Advances in downstream processing of biologics – Spectroscopy An emerging process analytical technology C R A e M I K a A R R A A K D P S C B C 1 t m p m p h 0 ARTICLE IN PRESSG Model HROMA 358040; No[.]
Trang 1Journal of Chromatography A
jou rn al h om ep a g e : w w w e l s e v i e r c o m / l o c a t e / c h r o m a
Review article
Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology, Engler-Bunte Ring 3, 76131
Karlsruhe, Germany
a r t i c l e i n f o
Article history:
Received 11 October 2016
Received in revised form 7 November 2016
Accepted 8 November 2016
Available online xxx
Keywords:
Downstream processing
Process analytical technology
Spectroscopy
Chemometrics
Biologics
a b s t r a c t
(http://creativecommons.org/licenses/by/4.0/)
Contents
1 Introduction 00
2 MultivariatedataanalysisforPAT 00
2.1 Multivariateprojectionmethods 00
2.2 Principalcomponentanalysis 00
2.3 Partialleastsquareregression 00
3 Spectroscopyforprocessmonitoringinchromatography 00
3.1 UV/visspectroscopy 00
3.2 FTIRspectroscopy 00
3.3 OtherspectroscopicPATtools 00
4 Conclusionandoutlook 00
Acknowledgements 00
References 00
1 Introduction
In2004,theUnitedStates’FDApublishedGuidancefor
indus-try.PAT–Aframeworkforinnovativepharmaceuticaldevelopment,
manufacturingandqualityassurance[1].Withintheguidance,FDA
promotestheimplementationofPATintoallunitoperationsto
monitorcriticalqualityattributes(CQAs).PATisdescribedasbeing
partof process design and furthermore intended tocontribute
∗ Corresponding author.
E-mail address: juergen.hubbuch@kit.edu (J Hubbuch).
toprocesscontrol,i.e.tobetakenactivelyintoaccountfor pro-cessdecisions.Whilebeingintendedforbothsmallmoleculesand biologics,theimplementationintothesetwodomainsof pharma-ceuticals is advancingat different paces In the past, PAT was adoptedmorequicklyintheproductionofsmallmolecules.For
anextensivereviewthereof,theauthorsdeferto[2].Thisarticle willfocusonbiologicsonly
In contrast tomost smallmolecules, biologicsare produced
inlivingorganismswhichareverysensitivetoawidevarietyof externalfactors.Mostbiologicsarecomplexproteins.Theydonot consistofonechemicalentitybutadistributionofmanyspecies Alreadyslightprocesschangescanaffecttheproductqualityprofile [3].Inordertoensureaconsistentproductqualityandtoreduce http://dx.doi.org/10.1016/j.chroma.2016.11.010
0021-9673/© 2016 The Authors Published by Elsevier B.V This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).
Trang 2liquidchromatography(HPLC)hasbeenusedtocontrolcolumn
loadingandpoolingdecisionsduringchromatographicpurification
steps[11,12].Subsequently,on-lineandat-lineHPLCwasfurther
usedforavarietyofapplications[13–15].Recently,at-lineHPLChas
beenalsoimplementedinthecontrolofcontinuous
chromatog-raphyequipment[16].HPLCprovideshighresolutionofdifferent
species.However,itiscomplexregardingtherequiredequipment,
consistingofadeviceforsamplingaswellasthechromatograph
itself.Thismaybeundesirableinamanufacturingenvironmentas
reliabilitymaybeanissue.Furthermore,automatedsamplingand
theanalyticalseparationalsoleadtonon-negligibletimedelays
Dependingonthedecisiontimeofaunitoperation,thismaylead
tolatenotice ofprocess deviationsor evencompletelyprevent
real-timemonitoring
Spectroscopyis apowerful tool forprocess monitoring[17]
Spectroscopicequipmenthassimilar investmentcosts($20k to
$200k)ason-lineHPLC.Measurementtimesarefast,typicallyin
thesubsecondrangeuptoafewminutes.Furthermore,
measure-mentscanoftenreadilybeperformedin-line.Fastmeasurement
timesareespecially important forpreparative chromatography,
theworkhorseincurrentDSP.Preparativechromatographic
pro-cessesarehighlynon-linearandfeaturesharpconcentrationfronts
[18].Thus,CQAsoftheeffluentsuchasthemassfractionof
impu-ritiesarequicklychanging.Toreliablycontrolsuchprocesses,the
