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Journal of Pharmaceutical Sciences 105 (2016) 97e105

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Journal of Pharmaceutical Sciences

journal homepage: www.jpharmsci .org

Pharmaceutics, Drug Delivery and Pharmaceutical Technology

Spectroscopic-Based Chemometric Models for QuantifyingLow Levels of Solid-State Transitions in Extended ReleaseTheophylline Formulations

Maxwell Korang-Yeboah 1, Ziyaur Rahman 1, Dhaval A. Shah 1, Mansoor A. Khan 2, *

1 Division of Product Quality and Research, Center for Drug Evaluation and Research, Food and Drug Administration, Maryland 209932 Irma Lerma Rangel College of Pharmacy, Texas A&M Health Science Center, College Station, Texas 77843

a r t i c l e i n f o

Article history:Received 28 August 2015Revised 2 September 2015Accepted 6 November 2015

Keywords:chemometricscontrolled releasepartial least squaresformulationnear-infrared spectroscopyRaman spectroscopypseudopolymorphsolid state stability

Disclaimer: This research publication reflects the vienot be construed to represent Food and Drug Admini* Correspondence to: Mansoor A. Khan (Telephon

436-0087).E-mail address: mkhan@pharmacy.tamhsc.edu (M

http://dx.doi.org/10.1016/j.xphs.2015.11.0070022-3549/© 2016 American Pharmacists Association

a b s t r a c t

Variations in the solid state form of a pharmaceutical solid have profound impact on the product quality andclinical performance. Quantitative models that allow rapid and accurate determination of polymorphicchanges in pharmaceutical products are essential in ensuring product quality throughout its lifecycle. Thisstudy reports the development and validation of chemometric models of Raman and near infrared spec-troscopy (NIR) for quantifying the extent of pseudopolymorphic transitions of theophylline in extendedrelease formulations. The chemometric models were developed using sample matrices consisting of thecommonly used excipients and at the ratios in commercially available products. A combination of scatterremoval (multiplicative signal correction and standard normal variate) and derivatization (Savitzky-Golaysecond derivative) algorithmwere used for data pretreatment. Partial least squares and principal componentregression models were developed and their performance assessed. Diagnostic statistics such as the rootmean square error, correlation coefficient, bias and Q2 were used as parameters to test the model fit andperformance. The models developed had a good fit and performance as shown by the values of the diag-nostic statistics. The model diagnostic statistics were similar for MSC-SG and SNV-SG treated spectra.Similarly, PLSR and PCR models had comparable performance. Raman chemometric models were slightlybetter than their corresponding NIR model. The Raman and NIR chemometric models developed had goodaccuracy and precision as demonstrated by closeness of the predicted values for the independent obser-vations to the actual TMO content hence the developed models can serve as useful tools in quantifying andcontrolling solid state transitions in extended release theophylline products.

© 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Introduction

About 56%-87% of pharmaceutical solids have more than onesolid state form.1 The various solid-state forms such as crystallinesolids, amorphous, cocrystals, salts, and solvates have dissimilarintermolecular and intramolecular interactions as well as free en-ergies, hence, differ in their physical, chemical, and mechanicalproperties. Changes in the solid-state form of the active ingredientmay therefore alter the stability, dissolution, bioavailability, and

ws of the author and shouldstration's views or policies.e: 979-436-0561; Fax: 979-

.A. Khan).

®. Published by Elsevier Inc. All rig

ultimately the clinical efficacy of the product.2,3 The extent of solid-state transitions in pharmaceuticals solids while in use shouldtherefore be adequately computed and controlled, to ensureconsistent clinical performance and safety. This can be attained bythe development of analytical methods that allow rapid and ac-curate quantification of solid-state transitions in pharmaceuticals.

X-ray powder diffraction (PXRD) is the most common and def-inite method for characterizing and quantifying pharmaceuticalsolids. However, the major limitation of this technique is the lengthof time required for data collection and the limited availability ofthe instruments because of its higher cost. Furthermore, for solidswith complex scattering patterns and in the presence of excipientswith overlapping peaks, the use of PXRD may not be ideal.4 Severalauthors have demonstrated the utility of other analytical tech-niques such as solid-state nuclear magnetic resonance (ssNMR),thermal techniques such as differential scanning calorimetry (DSC)

hts reserved.

