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Monitoring of the physical state of warfarin sodium (WS) in products is essential for minimizing product quality variability in order to ensure consistent clinical performance. This study reports the development of chemometric models for quantifying the crystalline and amorphous fractions of WS in commercial drug products using NIR spectroscopy. Formulations based on commercially available products with different API to excipient ratio were used for the study. For each content, two formulations containing either lactose monohydrate or lactose anhydrous as the predominant formulation excipient were prepared. Two formulations containing either 100% amorphous WS (AWS) or crystalline WS (CWS) were prepared and mixed in various ratios to obtain sample matrices containing AWS/CWS 0–100%. The uniformity of the samples was confirmed by near infrared chemical imaging. Data were mathematically pretreated by multiplicative signal correction and Savitzky–Golay second derivative. Principal component regression and partial least square regression models were developed from mathematically treated data. All the models showed linear trend for amorphous and crystalline fractions of the WS as indicated by correlation andR2 > 0.99 and >0.98, respectively. The models demonstrated good performance parameters with a low-root mean squared error, standard error and bias. The model predicted CWS and AWS contents were in very close agreement with the actual values. The study indicated the utility of NIR chemometric methods in quantification of the crystalline and/or amorphous fraction of WS in its products. [ABSTRACT FROM PUBLISHER]

Fermentanomics is an emerging field of research and involves understanding the underlying controlled process variables and their effect on process yield and product quality. Although major advancements have occurred in process analytics over the past two decades, accurate real-time measurement of significant quality attributes for a biotech product during production culture is still not feasible. Researchers have used an amalgam of process models and analytical measurements for monitoring and process control during production. This article focuses on using multivariate data analysis as a tool for monitoring the internal bioreactor dynamics, the metabolic state of the cell, and interactions among them during culture. Quality attributes of the monoclonal antibody product that were monitored include glycosylation profile of the final product along with process attributes, such as viable cell density and level of antibody expression. These were related to process variables, raw materials components of the chemically defined hybridoma media, concentration of metabolites formed during the course of the culture, aeration-related parameters, and supplemented raw materials such as glucose, methionine, threonine, tryptophan, and tyrosine. This article demonstrates the utility of multivariate data analysis for correlating the product quality attributes (especially glycosylation) to process variables and raw materials (especially amino acid supplements in cell culture media). The proposed approach can be applied for process optimization to increase product expression, improve consistency of product quality, and target the desired quality attribute profile. [ABSTRACT FROM AUTHOR]

The focus of this study was to evaluate the applicability of chemometrics to differential scanning calorimetry data (DSC) to evaluate nimodipine polymorphs. Multivariate calibration models were built using DSC data from known mixtures of the nimodipine modification. The linear baseline correction treatment of data was used to reduce dispersion in thermograms. Principal component analysis of the treated and untreated data explained 96% and 89% of the data variability, respectively. Score and loading plots correlated variability between samples with change in proportion of nimodipine modifications. The R2 for principal component regression (PCR) and partial lease square regression (PLS) were found to be 0.91 and 0.92. The root mean square of standard error of the treated samples for calibration and validation in PCR and PLS was found to be lower than the untreated sample. These models were applied to samples recrystallized from a cosolvent system, which indicated different proportion of modifications in the mixtures than those obtained by placing samples under different storage conditions. The model was able to predict the nimodipine modifications with known margin of error. Therefore, these models can be used as a quality control tool to expediently determine the nimodipine modification in an unknown mixture. [ABSTRACT FROM AUTHOR]

Abstract: As an emerging technology, THz spectroscopy has gained increasing attention in the pharmaceutical area during the last decade. This attention is due to the fact that (1) it provides a promising alternative approach for in-depth understanding of both intermolecular interaction among pharmaceutical molecules and pharmaceutical product quality attributes; (2) it provides a promising alternative approach for enhanced process understanding of certain pharmaceutical manufacturing processes; and (3) the FDA pharmaceutical quality initiatives, most noticeably, the Process Analytical Technology (PAT) initiative. In this work, the current status and progress made so far on using THz spectroscopy for pharmaceutical development and pharmaceutical PAT applications are reviewed. In the spirit of demonstrating the utility of first principles modeling approach for addressing model validation challenge and reducing unnecessary model validation “burden” for facilitating THz pharmaceutical PAT applications, two scientific case studies based on published THz spectroscopy measurement results are created and discussed. Furthermore, other technical challenges and opportunities associated with adapting THz spectroscopy as a pharmaceutical PAT tool are highlighted. [Copyright &y& Elsevier]

