Use of FT-NIR transmission spectroscopy for the quantitative analysis of an active ingredient in a translucent pharmaceutical topical gel formulation.

The objective of this study was to demonstrate the use of transmission Fourier transform near-infrared (FT-NIR) spectroscopy for quantitative analysis of an active ingredient in a translucent gel formulation. Gels were prepared using Carbopol 980 with 0%, 1%, 2%, 4%, 6%, and 8% ketoprofen and analyzed with an FT-NIR spectrophotometer operated in the transmission mode. The correlation coefficient of the calibration was 0.9996, and the root mean squared error of calibration was 0.0775%. The percent relative standard deviation for multiple measurements was 0.10%. The results prove that FT-NIR can be a good alternative to other, more time-consuming means of analysis for these types of formulations.

Prediction of drug content and hardness of intact tablets using artificial neural network and near-infrared spectroscopy.

The purpose of this study was to predict drug content and hardness of intact tablets using artificial neural networks (ANN) and near-infrared spectroscopy (NIRS). Tablets for the drug content study were compressed from mixtures of Avicel PH-101, 0.5% magnesium stearate, and varying concentrations (0%, 1%, 2%, 5%, 10%, 20%, and 40% w/w) of theophylline. Tablets for the hardness study were compressed from mixtures of Avicel PH-101 and 0.5% magnesium stearate at varying compression forces ranging from 0.4 to 1 ton. An Intact Analyzer was used to obtain near infrared spectra from the tablets with varying drug contents, whereas a Rapid Content Analyzer (RCA) was used to obtain spectral data from the tablets with varying hardness. Two sets of tablets from each batch (i.e., tablets with varying drug content and hardness) were randomly selected. One set of tablets was used to generate appropriate calibration models, while the other set was used as the unknown (test) set. A total of 10 ANN calibration models (5 each with 10 and 160 inputs at appropriate wavelengths) and five separate 4-factor partial least squares (PLS) calibration models were generated to predict drug contents of the test tablets from the spectral data. For the prediction of tablet hardness, two ANN calibration models (one each with 10 and 160 inputs) and two 4-factor PLS calibration models were generated and used to predict the hardness of test tablets. The PLS calibration models were generated using Vision software. Prediction of drug contents of test tablets using the ANN calibration models generated with 10 inputs was significantly better than the prediction obtained with the ANN calibration models with 160 inputs. For tablets with low drug concentrations (less than or equal to 2% w/w) prediction of drug content was better with either of the two ANN calibration models than with the PLS calibration models. However, prediction of drug contents of tablets with greater than or equal to 5% w/w drug was better with the PLS calibration models than with the ANN calibration models. Prediction of tablet hardness was better with the ANN calibration models generated with either 10 or 160 inputs than with the PLS calibration models. This work demonstrated that a well-trained ANN model is a powerful alternative technique for analysis of NIRS data. Moreover, the technique could be used in instances when the conventional modeling of data does not work adequately.

A comparison of reflectance and transmittance near-infrared spectroscopic techniques in determining drug content in intact tablets.

Drug contents of intact tablets were determined using non-destructive near infrared (NIR) reflectance and transmittance spectroscopic techniques. Tablets were compressed from blends of Avicel PH-101 and 0.5% w/w magnesium stearate with varying concentrations of anhydrous theophylline (0, 1, 2, 5, 10, 20 and 40% w/w). Ten tablets from each drug content batch were randomly selected for spectral analysis. Both reflectance and transmittance NIR spectra were obtained from these intact tablets. Actual drug contents of the tablets were then ascertained using a UV-spectrophotometer at 268 nm. Multiple linear regression (MLR) models at 1116 nm and partial least squares (PLS) calibration models were generated from the second derivative spectral data of the tablets in order to predict drug contents of intact tablets. Both the reflectance and the transmittance techniques were able to predict the drug contents in intact tablets over a wide range. However, a comparison of the results of the study indicated that the lowest percent errors of prediction were provided by the PLS calibration models generated from spectral data obtained using the transmittance technique.

Low-level determination of polymorph composition in physical mixtures by near-infrared reflectance spectroscopy.