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and isothermal micro-calorimetry, and vibrational techniques (i.e.,near-infrared [NIR] and Raman spectroscopy) in quantifying solid-state transitions in pharmaceutical solids.5-8 Of these techniques,vibrational spectroscopic techniques such as NIR and Raman havegarnered much attention because of their rapid data acquisitionrate, low cost, ease of use, nondestructive nature, and therequirement of little or no sample preparation. Moreover, thesetechniques are sensitive to structural, conformational, and envi-ronmental changes at the molecular level.9 Although both tech-niques measure molecular vibrations, NIR spectra is due toovertone and combination bands arising from light absorption,whereas Raman spectra arises from inelastic light scattering asso-ciated with loss of vibrational energy. The development of fiberoptic probes and instrumental advancements has allowed NIR andRaman to be used as a process analytical technique (PAT) tool for in-line, at-line, and off-line process monitoring. In addition, combi-nation of vibrational spectroscopy and data analytical tools such aschemometrics have significantly enhanced the sensitivity of thetechniques for quantitative analysis and improved the wealth ofinformation that can be obtained from the spectra.10

Theophylline is bronchodilator used in the treatment of asthmaand chronic obstructive pulmonary disease. More than 7 decadessince its discovery, theophylline still remains the most widely usedbronchodilator worldwide although its use has been limited to pa-tients with poorly controlled disease conditions.11,12 Currently, 4different polymorphs of theophylline, 3 anhydrous polymorphs andtheophylline monohydrate (TMO), have been identified. Theophyl-line anhydrous form II (THA) is the most stable form at room tem-perature and used in pharmaceutical formulations. The mostcommonly encountered alterations in the solid state of THA duringstorage and in use are the transitions to and from THA to TMO.13,14

Transition of THA to TMO is associated with a significant decreasein dissolution and bioavailability.13,15,16 Theophylline is a narrow

Figure 1. (a) DSC, (b) thermogravimetric analysis, (c) ssN

therapeutic index drug; in addition, its patient population consistsmainly of individuals with uncontrolled disease conditions, andhence, any minor variations in bioavailability may have a very sig-nificant impact on the clinical efficacy and incidence of side effects.

Several authors have reported the use of NIR and Raman as PATtools for monitoring transitions of THA during manufacturing.17-20

NIR has also been used in differentiating between the unboundand bound water content during wet granulation.18 Otsuka et al.also reported quantitative chemometric PXRD models for predict-ing the hydrate content of powders containing both THA andTMO.21 The present study extends the use of NIR and Ramanspectroscopy beyond their present role as PAT tools to quantifica-tion of low-level pseudopolymorphic transition (less than 5% of thetotal solid content) of THA in controlled release formulations oftheophylline. The authors also report on the effectiveness ofdifferent data preprocessing techniques and commonly usedregression models in chemometric method development.

Materials and Methods

Material

THA, magnesium stearate (MgS), and lactose monohydrate (LM)were purchased from Sigma Aldrich (St. Louis, MO). Hydroxy-propylmethylcellulose K100M was obtained from Colorcon (Har-leysville, PA). Colloidal silicon dioxide (Aerosil 200) was obtainedfrom (Evonik, Parsippany, NJ). TMO was prepared by recrystalliza-tion of saturated THA solution in deionized water at 70�C. Themonohydrate crystals obtained were filtered, dried overnight atambient temperature, and stored in a chamber maintained at 95%relative humidity. The TMO crystals were characterized by PXRD,ssNMR, and DSC before use.

MR, and (d) XRD characterization of THA and TMO.

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Preparation of Calibration Samples

Theophylline formulations were prepared in-house using ex-cipients commonly found in commercial theophylline products.The formulation consisted of THA 53%, K100M 33%, LM 11%, aerosil0.1%, and MgS 2.5%. A similar formulation was prepared using TMOin place of THA. The calibration samples were prepared by mixingthe THA and TMO formulations to obtain sample matrices with 0%-25% of the active pharmaceutical ingredient (API) being TMO.

