1960s. The basic idea was to provide variousresearchandproductionfacilitieswithonlineNIRmeasurementsofagriculturalproducts,whichwasthefieldofinterestatthetime[5,6].NIRSandchemometricsthen spread to other domains, such as food [7], and the chemical [8] and oilindustries [9], proving effective for both qualitative and quantitative analyses.

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NIRS is widely used in the pharmaceutical industry, with multiple practicalapplicationsandamassivelyincreasedpresenceinspecializedjournals.

NIRSisgenerallychosenfor itsspeed, lowcost,andnon‐destructiveness. It isaselective technique that is sensitive to thephysical chemistry of the samples itanalyzes. These can bemeasured intact or through packaging such as glass orplastic bags. NIR imaging is derived fromNIRS and involves the acquisition ofspatially located spectra that display compound distribution in the sample foranalysisalongwithfeaturessuchasmoistureorparticlesize.SeveralreviewsofNIRSarenowavailable[10‐13],togetherwithitsapplicationtothedevelopmentof solid dosage forms [14]. Interest in NIR has increased because of improvedinstrumentation and the development of fibre optics allowing non‐contactmeasurement;ithasalsobeenboostedbytheprogressincomputerscienceanddata processing that has facilitated interpretation of NIR spectra, notablyinvolving chemometrics, the body of mathematical and statistical techniquesdevelopedfortheprocessingofchemicalanalyticdata.Mid‐IR(MIR)spectra,ontheotherhand,especiallytheabsorbancebands,aredirectlyreadablethankstochemicalpeakspecificity.

TheaimofthischapteristopresentthetheoryandinstrumentationofNIRSandNIRimaging,brieflydescribesomeofthechemometrictoolsusedincalibratingandvalidatingNIRmethods,andfinallytofocusonsomeNIRapplicationsinthedevelopmentprocessanddrugdiscovery.

9.2. NIRSANDIMAGING:THEORYANDINSTRUMENTATION

9.2.1. Generalintroduction

Infrared (IR) spectroscopy is a versatile tool applied to the qualitative andquantitativedeterminationofmolecularcompoundsofalltypes.TheIRdomainissubdividedintoNIR,MIR,andfarIR(FIR)withthefollowingrangelimits(Figure9.1):

NearIR: 780nmto2500µm(12800to4000cm‐1)

MidIR: 2500µmto50µm(4000to200cm‐1)

FarIR: 50µmto500µm(200to20cm‐1)

Figure9.1:Electromagneticspectrum–theinfraredrange

cm-13.3202004 00012 50025 000105

farmiddlenear

MicrowaveInfraredVisibleUltaviolet

nm3.1065.10550 0002 500800400100

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MIRspectroscopyisbyfarthemostwidelyused,withabsorption,reflection,andemissionspectraemployedforbothqualitativeandquantitativeanalyses.FIRisprimarily used for absorption measurements of inorganic and metal‐organicsamples.

The NIR region is particularly used for routine quantitative determination incomplex samples containing functional groups of hydrogen bonded to carbon,nitrogen, or oxygen. This is of interest in agriculture, feed, food, and, morerecently,pharmaceuticalindustries.NIRSwasfirstusedfortherapidscreeningoffeed and food products for protein, moisture, starch, oil, lipid, and cellulosecontent.

9.2.2. CharacteristicsofNIRS

Themainvibrationsobserved in theNIRregionare thoseof ‐CH, ‐OH, ‐SH,and‐NHbonds.Alltheabsorptionbandsresultfromovertonesorcombinationsofthefundamental MIR bands [15,16]. The overtone and combination molecularabsorptions found within the NIR region are much less intense than thefundamentalIRabsorptions.

Oncetheappropriatechemometrictoolsweredeveloped,theNIRregionturnedouttobeofgreatinterestforindustrialapplications.NIRSisfast:onceamethodhas been developed and validated, measurement only takes seconds. Samplesrequirenopreparationandcanbemeasuredassuch, intact.Theyareavailablefor further analysis since NIRS is non‐destructive. Measurements can beperformedon‐andat‐line.NIRspectrometerstendtobeveryrobust.Glassfibreopticscanbeused toperformremoteanalysisbybringingradiationdirectly tothesample.Thefibreopticprobecanbeincontactwiththesampleorimmersedinit.AsNIRmeasurementscanbedonethroughglass,thismaterialcanbeusedfor windows, lenses, and any other optical components, which simplifiessampling.

Table9.1comparessomeprosandconsofNIR,IRandRamanspectroscopy.

Table9.1.Comparisonofnearinfrared(NIR),infrared(IR)andRamanspectroscopySpectroscopy NR IR Raman

Signalrange,cm‐1 12000–4000 4000–400 4000‐50

Signalintensity ++ +++ +

Microscopicanalysis No Yes Yes

Fiberopticinterfacing Yes Yes(limitlength) Yes

Samplingthroughglass Yes O Yes

Instrumentrobustness +++ + ++

Chemicalinterpretation Withchemometrictools Direct Direct

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NIRShasmanymorepracticaladvantagesinthepharmaceuticalprocessthanksto numerous applications enabled by developments in instrumentation andsampling.

9.2.3. NIRinstrumentation

The instrument design first depends on how measurements are performed.Diffuse reflectance and transmittance measurements are both used, althoughdiffuse reflectancemuchmorewidely because of its ease of use. In the diffusereflectance mode, radiation penetrates the particle surface layer, excites thevibrationalmodesoftheanalytemolecule,andisthenscatteredinalldirections.Reflectancemeasurementspenetrateonly1mm to4mm into the solid samplesurface.InthiscasetheordinateisthelogarithmofthereciprocalofreflectanceR, log(1/R),whereR is theratiooftheintensityofradiationreflectedfromthesampletoreflectancefromastandardreflector.Intransmittancemode,theentirepath through the sample is integrated into the spectralmeasurement, therebyreducing error due to sample non‐homogeneity. Transmittance is suitable formeasuring through compact samples, like intact tablets, but surface scatteringinducesa lossof transmittedenergywith theneteffectbeingadecrease in thesignal‐to‐noiseratio.

ManyspectrometershavebeenspecificallydesignedfortheNIRrange.Theidealinstrument has both transmittance and reflectance capabilities. However, thechoiceiswide,especiallywhencomparedwithMIRspectrometers.Grating,diodearray, and Fourier transform (FT) instruments are the most sophisticated. FTspectrometersaremostlybasedontheMichelsoninterferometer,althoughothertypes of optical systems are also encountered. Tungsten‐halogen lamps withquartz windows are used as sources while detectors are usuallymade of leadsulfide (PbS) or arsine‐gallium (AsGa). Handheld NIR spectrometers have alsobeendeveloped thatallowmeasurement in the fieldrather than in the lab [17‐19].FTspectrophotometersarepreferredformanyapplicationsbecauseoftheirspeed, reliability, and convenience. They appear to have better signal‐to‐noiseratios and a much larger energy throughput than dispersive instruments.Interferometric instruments also feature high resolutions, high accuracy, andreproducible frequency determination. Other designs include diode arraydetectors andNIR emittingdiode sources.Acousto‐optic tunable filters (AOTF)capableofmicrosecondscanningspeedsaredevicesbasedondiffraction.Othertechniques are available, such as ultrafast‐spinning interference filter wheels,interferometerswithnomovingparts,andtunablelasersources.