usedmonitoringmethodneedstohaveshortresponsetimes
Typ-icaldecisiontimesforpreparativeproteinchromatographyliein
therangeof30stoseveralminutes.Incontrasttoat-lineHPLC,
spectroscopyprovidessignalswithlimitedselectivityfordifferent
components.Toovercomethislimitation,acombinationof
mul-tivariatemeasurementsandmathematicaltoolsformultivariate
dataanalysis(MVDA)isgenerallyappliedtoextractinformation
fromspectroscopicmeasurements
Followingthisargumentation,thisarticleisfocusinginafirst
partonthereviewoftwowidelyusedchemometrictoolsforthe
analysisofspectroscopicdata.Subsequently,thecurrent
state-of-artofspectroscopicPATinDSPisdiscussed
2 Multivariate data analysis for PAT
TheimplementationofthePATframeworkisoften
accompa-niedby theapplication ofmultivariate mathematicalapproaches
[1],alsoknownaschemometrics.Inchemometrics,mathematical
andstatisticaltoolsareusedtoextractusefulchemical
informa-tionfromlargeamountsofmultivariatemeasurementsorrawdata
[19].ThemultivariatenatureofspectroscopicdataforPATarises
outofnecessity,sincenounivariateprocessanalyzerhas
signif-icantselectivitytomonitoraspecificCQAwithoutinterferences
fromotherproperties[17].Chemometricscanbeusedforawide
relationshipsbetweenobservationsbyplotting theobservations
ina space spannedby thek variablesin X In theobjectspace (Fig.1b),thecoordinatesystemisdefinedbythenobservations
Itvisualizesinformationabouttherelationshipbetweenvariables [22].Themaingoaloflatentprojectionmethodsistoreducethe dimensionsinthevariablespacebysummarizingvariableswith similarinformationinLVs.Alllatentprojectionmethodshelp get-tingfundamentalinsightsintocomplexmultivariatedataby(1) discoveringgroupingsinthedata,(2)datacompression,(3) regres-sion,andmore[24]
ThevariabledecompositionintoLVscangeometricallybe inter-pretedasaprojectionofthedatainthevariableandobjectspaceon a-dimensionalhyperplanes,wherebyarepresentsthenumberof LVs.Sincetheprojectionisperformedinbothspaces,themaximum numberofLVsismin(n,k).Theprojectioncoordinates(scores)of theobservationsinthevariablespaceontheithLVaresummarized
inthescorevector tiandareobtainedbyprojectingthesampleson thecorrespondingweightvectorwi[23].Thevectors tiandwiare orthogonalandorthonormal,respectively.Anylatentprojection methodcanbederivedoverthedefinitionofwi[20].The projec-tioncoordinates(loadings)ofthevariablesintheobjectspaceare summarizedintheloadingvectorpi Theloadingvectorspiarenot necessarilyorthogonal
2.2 Principalcomponentanalysis
PCAisacommontoolinexploratorydataanalysisandisusedfor datareduction,simplification,outlierdetection,classification,and noisereduction[25].DatadecompositionofamatrixXaccording to
isperformedwiththeobjectivetoexplainasmuchasofthe vari-anceinXbyalinearcombinationofacomplementarysetofscores
T=(t1, , ta andloadingsP=(p1, , pa).Inorderto differen-tiatethedatadecompositionbyPCAfromotherlatentprojection methods,theLVsarereferredtoasprincipalcomponents(PCs).In PCA,theloadingspiareequaltotheweightswiandthus orthonor-mal They give a quantitative measure of the part of variance andobservedvariableshareswiththePC[22].Thus, thewhole informationregardingthelinearrelationshipbetweenvariablesis compressedintheloadingmatrixP.ThehiddenstructureofX con-cerningtheobjectspacecanbevisualizedbyloadingplots,where theloadingspiareplottedagainsteachother[25].Variables hav-ingsimilarloadingvaluesonaPCarelineardependent(collinear) andareredundantconcerningthisPC.Formeancentereddata,as illustratedinFig.1,collinearitybetweentwovariablescan graph-icallybevisualizedbythecosine oftheanglebetweenthetwo variablesintheobjectspace.Inthesamemannerasrelationships betweenvariablescanbeillustratedbyloadingplots,relationships
