Evaluation of key sources of variability in the measurement of pharmaceutical drug products by near infrared reflectance spectroscopy

Tablet splitting: Product quality assessment of metoprolol succinate extended release tablets

Metoprolol succinate extended release tablets comprise a multiple unit system containing metoprolol succinate in a multitude of controlled release pellets. Each pellet acts as a separate drug delivery unit and is designed to deliver metoprolol continuously over the dosage interval. Despite the flexibility that controlled release pellets may offer, segregation is one of the challenges that commonly occur during tableting for such drug delivery system. Since all commercial metoprolol succinate extended release tablets are scored, they are deemed suitable for splitting. The present study was aimed at utilizing an innovative technology to determine the dose uniformity for split tablets. Four marketed drug products consisting of innovator and generics were evaluated for effect of splitting on weight, assay and content uniformity. Novel analytical tool such as near infrared (NIR) chemical imaging was used to visualize the distribution of metoprolol succinate and functional excipients on the surfaces of the marketed tablets. The non-homogeneous distribution of directly compressed metoprolol succinate beads on the surface of the tablets as well as the split intersection explained the large variation in the split tablets' weight and content uniformity results. The obtained results indicated the usefulness of NIR chemical imaging to determine the need for content uniformity studies for certain split tablets.

Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches

Near-infrared chemical imaging (NIR-CI) is the fusion of near-infrared spectroscopy and image analysis. It can be used to visualize the spatial distribution of the chemical compounds in a sample (providing a chemical image). Each sample measurement generates a hyperspectral data cube containing thousands of spectra. An important part of a NIR-CI analysis is the data processing of the hyperspectral data cube. The aim of this study was to compare the ability of different commonly used calibration methods to generate accurate chemical images. Three common calibration approaches were compared: (1) using single wavenumber, (2) using classical least squares regression (CLS) and (3) using partial least squares regression (PLS1). Each method was evaluated using two different preprocessing methods. A calibration data set of tablets with five constituents was used for analysis. Chemical images of the active pharmaceutical ingredient (API) and the two major excipients cellulose and lactose in the formulation were made. The accuracy of the generated chemical images was evaluated by the concentration prediction ability. The most accurate predictions for all three compounds were generated by PLS1. The drawback of PLS1 is that it requires a calibration data set and CLS, which does not require a calibration data set, therefore proved to be an excellent alternative. CLS also generated accurate predictions and only requires the pure compound spectrum of each constituent in the sample. All three calibration approaches were found applicable for hyperspectral image analysis but their relevance of use depends on the purpose of analysis and type of data set. As expected, the single wavenumber method was primarily found useful for compounds with a distinct spectral band that was not overlapped by bands of other constituents. This paper also provides guidance for hyperspectral image (or NIR-CI) analysis describing each of the typical steps involved.

The non-destructive identification of solid over-the-counter medications using single particle aerosol mass spectrometry

Single over-the-counter medication tablets were analyzed in real time using Single Particle Aerosol Mass Spectrometry (SPAMS). Dual-polarity time-of-flight mass spectra were obtained for micrometer-sized single particles dislodged from a single tablet without destroying the shape or markings of each tablet. The solid tablet was placed in a modified-top glass vial and shaken to dislodge and introduce micrometer-sized particles into the SPAMS system. Unique spectra from these particles were obtained in less than 1 s for single tablets of aspirin, ibuprofen, pseudoephedrine, phenylephrine, loratadine, or diphenhydramine. The signals obtained allowed the non-destructive identification of an individual tablet in seconds. SPAMS presents an ideal system for high-throughput analysis of solid drugs.

Near infrared spectroscopy and process analytical technology to master the process of busulfan paediatric capsules in a university hospital

The prescription of unlicensed oral medicines in paediatrics leads the hospital pharmacists to compound hard capsules, such as busulfan, an alkylating agent prescribed in preparative regimens for bone marrow transplantation. In this study, we have investigated how the general principle of process analytical technology (PAT) can be implemented at the small size of our hospital pharmacy manufacturing unit. Near infrared spectroscopy (NIRS) was calibrated for raw material identification, blend uniformity analysis and final content uniformity of busulfan hard capsules of 11 different strengths. Measurements were performed on capsules from 2 to 40 mg (n=440). After optimisation, accuracy and linearity of the NIRS quantitative method was demonstrated after comparison with a previously validated quantitative high performance thin layer chromatography (HPTLC) method. Such a comparison led to attractive NIRS precision: +/-0.7 to +/-1.0 mg for capsules from 2 to 40 mg, respectively. As NIRS is a rapid and non-destructive technique, the individual control of a whole batch of busulfan paediatric capsules intended to be administrated is possible. Actually, mastering the process of busulfan paediatric capsules with the NIRS integrated into the notion of PAT is a powerful analytical tool to assess the process quality and to perform content uniformity of at least 5mg busulfan-containing capsules.

Quantifying crystal form content in physical mixtures of (+/-)-tartaric acid and (+)-tartaric acid using near infrared reflectance spectroscopy

The objective of this study was to use diffuse reflectance near infrared spectroscopy (NIRS) to determine racemic compound content in physical mixtures composed primarily of the enantiomorph and to assess the error, instrument reproducibility and limits of detection (LOD) and quantification (LOQ) of the method. Physical mixtures ranging from 0 to 25% (+/-)-tartaric acid in (+)-tartaric acid were prepared and spectra of the powder samples contained in glass vials were obtained using a Foss NIRSystems Model 5000 monochrometer equipped with a Rapid Content Analyzer scanning from 1100 to 2500 nm. A calibration curve was constructed by plotting (+/-)-tartaric acid weight percent against the 2(nd) derivative values of log (1/R) vs lambda at a single wavelength, normalized with a denominator wavelength (1480 nm/1280 nm). Excellent linearity was observed (R(2)=0.9999). The standard error of calibration (SEC) was 0.07 and the standard error of prediction (SEP) for the validation set was 0.11. Instrument and method errors for samples in the 2% composition range ((+/-)-tartaric acid in (+)-tartaric acid) were less than 1% RSD and 3% RSD, respectively. The practical LOD and LOQ were 0.1% and 0.5%, respectively, and comparable to the calculated LOD and LOQ. These studies show that NIRS can be used as a rapid and sensitive quantitative method for determining racemate content in the presence of the enatiomerically pure crystal in the solid-state.

The effect of preprocessing methods in reducing interfering variability from near-infrared measurements of creams

This work is part of a study in which the possibility of NIR combined with some chemometrical methods is investigated as a suitable technique to classify clinical study samples of a cream. In this study, the influence of different preprocessing methods on the removal of spectral variations due to some variance sources has been investigated. The applied preprocessing methods are standard normal variate (SNV), detrend correction, offset correction, and first and second derivation. The investigated variance sources are different batches of ingredients, different samples of the same batch, different days and different positions of the sample cup in the sample drawer of the instrument. A nested ANOVA design has been applied in order to quantify the variances introduced by these variance sources. Since ANOVA is a univariate technique, the necessary variable (wavelength) selection has been performed by the Fisher criterion. The best results, i.e. largest reduction of interfering variability and clearest distinction between different clinical study samples, are obtained with the second derivative spectra.

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.

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.

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.