Specific instruments are used for chemical imaging. A complete spectrum isacquiredforeachpixel,meaningthatahyperspectralimageisinfactadatacube,i.e. a 3D matrix (Figure 9.2). Chemical imaging experiments yield an X×Y×λmatrixordatacube,whereXandYarethespatialdimensionsandλthespectraldimension [20]. In principle, it is possible to collect hyperspectral imageswithsingle‐point detectors, i.e. classicalmappingwithmicroscopes. However, array


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detectors measure all pixels simultaneously, reduce recording time, provideuniform background, and improve the signal to noise ratio [21]. Figure 9.3presentsanexampleofaNIRimageanalyzer.

Figure9.2. Nearinfrared(NIR)imagingconcept[1]

Figure9.3. Nearinfrared(NIR)imaging[1]

9.3. CHEMOMETRICSANDVALIDATIONOFNIRMETHODS

Chemometrics[22‐24]isusedtoselecttheoptimalexperimentalprocedureandprocessing technique for chemical analytic data. It draws on a variety ofspecialties,includingexperimentaldesign,dataextraction(modeling,regression,classification, hypothesis testing), and techniques for understanding chemicalmechanisms(seereviewbyLavine[25]andmanytextbooks[26‐28]).

9.3.1. DevelopmentandvalidationofNIRmethods

SampleselectionANIRmodel isbuiltandvalidatedwithseveralsamplesets,beginningwiththecalibrationsetused tocompute themodel.Thesecondvalidationset isused toevaluate the model’s ability to predict unknown samples. Both sets must beindependent, i.e. they must consist of samples from different batches. Setselection and preparation are critical issues. For instance, for quantitativemethods the analystmust collect orprepare sampleswhich span the completerangeofconstituentconcentrationsspreadoutasevenlyaspossible.Calibrationsets must comprise a correctly distributed number of sample types. Spectro‐

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scopic measurements with calibration samples should be performed underconditionsapproximatingtotheseforroutinesamples.

Table 9.2 offers an example of a quantitative NIR validation plan. Qualitativemethodsrequiretestingonlyforspecificityandrobustness.

Table9.2.Testsforquantitativenearinfrared(NIR)validationparametersParametersforvalidation

Tests

SpecificityPredictallsamplesoftheexternalvalidationsetusingthecalibratedmodel

Predictothersamples(challengesamples)

LinearityPlotanddetermineslopeandinterceptofNIRpredictedvalues(y)versus

referencevalues(x)ofcalibrationandvalidationsets

Accuracy

Determinethestandarderrorofprediction(SEP)withvalidationset

CompareSEPandaccuracyofthereferencemethod.

ComparereferenceandcalibrationsetNIRresultsusingpairedsamplet‐teststotestforsignificantdifferences

Recoveryexperiments

PrecisionRepeatability:e.g.10timesonthesameday

Intermediateprecision:3differentanalystsassayingthesameproductionsampleon3separatedays

RobustnessTesterrorsassociatedwithsamplingspeed,temperature,sampleposition,

fibreopticparameters,etc.

Once themodelhasbeenconstructedandvalidated it canbe routinelyused. Itcanberunonthedeviceusedduringdevelopmentoronanotherdevice;inwhichcasetransferabilitymustbeensured(someadjustmentisusuallynecessary).

UseofregressionforNIRquantitativemethodsBeforeaquantitativemodel is computed, thepurposeof thecalibrationand itsminimal accuracy and limits of validity need to be established.A robustmodelpresupposes the design of a range of samples comprising adequate lateralvariation[29‐31].

Calibrationisthefittingstage:themaindataset,containingonlythecalibrationsamples, is used to compute model parameters such as principal components(PCs)andregressioncoefficients.Themodelsmustbevalidatedtogetanideaofhowwellaregressionmodelperformsifusedtopredictnew,unknownsamples.A validation set consisting of sampleswith known variable values is used. Themostcommondistributionallocatestwo‐thirdsofthesamplestothecalibrationset and one third to the internal validation set. Sample selection studies havecompared Kennard‐Stone and successive projections algorithms, randomsampling,andfullcross‐validationforusewithmultiplelinearregression(MLR)andpartialleastsquares(PLS)models[32,33].

During the validation step, only the spectral information is introduced into themodel, fromwhich response values are predicted and compared to the known


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valuesofthereferencemethod.Iftheuncertaintyofpredictionisreasonablylow,themodel canbe consideredusable. Independent test‐setvalidationandcross‐validation are the most current methods of estimating prediction error. Withcross‐validation, the same samples are used for both model estimation andtesting.Thisrepresentsanalternativeformofvalidationifsamplenumbersaresmall.Themethodconsistsinleavingoutafewsamplesfromthecalibrationsetandcalibratingthemodelontheremainingdata.Theleft‐outsamplevaluesarethen predicted and the corresponding prediction residuals computed. Theprocessisrepeatedwithanothercalibrationsubset,andsoonuntileveryobjecthasbeenleftout.Figure9.4illustratesthestepsrequiredforcompletemodeling.

Figure9.4.Principleofmultivariatecalibration(NIR‐Nearinfrared)

The predicted Yvalues are then compared with the observed Yvalues (Fig‐ure9.5). This generates a prediction residual that can be used to compute avalidation residual variance, or a measure of the uncertainty of futurepredictions.

Goodnessoffitofapredictionmodelcanbeevaluatedbyfurthercriteriasuchasthelowstandarderrorofcalibration(SEC),lowSEP,highcorrelationcoefficient(R2),andlowbias.SEC,standarderrorofcross‐validation(SECV),SEP,bias,slope,andSEPwithbiascorrection(SEP(C))arethecriteriaofmodelaccuracy(formu‐

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las inFigure9.6).Naesandco‐workershavedescribed thestatistical strategiesavailable[34]andthereareotherexamplesofNIRvalidationmethods[35].

Figure9.5.Exampleofaregressionperformedonnearinfrared(NIR)spectra

andreferenceresults(KF‐KarlFischertitration)

Figure9.6.Formulasforgoodnessoffitcriteria(SEC‐standarderrorofcalibration;SEP‐standarderrorofprediction)

q1m

)y'(ySEC

m

1j

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y'y .)y,'ycov(

R

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whereyj = referencevalue forthesample# j,y’j predictedvalue forthe sample# j,m = numberof samplesin calibration,n = numberof samplesin validation,q =PC number.

n

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9.3.2. Mathematicalpreprocessingofspectroscopicdata

Spectral rawdatamayhaveadistributionorshape that isnotoptimal forana‐lysis.Backgroundeffects,baselineshifts,ormeasurementsunderdifferentcondi‐tionscancomplicate theextractionof information. It is thus important tomini‐mizethenoise introducedbysucheffectswithpreprocessingoperations.Theseincludecentering,weighting,andnumerousmathematicaltransformations.