The objective of this study was to develop an integrated process monitoring approach for evaluating powder blending process kinetics and determining blending process end-point. A mixture design was created to include 26 powder formulations consisting of ibuprofen as the model drug and three excipients (HPMC, MCC, and Eudragit L100-55). The mixer was stopped at various time points to enable near-infrared spectroscopy scan of the powder mixture for obtaining the time course of the blending process. The evaluation of the blending process kinetics and process end-point was studied through three quantitative approaches: (1) Spectra linear superposition method; (2) Characteristic peak method; (3) Moving block standard deviation method. It was found that the powder blending experienced an initial rapid process to reach a quasi- end point within the first few minutes. Afterwards, a demixing occurred. Then, a real blending end-point was reached as characterized by an inflection point. ANOVA shows that the compositions of ibuprofen and MCC are the most statistically significant variables that impact the time required to reach the blending end-point. This highlighted the critical importance of developing quantitative chemometric approaches to extract critical process information and generate essential process knowledge to enable real-time release of the blending process. © 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:2784–2798, 2009 [ABSTRACT FROM AUTHOR]

There is an urgent need for the analysis of melamine in the global pharmaceutical supply chain to detect economically motivated adulteration or unintentional contamination using a simple, nondestructive analytical technique that confirms the extent of adulteration in a shorter time period. In this work, different analytical techniques (thermal analysis, X-ray diffraction, Fourier transform infrared ( FT- IR), FT- Raman, and near-infrared ( NIR) spectroscopy) were evaluated for their ability to detect a range of melamine levels in gelatin. While FT- IR and FT-Raman provided qualitative assessment of melamine contamination or adulteration, powder X-ray diffraction and NIR were able to detect and quantify the presence of melamine at levels as low as 1.0% w/w. Multivariate analysis of the NIR data yielded the most accurate model when three principal components were used. Data were pretreated using standard normal variate transformation to remove multiplicative interferences of scatter and particle size. The model had a root-mean-square error of calibration of 2.4 ( R2 = 0.99) and root-mean square error of prediction of 2.5 ( R2 = 0.96). The value of the paired t test for actual and predicted samples (1%-50% w/w) was 0.448 ( p < 0.05), further indicating the robustness of the model. Published 2013. This article is a U.S. Government work and is in the public domain in the USA 103:539-544, 2014 [ABSTRACT FROM AUTHOR]

The focus of present investigation was to characterize and evaluate the variability of solid dispersion ( SD) of amorphous vancomycin ( VCM), utilizing crystalline polyethylene glycol ( PEG-6000) as a carrier and subsequently, determining their percentage composition by nondestructive method of process analytical technology ( PAT) sensors. The SD were prepared by heat fusion method and characterized for physicochemical and spectral properties. Enhanced dissolution was shown by the SD formulations. Decreased crystallinity of PEG-6000 was observed indicating that the drug was present as solution and dispersed form within the polymer. The SD formulations were homogenous as shown by near infrared ( NIR) chemical imaging data. Principal component analysis ( PCA) and partial least square ( PLS) method were applied to NIR and PXRD (powder X-ray diffraction) data to develop model for quantification of drug and carrier. PLS of both data showed correlation coefficient >0.9934 with good prediction capability as revealed by smaller value of root mean square and standard error. The model based on NIR and PXRD were two folds more accurate in estimating PEG-6000 than VCM. In conclusion, the drug dissolution from the SD increased by decreasing crystallinity of PEG-6000, and the chemometric models showed usefulness of PAT sensor in estimating percentage of both VCM and PEG-600 simultaneously. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:4337-4346, 2013 [ABSTRACT FROM AUTHOR]

The objective of the present work was to evaluate and characterize a pediatric-friendly formulation of a bitter tasting drug, oseltamivir phosphate (drug). Amberlite IRP64 (resin) was used to make ionic complexes for masking its bitterness. Complexes of four drug-to-resin ratios, 1:1, 1:2, 1:4, and 1:6 (w/w), were prepared and characterized. At buccal pH of 6.8, drug-resin complexes of 1:1, 1:2, 1:4, and 1:6 ratios released 42.13%, 23.26%, 4.13%, and 14.94%, respectively, of loaded drug after 20 s. However, at stomach pH of 1.2 (0.1 N HCl), 61.96%, 70.18%, 85.88%, and 91.42% of drug was released from the same complexes in 6 min. Near-infrared (NIR) chemical imaging of the complexes showed homogeneous distribution of drug in the resin. Chemometric partial least squares model using NIR data of the drug showed a high correlation between calibration and predicted data ( R2 > 0.998). Overall, these results indicated the complex formation between drug and resin. The pH dependence of drug release from these complexes could minimize drug release in the mouth, whereas immediately releasing it in the stomach. Electronic tongue used to evaluate taste indicated that conductivity taste signals were different from control, suggesting taste masking of the drug. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:1800-1812, 2013 [ABSTRACT FROM AUTHOR]