Near-infrared reflectance spectroscopy (NIRS) was employed to quantify sulfathiazole (STZ) forms I and III in binary physical mixtures in which one form was the dominant component. Physical mixtures of the polymorph pair were made by weight, ranging from 0 to 5% STZ form I mixed with STZ form III, and near-infrared spectra of the powder samples contained in glass vials were obtained over the wavelength region of 1100 to 2500 nm. A calibration plot was constructed by plotting STZ form I weight percent against a ratio of second-derivative values of log (1/R') (where R' is the relative reflectance) versus wavelength. The coefficients of determination, R(2), were > 0.9983 and standard errors were low for these calibration models. The instrument reproducibility, method error, and limits of detection (LOD) and quantification (LOQ) of the method were assessed. The LOD and LOQ were determined from the standard deviation of the response of the 0% analyte sample (0% STZ form I containing 100% STZ form III). The LOQ was subsequently validated with independently prepared samples. The results show that polymorphs can be quantified in binary physical mixtures in the 0.3% polymorph composition range. These studies indicate that NIRS is a precise and accurate quantitative tool for determination of polymorphs in the solid state, is comparable to other characterization techniques, and is more convenient to use than many other methods.

Quantitative analysis of polymorphs in binary and multi-component powder mixtures by near-infrared reflectance spectroscopy.

Near-infrared reflectance spectroscopy was employed to quantify polymorphs in binary and multi-component powder mixtures. Sulfamethoxazole (SMZ) forms I and II were used as model polymorphs for this study. The instrument reproducibility, method error, precision, and limits of detection and quantification of the method were assessed. Physical mixtures of the polymorph pair were made by weight, ranging from 0 to 100% SMZ form I in II. Near-infrared spectra of the powder samples contained in glass vials were obtained over the wavelength region of 1100-2500 nm. A calibration plot was constructed by plotting SMZ form I weight percent against a ratio of second derivative values of log(1/R') (where R' is the relative reflectance) versus wavelength. The coefficients of determination, R(2), were generally greater than 0.9997 and standard errors were low for all the systems. Instrument error was assessed by analyzing a sample 10 times without perturbation. Method error was assessed in the same manner except the sample was re-mixed between analyses. A precision study was conducted by analyzing aliquots from a larger homogeneous sample. Limits of detection (LOD) and quantification (LOQ) were determined from the standard deviation of the response of the blank samples (100% SMZ form II, undiluted or diluted with 60% lactose). These limits were subsequently validated with independent samples. The results show that polymorphs can be quantified in binary and multi-component mixtures in the 2% polymorph composition range. These studies indicate that NIRS is a precise and accurate quantitative tool for determination of polymorphs in the solid-state, is comparable to other characterization techniques, and is more convenient to use than many other methods.

Application of diffuse reflectance near-infrared spectroscopy for determination of crystallinity.

Studies were conducted to investigate the use of near-infrared spectroscopy (NIRS) for determining degree of crystallinity. Physical mixtures of amorphous/crystalline indomethacin and amorphous/crystalline sucrose were prepared over several composition ranges. Spectra were obtained on powder samples contained in glass vials using diffuse reflectance sampling. Parallel studies were conducted using X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC) for comparison. NIRS standard curves were constructed by plotting crystalline weight percent against the ratio of responses at two wavelengths or by partial least squares regression. NIRS standard curves demonstrated higher coefficients of determination and lower standard errors than either XRPD or DSC. Validation standards confirmed the accuracy of NIRS over XRPD. Method error analysis demonstrated comparable accuracy for NIRS and XRPD, with NIRS showing slightly better precision in repeated crystallinity determinations for a 50% crystalline sucrose sample. Interpretive analysis of the NIRS spectra was performed using neutron scattering and polarized Raman spectroscopy data obtained from the literature. Results indicated that the NIRS differences between crystalline and amorphous sucrose may be attributed to the disruption of regular vibrational modes when crystalline sucrose is rendered amorphous.

Quantifying crystalline form composition in binary powder mixtures using near-infrared reflectance spectroscopy.