Mean‐centering consists of subtracting average spectra from each spectrum toensure that all results will be interpretable in terms of variation around themean. Weights based on the standard deviation (SDev, square root of thevariance)mayalsobeusedforscaling.Apossibleweightingoptionisthe1/SDevstandardization,whichgivesallvariablesthesamevariancesothattheyhavethesamechance to influenceestimationof thecomponents.This isadvisable if thevariables are measured with different units, have different ranges, or are ofdifferent types. It is also possible to fix a constant weight for each variablemanually.Thisinvolvesstretchingandshrinkingbymeasuringapositionrelativetotheextremes.However,thisemphasizestherelativeinfluenceofunreliableornoisy attributes. Smoothing is relevant for variables which themselves are afunctionofsomeunderlyingvariable,forinstancetime.Itisalsooneofthefirstoperations performed on recorded NIR spectra. It removes as much noise aspossible without degrading important information content. Polynomialsmoothing,alsocalledSavitzky‐Golaysmoothing,involvesleastsquarefittingofapolynomialequationtoawindowofnsequentiallyselectedspectraldatapoints.Normalizationisafamilyoftransformationswhicharecomputedsample‐wise,inthis case to improve specific properties. Mean normalization is the classicalgorithm. It consists individingeach rowofadatamatrixby its average, thusneutralizingtheinfluenceofpossiblehiddenfactors.Maximumnormalizationisanalternativeprocedurewhichdivideseachrowbyitsmaximumabsolutevalueinsteadoftheaverage.Multiplicativescattercorrection(MSC)isatransformationmethodusedtocompensateforadditiveand/ormultiplicativeeffectsinspectraldata. It successfully treats multiplicative scattering and similar effects such aspath length problems, offset shifts, and interference. Derivation is typicallyrelevant for spectral data that are a function of some underlying variableinfluencingabsorbanceatvariouswavelengths. It is alsoa simplebutpowerfultechniqueformagnifyingfinestructureinrawspectralackingstructure,whichiscommoninNIRS.Byincreasingtheorderofderivation,bandstructureresolutionisincreased.TheSavitzky‐Golayalgorithmpermitscomputationtohigherorderderivatives, includingasmoothing factorwhichdetermineshowmanyadjacentvariableswillbeused toestimate thepolynomialapproximation forderivation.Norris derivation is an alternative algorithm for computing first derivatives. Abaseline correction algorithm is the standard normal variate (SNV) methodwhichdoesnotaffectoverallspectralayout.

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9.3.3. Principalcomponentanalysis(PCA)

Large tables contain information partly hidden by data complexity; a featuretypicalofNIRspectracollection.PCAisaprojectionmethodthatvisualizesalltheinformationcontainedinadatatable.Itcanbeusedtoshowinwhatrespectonesample differs from another, which variables contribute the most to thatdifference, and whether these variables contribute in the same way, and arecorrelatedor independent fromeachother. It also reveals samplepatternsandoutliers.

PCAmodelingformsthebasisforseveralclassificationandregressionmethods.Theunderlyingideaistoreplaceacomplexdatasetbyasimplerversionhavingfewer dimensions, but still fitting the original data closely enough to beconsidered a good approximation. Two samples can be described as similar iftheyhaveclosevalues formostvariables.Fromageometricperspective, in thecase of close coordinates in the multidimensional space of variables, the twopointsarelocatedinthesamearea(Figure9.7).

Figure9.7.Thegeometricconceptofprincipalcomponentanalysis(PCA)

A,B,C‐threesamples;‐wavelength;PC‐principalcomponent

PCAconsistsinfindingthedirectionsinspaceknownasprincipalcomponents(PCs) along which the data points are furthest apart. It requires a linearcombination of the initial variables that contribute the most to making thesamplesdifferentfromeachother.PCsarecomputediteratively,withthefirstPCcarrying themost information, i.e. themostexplainedvariance, and the secondPC carrying most of the residual information not taken into account by theprevious PC, and so on. This process can go on until as many PCs have beencomputed as there are potential variables in the data table. At that point, allbetween‐samplevariationhasbeenaccountedfor,andthePCsformanewsetofaxes.Usually,only the firstPCscontainpertinent information,with the lastPCsbeingmore likely to describenoise. Deciding on thenumber of components toretaininaPCAmodelinvolvesacompromisebetweensimplicity,completeness,andeffectiveness.

EachPCAcomponentischaracterizedbyattributes,e.g.variances, loadings,andscores. Variances are data scatteringmeasure showing howmuch information


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thePCstakeintoaccount.Residualvariancedesignatesthevariationinthedatathat remains to be explained once the current PChas been taken into account,whileexplainedvariancemeasuresthevariationinthedataaccountedforbythecurrentPC.Loadingsdescribethedatastructureintermsofvariablecorrelations.VariableswithhighloadingsforagivenPCcontributegreatlytothemeaningofthatparticularPC.Scoresdescribethedatastructureintermsofsamplepatternsand emphasize differences or similarities. Each sample has a score on each PCwhichisthecoordinateofthesampleonthePC.Itdescribesthemajorfeaturesofthesample,relativetothevariableswithhighloadingsonthesamePC.SampleswithclosescoresalongthesamePCareconsideredassimilarbecausetheyhaveclosevaluesforthecorrespondingvariables.

OnceNIRspectrahavebeenmeasured,buildingandusingaPCAmodelinvolvesthreesteps:selectingtheappropriatepreprocessings,runningthePCAalgorithmand diagnosing themodel, and interpreting the loading and score plots (Figu‐re9.8)[36].

Figure9.8.Principalcomponentanalysis(PCA)scoreplotidentifyingsevenclusters

withthefirstthreeprincipalcomponents(PCs)[36]

9.3.4. Patternrecognition

Pattern recognitionmethods are often applied in chemistry [37], biology [38],and food science [39]. The main goal is to assign new samples reliably topreexistingclasses.Classificationresultscanalsobeusedasadiagnostictooltoidentify the most important variables or find outliers. Applications includepredicting whether a pharmaceutical product meets specified quality

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requirements or more generally confirming substance identity. PCA anddiscriminant analysis are techniques that have found extensive use in NIRanalysisforthispurpose.

The classification techniques can be divided into two categories: unsupervisedand supervised. In unsupervised classification, samples are classified withoutprior knowledge, except the spectra. The spectroscopist’s job is to explain theclusters obtained. Many clustering algorithms can be used, such as thehierarchicalmethod(Figure9.9)[36].

Figure9.9.Hierarchicalclusteranalysis(HCA)dendrogram[36]

Supervised methods are those requiring prior knowledge, i.e. the categorymembershipofsamples.Thus,theclassificationmodelisdevelopedonatrainingset of samples with known categories [28]. Then the model performance isevaluatedbycomparingtheclassificationpredictionstothetruecategoriesofthevalidationsamples.Therefore,mathematicalmodelsarecomputedinafirststepwith a calibration set containing spectra and class information. FeatureextractionmethodssuchasPCAareoftenappliedbeforeclusteranalysis.

Currentmethodsforsupervisedpatternrecognitionarenumerous.Typicallinearmethods are linear discriminant analysis (LDA) based on distance calculation,soft independent modeling of class analogy (SIMCA), which emphasizessimilarities within a class, and PLS discriminant analysis (PLS‐DA), whichperforms regression between spectra and class memberships. More advancedmethodsarebasedonnon‐lineartechniques,suchasneuralnetworksorsupportvectormachines(SVM).


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9.3.5. Regressionmethods

FromunivariatetomultivariateregressionRegressionconcernsallmethodsattemptingtofitamodeltotheobserveddata.In spectroscopy the simplest method of quantitative calibration is based on asingle independent variable, e.g. wavelength, since a sample attribute such asanalyte concentration is a linear function of absorbance at a givenwavelength.This is called univariate regression. In this approach, a wavelength is selectedwhen it shows a high degree of correlation between concentration andabsorbance.Correlationisanindicatorofhowwellthecalibrationdescribesthedataset.Thelinearrelationshippermitsdirectandvisualestimationofgoodnessof fit, thus enhancing the analyst’s trust in their data collection. Wherepharmaceutical samples are concerned, the linear approach rapidly reaches itslimitsandanotherapproach,multivariateregression,isrequired.