The objective of the current study was to evaluate the performance of Raman and near-infrared (NIR) techniques combined with chemometrics in characterizing the critical quality attributes of brompheniramine (BP)-tannate complexes. Seven complexes were prepared and evaluated for chemical interactions, solubilities, dissolutions, and spatial distributions by NIR chemical imaging (CI). Principal component analysis (PCA) was applied before either partial least squares regression (PLSR) or principal component regression (PCR) models were developed. Complexation was confirmed by Fourier transform IR analysis to yield complexes of lower drug solubilities and sustained-release characteristics in alkaline media. PCA results showed better discrimination ability by NIR than by Raman spectroscopy. Compared with PCR, the PLSR predictions errors, calculated from the Raman and NIR data with second-derivative pretreatment, showed lesser values of 2.68, 0.37, 1.79, and 5.60 and 0.58, 0.25, 0.93, and 0.58 for complex solubilities in acidic and alkaline media and percentages dissolved after 1 and 20 h, respectively. In addition, good correlation (>0.95) was obtained for predicting the drug concentration using PLSR score images explaining the validity of the NIR-CI model for spatial quantitation of BP within its tannate complexes. In conclusion, the chemometric analysis of NIR and/or Raman spectra represented an innovative approach to determine the tannate complexation variability. © 2011 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 101:1450-1461, 2012 [ABSTRACT FROM AUTHOR]

Abstract Carbamazepine (CBZ) exists in anhydrous and dihydrate forms. These forms differ in their solubility, dissolution rate, and subsequently in their oral bioavailability. The objective of this study is to develop multivariate chemometric models for estimation of the low level of carbamazepine dihydrate (CBZ-DH) in the CBZ formulations containing excipients of the commercial formulation. The selected excipients were mixed in proportions to make sample matrices ranging from 0% to 50% CBZ-DH. Fourier transform infrared (FTIR), near infrared (NIR), and hyperspectral imaging data were mathematically pretreated before the development of partial least square and principal component analysis regression models. The developed partial least squares regression and principal component analysis models demonstrated predictability of CBZ and CBZ-DH by multiple scattering correction and standard normal variate processing methods. Among the spectroscopic techniques used the model performance parameters such as root-mean-square error, standard error, and bias were found to be low for NIR compared to FTIR. The treated data have shown better model fitting than without treatment, which was demonstrated by correlation coefficient of 0.9778, 0.9824, and 0.9852 for FTIR, NIR, and hyperspectral imaging, respectively. Furthermore, the predicted values were found to be very close to the selected low level of independent samples having 5% CBZ-DH in tablet formulation. [ABSTRACT FROM AUTHOR]

Phenytoin sodium (PS) has a tendency to convert to its base form; phenytoin base (PHT) during manufacturing, packaging, shelf life and in-use conditions that can influence its clinical performance. The objective of the present work was to develop a non-destructive, quick and easy analytical method for quantification of PHT in the drug product. A formulation was prepared to contain the excipients of commercial capsule formulation of PS. The formulation containing either 100% PHT or PS was prepared and these formulations were mixed in different proportion to achieve 0–100% PHT matrices. FTIR, NIR and Raman spectra of samples were collected. Data were truncated and mathematically pretreated before development of partial least squares (PLS) and principal component analysis (PCA) regressions model. The models were assessed by slope, intercept, R, R 2 , root mean square error (RMSE) and standard error (SEP). The models exhibited good linearity over the selected range of PHT in the formulations with low error as indicated by slope that was close to one and small values of intercept, RMSE and SE. The models of NIR based data were more accurate and precise than Raman data based models as indicated by the low values of RMSE and SE. Prediction accuracy of independent samples containing 25% PHT using NIR models were similar to Raman models. On the other hand, the prediction was more precise for the independent sample containing 5% PHT using NIR data based models compared to Raman data based models as indicated by standard deviation. In conclusion, chemometric models based on NIR and Raman spectroscopies provides a fast and easy way to monitor the disproportionation of PS in the drug products. [ABSTRACT FROM AUTHOR]