Trang 3Fig 1. Visualization of a data matrix X consisting of three observations with three variables in the variable space (a) and object space (b) Observations in the variable space are projected on a latent structure defined by the weight vectors w i , leading to the projection coordinates (scores) t i Projection coordinates (loadings) of the variables in the object space are summarized in the loading vectors p i
betweenobservationscanbevisualizedbyscoreplots[25].Score
plotscanrevealpatterns,clusters,andoutliersintheobservations
(measurements).Usually,twoorthreePCsarealreadysufficient
torevealhiddenpatternsinXbyloadingandscoreplots,sincethe
mostusefulinformation(variance)inXisexplainedbythefirstfew
PCs.Theremainingonesareassumedtocomprisepredominantly
noise[25].ByneglectingtheseminorPCs,PCAachievesadata
sim-plificationandnoisereductioninX.Sincebothscoresandloadings
areorthogonal,PCAisalsoabletoreducecollinearityinX,whichis
whyitalsoplaysacentralroleinregressionanalysis
2.3 Partialleastsquareregression
LinearregressionmethodslikePLSaretoolsinexploratorydata
analysis,relating oneormoreresponsevariablesYwithseveral
predictorvariablesX,byalinearmultivariatemodel
whereBcontainstheregressioncoefficientsconnectingthe
pre-dictorvariablestotheresponses.Thedeviationbetweenmodel
responsesandmeasurementsissummarizedintheresidualmatrix
EY.In thesimplestcase,whenthematrixXisoffullrank,
mul-tiplelinear regression(MLR) canbeappliedand theregression
coefficientscanbeobtainedbytheleastsquaresolution
InmostPATapplications,however,theobservationtovariable
ratioisratherlowandtheX-variablesarecollinearandnoisy.In
suchcases,predictionabilitiesof MLRmodelscanbevery poor
sincetheestimatedregressioncoefficientsbecomeunstableand
candeviatesubstantiallyfromtheirexpectedvalues[26,27]
AnalternativewaytodeterminetheregressioncoefficientsB
is byusinglatentprojection methodslikeprincipal component
regression(PCR)andPLS.InPCRthecollinearityproblemissolved
by(1)decomposingthepredictormatrixXtoorthogonalPCsand(2)
regressingtheresponsesYontheorthogonalscoresTinsteadofX
ThescorematrixTisoffullrankandallowsthepredictionofstable
regressioncoefficients.Furthermore,datadecompositionpriorto
regressionallowsnoisereductionandthusthecalibrationofmore
robustmodels.AmajordrawbackofPCRisthatdatadecomposition
isperformedundertheobjectivetoexplainasmuchaspossibleof
thevarianceinX.However,thevarianceinXthatisrelevantforthe
predictionofYcouldberathersmallincomparisonwiththetotal
varianceinX.Thus,muchoftherelevantvariancecouldbelostby
PCA[17]
IncontrasttoPCR,PLSperformsasimultaneousdecomposition
ofXandYwiththeobjectivetoexplainasmuchaspossibleofthe covariancebetweenthedatasets[28].ThedecompositionofXand
Ycanbedescribedby
and
whereU=(u1, , ua containsthecorrespondingY-scoresuion the ith latent variable, EC represents the Y-residuals, and C= (c1, , ca denotesthelineartransformationdefinedbythe ortho-gonalY-loadingsci SincetheweightmatrixWisdeterminedunder theobjectiveofmaximizingcovariancebetweenXandY,thescores
TaregoodpredictorsoftheoriginaldataX
andmodelalsotheresponses[29]
IncontrasttoPCA,weightswiandloadingspiarenotequal.The orthonormalweightscanbeconsideredastiltedX-loadingssince theydescribetheeffectiverelationshipbetweenXandY.Depending
onhow strongYeffectsW,theweightswideviatemoreorless fromtheloadingspi[30].TheX-loadingsarenotorthogonaltoeach other[24].ComparingEq.(2)withEq.(7)leadstotheregression coefficient
SincetheregressionmodelBiscalculatedfromtheorthogonal latentstructuresWandC,PLSisabletoanalyzedatawithstrongly collinear,noisy,andnumerousX-variables[29]
3 Spectroscopy for process monitoring in chromatography
Inthepast,spectroscopicmethodshavebeenwidelyusedas toolsforstructuralanalysisofproteins[31–33].Fromabiochemical pointofviewtheanalysisofproteinscanbesplitintothe assess-ment ofprimary,secondary, tertiaryand quaternarystructures Spectroscopicmethodsprovideinformationabouteachof these layersofabstractionwithintheproteinstructure(cf.Fig.2)[31]