Multivariate regression takes several predictive variables simultaneously intoaccountforgreateraccuracy.Thefittedmodelmaythusbeusedtodescribetherelationship between two groups of variables, or to predict the values ofunknownsamples.

Multiplelinearregression(MLR)MLR extends linear regression to onewavelength by least squares, withmorethan one wavelength selected to perform a calibration. The method requiresindependent variables in order to explain the data set. More samples thanpredictorsarenecessaryandnomissingvaluesmustbepresentinthedatatable.Ifitcomplieswiththeseconditions,MLRwillapproximatetheresponsevaluesbya linear combination of predictor values, yielding regression coefficients. It isworth mentioning that MLR is the only multivariate method for which formalstatistical tests of significance for regression coefficients are available.Yvariances provide the relevantmeasureofMLRmodelperformance, showinghowmuchvariationremainsintheobservedresponseafterthemodeledpartisremoved,andactingasanoverallmeasureofmisfit.

Principalcomponentregression(PCR)andPLSregressionWith PLS multivariate regression, spectral and constituent data are modeledsimultaneously according to an iterative algorithm. PCR and PLS are bothprojectionmethods, like PCA. PLS1 dealswith only one response variable at atime (likeMLR and PCR). PLS2 handles several responses simultaneously. Theunexplained part of the data set ismade up of residuals. The original data arecombinedinfactorsorPCs.AcriticalstepinPLSmodelingistheselectionofthenumber of factors. Selecting too few factors will provide an inadequateexplanation of variability, while too many factors will cause overfitting andinstability in the resulting calibration. Coefficients, loadings and scores arecalculatedtoindicatetheextentoforiginaldatainvolvementinthecomputationofeach factor. Ina finalstep, theamountofvariancemodeled ismaximizedfor

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each factor and the residuals are minimized. Either an additional and inde‐pendent data set is used or the training data set is split into subsets for con‐tinuous internal validation at each iterative step (cross‐validation). In addition,theresultingPLScalibrationmustproveabletopredictunknownsamples

9.4. QUALITATIVEANALYSESBYNIRS

Qualitativeanalysesmeanclassificationsofsamplesbasedonpatternrecognitionmethods and their NIR spectra. Classification is simply amatter of finding outwhether new samples are similar to classes of samples that have beenused tomake themodel. If a new sample fits aparticularmodelwell, it is said tobe amemberofthatclass.Manyanalyticaltasksfallintothiscategory.Theaimofthispart is to present an overview of pharmaceutical applications for qualitativeanalyses,especiallytheidentificationandqualificationofrawandfinalmaterial.

9.4.1. Identificationandqualification

AnalysisofrawmaterialsandpharmaceuticalintermediatesThe International Conference onHarmonisation of Technical Requirements forRegistration of Pharmaceuticals for Human Use (ICH) guidelines describe theimportance of identity tests [40‐43]. Pharmacopoeias [44] have selectedanalyticalmethods like HPLC, optical rotation, and colorimetry to identify rawmaterials. The NIR application for qualitative analysis is, however, now alsodescribedbytheEuropeanPharmacopoeiainchapter2.2.40.Thequalificationofa sample will determine whether it is within the normal variability range orsubjecttooverlimitdeviations.Keyqualityparameterscanbeevaluatedforthispurpose [45].Distancebasedmethodsareoftenapplied for thequalificationofproducts.Ifthesamplebelongstothesamepopulationasthereferenceproduct,thenthereisaprobabilityof99.7%thatthedistancewillbelessthanthreetimesthestandarddeviation. If themaximumdistance ishigher than thatvalue, thenthesampleisfromadifferentpopulation.

TheidentificationofincomingrawmaterialsisnowacommonNIRSapplication[46‐48]thankstothelimitedsamplepreparationitrequires(Figure9.10).Alotof publications describe the application of NIRS for the control of excipients,active pharmaceutical ingredients (APIs) and final products. Ulmschneider andco‐workers applied NIRS to identify different types of starch, sugar, cellulose,intermediatesandAPIswithPCAandclustercalibration[49‐52].NIRSwasusedby Ebube and co‐workers to differentiate between Avicel products [53]. Thediscriminationofcellulose[54]isindeedstatisticallysignificant.Celluloseetherswere identified by NIRS, but the separation of methylcellulose and celluloseethers with methyl or hydroxyalkyl groups was not possible. Several types ofpoly(vinylpyrrolidone) (povidones) are characterized by their viscositymeasuredinwater.Kreftandco‐workers[55]havedevelopedaSIMCAmethodfor their differentiation by NIRS. The identification of raw materials can be


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performeddirectlyat‐lineinthedispensingoratthereceptioninthewarehouse.NIRS is now used in the manufacture of solids and also in biotechonolgyproduction for identification of cell culturemedium [56]. NIRS identification isusedinmanufacturingplantsandduringprocessdevelopment.

Figure9.10.Rawmaterialidentificationthroughplasticbagswith

afibreopticprobeinawarehouse

Finalproduct:identificationandcounterfeitdetectionDiscriminating substances in tablet matrixes is possible and was studied byChongandco‐workers[57].NIRcanbeandiscommonlyusedforidentitytestsinqualitycontroldepartments[58].NIRtransmissionspectroscopycombinedwithchemometricmethods ismoreoverapplicable to confirm the identityof clinicaltrialtablets[59].

NIRSisbesidesasuitablemethodforthefastdetectionofcounterfeitmedicines[60‐66]. In Figure 9.11 a discriminant analysis model is presented that wascomputed on NIR spectra of authentic and suspect capsules. For example,genuinecapsuleslabelledastypes1and2couldbeseparatedafterpretreatment.Counterfeits of both types of capsules were successfully separated from thegenuinecapsulesasaresultofMahalanobisdistance.Thesameprocedurecanbeappliedtodetectplacebosinclinicalstudies.

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Figure9.11.DiscriminantAnalysisperformedonnearinfrared(NIR)spectra

ofgenuine(reference)andcounterfeitcapsules

9.4.2. Polymorphism

The ability of a substance to be present in different crystalline forms is calledpolymorphism.Thesolidstatepropertieshavean influenceonthestabilityanddissolutionpropertiesofthepharmaceuticalproduct.Thisneedstobecontrolledby analytical methods, which could also help in galenical production anddevelopment.RamanspectroscopyhasbeensuccessfullyappliedtocharacterizeAPIpolymorphic forms,but thispharmaceutical issuecanbesolvedbyNIRSaswell. Information concerning the crystalline form ofmiokamycin could, e.g. bedeterminedbyNIRS[67].Thistoolcouldmoreoverimprovetheunderstandingofphysical forms of theophylline [68] and polymorphic transformation ofpazopanib hydrochloride [69]. In addition, the characterization and analysis ofazithromycin,anantibioticderivedfromerythromycinA,wasstudiedbyBlancoand co‐workers [70]. The suitability of NIRS to follow changes in both theamorphous and crystalline forms of lactose at room temperature was alsoinvestigated [71]. Also, their differentiation was possible by studying NIRfrequencies of water peaks. A comprehensive review dealing with polymorphanalysiswasrecentlypublished[72].