Variations in the solid state form of a pharmaceutical solid have profound impact on the product quality and clinical performance. Quantitative models that allow rapid and accurate determination of polymorphic changes in pharmaceutical products are essential in ensuring product quality throughout its lifecycle. This study reports the development and validation of chemometric models of Raman and near infrared spectroscopy (NIR) for quantifying the extent of pseudopolymorphic transitions of theophylline in extended release formulations. The chemometric models were developed using sample matrices consisting of the commonly used excipients and at the ratios in commercially available products. A combination of scatter removal (multiplicative signal correction and standard normal variate) and derivatization (Savitzky-Golay second derivative) algorithm were used for data pretreatment. Partial least squares and principal component regression models were developed and their performance assessed. Diagnostic statistics such as the root mean square error, correlation coefficient, bias and Q 2 were used as parameters to test the model fit and performance. The models developed had a good fit and performance as shown by the values of the diagnostic 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 slightly better than their corresponding NIR model. The Raman and NIR chemometric models developed had good accuracy and precision as demonstrated by closeness of the predicted values for the independent observations to the actual TMO content hence the developed models can serve as useful tools in quantifying and controlling solid state transitions in extended release theophylline products. [ABSTRACT FROM AUTHOR]

Clinical performance of an amorphous solid dispersion (ASD) drug product is related to the amorphous drug content because of the greater bioavailability of this form of the drug than its crystalline form. Therefore, it is paramount to monitor the amorphous and the crystalline fractions in the ASD products. The objective of the present investigation was to study the feasibility of using a standardized X-ray powder diffraction (XRPD) in conjunction with chemometric methods to quantitate the amorphous and crystalline fraction of the drug in several tacrolimus ASD products. Three ASD products were prepared in which drug to excipients ratios ranged from 1:19 to 1:49. The amorphous and crystalline drug products were mixed in various proportions so that amorphous/crystalline tacrolimus in the samples vary from 0 to 100%. XRPD of the samples of the drug products were collected, and PLSR and PCR chemometric methods were applied to the data. The R 2 was greater than ‘0.987’ for all the models and bias in the models were statistically insignificant ( p > 0.05). RMSEP and SEP values were smaller for PLSR models than PCR models. The models prediction capabilities were good and can predict as low as 10% when drug to excipient ratio is as high as 1:49. In summary, XRPD and chemometric provide powerful analytical tools to monitor the crystalline fractions of the drug in the ASD products. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop powder X-ray diffraction ( XRPD) chemometric model for quantifying crystalline tacrolimus from solid dispersion ( SD). Three SDs (amorphous tacrolimus component) with varying drug to excipient ratios (24.4%, 6.7%, and 4.3% drug) were prepared. Placebo SDs were mixed with crystalline tacrolimus to make their composition equivalent to three SD (crystalline tacrolimus component). These two components were mixed to cover 0%-100% of crystalline drug. Uniformity of the sample mixtures was confirmed by near-infrared chemical imaging. XRPD showed three distinct peaks of crystalline drug at 8.5°, 10.3°, and 11.2° (2 θ), which were nonoverlapping with the excipients. Principal component regressions ( PCR) and partial least square (PLS) regression used in model development showed high R2 (>0.99) for all the mixtures. Overall, the model showed low root mean square of standard error, standard error, and bias, which was smaller in PLS than PCR-based model. Furthermore, the model performance was evaluated on the formulations with known percentage of crystalline drug. Model-calculated crystalline drug percentage values were close to actual value. Therefore, these studies strongly suggest the application of chemometric- XRPD models as a quality control tool to quantitatively predict the crystalline drug in the formulation. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 103:2819-2828, 2014 [ABSTRACT FROM AUTHOR]