Toassessthesequenceandtotalconcentrationofprotein, espe-ciallyUV/visspectroscopyandFourier-TransformInfrared(FTIR) spectroscopyareofinterest.UV/visspectroscopymainlymeasures theprimarystructure,i.e.thecontentofaromaticaminoacidsas
Trang 5Thesecondarystructureofproteinshasbeenfrequentlymeasured
byvibrationalspectroscopysuchasFTIRandRamanspectroscopy
[32,34,35].Themethodsallowtomeasurethevibrationalmodesof
thebackboneofpolypeptides.Thetertiarystructureofproteinsis
accessibleoverthefluorescenceofthearomaticaminoacids.The
tryptophanemissionis solvatochromatic,reactingtochangesin
thelocalpolarityaroundtryptophanresidues[31,33].Thus,
struc-turalchanges whichaffectthelocalenvironmentof tryptophan
residuescanbedetectedbyfluorescencespectroscopy.Finally,the
quaternarystructureofproteins,i.e.assemblyofmultiplesubunits
ornativeaggregationofproteinmonomers,maybeassessedover
theproteinsizebyquasi-elasticlightscatteringmethods
includ-ingstaticlightscattering(SLS)anddynamiclightscattering(DLS)
[31,4]
Alloftheabovementionedmethodsareofmajorinterestfor
processmonitoringaseachmethodprovidesaccesstoorthogonal
informationabouttheproduct.Keyaspectsofthedifferent
meth-ods have been summarized in Table1 In literature, especially
UV/visabsorptionandFTIRhavebeenusedforavarietyofPAT
applications(cf.Sections3.1and3.2).Literatureforfluorescence
spectroscopy aswell asDLS is less broad.However interesting
applicationsexist(cf.Section3.3).In thefollowingsections,the
differentapplicationswillbediscussed
3.1 UV/visspectroscopy
UV/visspectroscopymeasurestheabsorptionofproteins
gen-erally in the range between 240 and 340nm Mainly due to
the content of aromatic amino acids (phenylalanine, tyrosine,
and tryptophan)proteinssignificantly absorbin thisregion (cf
Fig.2,primarystructure)[18,31,36].Duetothesensitivity,
repro-ducibilityofsignalsandrobustnessofthespectrometers,UV/vis
absorptionat280nmiswidelyusedasaprimarydetectionmethod
ofproteinconcentrations.Whilecurrentapplicationsmainlyrely
onunivariateUV/vismeasurements,ithasbeenshownthatUV/vis
spectracontainasignificantamountofinformationonproteinsand
maybeusedforselectivequantificationevenifonlyminutespectral
differencesexist[36]
MultivariateUV/visspectroscopyinconjunctionwithPLS
mod-elingforselectiveproteinquantificationfirstappearedin1994[37]
Arteagaetal.demonstratedthequantificationofthethreemain
bovinecaseinsbyPLSregressiononthefourthderivativeUV/vis
spectra.ThePLSmodelwascalibratedbasedondesignedmixing
ratios.Incontrasttolatterpublicationswhichfocuson
(near)-real-time assays,Areaga et al.intended theproposedmethodasan
off-lineanalyticalassay.Inthescopeofthepublication,themethod
wasnotappliedtoprocesssamples
The first at-line application for chromatography was only
reportedin 2011 asa tool tocircumvent theanalytical
bottle-neckcreatedbyhighthroughputexperimentation[38].Similarto
Arteagaetal.,aPLSmodelwascalibratedbasedondesignedmixing
ratiosofpureproteincomponents.ThecalibratedPLSmodelwas
usedtoselectivelyquantifytheproteincontentinelutionfractions
ofmultipleco-elutingspeciesfromminiaturizedandparallelized
chromatographyexperiments.Theresultswerelaterconfirmedby
[39].Subsequently,themethodwastransferredtoanin-linesetup
withadiodearraydetectorandappliedforaselectiveand
real-timequantificationof3modelproteins[40].Itwasshownthatthe
deconvolutedsignalfromthedetectorcouldbedirectlyusedin
afeed-forwardcontrollertotriggerproductpooling.Experiments wereperformedindilutedconditionstopreventdetector satura-tion