9.4.3. Otherapplications

AfterthepresentationofthemainqualitativeapplicationsofNIRS,otherstudiescan be mentioned as well, such as the discrimination of production sites oftablets. This is valuable tomanufacturers, customers and industry regulations.Yoonandco‐workers[73]usedPCAforthispurpose,whichscoreplotsshowedthat spectra of tablets originating from differentmanufacturing sites are stati‐sticallydifferent.


449

NIRS and chemometrics are besides combined to understand process (Figu‐re 9.12) and dissolution issues [74]. This shows how NIRS can be useful forunderstanding batch differences due to variations in process conditions.BeginningwithaqualitativeanalysisofthepotentialapplicationofNIRSandIRimaging for solids analysis, the ability of NIRS to detect the effects of meltgranulation time‐temperature gradient, compaction force, coating formulationandcoatingtimewastestedonpilotproductionsamples.

Asanewfieldofapplication,NIRqualitativeanalysesofbiotechnologyproductsarealsoincreasinglyperformedsuchasidentificationofbacteriastrains[75,76].

Figure9.12.Comparisonofproductionsitesbeforeandafterprocessharmonization

9.5. QUANTITATIVEANALYSESBYNIRS

Once the classification of samples has been achieved it can be useful to knowmore precisely to what extent they differ. Therefore, the development of aquantitativemodelappearsuseful.Historically, the firstmodelswerecomputedforthedeterminationofsamplemoisture,accordingtotwostrongwaterbandsabsorbingat1450and1940nm[16].

9.5.1. Physicalparameters

NIR spectra contain information about the chemical and physical properties ofthe analyzed samples. NIRS is nowadays used to determine a large panel ofphysical parameters on powders and tablets. Various biopharmaceuticalparameters can be quantitatively analyzed by NIRS, such as hardness (for

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instance, tablet hardness [77]), particle size [78‐80], compaction force, flowproperties[81]anddissolutionrate[82,83].

Tablethardness isdetermined indifferent studieswithPLSandMLRmethods.Morisseauandco‐workers[84]usedthesewellestablishedregressionmethodsfor this purpose and concluded that the accuracy of the results are highlydependentontheproductsandtheirformulation.InastudyondrugcontentandtablethardnessbyChenandco‐workers[85],goodresultswereachievedontwodifferentmodelscomputedbyanartificialneuralnetwork(ANN).Thecorrelationbetween compression force and NIR spectra at a specific wavelength isdemonstrated by Guo [86]. Blanco and co‐workers [87] have more recentlyshownthepossibilityofpredictingthepressureofcompactiononalabsamplebyusingaPLSmodel.

Theuseofdifferentregressionmethodsenablechemiststofollowthepercentageof the drug released in the medium by a tablet. The dissolution profile wasdeterminedwithNIRSbyDonosoandGhaly[88].

Berntsson published results on the determination of particle size whenmeasuring powder blends with NIRS in reflectance [89,90]. In 2004, OtsukaanalyzedthescatteringeffectduetoparticlesizethatwasmeasuredwithaPCRmodel[91].

9.5.2. Polymorphsdetermination

The polymorphic form of a product is a key parameter as it influences itsdissolutionproperties.Thedetermination of the ratiobetween amorphous andcrystalline formsofproducts isusuallyperformedbyX‐Raydiffraction. Severalstudies showed that this analysis can also be done by NIRS [92‐94]. Bai [95]observed great agreement between NIRS and X‐Ray in the analysis of glycinecrystallinity.The SEPobtainedbyNIRSwas3.2%and this toolwasproven todetect crystallized glycine at a lower content than X‐Ray diffraction. NIRScombined with regression methods has already been used for severalpolymorphismorcrystallizationapplications[67,70,96‐99].

9.5.3. Moisturedetermination

ThequantitativeanalysisofmoistureisoneoftheveryfirstapplicationsofNIRSin thepharmaceutical field.The existenceofwater inmedicines is critical as itensures stability.Water content is indicated by the presence of two importantwaterbandsat1450nmandespeciallyaround1940nm(Figure9.13).

NIR spectroscopy is now used to determine the water content in powders orgranules[100‐102],tabletsorcapsules[103‐105],aswellasinlyophilisedvialsor solutions [106]. Use of NIRS in moisture determination is long established,therefore most of the relevant applications for this purpose are now imple‐mentedon‐line(Figure9.14).


451

Figure9.13.Watercontentevolutioninlyophilisedvials(peakat1940nm)

Figure9.14.Nearinfrared(NIR)inspectionmachinefor100%on‐linewatercontent

determinationoflyophilisedvials

9.5.4. Contentdetermination

Many studies have been published during the last few years concerning thedeterminationof thechemical contentof compoundssuchasAPI,excipientsormoistureinmedicines.Potentialsamplesforanalysiscanbeofvariousformslikepowders,granulates,tablets,liquids,gels,filmsorlyophilisedvials[107‐113].

AstudywaspublishedcomparingNIRspectrometers,suchasFT‐NIR,FTIR‐PAS(PhotoacousticSpectroscopy),FTIR‐ATR(AttenuatedTotalReflectance),DRIFTS(Diffuse Reflectance Infrared Fourier Transform Spectroscopy) and FT‐RamanforthedeterminationofvitaminCinpowdersandsolutions[114].

Wavelength, nm

Abs

orb

ance

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Chalusandco‐workerspresenteddifferentdatapretreatmentsandregressionme‐thodsforpredictionmodelsofAPIinlow‐dosagetablets[115].AnexampleofthequantitativeanalysisofAPIispresentedinFigure9.15wheretheresultsofthePLSregression can be observed. Figure 9.15a presents the pretreated NIR spectra,whiletheselectionof13factorsfromthecrossvalidationisshowninFigure9.15b.TheresultsofthevalidationwithreferenceandNIRpredicteddataaredisplayedinFigure9.15c,andthestatisticsofthemethodinFigure9.15d[116].

Figure9.15.DeterminationofAPIcontentbyNIRS(range:1‐8mgAPI/tablet)[116].PC‐principalcomponent;SEC‐standarderrorofcalibration;SEP(C)‐SEPwithbiascorrection;API‐activepharmaceuticalingredient;NIRS‐nearinfraredspectroscopy

An increasing number of papers are published about the use of NIRS for thefollow‐up of the tablet production process, from the rawmaterials to the finalproduct,with tablets being coatedornot orpackagedornot [74,106,117,118].Successfulimplementationduringearlystageformulationdevelopmentwasalsopresented by Li [119]. NIRS spectroscopy allows a 100% packaging check oftabletswithabuilt‐inPCAmodeltosortupto12000tabletsperminute[120].ManyotherquantitativeapplicationsaresummarisedinTable9.3.


453

Table9.3.ExamplesofquantitativeapplicationsofNIRS.Sampleform Analyte

RegressionMethod* Remarks† Ref.

Tablets

Coatingthickness PLS Measurementsaremadedirectlyinafluidisedbedandthecoatingthicknessisfollowed.

[121]

Coatingthickness PLSTabletsarecomposedoftwodifferentchemical

compositions. [122]

Ibuprofen800mg PLSTabletsof7.6mmthicknesshavetobereducedto3.6mm.Thetransmittancemeasurementis

thususableonadedicateddevice.[123]

MetforminPLSLinear

regression

PLSappearedtobemoreaccuratethansinglewavelengthregression. [124]

Caffeine PLS Therangeofcaffeinecontentis0‐100%m/m. [125]

Steroid PLSTransmittanceofthetabletsismeasured.TheSEPallowedtheuseofNIRSfortheassayof

tabletsforbatchrelease.[126]

Gemfibrozil PLS

Tabletsoftworelatedpreparationsareidentifiedbyaclassificationmodel.Theircontentispredictedfor751mg/gand810mg/g

formulation.