NIR Spectroscopy

NIR spectra of THA, TMO, calibration, and the independent testsamples were collected over the wavelength range of 1100-2500nm in 2-nm increments with FOSS NIR Spectrophotometer 6500(FOSS NIR Systems, Inc., Laurel, MD) equipped with rapid contentanalyzer. The spectra were acquired with the Vision software,version 3.2 (FOSS NIR System Inc.). Each spectrum was an averageof 60 scans. The spectra of polytetrafluorethylene were used as thereference spectra.

Raman Spectroscopy

Raman spectra were measured with a noncontact Raman probe(Raman PhAT-RXN1 Analyzer; Kaiser Optical System Inc., Ann Ar-bor, MI) with a spot size of 6mm and laser wavelength and strengthof 785 nm and 350 W, respectively. The spectra were recorded intriplicates over the wavelength range of 175-1875 cm�1 at a

Figure 2. Raw (a, b) and MSC-SG second derivative, (c, d) NIR spectra of THA,

resolution of 1 cm�1 and measurement time of 1 min (exposuretime of 15 s and 4 scans). Data acquisition was performed with iC®Raman software, version 4.1 (Kaiser Optical System Inc.).

Data Analysis

Data analysis and chemometricmodelswere developedwith theUnscrambler X software (version 10.1; CAMO ASA, Norway) andSIMCA, version 14 (Umetrics AB, Umea, Sweden). Principal compo-nent regression (PCR) analyses were performed with Unscrambler,whereas all partial least squares regression models (PLSR) weredevelopedwith the SIMCA software.Model developmentwas basedon the current US Food and Drug Administration guidelines ondevelopment and submission of NIR analytical procedures.22

Results and Discussion

Characterization of TMO and THA Crystals

The ssNMR spectra, PXRD spectra, and DSC thermogram of theTHA and TMO crystals used are shown in Figure 1. The DSC scan ofTHA had a sharp melting endotherm at 273�C. An additional broadendotherm was observed between 50�C to 102�C in the DSC ther-mogram of TMO. This was due to the loss of water from the mon-ohydrate crystals. This was confirmed by the thermogravimetricanalysis thermogram, which showed a weight loss of about 8.9%.The extent of weight lossmatches the theoretical value of 9%, whichfurther confirms the transformation of THA to TMO. Also, the PXRD

TMO, THA- and TMO-formulated drug products, and of sample matrices.

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pattern of the prepared TMO crystals showed the absence ofcharacteristic THA peaks at 7.2� and 12.5� 2q, and the presence ofdistinctive TMO peaks at peaks at 8.8�, 11.5�, and 27�. In addition,the conversion of THA to TMO resulted in a change in the chemicalshift position of the carbonyl carbon in the ssNMR spectra from150.9 ppm to 148.4 ppm. However, the use of PXRD and ssNMR forquantification of low levels of solid-state transition required a runtime of more than 2 h and 8 h, respectively, to obtain an optimumsignal-to-noise ratio. For these reasons, quantitative models basedon PXRD and ssNMR were not pursued further.

NIR Spectroscopy

The NIR spectra of THA, TMO, and the sample matrices areshown in Figure 2. The NIR spectra of THA were mainly due to CHstretching and deformation bands of the methyl carbon (1170 nmand 1368 nm), CH stretching bands of the methine carbons (1660nm) and combination bands due to NH stretching vibration (2258nm). Pseudopolymorphic transition of THA to TMO results inchanges in the NIR spectra along the entire wavelength range

Figure 3. Raman spectra of (a) THA, TMO, THA- and TMO-for

because of the high susceptibility of this technique tomoisture. Themost notable difference between the NIR spectra of THA and TMOwas the appearance of OH peaks from adsorbed and crystallinewater (1937 nm and 1970 nm) and an OH stretch first overtonepeak (1476 nm). The presence of excipients did not significantlyinterfere with peaks of THA and TMO other than the appearanceof crystalline water peaks of LM (1934 nm). The differences be-tween THA and TMO sample matrices were further enhanced bythe application of mathematical algorithms, which unraveledoverlapping bands and increased the signal-to-noise ratio. Themathematical algorithms used are discussed further in the latersection.

