Quality control of pharmaceuticals with NIR: From lab to process line

This work describes a general framework for assessing the active pharmaceutical ingredient (API) and excipient concentrations simultaneously in pharmaceutical dosage forms based on laboratory-scale measurements. The work explores the comprehensive development of a near infrared (NIR) analytical protocol for the quantification of the API and excipients of a pharmaceutical formulation. The samples were based on a paracetamol (API) formulation with three excipients: microcrystalline cellulose, talc, and magnesium stearate. The developed method was based on laboratory-scale samples as calibration samples and pilot-scale samples (powders and tablets) as model test samples. Both types of samples were produced according to an experimental design. The samples were measured in reflectance mode in a Fourier-transform NIR spectrometer. Additionally, a new method for determining the minimum number of calibration samples was proposed. It was concluded that the use of laboratory-scale samples to construct the calibration set is an effective way to ensure the concentration variability in the development of calibration models for industrial applications. With this method, both API and excipients can be determined in high-throughput applications in the pharmaceutical industry.     

Development of a univariate calibration model for pharmaceutical analysis based on NIR spectra

Near-infrared spectroscopy (NIRS) has been widely used in the pharmaceutical field because of its ability to provide quality information about drugs in near-real time. In practice, however, the NIRS technique requires construction of multivariate models in order to correct collinearity and the typically poor selectivity of NIR spectra. In this work, a new methodology for constructing simple NIR calibration models has been developed, based on the spectrum for the target analyte (usually the active principle ingredient, API), which is compared with that of the sample in order to calculate a correlation coefficient. To this end, calibration samples are prepared spanning an adequate concentration range for the API and their spectra are recorded. The model thus obtained by relating the correlation coefficient to the sample concentration is subjected to least-squares regression. The API concentration in validation samples is predicted by interpolating their correlation coefficients in the straight calibration line previously obtained. The proposed method affords quantitation of API in pharmaceuticals undergoing physical changes during their production process (e.g. granulates, and coated and non-coated tablets). The results obtained with the proposed methodology, based on correlation coefficients, were compared with the predictions of PLS1 calibration models, with which a different model is required for each type of sample. Error values lower than 1-2% were obtained in the analysis of three types of sample using the same model; these errors are similar to those obtained by applying three PLS models for granules, and non-coated and coated samples. Based on the outcome, our methodology is a straightforward choice for constructing calibration models affording expeditious prediction of new samples with varying physical properties. This makes it an effective alternative to multivariate calibration, which requires use of a different model for each type of sample, depending on its physical presentation.     

Development of a rapid process monitoring method for dry-coated tableting process by using near-infrared spectroscopy

A nondestructive transmittance near-infrared (NIR) method for detecting off-centered cores in dry-coated (DC) tablets was developed as a monitoring system in the DC tableting process. Caffeine anhydrate was used as a core active pharmaceutical ingredient (API), and DC tablets were made by the direct compression method. NIR spectra were obtained from these intact DC tablets using the transmittance method. The reference assay was performed with HPLC. Calibration models were generated by partial least squares (PLS) regression and principal component regression (PCR) utilizing external validations. Hierarchical cluster analysis (HCA) of the results confirmed that NIR spectroscopy correctly detected off-centered cores in DC tablets. We formulated and used the Centering Index (CI) to evaluate the precision of core alignment and generated an NIR calibration model that could correctly predict this index. The principal component (PC) 1 loading vector of the final calibration model indicated that it could specifically detect the misalignment of tablet cores. The model also had good linearity and accuracy. The CIs of unknown sample tablets predicted by the final calibration model and those calculated through the HPLC analysis were closely parallel with each other. These results demonstrate the validity of the final calibration model and the utility of the transmittance NIR spectroscopic method developed in this study as a monitoring system in DC tableting process.     