[127]

Paracetamol MLR,PLSTwowavelengthselectionmodesweretriedfor

theMLR. [128]

Paracetamol MLR TheMLRmodeliscomputedontwowavelengths. [129]

Acetylsalicylicacid PLS

Thisstudyassaysacetylsalicylicacidinthreedif‐ferentformulations:onlyAPI,APIcombinedwithvitaminC,orwithvitaminCandparacetamol.Measurementsareperformedinreflectanceandtransmittanceonintacttabletsandreflectanceon

milledtablets.

[130]

TabletsPowders

ParacetamolAmantadineHydrochloride

ANNAssaysofparacetamolandamantadinehydroxidearedeterminedbyanANNmodel.Modelsare

basedontabletsandpowders.[131]

Diphenhydramine PLS

Tabletsofdiphenhydraminearemeasuredinreflectanceandtransmittance.Theirmilledform

ismeasuredinreflectance.Resultsarecomparableforthethreekindsofmeasurements.

[132]

Mirtazapine PLS

ThePLSmodelfordeterminationofcontentisbasedonlabpowdersamplesandproductiontablets.Thefirstfactorsofthemodelhadtobeexcludedandthefinalselectedmodelused4

factors.

[87]

Powder

ParacetamolDiphenhydramineHydrochloride

Caffeine

ANN,PLSThestudycomparesdifferentpretreatmentsandPLStoANN.ANNimprovestheresultscompared

toclassicalPLS.[133]

Amylose Peakratio ThecomputedmodelpresentsaRMSEPof1.2%. [134]

PowderGranulate

FerrouslactateDihydrate

PLS

Theconcentrationrangewas650‐850mg/g.Identificationisfirstperformedonthesamples.Thelabsamplesarepowderswhileproductiononesaregranulatesandbothareincludedinthe

model.

[135]

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Sampleform

AnalyteRegressionMethod*

Remarks† Ref.

Lyophilisedsamples

Water PLSTwoPLSmodelsarebuilt,thefirstoneforwatercontentof1–40%w/wandthesecondonefor

contentbetween1and10%w/w.[136]

Extrudedfilm

Clotrimazole PLSThedrugcontentrangewas0‐20%inahotmelt

extrudedfilmofpolyethyleneoxide. [137]

Translucentgel

Ketoprofen MLR,PLS MLRispreferredtoPLSbecauseitgivesagoodideaoftheabilityofNIRtopredictcontent.

[138]

* Partialleastsquares(PLS);multiplelinearregression(MLR);artificialneuralnetwork(ANN)† Nearinfraredspectroscopy(NIRS);activepharmaceuticalingredient(API);rootmeansquareerrorofprediction(RMSEP)

9.6. ON‐LINECONTROLBYMEANSOFNIRS

9.6.1. PowderBlending

TheblendingofAPIwithexcipientsisacriticalstepinthemanufacturingofphar‐maceutical solids.Without a homogenous blend it is impossible to get uniformdoseswiththerightcontentofAPIinthefinalproduct.However,thedetermina‐tionofhomogeneityisproblematic.Currently,samplesaremostlyremovedfromthe blender and analyzed by conventionalmethods like HPLC or UV/VIS‐spec‐troscopy. The API distribution is thus determined and the homogenous distri‐butionof theexcipientsassumed if theAPI ishomogenouslydistributed.More‐over,thesamplingoftenchangesthepowderdistributionandisconsequentlythesourceofsignificantsamplingerrors.Classicalmethodsarebesidesdestructive,time and cost consuming, labor intensive, require solvents and are responsiblefor longcycle timesas theyareperformedoff‐line.Therefore, theuseof a fast,non‐destructivemethod is advisable.NIRSoffers these advantages and enablestheanalysisofall thecompoundsofapowdermixture.On‐lineor in‐lineappli‐cationispossibleforhomogeneity(Figure9.16)andendpointdetermination.

Figure9.16.Implementationofanon‐linenearinfrared(NIR)spectrometerinsolid

developmentforblendingmonitoring


455

Numerous studies have been carried out to explore the use ofNIRS for powderblendingcontrol.ThefactthatNIRShasgreatpotentialinthisapplicationhasbeenshown by Wargo and Drennen [139‐142]. Cho and co‐workers dealt with theeffectivemass that is sampled by NIR fibre‐optic reflectance probes in blendingprocessesanddemonstratedthatthesampledmassmetFDArequirements[143].Hailey and co‐workers showed that by using a fibre‐optic reflectanceprobe it ispossible,eitherinay‐coneorblender,touseNIRSforin‐lineblendanalysis[144].Sekulić and co‐workers also evaluated NIRS for on‐line monitoring of powderblending processes by using a fibre‐optic reflectance probe and showed itsfeasibilitywithamodel‐freeapproach[145].NIRimagingwasusedbyEl‐Hagrasyandco‐workerswhodemonstratedthepossibilityofusing it foron‐lineblendingcontrol. However, they pointed out the fact that multiple sampling points arenecessary for correct process control [21]. Sekulić and co‐workers focused onqualitativeapproachesofblendevaluationinastudyusingasmallblenderandareflectancefibreopticprobe.Differentblendswereproduced,monitoredviaNIRSanddifferentmathematicalpre‐processingperformedontheresultingdata[146].Skibsted and co‐workers presented a qualitative and quantitative method andcontrolcharts,withwhichtheywereabletomonitorthehomogeneityofablend[147].TheuseofNIRSforthequantificationofthedrugcontenthasalsobeenusedby Popo and co‐workers.However, theymeasured samples obtained by stream‐samplinginsteadoftakingspectradirectlyintheblender[148].Berntssonandco‐workersdescribed thequantitative in‐linemonitoring inamixer,both in the laband at the production scale. With high speed sampling, average content anddistributionofthemixturecontentwereassessed[149].

Figure9.17presentsanexampleofablendprocessmonitoringofAPIusingthemoving block method. In Figure 9.18, a quantitative determination of API isperformedduringtheblendprocess.Since2007,alargenumberofpublicationshavebeenwrittenaboutNIRSandtheblendprocess[150‐160].Theuseof thisspectroscopyforblendmonoritinghasthereforebeenfullydemonstrated.

Figure9.17.Movingblockstandarddeviationataspecificwavelengthofactive

pharmaceuticalingredient(API)duringtheblendprocess

0 10 20 30 40 50 60Time, min

–

0

0.050.1

0.150.2

0.25

0.0.35

0.0.45

0.5

Sta

ndar

d de

viat

ion

– 11

poi

nts

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Figure9.18.On‐linequantitativedeterminationoftheactivepharmaceuticalingredient

(API)contentduringtheblendprocess.