Whiletheabovementionedpublicationsprovidedaccurate pre-dictionsofproteinconcentrationsinmulti-proteinmixtures,they allrelied ondesigned mixing ratiosofpureproteins This may posemajordifficultieswhencalibratingaPLSmodelforanapplied example, e.g thepurification of a monoclonal Antibody(mAb) fromitshighmolecularweightimpurities(HMWs).Brestrichetal addressedthis problemby usingprocessbasedsamplesfor the PLS model calibration[41] Instead of using pureprotein sam-plestoproducedesignedmixingratios,chromatographicrunsat variableconditionswereperformedtospanamodelcalibration space The column effluentof those experiments was fraction-atedandanalyzedbysuitableoff-lineanalytics.Theyappliedthe newlydesignedmethodtodifferentdilutedseparationproblems includingtheseparationofamAbfromitsimpuritiesandthe mea-surementofdifferentproteinspeciesinhumanbloodfractionation Sincethen,UV/visspectroscopyinconjunctionwithPLS mod-elinghasbeenusedinmultiplestudies.Asasupportivetool,itwas appliedtogetherwithmechanisticmodeling fora generic root-causeinvestigation[10].Inafirstpreparativein-lineapplication, thetoolhasbeenusedtomonitorandcontrolachromatographic ProteinAcapturestep,anapplicationwhich maybeofinterest forcontrollingcontinuouschromatographyequipment[42] Cur-rentresearchaimstoextendtheapplicableconcentrationrangefor theapproach.Duringtheelutionofpreparativechromatographic steps,veryhighproteinconcentrationsmayoccurandcause detec-torsaturationintheUV/visrange.Byapplyingvariablepathlength spectroscopy,thelinearrangeofUV/visabsorptionspectroscopy canbegreatlyextended.PLSmodellingallowedthedeconvolution
ofco-elutingspeciesinmultiplecasestudies[43]
3.2 FTIRspectroscopy FTIRspectroscopyisfrequentlyappliedasaPATtechnologyfor smallmoleculeproduction[2].Forproteins,FTIRwasfirst estab-lishedasatoolforassessingthesecondarystructure[31,32,34,35] Proteinsaredetectedbythevibrationofthepolypeptidebackbone Multiplevibrationalmodescorrespondtodifferentdetectedamide bands(cf.Fig.2,primaryandsecondary structure).The absorp-tionoftheamidebandsisdirectlyproportionaltotheamountof polypeptidebackbone.Themostprominentproteinogenicband, theamideIband,ismainlycausedbyC Ostretching.Secondary structuralelementsinducebandshifts oftheamidebands.This phenomenoncanbeusedtoquantifytheproportionofdifferent secondarystructuralelements,e.g.bytakingthesecondderivative
or applying Fourier self-deconvolution Thus, FTIRis a promis-ingcandidateformonitoringtheoverallproteinmassaswellas thestructuralintegrityofproteinsbytheirsecondarystructure The application is however hindered by the strong absorption
of water in thesame spectral region.It is a non-trivial taskto correct for thewater absorption.Topreventtotal extinction in thetransmissioncell,typicalpathlengthsneedtobevery short (approximately5m),whichhoweveralsoreducesthe sensitiv-itytowardsproteins.Despitetheexistingproblems,anumberof promisingapplicationshavebeenreported
Publicationsdemonstratedthepossibilitytoselectivelydetect mAbs,HMWsandhostcellproteins(HCPs)[44,45]withFTIRfor biopharmaceuticalapplications.Experiments wereperformedin
Fig 2. Based on the example of ovomucoid, the four different level of protein structure are illustrated To each level, suitable spectroscopic methods are listed with a short explaination of what is measured The lists are not extensive but rather correspond to the most promising methods in the authors eyes Protein structure retrieved from PDB ID: 1OVO [53,54] UV/vis spectra obtained from [36]
Trang 6absorption, fiber probes readily available Fluorescence
spectroscopy
Excitation:
240–300 nm Emission:
260–450 nm
and their solvochromatic environment
Broad measurement ranges feasible, calibration may be challenging
[33,31,49]
e.g 633 nm
macromolecules
Based on time correlation, limited suitability for flow-through
[50]
e.g 633 nm
particle size
Difficult to obtain stable baselines
[31]
Circular
dichronism
structure
Impractical at high concentrations
[31]
anat-linesetup.Theapproachwaslaterextendedtofurther
down-streamprocessingunitoperations[46].Capitoetal.demonstrated