Robust calibrations on reduced sample sets for API content prediction in tablets: Definition of a cost-effective NIR model development strategy

Owing to spectral variations from other sources than the component of interest, large investments in the NIR model development may be required to obtain satisfactory and robust prediction performance. To make the NIR model development for routine active pharmaceutical ingredient (API) prediction in tablets more cost-effective, alternative modelling strategies were proposed. They used a massive amount of prior spectral information on intra- and inter-batch variation and the pure component spectra to define a clutter, i.e., the detrimental spectral information. This was subsequently used for artificial data augmentation and/or orthogonal projections. The model performance improved statistically significantly, with a 34–40% reduction in RMSEP while needing fewer model latent variables, by applying the following procedure before PLS regression: (1) augmentation of the calibration spectra with the spectral shapes from the clutter, and (2) net analyte pre-processing (NAP). The improved prediction performance was not compromised when reducing the variability in the calibration set, making exhaustive calibration unnecessary. Strong water content variations in the tablets caused frequency shifts of the API absorption signals that could not be included in the clutter. Updating the model for this kind of variation demonstrated that the completeness of the clutter is critical for the performance of these models and that the model will only be more robust for spectral variation that is not co-linear with the one from the property of interest.     

Development and validation of a near infrared method for the analytical control of a pharmaceutical preparation in three steps of the manufacturing process

A near infrared diffuse reflectance spectroscopy (NIRS) procedure for the quantitative control analysis of the active compound (otilonium bromide) in a pharmaceutical preparation in three steps of the production process (blended product, cores and coated tablets) and a methodology for its validation are proposed. The analytical procedure is composed by two consecutive steps. First, the sample is identified by comparing its spectrum with a second derivative spectral library. If the sample is positively identified, the active compound is quantified by using a previously established partial least squares (PLS) calibration model. The procedure was validated by studying repeatability, intermediate precision, accuracy and linearity. To this end, an adaptation of ICH (International Conference on Harmonisation) validation methodology to an NIR multivariate calibration procedure is proposed. The relative standard error of prediction (RSEP) was < or = 1% and the suitability of the procedure for control analysis was confirmed by the results obtained analysing new production samples produced over a three-month period.     

Development and validation of a near infrared method for the analytical control of a pharmaceutical preparation in three steps of the manufacturing process

A near infrared diffuse reflectance spectroscopy (NIRS) procedure for the quantitative control analysis of the active compound (otilonium bromide) in a pharmaceutical preparation in three steps of the production process (blended product, cores and coated tablets) and a methodology for its validation are proposed. The analytical procedure is composed by two consecutive steps. First, the sample is identified by comparing its spectrum with a second derivative spectral library. If the sample is positively identified, the active compound is quantified by using a previously established partial least squares (PLS) calibration model. The procedure was validated by studying repeatability, intermediate precision, accuracy and linearity. To this end, an adaptation of ICH (International Conference on Harmonisation) validation methodology to an NIR multivariate calibration procedure is proposed. The relative standard error of prediction (RSEP) was ≤ 1% and the suitability of the procedure for control analysis was confirmed by the results obtained analysing new production samples produced over a three-month period.     

Quantification of paracetamol in intact tablets using near-infrared transmittance spectroscopy.

Production batch samples of paracetamol tablets and specially prepared out-of-specification batches covering the range 90-110% of the stated amount (500 mg) were analysed by the BP official UV assay and by NIR transmittance spectroscopy. NIR measurements were made on 20 intact tablets from each batch, scanned five times each (10 min measurement time per batch) over the spectral range 6000-11,520 cm-1. An average spectrum was calculated for each batch. Partial least squares (PLS) regression models were set up using a calibration set (20 batches) between the NIR response and the reference tablet paracetamol content (UV). Various pre-treatments of the spectra were examined; the smallest relative standard error of prediction (0.73%) was obtained using the first derivative of the absorbance over the full spectrum. Only two principal components were required for the PLS model to give a good relationship between the spectral information and paracetamol content. Applying this model to the validation set (15 batches) gave a mean bias of -0.08% and a mean accuracy of 0.59% with relative standard deviations of 0.75 and 0.44%, respectively. The proposed method is non-destructive and therefore lends itself to on-line/at-line production control purposes. The method is easy to use and does not require a knowledge of the mass of the tablets.     