Raman Spectroscopy

As water is a weak Raman scatter, any variations in the Ramanspectra of THA and TMO were mainly because of the changes inmolecular vibrations due to hydrogen bond interactions betweentheophylline and water molecules. The Raman spectra of THA,TMO, and the sample matrices can be seen in Figure 3. The most

mulated drug products. (b) Calibration sample matrices.

Table 1Statistical Figures of Merit and Extent of Variance Explained by PLSR and PCR Latent Variables

Method Pretreatment Model LatentVariables

Variance Explained Statistical Figures of Merit

X Block Y Block

Factor Accumulated Factor Accumulated Slope Offset R2 Correlation RMSEE/C RMSECV

NIR MSC-SG PLSR 1 0.85 0.85 0.98 0.98 1 8.8E-07 0.989 0.994 0.831 0.8462 0.10 0.95 0.01 0.99

PCR 1 0.85 0.85 0.98 0.98 0.985 0.240 0.986 0.993 0.881 0.9572 0.08 0.93 0.01 0.99

SNV-SG PLSR 1 0.85 0.85 0.98 0.98 1 1.6E-07 0.999 0.995 0.851 0.8552 0.10 0.95 0.01 0.99

PCR 1 0.86 0.86 0.98 0.98 0.982 0.221 0.985 0.992 0.992 1.0772 0.08 0.99 0.01 0.99

Raman MSC-SG PLSR 1 0.86 0.86 0.99 0.99 0.999 1.6E-07 0.998 0.999 0.405 0.3832 0.13 0.99 0.01 1.00

PCR 1 0.86 0.86 0.98 0.98 0.998 0.019 0.998 0.999 0.393 0.4342 0.13 0.99 0.02 1.00

SNV-SG PLSR 1 0.81 0.81 0.99 0.99 1 2.1E-07 0.998 0.999 0.392 0.3152 0.18 0.99 0.01 1.00

PCR 1 0.86 0.86 0.98 0.98 0.998 0.037 0.999 0.999 0.387 0.4372 0.13 0.99 0.02 1.00

RMSEE, root mean error of estimate; RMSEC, root mean square error of calibration.

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notable difference between the spectra of THA and TMO was thereplacement of the double carbonyl peaks at 1662 cm�1 and 1704cm�1 with a single sharp peak at 1686 cm�1. THA Raman peaks at1612 cm�1 (C¼C), 1572 cm�1 (C¼N), 1427 cm�1(CH3 deformation),1190 cm�1(C-C), and 928 cm�1 (CH3 rocking) shifted to lower wavenumbers in TMO. On the other hand, THA peaks at 1316 cm�1, 1286

Figure 4. (a) Weighted residual, (b) Hotelling T2 plots of PLSR Raman models and

cm�1 (C-N stretching), and 558 cm�1 (O¼C-N bend) shifted tohigher wave numbers. LM and MgS also had Raman peaks in be-tween 1500 cm�1 and 500 cm�1. However, these peaks did alter thedifferences between varying THA and/or TMO contents in thesample matrices as APIs have higher Raman activity than the ex-cipients. The Raman spectra of the sample matrix showed

PLSR score plots for (c) MSC-SGetreated and (d) SNV-SGetreated NIR models.

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discernable differences in spectra at the concentration rangestudied (0%-25% wt/wt TMO).

Chemometric Analysis

Spectra PretreatmentAlthough proper data collection is essential in developing

quantitative NIR models, NIR spectra is inherently confounded bysystemic variations due to light scattering from smaller particleswith size comparable to the NIRwavelength, surface roughness andshape, crystalline defects, and density fluctuations.23,24 Anothersource of variation is the differences in effective path length. Thesefactors may lead to baseline shift (multiplicative scatter effect) andnonlinearity in the spectra data. On the other hand, Raman spectraare less affected by physical variations. However, sample fluores-cence, subsampling, and sample inhomogeneity can alter the dataof the Raman spectra. These variations were removed by mathe-matical treatment of the spectra data.

