Application of FT-NIR Analysis for In-line and Real-Time Monitoring of Pharmaceutical Hot Melt Extrusion: a Technical Note

Continuous manufacturing, a gaining interest paradigm in the pharmaceutical industry, requires in-process monitoring of critical process parameters to ensure product consistency. This study demonstrated the application of Fourier transform near-infrared (FT-NIR) spectroscopy in conjunction with chemometrics modeling for in-line hot melt extrusion process monitoring. The obtained results suggested that inline FT-NIR analysis, along with a tailored NIR reflector, is a viable process analytical tool to monitor active pharmaceutical ingredient concentration as well as processing parameters.     

Polymorph Transformation in Paracetamol Monitored by In-line NIR Spectroscopy During a Cooling Crystallization Process

The reliable in-line monitoring of pharmaceutical processes has been regarded as a key tool toward the full implementation of process analytical technology. In this study, near-infrared (NIR) spectroscopy was examined for use as an in-line monitoring method of the paracetamol cooling crystallization process. The drug powder was dissolved in ethanol-based cosolvent at 60°C and was cooled by 1°C/min for crystallization. NIR spectra acquired by in-line measurement were interpreted by principal component analysis combined with off-line characterizations via X-ray diffraction, optical microscopy, and transmission electron microscopy. The whole crystallization process appeared to take place in three steps. A metastable form II polymorph of paracetamol was formed and transformed into the stable form I polymorph on the way to the growth of pure form I by cooling crystallization. These observations are consistent with a previous focused beam reflectance method-based study (Barthe et al., Cryst Growth Des 8:3316–3322, 2008).     

Next generation fluidized bed granulator automation

A system for fluidized bed granulator automation with in-line multichannel near infrared (NIR) moisture measurement and a unique air flow rate measurement design was assembled, and the information gained was investigated. The multivariate process data collected was analyzed using principal component analysis (PCA). The test materials (theophylline and microcrystalline cellulose) were granulated and the calibration behavior of the multichannel NIR set-up was evaluated against full Fourier Transform (FT) NIR spectra. Accurate and reliable process air flow rate measurement proved critical in controlling the granulation process. The process data describing the state of the process was projected in two dimensions, and the information from various trend charts was outlined simultaneously. The absorbence of test material at correction wavelengths (NIR region) and the nature of material-water interactions affected the detected in-line NIR water signal. This resulted in different calibration models for the test materials. Development of process analytical methods together with new data visualization algorithms creates new tools for in-process control of the fluidized bed granulation.     

Assessment of near-infrared spectral information for rapid monitoring of bioprocess quality.

Access to real-time process information is desirable for consistent and efficient operation of bioprocesses. Near-infrared spectroscopy (NIRS) is known to have potential for providing real-time information on the quantitative levels of important bioprocess variables. However, given the fact that a typical NIR spectrum encompasses information regarding almost all the constituents of the sample matrix, there are few case studies that have investigated the spectral details for applications in bioprocess quality assessment or qualitative bioprocess monitoring. Such information would be invaluable in providing operator-level assistance on the progress of a bioprocess in industrial-scale productions. We investigated this aspect and report the results of our investigation. Near-infrared spectral information derived from scanning unprocessed culture fluid (broth) samples from a complex antibiotic production process was assessed for a data set that incorporated bioprocess variations. Principal component analysis was applied to the spectral data and the loadings and scores of the principal components studied. Changes in the spectral information that corresponded to variations in the bioprocess could be deciphered. Despite the complexity of the matrix, near-infrared spectra of the culture broth are shown to have valuable information that can be deconvoluted with the help of factor analysis techniques such as principal component analysis (PCA). Although complex to interpret, the loadings and score plots are shown to offer potential in process diagnosis that could be of value in the rapid assessment of process quality, and in data assessment prior to quantitative model development.     