theuseofacalibratedPLSmodeltoselectivelyquantifymAb,HMW
andHCPconcentrationsofsamplesdrawnfromdifferentunit
oper-ations.Againexperimentswereperformedat-line.mAbcouldbe
quantifieddowntoconcentrationsof0.7g/lwhileHMW
concen-trationsaslowas1%[w/w]weredetected
Duringtherefoldingprocessofaninclusionbodyofan
auto-protease,FTIRwasappliedasanin-linePATtooltomonitorthe
relativecontentofdifferentsecondarystructuralelements[47].A
timeevolutionoftherelativecontentofstructuralelementscould
beshownduringtherefoldingprocess.However,theresultsdid
notallowpredictionoftherefoldingyieldbasedonthecomputed
contentofsecondarystructuralelements
Recently,anapproachwaspublishedtomonitorthein-column
bindingbehaviourofmAbduringaProteinAcapturestepbyFTIR
[48].Amicro-columnwaspackedontopofanattenuatedtotal
reflection(ATR)crystal.WithaPLSmodel,thetotalproteincontent
ofresinincontactwiththeATRcrystalwasmeasuredover
multi-pleprocesssteps.Thepublicationshowed,thattheclean-in-place
stepsdonotseemtobeabletoreliablyremoveallboundprotein
Whilebeinganinterestingscientificapproach,atransfertoalarger
scalesystemmaybedifficult.Theproposedsetupsamplestheresin
verylocally,whichmaynotberepresentativefortheoverall
col-umn.Furthermore,lateralstresshadtobeappliedtogeneratean
increasedcontactareabetweenresinandATRcrystal.Nevertheless,
theapproachshowstheversatilityofFTIRspectroscopy
3.3 OtherspectroscopicPATtools
Tothebestofourknowledge,otherspectroscopicmethodshave
onlybeenstudiedbytwoarticlesasPATtechnologiesforDSPof
biologics.Fluorescencespectroscopywasproposedasanat-line
PATtoolforachromatographicpurificationstepofafusion
pro-tein[49].Here, itwasshownthatthefluorescencesignalcould
becorrelatedwiththefractionpurityfromanhydrophobic
inter-actionchromatographystepseparatingmisfoldsfromtheproduct
DLSwasusedtoinvestigatetheunfoldingandrefoldingprocessofa
recombinantfusionproteinfromaninclusionbodyandits depend-enceonachaotropicagent[50].Yuetal.couldaccuratelypredict theaggregationandfoldingstatecomparedtoreference analyt-ics.Themethodwashowevernotappliedfor real-timeprocess monitoringorcontrol
4 Conclusion and outlook
In summary, PATfor biologicshasadvanced in recent years towards real-time monitoring and control of critical quality attributes Spectroscopy based PAT tools have been success-fully applied to a variety of applications Compared to other methodologies,theyfeaturefastmeasurementtimes,easyin-line implementationandmaintainablecosts
Amajorchallengeinfuturerelatestoaflexibleimplementation
ofPATtoolsintodifferentunitoperations.Currently,disposable andsingle-usetechnologiesaregainingmarketsharesespecially duringclinicalphases[51].Atthesametime,theproduct port-folioof biologics is broadening New formats such asantibody fragments,nanobodies,conjugatedproteinsandvaccines,and Fc-fusionproteinsareemerging[52].Dependingontheunitoperation andbiopharmaceuticalproduct,differentsensorsorsensor com-binations may be of interest Ideally, detectors could therefore
beexchangedwithlittleeffort.Such aflexible approachtoPAT howeverrequiresstandardizedcommunicationbetweendifferent components,e.g.throughOPCFoundation’sOPCunified architec-ture(OPCUA).Here,thesupportoftheequipmentmanufactureras wellasdedicatedsensormanufactureriskey[17].Byprovidinga flexibleplatformwhichallowstocombinedifferentmanufacturing equipmentwitharangeofsensortechnologies,aversatileapproach towardsfuturePATchallengescouldbeimplemented
Acknowledgements
This work hasreceived funding fromthe European Union’s Horizon2020 researchand innovationprogramme under grant agreementNo.635557
Trang 7Bau-mannandStefanHeisslerforthereviewofthemanuscript.They
arealsothankfulforthevaluableinputstheyreceivedfromother
membersoftheresearchgroupMABatKIT
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