Scatter removal techniques (multiplicative signal correction[MSC] and standard normal variate [SNV]) were used to minimizeunwanted variations. The effectiveness of these techniques wascompared using the root mean square error of prediction (RMSEP)

Figure 5. PLSR and PCR loading plots for (a

and standard error of prediction (SEP) of their respective models.Both MSC and SNV remove physical light scattering effect andcorrect baseline shifts.23,25,26 MSC removes artifacts and imper-fections by estimating the correction coefficients by the leastsquaresmethod using the average spectrum of the calibration set asthe reference spectrum and correcting the recorded spectrum usingthe slope and intercept of the linear regression model. The offsetcorrection concept for SNV is similar to MSC; however, each spec-trum is processed on its own without the need for a referencespectrum or linear regression.23,24 Application of MSC and SNValone did not significantly improve the RMSEP and SEP whencompared to the raw data therefore the spectral data was furthersubjected to secondary derivative treatment based on theSavitzkyeGolay (SG) with a third-order polynomial and 15-pointsmoothing. This technique further removed any additive andmultiplicative effects in the data without decreasing the signalstrength or signal-to-noise ratio. Further data treatment by secondderivative SG led to a significant improvement in RMSEP valuewhen compared to the untreated data and SG only treated data. TheRMSEP values for MSC-SG and SNV-SG treated data were not sta-tistically different. (Table 1) Spectral truncation did not improve themodels, hence, was not pursued further.

) NIR and (b) Raman sample matrices.

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Outlier DetectionThe normalized weighted residual plot (DModX norm) and the

Hotelling T2 plots were used in identifying outliers and extremesamples that may alter the prediction capability of the models(Fig. 4). The residual plot is a measure of the normalized distance ofan observation in the training set from the model at a critical valueof 0.05. An observation is considered a moderate outlier if theweighted residual is more than twice the critical value computedfrom the F-distribution. All the observations used in model devel-opment were below twice the critical values (Dcrit of 1.235 and1.243 for Raman and NIR models, respectively). In addition, theHotelling T2 and the score plots were used as complementarytechniques in the detection of sample outliers and the influence of asample on the model. A T2 value greater than the critical value at95% and 99% confidence limit indicates the observation is furtheraway from similar observations in the score space. These values areproportional to the sample leverage which is a measure of the in-fluence of a sample on the model. All the sample matrices used indeveloping the Raman models had T2 values lower than the criticalvalues of 6.853 and 11.14 for 95% and 99% confidence limit,respectively. A similar observation was obtained for all modelsdeveloped from the NIR spectra.

PLSR and PCR ModelsThe 2 most used regression models in multivariate analysis,

PCR and PLSR, were used for quantitative model development forboth NIR and Raman spectra. PCR models are based on spectraldecomposition of the X matrix into principal components (prin-cipal component [PC] or X-scores), which explain the maximumvariation in the data. However, the predictive variables or PCs in

Figure 6. Permutation plots for (a) Raman and (b) NIR PLSR models and predicted versus

PCR models may not necessarily correlate with the predictedresponse (Y matrix or concentration). On the other hand, in PLSRmodels, the spectra are decomposed into X and Y scores such thatthere exist a strong correlation between the predictive variablesand the predicted response.27-29 All models were developed andvalidated taking into account the European Medicine Agency andUnited States Food and Drug Administration guidelines on devel-opment and submission of NIR analytical procedures guidance forindustry.22,30 The models were validated using the cross validationapproach in which the same data set was used for model cali-bration and validation. Two latent variables (PLSR factors or PCRPCs) were used in developing all the models. This was because itgave better statistical values for predicted residual error sum ofsquares and root mean square error of cross validation (RMSECV).Choosing the right number of latent variables is essential to avoidan over fitted or under fitted model. The 2 latent variables of thePLSR and PCR models accounted for �93% and �99% of the vari-ance in the X and Y blocks in both the MSC-SG and SNV-SG NIRmodels (Table 1). Similarly, more than 99% of the variance in the Xand Y blocks were explained in all models based on the Ramanspectra. In addition, the first latent variables (PC-1 and factor-1 forPCR and PLSR, respectively) for all the models accounted for mostof the X and Y variance in the data set. This suggests a possiblecorrelation between the latent variables and increasing TMOcontent.