The objectives of this study were to assess the utility of near-infrared reflectance spectroscopy (NIRS) in differentiating crystalline forms of pharmaceutical materials and determine the accuracy of this technique in quantifying crystalline forms of solids in binary mixtures. Various crystalline forms of sulfamethoxazole, sulfathiazole, lactose, and ampicillin, independently characterized with other methods, were analyzed qualitatively and quantitatively. The observed differences in near-infrared (NIR) spectra of crystalline form pairs were interpretable on the basis of the features of their crystalline and molecular structures and mid-infrared spectra. NIR spectra of binary physical mixtures of crystalline form pairs were obtained directly through glass vials over the wavelength range of 1100-2500 nm. The calibration lines were constructed using an inverted least-squares regression method. The ratio of the response of the second derivative of the reflectance spectra at two wavelengths was plotted versus crystal form composition. The correlation coefficients for plots of predicted versus theoretical composition were generally greater than 0.99 and standard errors were all low. Parallel studies comparing the NIRS method to a quantitative x-ray powder diffraction method using sulfamethoxazole and sulfathiazole confirmed the accuracy of the results. Additional NIRS studies were conducted in the 0-10% composition range with ampicillin and sulfamethoxazole. These results indicated that prediction down to the 1% level was possible. This study demonstrates that NIRS can be used as a quantitative physical characterization method, is comparable in accuracy to other techniques, and is capable of detecting low levels of one crystal form in the presence of another.

Diffuse reflectance near-infrared spectroscopy as a nondestructive analytical technique for polymer implants.

A near-infrared spectroscopic method to quantify drugs or excipients within polymeric matrixes is proposed. Cylindrical implants were fabricated by a melt-mold technique containing various ratios of poly(epsilon-caprolactone) (PCL) and poly(ethylene glycol) (PEG) and various loadings of lomefloxacin HCl with a constant ratio (70:30 w/w) of PCL/PEG. Near-infrared (NIR) spectra were obtained on intact sections of larger implants using a Foss NIRSystems Model 5000 monochrometer equipped with a Rapid Content Analyzer. Spectral data were treated with second derivative transformation followed by linear regression and PLS to obtain correlation with lomefloxacin or PEG content. Lomefloxacin content was separately determined by UV analysis (287 nm) using a validated extraction procedure. The NIR method was tested by comparing predicted loadings of test implants with either theoretical values based on weight (PEG) or with UV analysis results (lomefloxacin). Second derivative spectral values at particular wavelength ratios (PEG, 2064 nm/1698 nm; lomefloxacin, 2172 nm/2226 nm and 1824 nm/1862 nm) yielded linear results for PEG or lomefloxacin content. PEG content determined by NIR spectroscopy was in excellent agreement with theoretical content. Lomefloxacin content determined by NIR spectroscopy was also in excellent agreement with UV analysis. NIR analysis is interpreted through the use of corresponding mid-infrared spectral data.

Application of near-infrared spectroscopy for nondestructive analysis of Avicel powders and tablets.

The purpose of this study was to use near-infrared spectroscopy (NIRS) as a nondestructive technique to (a) differentiate three Avicel products (microcrystalline cellulose [MCC] PH-101, PH-102, and PH-200) in powdered form and in compressed tablets with and without 0.5% w/w magnesium stearate as a lubricant; (b) determine the magnesium stearate concentrations in the tablets; and (c) measure hardness of tablets compressed at several compression forces. Diffuse reflectance NIR spectra from Avicel powders and tablets (compression forces ranging from 0.2 to 1.2 tons) were collected and distance scores calculated from the second-derivative spectra were used to distinguish the different Avicel products. A multiple linear regression model was generated to determine magnesium stearate concentrations (from 0.25 to 2% w/w), and partial least squares (PLS) models were generated to predict hardness of tablets. The NIRS technique could distinguish between the three different Avicel products, irrespective of lubricant concentration, in both the powdered form and in the compressed tablets because of the differences in the particle size of the Avicel products. The percent error for predicting the lubricant concentration of tablets ranged from 0.2 to 10% w/w. The maximum percent error of prediction of hardness of tablets compressed at the various compression forces was 8.8% for MCC PH-101, 5.3% for MCC PH-102, and 4.6% for MCC PH-200. The NIRS nondestructive technique can be used to predict the Avicel type in both powdered and tablet forms as well as to predict the lubricant concentration and hardness.