The objective of the present research was to study the feasibility of using near-infrared ( NIR) and Fourier transform infrared ( FTIR)-based chemometric models in quantifying crystalline and amorphous tacrolimus from its sustained-release amorphous solid dispersion ( ASD). ASD contained ethyl cellulose, hydroxypropyl methyl cellulose, and lactose monohydrate as carriers, and amorphous form of tacrolimus in it was confirmed by X-ray powder diffraction. Crystalline physical mixture was mixed with ASD in various proportions to prepare sample matrices containing 0%-100% amorphous/crystalline tacrolimus. NIR and FTIR of the samples were recorded, and data were mathematically pretreated using multiple scattering correction, standard normal variate, or Savitzky- Golay before multivariate analysis, partial-least-square regression ( PLSR), and principle component regression ( PCR). The PLSR models were more accurate than PCR for NIR and FTIR data as indicated by low value of root-mean-squared error of prediction, standard error of prediction and bias, and high value of R2. Additionally, NIR data-based models were more accurate and precise than FTIR data models. In conclusion, NIR chemometric models provide simple and fast method to quantitate crystalline tacrolimus in the ASD formulation. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 103:2376-2385, 2014 [ABSTRACT FROM AUTHOR]

The objective of this study was to assess the performance of the chemometric model to predict the proportion of the recrystallized polymorphs of nimodipine from the cosolvent formulations. Ranging from 100% to 0% (w/w) of polymorph I, the two polymorphs mixtures were prepared and characterized spectroscopically using Fourier transformed infrared spectroscopy ( FTIR), near-infrared spectroscopy ( NIR), and Raman spectroscopy. Instrumental responses were treated to construct multivariate calibration model using principal component regression ( PCR) and partial least square regression approaches. Treated data showed better model fitting than without treatment, which demonstrated higher correlation coefficient ( R2) and lower root mean square of standard error ( RMSE) and standard error ( SE). Multiple scattering correction and standard normal variate exhibited higher R2 and lower RMSE and SE values than second derivative. Goodness of fit for FTIR and NIR ( R2 ∼ 0.99) data was better than Raman ( R2 ∼ 0.95). Furthermore, the models were applied on the recrystallized polymorphs obtained by storing nimodipine-cosolvent formulations at selected stability conditions. The relative composition of the polymorphs differed with storage conditions. NIR-chemical imaging on recrystallized sample of nimodipine at 15°C qualitatively corroborated the model-based prediction of the two polymorphs. Therefore, these studies strongly suggest the importance of the potential utility of the chemometric model in predicting nimodipine polymorphs. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:4024-4035, 2013 [ABSTRACT FROM AUTHOR]

Abstract: The focus of present investigation was to evaluate the tannic acid (TA) complexes of cholorpheniramine maleate (CPM) and characterize it by a variety of physicochemical, dissolution, and electronic tongue methods. The complexes were prepared in various molar ratios by solvent evaporation method. They were characterized by spectroscopic, thermal, powder X-ray, electronic tongue, solubility and dissolution methods. FTIR (infrared red) spectra showed complex formation between the TA and CPM. Complex formation has significantly lowered the drug solubility and sustained its release for more than 24h in phosphate buffer pH 6.8. On the contrary, the release was much faster in the presence of Avicel PH 113 in the same molar ratio complex. The complex formulation has suppressed the bitter taste of CPM as indicated by Euclidean distance in electronic tongue evaluation. NIR-CI (near infrared chemical imaging) showed lower skew value that indicated the homogenous distribution of formulation components. The chemometric models were also developed using the NIR data. The model based on second derivative data was better in predicting the TA and CPM loading as indicated by higher values of R, R 2 and lower values of root mean square error and standard errors. Furthermore, it has a better accuracy and less biased in comparison to other models. In conclusion, the CPM tannate has a sustained release behavior and excipients play a major role in modifying its release. Additionally, the complexes with varying molar ratio of tannate to CPM have differential taste masking abilities than that of the pure drug. [Copyright &y& Elsevier]

Abstract: Different destructive and nondestructive analytical methods, namely powder X-ray diffractometry (PXRD), differential scanning calorimetry (DSC), Raman and near-infrared (NIR) spectroscopy and imaging, to detect and characterize tacrolimus trace crystallinity in an amorphous solid dispersion (SD) using chemometric analysis were developed. The SD was spiked with different percentages of the crystalline drug to construct an array of SDs with different crystallinity percentages. Partial least square (PLS) regression analysis was employed to compare the performance of the calibration models created using these analytical methods. The obtained results indicated a significant interaction between tacrolimus and the employed polymer and a drug dissolution dependency on the crystalline fraction within the SDs. Using two PLS factors, these analytical methods were ranked according to its specificity to detect the trace crystallinity of SDs as NIR>PXRD>Raman>DSC. Through the application of PLS, root-mean-squared error of calibration values of 2.91%, 5.36%, 7.07% and 11.58% were calculated for the calibration models constructed by NIR, PXRD, Raman and DSC, respectively. Having a prediction error of 2.1% and a correlation coefficient of 0.99, it is demonstrated that combined NIR imaging and chemometric analysis outperformed the other methods in detecting trace crystallinity in tacrolimus amorphous systems. The spatial distributions of amorphous and crystalline drug were also obtained in order to allow for studying the crystallization dissemination in the solid dispersions. Consequently, NIR and NIR imaging coupled with chemometry was shown to be a powerful tool for the prediction of drug crystallinity within SDs. [Copyright &y& Elsevier]