Furthermore, the score plots reinforced the possible correlationbetween the latent variables and increasing TMO content asdemonstrated by an increase in score number with increasingamounts of TMO (Fig. 4). The loading and coefficient plots were alsoused as complimentary parameters in assessing the relationship

actual % TMO plots for (c) MSC-SGetreated and (d) SNV-SGetreated Raman models.

Table 2Model Predicted and Actual TMO Percentage Content for Independent Samples

Method Pretreatment Actual % TMO(wt/wt)

Predicted % TMO content (wt/wt)

PLSR PCR

NIR MSC-SG 2.5 3.53 ± 0.69 3.12 ± 0.885 4.77 ± 0.08 4.06 ± 0.787.5 7.52 ± 0.46 7.13 ± 1.10

10 11.76 ± 0.63 9.76 ± 0.70SNV-SG 2.5 3.72 ± 0.70 3.16 ± 1.93

5 5.20 ± 0.72 4.03 ± 0.927.5 7.47 ± 0.47 6.93 ± 1.30

10 10.16 ± 0.07 9.55 ± 0.79Raman MSC-SG 2.5 2.72 ± 0.10 2.43 ± 0.54

5 4.85 ± 0.06 4.79 ± 0.457.5 6.95 ± 0.06 6.86 ± 0.41

10 10.10 ± 0.04 11.28 ± 0.53SNV-SG 2.5 2.61 ± 0.20 2.48 ± 0.53

5 4.74 ± 0.10 4.74 ± 0.487.5 6.87 ± 0.21 6.88 ± 0.42

10 10.90 ± 0.02 11.0 ± 0.48

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between the latent variables and changes in the concentration ofTMO (Y variable). The coefficient plot represents changes in the Yvariable due to variations in an X variable, whereas all the other Xvariables are kept at the average value. The NIR loading plots forboth PLSR and PCR models showed peaks and very high coefficientvalues at 1970 nm and 2340 nm. Both peaks are common to TMOand also increased in intensity as the amount of TMO in the samplematrix increased (Figs. 2 and 5). Similarly, the most prominentpeaks of the latent variables (PC-1 and factor-1) for PCR and PLSRloading plots of the Raman spectra were stretching and bendingvibrations of TMO at 1686 cm�1 (C¼O), 1322 cm�1(C-N), 1250cm�1(H-N¼C), and 674 cm�1(O¼C-N).

Model Fit and Performance AssessmentThe fitness of the models was assessed using statistical param-

eters: R2, correlation, bias, residuals, root mean square error ofcalibration and RMSECV. The R2 and correlation coefficients for allthe models were�0.985. The slopes were all very close to 1 (�0.98)which indicated the low levels of systematic errors. In addition, allthe models had very low offset values. The RMSECV values werealso very low: not more than 1% in all models. The model perfor-mance and prediction accuracy were assessed by their RMSEP, SEP,bias, and Q2 values. The RMSEP value is a measure of the total erroror the average uncertainty expected when the model is used inpredicting the concentration of an independent sample. The RMSEPvalues were less than 1.25% wt/wt and 0.5% wt/wt for PSLR and PCRmodels of the NIR and Raman spectra, respectively. The biasesassociated with all the models were very low and statisticallyinsignificant at p < 0.05. This further confirmed the precision andthe low levels of systematic and random errors in the models.

The Q2 also referred to as the cross-validated R2 value, measuresthe fraction of the total variation in the TMO content that can bepredicted by a component as estimated by cross validation at ap value of 0.05. This serves as a measure of the predictive ability ofthe model. The Q2 value was computed as:

Q2 ¼ 1� PRESSSSYðTotalÞ

where SSY is the residual sum of squares and PRESS is the predic-tion error sum of squares.31,32 Models with a Q2 value �0.5 havegood predictivity. The Q2 values were greater than 0.98 which is anindication of good predictive ability.