A rapid quantitative assay of intact paracetamol tablets by reflectance near-infrared spectroscopy

Near-infrared (NIR) reflectance spectroscopy was used to determine rapidly and non-destructively the content of paracetamol in bulk batches of intact Sterwin 500 mg tablets by collecting NIR spectra in the range 1100-2500 nm and using a multiple linear regression calibration method. The developed NIR method gave results comparable to the British Pharmacopoeia 1993 UV assay procedure, the standard errors of calibration and prediction being 0.48% and 0.71% m/m, respectively. The method showed good repeatability, the standard deviation and coefficient of variation for six NIR assays on the same batch on the same day being 0.14 and 0.16% m/m, respectively, while measurements over six consecutive days gave 0.31 and 0.36% m/m, respectively. Applying the calibration to a parallel test set gave a mean bias of -0.22% and a mean accuracy of 0.45%. The developed method illustrates how the full potential of NIR can be utilised and how the ICH guidelines which recommend the validation of linearity, range, accuracy and precision for pharmaceutical registration purposes can be applied. Duplicate determinations on bulk batches could be performed in under 2 min, allowing the potential use of the method on-line for real time monitoring of a running production process.     

Classification and quantification analysis of Radix scutellariae from different origins with near infrared diffuse reflection spectroscopy

A rapid near infrared spectroscopy analysis method was developed for the geographical origin discrimination and content determination of Radix scutellariae, a kind of Traditional Chinese Medicine (TCM). 81 R. scutellariae samples from six different origins were analyzed with HPLC-UV as reference method. The NIR spectra were collected in integrating-sphere diffused reflection mode and processed with different spectra pretreated methods. Discriminant analysis (DA) and discriminant partial least squares (DPLS) were applied to classify the geographical origins of those samples, and the latter had a better predictive ability with 100% accuracy after two exceptional samples eliminated from the calibration set. For the quantitative calibration, the samples were divided into calibration set and validation set by Kennard-Stone algorithm. The models of baicalin, wogonoside, baicalein, wogonin were established with partial least squares (PLS) algorithm and the optimal principal component (PC) numbers were selected with Leave-One-Out (LOO) cross-validation. The established models were evaluated with the root mean square error of prediction (RMSEP) and corresponding correlation coefficients. The correlation coefficients of all the four calibration models are above 0.920, and the RMSEPs of baicalin, wogonoside, baicalein and wogonin are 0.752%, 0.094%, 0.418% and 0.139%, respectively. This research indicated that the NIR diffuse reflection spectroscopy could be used for the rapid analysis of R. scutellariae, which is beneficial to the quality control of this raw material in TCM pharmaceutical factory, and will also help to solve analogous problems.     

Content uniformity determination of pharmaceutical tablets using five near-infrared reflectance spectrometers: a process analytical technology (PAT) approach using robust multivariate calibration transfer algorithms

Near-infrared calibration models were developed for the determination of content uniformity of pharmaceutical tablets containing 29.4% drug load for two dosage strengths (X and Y). Both dosage strengths have a circular geometry and the only difference is the size and weight. Strength X samples weigh approximately 425 mg with a diameter of 12 mm while strength Y samples, weigh approximately 1700 mg with a diameter of 20 mm. Data used in this study were acquired from five NIR instruments manufactured by two different vendors. One of these spectrometers is a dispersive-based NIR system while the other four were Fourier transform (FT) based. The transferability of the optimized partial least-squares (PLS) calibration models developed on the primary instrument (A) located in a research facility was evaluated using spectral data acquired from secondary instruments B, C, D and E. Instruments B and E were located in the same research facility as spectrometer A while instruments C and D were located in a production facility 35 miles away. The same set of tablet samples were used to acquire spectral data from all instruments. This scenario mimics the conventional pharmaceutical technology transfer from research and development to production. Direct cross-instrument prediction without standardization was performed between the primary and each secondary instrument to evaluate the robustness of the primary instrument calibration model. For the strength Y samples, this approach was successful for data acquired on instruments B, C, and D producing root mean square error of prediction (RMSEP) of 1.05, 1.05, and 1.22%, respectively. However for instrument E data, this approach was not successful producing an RMSEP value of 3.40%. A similar deterioration was observed for the strength X samples, with RMSEP values of 2.78, 5.54, 3.40, and 5.78% corresponding to spectral data acquired on instruments B, C, D, and E, respectively. To minimize the effect of instrument variability, calibration transfer techniques such as piecewise direct standardization (PDS) and wavelet hybrid direct standardization (WHDS) were used. The PDS approach, the RMSEP values for strength X samples were lowered to 1.22, 1.12, 1.19, and 1.08% for instruments B, C, D, and E, respectively. Similar improvements were obtained using the WHDS approach with RMSEP values of 1.36, 1.42, 1.36, and 0.98% corresponding to instruments B, C, D, and E, respectively.     