Comparative determination of polymorphs of indomethacin in powders and tablets by chemometrical near-infrared spectroscopy and X-ray powder diffractometry

The purpose of this research was to develop a rapid chemometrical method based on near-infrared (NIR) spectroscopy to determine indomethacin (IMC) polymorphic content in mixed pharmaceutical powder and tablets. Mixed powder samples with known polymorphic contents of forms α and γ were obtained from physical mixing of 50% of IMC standard polymorphic sample and 50% of excipient mixed powder sample consisting of lactose, corn starch, and hydroxypropyl-cellulose. The tablets were obtained by compressing the mixed powder at 245 MPa. X-ray powder diffraction profiles and NIR spectra were recorded for 6 kinds of standard materials with various polymorphic contents. The principal component regression analysis was performed based on normalized NIR spectra sets of mixed powder standard samples and tablets. The relationships between the actual and predicted polymorphic contents of form g in the mixed powder measured using x-ray powder diffraction and NIR spectroscopy show a straight line with a slope of 0.960 and 0.995, and correlation coefficient constants of 0.970 and 0.993, respectively. The predicted content values of unknown samples by x-ray powder diffraction and NIR spectroscopy were reproducible and in close agreement, but those by NIR spectroscopy had smaller SDs than those by x-ray powder diffraction. The results suggest that NIR spectroscopy provides a more accurate quantitative analysis of polymorphic content in pharmaceutical mixed powder and tablets than does conventional x-ray powder diffractometry.     

Evaluation of Three Approaches for Real-Time Monitoring of Roller Compaction with Near-Infrared Spectroscopy

Three different approaches have been evaluated for monitoring ribbon density through real-time near-infrared spectroscopy measurements. The roll compactor was operated to produce microcrystalline cellulose (MCC) ribbons of varying densities. The first approach used the slope of the spectra which showed a variation through the ribbon that could be attributed to density. A second qualitative approach was also developed with a principal component analysis (PCA) model with spectra taken in-line during the production of ribbons in an ideal roll pressure range. The PCA (i.e., real-time) density scans show that the model was able to qualitatively capture the density responses resulting from variation in process parameters. The third approach involved multivariate partial least squares (PLS) calibration models developed at wavelength regions of 1,120–1,310 and 1,305–2,205 nm. Also, various PLS models were developed using three reference methods: caliper, pycnometer, and in-line laser. The third approach shows a quantitative difference between the model-predicted and the measured densities. Models developed at high-wavelength region showed highest accuracy compared with models at low-wavelength region. All the PLS models showed a high accuracy along the spectra collected throughout the production of the ribbons. The three methods showed applicability to process control monitoring by describing the changes in density during in-line sampling.     

Acoustic-resonance spectrometry as a process analytical technology for the quantification of active pharmaceutical ingredient in semi-solids

The purpose of this study was to demonstrate acoustic resonance spectrometry (ARS) as an alternative process analytical technology to near infrared (NIR) spectroscopy for the quantification of active pharmaceutical ingradient (API) in semi-solids such as creams, gels, ointments, and lotions. The ARS used for this research was an inexpensive instrument constructed from readily available parts. Acoustic-resonance spectra were collected with a frequency spectrum from 0 to 22.05 KHz. NIR data were collected from 1100 to 2500 nm. Using 1-point net analyte signal (NAS) calibration, NIR for the API (colloidal oatmeal [CO]) gave anr ² prediction accuracy of 0.971, and a standard error of performance (SEP) of 0.517%CO. ARS for the API resulted in anr ² of 0.983 and SEP of 0.317%CO. NAS calibration is compared with principal component regression. This research demonstrates that ARS can sometimes outperform NIR spectrometry and can be an effective analytical method for the quantification of API in semi-solids. ARS requires no sample preparation, provides larger penetration depths into lotions than optical techniques, and measures API concentrations faster and more accurately. These results suggest that ARS is a useful process analytical technology (PAT). Published: July 14, 2006     

Integration of distributed multi-analyzer monitoring and control in bioprocessing based on a real-time expert system