9.6.2. Granulation

Inmanycases,powderblendingisfollowedbyagranulationstep,whichisoftennecessaryfortabletcompressionorcapsulefilling.Theseareproducedeitherbydrygranulation,likerollercompaction,wetgranulation,suchasfluidbedspray,orhighshearmixergranulation.Themoisturecontentofgranulatesisimportantfortherestoftheprocessasit influencestheirpropertiesand,forexample,thehardeningoftabletsduringthestorage.Classicalmeasurementmethodssuchasinfrared dryers for moisture content determination require time andconsequentlyslowdownthemanufacturingonthedevelopmentprocess.AsNIRScanbeperformedinrealtime,theprocessmightbemonitoredmoreefficiently,resultingingreaterprocessreliabilityandoptimizedproductcharacteristics.NIRcanbeusedduringtheprocessoptimizationstep.Rantanenandco‐workersusedNIR‐reflectance spectroscopy for in‐line moisture content determination influidisedbedgranulation.TheyfollowedsprayinganddryingphasesbyNIRSandwereabletodeterminedryingendpoints[102,161]andtheeffectsofbinderandparticle size on moisture determination [162]. A non‐linear calibration modelwas developed with a combination of NIRS and other process measurements[163].Frakeandco‐workersappliedin‐lineNIRStoafluidisedbedgranulationtocontrolthegranulemoisturecontentandchangesinparticlesize[164].Findlayandco‐workersshowedthatthisspectroscopyenablesthecontrolofafluidisedbedgranulation.Theydetermined thedryingendand timepointswhenbinderaddition should be stopped [100]. Gupta and co‐workers determined contentuniformity,moisturecontentandstrengthofcompacts[165].Ultimately,NIRSisausefultoolforthemonitoringofthedifferentphasesofthegranulationprocessanddeterminationofparticulesizeandAPIcontent[166].ThegranulationratecanbeestimatedbyNIRSaswell[167].

AP

I, %


457

9.6.3. Drying

Dryingismostofthetimeanothercriticalstepintheproductionofmedicines.Itisusedatdifferentstagesoftheprocess,suchasgranulationorlyophilisation.Asthe O‐H bands are characteristic by NIRS, the first on‐line applications of thisspectroscopyweremainlyforthemonitoringofdryingprocesses.InFigure9.19,theevolutionofNIRspectraduringadryingprocesscanbeobserved.Indeedatthe position of the water bands, especially at 1950 nm, the analyzed spectrapresent a variation of the intensity, which can be directly correlated to themoistureofthesamples.

Figure9.19.On‐linewatercontentdeterminationduringtheactivepharmaceuticalingredient(API)dryingstep.InFigure9.19A,theanalyzedspectrapresentavariationoftheintensityofthewaterpeakat1950nm.Thedifferencesbetweenthespectra

arehighlightedinFigure9.19B

Brüllsandco‐workerspresentedthepossibilityoffollowingthetransitioninthecakeduringafreezedryingstep.Themeasurement,performedbyintroducingafibreopticprobe inavial, showedagoodcorrelationwith theclassicalmethod[168].SukowskiandUlmschneiderstudiedtheanalysisof100%productionvialsdirectly in‐line [169]. Theuse ofNIR for the dryingprocesswas validated andtransferredbyPeinadoandco‐workers[170].

9.6.4. Crystallinityandpolymorphism

Duringthedryingphaseofwetgranulation,polymorphicchangescanoccurinanAPI or in some of the excipients. The polymorphic changes of glycine involveimportant modifications in the hydrogen bonding of crystals. They werequantifiedbyDavisandco‐workerswithNIRSduringwetgranulation[171].

Crystallisationmayalsobe followed in anearlier step, i.e. theproductionof anAPI.Févotteandco‐workersusedafibreopticprobetomonitorcrystallisationbyNIRS.Thismethod showed thepossibility of using this typeof spectroscopy tofollowtheAPIcrystallisationon‐line[98].

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9.6.5. Coating

Oneofthelaststepsinthepreparationofadrugmayinvolvethecoatingofsometabletsorgranulates. It is important toensurethe integrityandgoodqualityofthe drug because coating may influence the release of the drug or assure itsstability.NIRdiffusereflectancespectroscopywasusedwithafibreopticprobetodeterminethefilmcoatingthicknessofpelletsbymeansofaPLSmodel.Theprobe was inserted on a side port of the fluidized bed reactor, and locatedverticallytothepelletbed[121].InthecaseofPérez‐Ramosandco‐workerswhodealtwith tablets, the probewas placed directly in the coating pan for diffusereflectance measurement. A univariate model was used, which followed thedecrease and increase of specific bands of a core compound and the coating,respectively [172]. NIR and Raman spectroscopy were used simultaneouslyduringthefluidbedpelletcoatingprocessbyDogomolovandco‐workers[173].The coating thickness of pellets [174,175] or tablets [176] was proven to bemeasurablebyNIRS.

9.6.6. Biotechnology

A new field of application for NIRS, following trends in biotechnologicalmanufacturing processes, has lately emerged in the pharmaceutical field. Thistool,combinedwithchemometrics,enablescellculturemonitoring,forinstance,asinthesystempresentedinFigure9.20.

Figure9.20.Nearinfrared(NIR)andcellculturemonitoring

Probeandinstrumentation


459

In2002and2003,Arnoldandco‐workerspresentedtheacquisition,calibration,validationandimplementationofthefermentationprocessincludingcontrollingandmonitoring. In thesestudies, theauthorsmanagedtocollectNIRspectra intransmittance and reflectance modes. For the direct on‐line or in‐lineimplementation, developmentwas advisedbecause the transfer from at‐line toin‐line analyses would be a challenge [177,178]. Cimander and Mandenius in2002appliedthemethodologyonthefermentationofEscherichiacolitoproduceantibiotics. Spectrawere acquiredwith an immersion probe and PCA and PLSwere used as chemometric tools. Models to track the amounts of biomass,tryptophan,phosphate,glucoseandacetateweredevelopedandvalidated[179].VibriocholeraefermentationsusedtoproducetoxinsorplasmidsweremeasuredbyNavratilandco‐workersin2004.NIRspectrawereacquiredwithafibreopticprobeandPLScomputedtodevelopcalibrationsofbiomass,glucoseandacetate.Interference problems that occurred when applying the chosen models in thebioreactor were discussed and corrected. Finally, the authors applied NIRpredictionmodels in theproduction [180]. In2003,Tamburini andco‐workerstried to monitor the fermentation of Staphylococcus and Lactobacillus. Spectrawere also acquiredwith a fibre opticprobe andPLS regressionwas applied todevelopmodels for glucose, lactic acid, acetic acid and biomass. Thesemodelswere then used for automatic control [181]. The determination of biomass,glucose,lacticacidandaceticacidduringfermentationsofStaphylococcusxylosuswas performed by Tosi and co‐workers. Models were developed by PLS forglucose,biomass, lacticacidandaceticacid.TheSECandSEPweresatisfactoryandthemodelswerethenappliedtoothermicroorganismsinthesamemedium[182].ThestudyofYeungandco‐workers[183]comparedtwostrategiesforthepreparationofcalibrationsamplesofaSaccharomycescerevisiaebioprocess.PLSwas applied to the NIR spectra to obseve cell debris, protein and RNA. ThecalibrationmodelsselectedaccordingtotheSEPvalueswerefinallyvalidated.