A new strategy to iteratively update scalable universal quantitative models for the testing of azithromycin by near infrared spectroscopy

The training set of a universal near infrared (NIR) model for quantitative analysis of a drug should cover as many samples of this drug in the market as possible. Inevitably the model may fail for new products that have different excipients and production processes. In such circumstances the model should be updated. We here propose a new strategy to iteratively update a universal NIR quantitative model for azithromycin. We prove that universal quantitative models generated from this new strategy are comparably effective for azithromycin injection powders and azithromycin tablets, compared to the strategy using hierarchical clustering method which we reported previously. Furthermore, we establish the correlation coefficient r between a new sample and the training set samples can be used to decide whether or not the model should be updated.     

The use of FT-NIR for API content assay in organic solvent media: a single calibration for multiple downstream processing streams

The use of near infrared spectroscopy (NIRS) in downstream solvent based processing steps of an active pharmaceutical ingredient (API) is reported. A single quantitative method was developed for API content assessment in the organic phase of a liquid–liquid extraction process and in multiple process streams of subsequent concentration and depuration steps. A new methodology based in spectra combinations and variable selection by genetic algorithm was used with an effective improvement in calibration model prediction ability. Root mean standard error of prediction (RMSEP) of 0.05 in the range of 0.20–3.00% (w/w) was achieved. With this method, it is possible to balance the calibration data set with spectra of desired concentrations, whenever acquisition of new spectra is no longer possible or improvements in model's accuracy for a specific selected range are necessary. The inclusion of artificial spectra prior to genetic algorithms use improved RMSEP by 10%. This method gave a relative RMSEP improvement of 46% compared with a standard PLS of full spectral length.     

Near infrared spectroscopy in the study of polymorphic transformations

The potential of near infrared (NIR) spectroscopy for the characterization of polymorphs in the active principle of a commercial formulation prior to and after the manufacturing process was assessed. Polymorphism in active principles is extremely significant to the pharmaceutical industry. Polymorphic changes during the production of commercial pharmaceutical formulations can alter some properties of the resulting end-products. Multivariate curve resolution-alternating least squares (MCR-ALS) methodology was used to obtain the “pure” NIR spectrum for the active principle without the need to pretreat samples. This methodology exposed the polymorphic transformation of Dexketoprofen Trometamol (DKP) in both laboratory and production samples obtained by wet granulation. No polymorphic transformation, however, was observed in samples obtained by direct compaction. These results were confirmed using by X-ray powder diffractometry (XRD) and differential scanning calorimetry (DSC) measurements. Pure crystalline polymorphs of DKP were available in the laboratory but amorphous form was not, nevertheless the developed methodology allows the identification of amorphous and crystal forms in spite of the lack of pure DKP.     