Moreover, the validity of the NIR and Raman models werefurther confirmed by the permutation plots (Fig. 6). The permuta-tion plots was used to assess the legitimacy of the low risk asso-ciated with the models and how well the models will predict theTMO content for new independent observations. In the permuta-tion test, the order of the observations was randomly permuted,new models developed from the data and their performanceassessed using the R2 and Q2 diagnostic statistic. The procedure isthen repeated several times (N¼ 20) to obtain a null distribution ofeach diagnostic statistic (R2 and Q2) and the validity of the modelassessed by comparing the R2 and Q2 of the original (unpermuted)model with the newmodels. The original model is said to be valid ifthe Q2 and R2 are on the lower left of that of the original model inthe permutation plot.33

Furthermore, there was not any significant difference in statis-tical parameters for NIR models in which the spectra data werepreprocessed using MSC-SG and those in which SNV-SG was used.A similar observation was for models based on the Raman spectra.Also the model fit parameters for PCR and PLSR models weresimilar. However, the regression models of the Raman spectra hadbetter statistical parameters of RMSECV, RMSEP, bias, and offsetvalues than their corresponding NIR models. The better

performance of Raman spectroscopy could be attributed to Ramanspectra usually having sharper and better-defined spectra peaksthan NIR. This improves the discriminatory power and sensitivity ofRaman spectroscopy in the presence of excipients. In addition, mostAPIs usually have much higher Raman activity than excipients andRaman spectroscopy unlike NIR is sensitive to lattice vibrationswhich are significantly altered when there are changes in thecrystal lattice due to polymorphism.

TMO Quantification

The prediction accuracy of the models was tested on indepen-dent observations with known TMO contents. The independentsamples consisted of 2.5%, 5%, 7.5%, and 10% of the total API contentas TMO. This corresponds to 1.33%, 2.66%, 3.98%, and 5.33% of thetotal solid content of the formulation, respectively. The indepen-dent samples had the same formulation composition as the samplematrices used in developing the models. The spectra were mean-centered and preprocessed either by SNV-SG or MSC-SG as wasdone for sample matrices used for actual model development. Themodels had good precision and accuracy. The model predictedvalues were very close to the actual TMO content in the indepen-dent samples with an absolute difference between the modelpredicted and actual TMO content less than 1.76% and 1.28% for NIRand Raman models, respectively (Table 2). In addition, the RMSEPvalues for the independent samples were low (�0.44% wt/wt and1.24% wt/wt) for both Raman and NIR models, respectively.

Conclusion

The relationship between the solid-state form of a pharma-ceutical solid and its product performance has been well estab-lished. The risk associated with these polymorphic changes inpharmaceutical solids is even greater for products such astheophylline, which has a narrow therapeutic index. Controllingthe quality of these products is therefore essential in ensuringconsistent product performance and product safety. Chemometricmodels that allow easy, rapid, and accurate quantification of theamount of TMO in extended release formulations were developedusing NIR and Raman spectroscopy. The model accuracy anddiagnostic statistics such as RMSECV, SEP, bias, and Q2 values werenot significantly different for spectra data pretreated by a combi-nation of SNV and second derivative S-G and that in which multi-plicative scatter correction and second derivative SavitzkyeGolay

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were used. The performance of PLSR and PCR models was similar.The models were able to accurately quantify low levels of pseu-dopolymorphic changes in extended release theophylline formu-lations. However, the performance of the Raman chemometricmodels was better than the ones based on the NIR spectra. Theperformance of the chemometric models may be affected byproduct-induced variations such as changes in formulation excip-ients, the type, grade, and physical properties of excipients used,and possible process-induced variations. All anticipated variationsmust be accounted for in the sample matrix used in model devel-opment to ensure the model accuracy and robustness. This workfurther highlights the utility of NIR and Raman chemometricmodels as quality control tools.

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