A computer system solution for integration of a distributed bioreactor monitoring and control instrumentation on the laboratory scale is described. Bioreactors equipped with on-line analyzers for mass spectrometry, near-infrared spectroscopy, electrochemical probes and multi-array gas sensors and their respective software were networked through a real-time expert systems platform. The system allowed data transmission of more than 1800 different signals from the instrumentation, including signals from gas sensors, electrodes, spectrometer detectors, balances, flowmeters, etc., and were used for processing and carrying out a number of computational tasks such as partial least-square regression, principal component analysis, artificial neural network modelling, heuristic decision-making and adaptive control. The system was demonstrated on different cultivations/fermentations which illustrated sensor fusion control, multivariate statistical process monitoring, adaptive glucose control and adaptive multivariate control. The performance of these examples showed high operational stability and reliable function and meet typical requirements for production safety and quality.     

Application of Near-Infrared Spectroscopy in Real-Time Monitoring of Product Attributes of Ribbed Roller Compacted Flakes

This study assessed the utility of near-infrared (NIR) spectroscopy for the real-time monitoring of content uniformity and critical quality attributes (tensile strength, Young’s modulus, and relative density) of ribbed roller compacted flakes made by axially corrugated or ribbed rolls. A custom-built setup was used to capture off-line NIR spectra from the flakes containing micronized chlorpheniramine maleate, microcrystalline cellulose, lactose, and magnesium stearate. The partial least square regression method was employed to build calibration models from these off-line NIR spectra using experimental design and validated using test set validation. During calibration model development, various factors, such as spectral acquisition mode, probe positioning, spectral preprocessing method, and beam size, were investigated to improve the prediction ability of the models. The statistical results obtained for calibration models and their validation revealed that dynamic spectral acquisition and proper probe positioning were very crucial to minimize the incorporation of variability in NIR spectra resulting from the flake’s undulation. Calibration and validation statistics also suggested the importance of selecting appropriate spectral preprocessing method and beam size. In this study, best calibration models resulted from standard normal variate followed by first derivative preprocessed dynamic spectra captured using beam size ~1.2 mm. Best calibration models constructed from off-line NIR spectra were used in real-time analysis of flake attributes. Finally, adequacy of best calibration models was established from real-time prediction results. Overall, with the proposed setup, it was possible to monitor the roller compaction process in real time for various properties associated with the ribbed flakes in a rapid, efficient, and nondestructive manner.     

Quantitative analysis of film coating in a pan coater based on in-line sensor measurements

A method was developed that enables in-line analysis of film coating thickness on tablets during a pan coating operation. Real-time measurements were made using a diffusereflectance near-infrared (NIR) probe positioned inside the pan during the coating operation. Real-time spectra of replicate batches were used for modeling film growth. Univariate analysis provided a simple method for in-line monitoring of the coating process using NIR data. An empirical geometric 2-vector volumetric growth model was developed, which accounts for differential growth on the face and band regions of biconvex tablets. The thickness of the film coat was determined by monitoring the decrease of absorption bands characteristic of a component of the tablet core and monitoring the increase of bands characteristic of a component in the coating material. There was good correlation between values estimated from the NIR data and the measured tablet volumetric growth. In-line measurements allow the coating process to be stopped when a predetermined tablet coating thickness is achieved. Published: September 20, 2005     

Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis

A method for simultaneous, nondestructive analysis of aminopyrine and phenacetin in compound aminopyrine phenacetin tablets with different concentrations has been developed by principal component artificial neural networks (PC-ANNs) on near-infrared (NIR) spectroscopy. In PC-ANN models, the spectral data were initially analyzed by principal component analysis. Then the scores of the principal components were chosen as input nodes for the input layer instead of the spectral data. The artificial neural network models using the spectral data as input nodes were also established and compared with the PC-ANN models. Four different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV), and multiplicative scatter correction) were applied to three sets of NIR spectra of compound aminopyrine phenacetin tablets. The PC-ANNs approach with SNV preprocessing spectra was found to provide the best results. The degree of approximation was performed as the selective criterion of the optimum network parameters.     