Reflectance near-infrared spectroscopic method with Chemometric techniques for simultaneous determination of Chondroitin, glucosamine, and methyl sulfonyl methane

Reflectance near-IR (RNIR) spectroscopy was used for the simultaneous determination of chondroitin (CH), glucosamine (GO), and methyl sulfonyl methane (MSM) in tablets. Simple sample preparation was done by grinding, sieving, and compression of the tablets for improving RNIR spectra. Principal component regression and partial least squares (PLS-1 and PLS-2) were successfully applied to quantify the three components in the studied mixture using information included in RNIR spectra in the range of 4350-9100 cm(-1). The calibration model was developed with drug concentration ranges of 14.5-44.2% (w/w) for CH, 18.4-55.3% (w/w) for GO, and 6-18.6% (w/w) for MSM with addition of tablet excipients to the calibration set in the same ratio as in the tested tablets. The calibration models were evaluated by internal validation, cross-validation, and external validation using synthetic and pharmaceutical preparations. The proposed method was applied for analysis of six batches of the pharmaceutical product. The results of the proposed method were compared with the results of the pharmacopoeial method for the same batch of the pharmaceutical product. No significant differences between the results were found. The RNIR method is accurate and precise, and can be used for QC of pharmaceutical products.     

Content uniformity of pharmaceutical solid dosage forms by near infrared hyperspectral imaging: A feasibility study

Near-infrared imaging systems simultaneously record spectral and spatial information. Each measurement generates a data cube containing several thousand spectra. Chemometric methods are therefore required to extract qualitative and quantitative information. The aim of this study was to determine the feasibility of quantifying active pharmaceutical ingredient (API) and excipient content in pharmaceutical formulations using hyperspectral imaging.Two kinds of tablets with a range of API content were analysed: a binary mixture of API and cellulose, and a pharmaceutical formulation with seven different compounds. Two pixel sizes, 10 μm/pixel and 40 μm/pixel, were compared, together with two types of spectral pretreatment: standard normal variate (SNV) normalization and Savitzky-Golay smoothing. Two methods of extracting concentrations were compared: the partial least squares 2 (PLS2) algorithm, which predicts the content of several compounds simultaneously, and the multivariate classical least squares (CLS) algorithm based on pure compound reference spectra without calibration.Best content predictions were achieved using 40 μm/pixel resolution and the PLS2 method with SNV normalized spectra. However, the CLS method extracted distribution maps with higher contrast and was less sensitive to noisy spectra and outliers; its API predictions were also highly correlated to real content, indicating the feasibility of predicting API content using hyperspectral imaging without calibration.     

Nir-chemical imaging study of acetylsalicylic acid in commercial tablets

Near Infrared Chemical Imaging (NIR-CI) is demonstrating an increasing interest in pharmaceutical research since it meets the challenging analytical needs of pharmaceutical quality and may serve as a versatile adjunct to conventional NIR spectroscopy in many fields.The direct analysis of samples by using hyperspectral imaging techniques, which provide a NIR spectrum in each pixel of the image, generates a big amount of information from one sample. Focusing the interest in pharmaceutical research, several chemometric algorithms are demonstrating their usefulness extracting the relevant information (i.e. quantitative determination of the component in one sample) in tablets with only one sample and without damaging it.In this work, a quantitative method to analyze different commercial Acetylsalicylic acid tablets is proposed by using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) method to the hyperspectral image and without any previous calibration model. For this purpose, a large concentration range of active pharmaceutical ingredient (ASA, Acetylsalicylic acid in this work), between 82% and 12%, was covered depending on the manufacturer. MCR-ALS allowed obtaining a concentration maps for acetylsalicylic acid and therefore, consequent analysis of the ASA distribution in the tablet was developed by using the histograms of the distribution of concentration.Results certified the good distribution of ASA despite the different origins of the tablets. Moreover, the obtained values of concentration showed a very good concordance with the nominal value of ASA. As a matter of fact, the quality of the results demonstrated the useful of encompassing NIR-CI techniques with MCR-ALS and, consequently, the well development on the production of Acetylsalicylic acid tablets.     