Traditionally, many fermentation products come from microbial bioprocesses.However, lately,mammalianand insect cell cultivationswerealsoexploited forthe high‐cost products they can be engineered to produce. Arnold and co‐workers [184]developedamethod tomonitormammaliancell cultivation.NIRspectrawere acquiredwith an immersion probe andmodelswere constructedforglucose,lactate,glutamineandammonia.Externalandinternalvalidationwasperformed.ThemonitoringofinsectcellculturewasalreadydevelopedbyRileyandco‐workersin1996[185].Calibrationmodelswereestablishedforglutamineand glucose with PLS and the models could be used for high concentrations.StudiesaboutcellculturemediaweremadeLewisandco‐workersin2000[186]andJungandco‐workersin2002[187].Inthefirststudy,theauthorsdevelopedmodels to predict glucose production using PLS. The results were comparedaccordingtotheSEC,SEPandmeanpercenterror(MPE)andthebestmodelforthe control of culture was retained. This demonstrated the ability of NIRS tomonitoron‐linefermentationsandcellcultures.Inthesecondstudy,thesystemwas coupled with a lab‐system to provide a real‐time spectral background

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reference. Smoothing and PLS were applied to develop calibration models forglucoseandlactate.

Duringthelast3years,thenumberofpublicationsdealingwithfermentationorcellculturemonitoringhassignificantlyincreased[188‐193],someofthembeingof very good quality like the report by Henriques and Buziol [194]. NIR canthereforebeusedforthedevelopmentofbioprocess.

9.7.APPLICATIONSOFNIRSPECTRALIMAGING

9.7.1. GeneraluseofNIRchemicalimagingforpharmaceuticalapplications

Thechemicalcompoundhomogeneityisanimportantissueforthedevelopmentof pharmaceutical solids. A classical NIR spectrometer integrates spatialinformation[130,195,196].However,theuseofameanspectrumonthesurfacecanbeadrawbackinsolidformanalysis.Onthecontrary,hyperspectralimagingprovides information that is spatial and spectral, and both qualitative andquantitative.Itcanmapchemicalcompounddistributionanddetermineparticlesize.Inthepharmaceuticalindustry,itisforinstanceimportanttomapthedistri‐butionofAPIsandexcipientsinatabletasthisrevealsphysical interactionbet‐weencomponentsandhelpssolvehomogeneityissues.Thisexplainstheincreas‐ing number of spectroscopic imaging studies on the visualization of chemicalcomponent homogeneity [21,197‐201]. The method was applied to qualitycontrol and to process problems affecting pharmaceutical tablets: dissolution,polymorph distribution, moisture content determination, API localization andcharacterization,counterfeits’detection,blending,andgranulation.

Spectral imaging is a complex and multidisciplinary field. The introduction ofnew detectors is making its use increasingly powerful and attractive. It hasproven its potential for qualitative pharmaceutical analyses and can be usedwhenspatial informationbecomesrelevantforananalyticalapplication.Evenifonlineapplicationsand regulatorymethodvalidationrequire furtherstudy, thepotential contribution of imaging to quality control and PAT needs no furtherdemonstration. A detailed overview of the pharmaceutical applications of NIRimagingandchemometrictoolsforimageanalysisisavailableinseveralreviews[202,203].Onlytwoexampleswillbediscussedhere.

9.7.2. TabletcompositionanalysiswithNIRimaging

AnexampleofthereconstructionofatabletbyNIRspectralimagingispresentedbelow.Atabletwascutlengthwisewithatrimmertogetaplanesurfaceandthecoating was then removed. The sample and references were analyzed using achemical imaging NIR spectrometer (SapphireTM, Malvern) with the followingacquisition parameters: detector size of 320×256 array, spectral range of1100–2450nmandaspatialresolutionof40µm/pixel.Theacquisitionlastedabout5minutes.


461

Afterthepretreatmentofthespectrawithasecondderivative,wavelengthswereselectedtogivecontrastimagesanddisplaythelocalizationofmannitol,APIandcrospovidonewithNIRimagesatspecificwavelengths(Figure9.21).

After the aforementioned steps were performed, tablet reconstruction by NIRimagingwas then possible. This highlights themain advantage of imaging: thelargeareaofanalysis.Theimagesareindeedmorerepresentativeofthesamplethanameanspectrumofanentiretablet.

Figure9.21.Useofmultivariatecurveresolution–alternatingleastsquares(MCR‐ALS)

inordertoobtaindistributionmaps–nearinfrared(NIR)imaging[1]API–activepharmaceuticalingredient

9.7.3. ApplicationofNIRimagingtoprocessoptimization

TheaimofthisstudywastouseNIRimagingtosolvegranulationissuesinanewformulationdevelopment.Undesiredpowderagglomerationswereindeeddeve‐loped during the granulation step and imagingwas applied to characterize thestructure.

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ThemeasuredsamplecontainedAPI,starch,Avicel®,crospovidone,andsodiumlauryl sulfate. The sample and references were analyzed by NIR imaging(Malvernsystem)with20co‐addsonaspectralrangeof1100–2450nm.Thefull image size was 320×256 pixels (or 4.1×3.3 mm). The images wereinterpretedandsamplerawmaterialsmappedusingPLSclassificationwithfiveloadings. This was based on the reference spectra of starch, API, Avicel®,crospovidone,andsodiumlaurylsulfate.

The PLS model allowed the identification all five chemical compounds. The PLSmultivariateanalysisshowedthatthecorecontainedAvicel®andAPIandstarchand crospovidone in theperiphery (Figure 9.22). Thanks to this information, asolution to the granulation issue couldbe found consisting of the additionof apremixing step to avoid agglomeration.NIR imagingproved tobeuseful in theimprovementofprocessunderstanding. Inour case, thepowderagglomerationwasheterogeneousandthelayershadhighexcipientcontent,makingitpossibletoapplysupervisedclassification.

Figure9.22.Imagesobtainedbyclassificationmethods–nearinfrared

(NIR)imaging[1].API‐activepharmaceuticalingredient

millimeters0 20 40 60 80 100 120

millimeters0 20 40 60 80 100 120

Avicel

Sodiumlauryl sulfate

API

Starch

Crosspovidone

Image #1 Image #2 Image #3


463

9.8. CONCLUSION

ThepotentialofNIRSforprocessdevelopmentneedsnofurtherdemonstration,as NIRS is a powerful way to discriminate pharmaceutical compounds. Thismethodcanbeusedqualitativelytodetect,identifyandqualifyaswellascontrolraw materials and final products. Additionally, it is a suitable tool for theclassificationandquantificationofpharmaceutical samples.NIRS ismoreoverapotentially precious diagnostic technique in process trouble‐shooting and canprovidespectralprofilesofpharmaceuticalproducts.Besides,itcansupportthedevelopmentofnewprocesses anddrugdiscovery.NIRS canbeapplied acrossthepharmaceuticalproductionprocess in chemistry, biotechnology andgalenicfields[204,205].Thesuccessofthisanalyticaltechniquereliesonkeyadvantages[46].Aspreviouslyobserved,NIRSisinfluencedbythechemicalandthephysicalproperties of the samples. This spectroscopy requires limited or no samplepreparation and is non‐destructive. Moreover, the measurement is fast withperformance in less thana second foron‐lineapplicationsandNIR frequenciesare transmitted through glass. Other vibrational techniques like Ramanspectroscopy[206]andmid‐IRshouldbementioned.Thesetechniquescanalsobe applied successfully to solve pharmaceutical issues in order to support thedevelopmentofnewdrugsandnewprocesses.Finally,NIRimagingsystemsweredeveloped in recent years. Namely, a hyper‐spectral imaging spectrometerrecordssimultaneouslyspectraandspatialinformationofsamples.NIRimaging[207] completes NIR spectroscopy and is used when spatial distribution is animportantissueoftheanalysis.

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2. W.Chew,P.Sharratt.Trends inprocessanalytical technology.AnalyticalMethods2(10)(2010)1412‐1438.

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