Quantification analysis with near-infrared (NIR) spectroscopy typically requires utilizing chemometric techniques, such as partial least squares (PLS) method, to achieve the desired selectivity. This article points out a major limitation of these statistical-based calibration methods. The limitation is that the techniques suffer from the potential for chance correlation. In this article, the impact of chance correlation on the robustness of PLS model was illustrated via a pharmaceutical application with NIR to the content uniformity determination of tablets. The procedure involves evaluating the PLS models generated with two sets of calibration tablets incorporated with distinct degree of concentration correlation between the active pharmaceutical ingredient (API) and excipients. The selectivity and robustness of the two models were examined by using a series of data sets associated with placebo tablets and tablets incorporated with variations from excipient content, hardness and particle size. The result clearly revealed that the strong correlation observed in the PLS model created by the correlated design was not solely based on the API information, and there was an intrinsic difference in the variances described by the two calibration models. Diagnostic tools that enable the characterization of the chemical selectivity of the calibration model were also proposed for pharmaceutical quantitative analysis. © 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:1155–1166, 2009 [ABSTRACT FROM AUTHOR]

Terahertz (THz) spectroscopy and chemometric analysis of resultant absorption spectra in the 30–500 cm-1 range has been applied to perform quantitative determination of both active ingredient and excipient concentrations of tablets. Tests were performed on a series of tablets composed of various concentrations and processes of theophylline formulated with lactose, magnesium stearate, starch or Avicel, and as a function of tablet hardness. Transmission spectra of polyethylene pellets derived from each of the samples were analyzed using three approaches. Spectral superposition method was used as an indirect measure to examine whether and when the interaction among various pharmaceutical components and the tableting history could be considered insignificant for quantification purpose. Spectral characteristic peak method was able to correlate peak maxima with correction for tablets having the same hardness. Multivariate analysis (PCR and PLS 1) was capable of correlating THz spectra with tablet concentrations. The predicted concentrations of independent samples using multivariate models agreed well with nominal concentrations. The best correlations were obtained using multivariate analysis. With these studies, the advantage of using multivariate approach was demonstrated for process analytical technology (PAT) application. Further, the feasibility of integrating THz spectroscopy and chemometrics for the purpose of quantifying pharmaceutical tablet concentrations was demonstrated. © 2007 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97: 958–971, 2008 [ABSTRACT FROM AUTHOR]

The objective of this work was to understand the product variability due to size and other characteristics of the SNEDDS by utilizing near infrared (NIR) and chemometric analysis, as well as several other well-known procedures. Pseudo-ternary phase diagrams were constructed to identify the efficient self-emulsification region using CyA solutions in sweet orange oil (oily phase), Emulphor EL-620 (surfactant), and Capmul MCM-C8 (cosurfactant). The formulated SNEDDS were characterized by droplet size, turbidity, zeta potential, and Fourier transform infrared (FTIR) analysis. Drug release studies were performed by dissolution in conjunction with turbidimetry. Permeability studies were performed in a Franz diffusion cell assembly. The results indicated an optimum surfactant to cosurfactant ratio of 2:1. Above this ratio, the resultant nanoemulsions had a particle size of 10 nm and turbidity of 10 nephlometric units (NTU). All the prepared systems were positively charged. The FTIR spectra and the DSC thermograms obtained showed no incompatibility between the SNEDDS ingredients. Turbidity time profiles revealed three distinctive regions: lag phase, plateau, and pseudolinear phase. Emulsification rate was obtained from the corrected slope of the pseudolinear phase of the profile. Permeability data indicated that the product variability is more with smaller droplet size. The size of the droplets showed good correlation with NIR spectral data by partial least square (PLS) regression plots. In conclusion, this study demonstrated the ability to understand the impact of nanodroplets size on the SNEDDS variability by different product analyzing tools. © 2007 Wiley-Liss, Inc. and the American Pharmaceutical Association J Pharm Sci 96: 2409–2423, 2007 [ABSTRACT FROM AUTHOR]