Comparison of near infrared and microwave resonance sensors for at-line moisture determination in powders and tablets

In this paper we demonstrate the feasibility of replacing KF for water content testing in bulk powders and tablets with at-line near infrared (NIR) or microwave resonance (MR) methods. Accurate NIR and MR prediction models were developed with a minimalistic approach to calibration. The NIR method can accurately predict water content in bulk powders in the range of 0.5-5% w/w. Results from this method were compared to a MR method. We demonstrated excellent agreement of both NIR and MR methods for powders vs. the reference KF method. These methods are applicable to in-process control or quality control environments. One of the aims of this study was to determine if a calibration developed for a particular product could be used to predict the water content of another product (with related composition) but containing a different active pharmaceutical ingredient (API). We demonstrated that, contrary to the NIR method, a general MR method can be used to predict water content in two different types of blends. Finally, we demonstrated that a MR method can be developed for at-line moisture determination in tablets.     

The use of NIR as a multi-parametric in situ monitoring technique in filamentous fermentation systems

The use of Fourier transform near infrared (FT-NIR) spectroscopy for simultaneous determination of multiple properties in an active pharmaceutical ingredient (API) fermentation process is described, together with procedures for developing accurate NIR calibrations with a performance independent of scale and the specific bioreactor used. Measurements were made in situ, by insertion of transflection probes into pilot and industrial bioreactors providing direct contact with the fermentation culture media. The ultimate goal was to establish methods for real time process monitoring aimed at enhanced process supervision, fault detection diagnosis and control of bioreactors. The in situ acquired spectra were related to lab results of samples taken from the reactors during the course of the manufacturing process. Suitable spectral wavenumber regions were selected and calibration models based on partial least squares (PLS) were developed. The root mean square errors of prediction for API content, viscosity, nitrogen source and carbon source concentration were all within acceptable ranges as compared to the off-line lab measurements, respectively, 0.03% (w/w), 150 cp, 0.01% (w/w), and 0.4% (w/w).     

Near-infrared determination of active substance content in intact low-dosage tablets

Near-infrared (NIR) spectroscopy can be applied to determine the active substance content of tablets. Its great advantage lies in the minimal sample preparation required, which helps to reduce the potential for error. The aim of this study is to show the feasibility of this method on low-dosage tablets. The influence of various spectral pretreatments [standard normal variate (SNV), multiplicative scatter correction (MSC), second derivative (D2), orthogonal signal correction (OSC), separately and combined] and regression methods on prediction error are compared. Partial least square (PLS) regression provided better prediction than principal component regression (PCR). SNV was applied to the first data set and SNV and a second derivative to the second set to maximise model accuracy for quantifying the active substance of intact pharmaceutical products using diffuse reflectance NIR. The models yielded standard errors of prediction (SEP) of 0.1768 and 0.0682 mg for the two products. The experiments were conducted with two low-dosage pharmaceutical forms and results of NIR predictions were comparable to currently approved methods. Diffuse reflectance NIR has the potential to become a reliable and robust quality control method for determining active tablet content.     

Characterization of paper finishes by use of infrared spectroscopy in combination with canonical variate analysis

The finishing process used by the paper industry involves subjecting the paper surface to the action of chemicals and physical treatments in a series of operations intended to provide an end-product suitable for its intended use. In this work, we studied various paper finishes by using infrared spectra processed with appropriate chemometric techniques. To this end, we used a wide range of paper samples supplied in various finishes (coated, offset and cast-coated) by several paper manufacturers. Fourier transform middle-infrared (FTIR) spectra for the paper samples were recorded by using an ATR module, and reflectance near-infrared (NIR) spectra with the aid of a fibre-optic probe. Both techniques are fast and require no sample pretreatment.The primary aim of this work was to develop a new methodology affording the accurate classification and identification of paper finishes in samples other than those used to construct the calibration model. To this end, we used the discriminant chemometric techniques principal component analysis (PCA) and canonical variate analysis (CVA), application of which was followed by that of the k-nearest neighbour algorithm to the samples in the prediction set. This procedure was also used to classify the coated samples into three subgroups. Both FTIR and NIR spectroscopy allowed most of the samples in the prediction sets to be accurately classified and identified.