PAT-Based Control of Fluid Bed Coating Process Using NIR Spectroscopy to Monitor the Cellulose Coating on Pharmaceutical Pellets

Current endeavor was aimed towards monitoring percent weight build-up during functional coating process on drug-layered pellets. Near-infrared (NIR) spectroscopy is an emerging process analytical technology (PAT) tool which was employed here within quality by design (QbD) framework. Samples were withdrawn after spraying every 15-Kg cellulosic coating material during Wurster coating process of drug-loaded pellets. NIR spectra of these samples were acquired using cup spinner assembly of Thermoscientific Antaris II, followed by multivariate analysis using partial least squares (PLS) calibration model. PLS model was built by selecting various absorption regions of NIR spectra for Ethyl cellulose, drug and correlating the absorption values with actual percent weight build up determined by HPLC. The spectral regions of 8971.04 to 8250.77 cm^?1, 7515.24 to 7108.33 cm^?1, and 5257.00 to 5098.87 cm^?1 were found to be specific to cellulose, where as the spectral region of 6004.45 to 5844.14 cm^?1was found to be specific to drug. The final model gave superb correlation co-efficient value of 0.9994 for calibration and 0.9984 for validation with low root mean square of error (RMSE) values of 0.147 for calibration and 0.371 for validation using 6 factors. The developed correlation between the NIR spectra and cellulose content is useful in precise at-line prediction of functional coat value and can be used for monitoring the Wurster coating process.     

Chemometric evaluation of pharmaceutical properties of antipyrine granules by near-infrared spectroscopy

The purpose of this research was to apply near-infrared (NIR) spectroscopy with chemometrics to predict the change of pharmaceutical properties of antipyrine granules during granulation by regulation of the amount of water added. The various kinds of granules (mean particle size, 70–750 μm) were obtained from the powder mixture (1 g of antipyrine, 6 g of hydroxypropylcellulose, 140 g of lactose, and 60 g of potato starch) by regulation of the added water amount (11–19 wt/wt%) in a high-speed mixer. The granules were characterized by mean particle size, angle of repose, compressibility, tablet porosity, and tablet hardness as parameters of pharmaceutical properties. To predict the pharmaceutical properties, NIR spectra of the granules were measured and analyzed by principal component regression, (PCR) analysis. The mean particle size of the granules increased from 81 μm to 650 μm with an increase in the amount of water, and it was possible to make larger spherical granules with narrow particle size distribution using a high-speed mixer. Angle of repose, compressibility, and porosity of the tablets decreased with an increase of added water, but tablet hardness increased. The independent calibration models to evaluate particle size, angle of repose, and tablet porosity and hardness were established by using PCR based on NIR spectra of granules, respectively. The correlation coefficient constants of calibration curves for prediction of mean particle size, angle of repose, tablet porosity, and tablet hardness were 0.9109, 0.8912, 0.7437, and 0.8064, respectively. It is possible that the pharmaceutical properties of the granule, such as mean particle size, angle of repose, tablet porosity, and tablet hardness, could be predicted by an NIR-chemometric method.     

In-Line Monitoring of a High-Shear Granulation Process Using the Baseline Shift of Near Infrared Spectra

Although near infrared (NIR) spectra are primarily influenced by undesired variations, i.e., baseline shifts and non-linearity, and many applications of NIR spectroscopy to the real-time monitoring of wet granulation processes have been reported, the granulation mechanisms behind these variations have not been fully discussed. These variations of NIR spectra can be canceled out using appropriate pre-processing techniques prior to spectral analysis. The present study assessed the feasibility of directly using baseline shifts in NIR spectra to monitor granulation processes, because such shifts can reflect changes in the physical properties of the granular material, including particle size, shape, density, and refractive index. Specifically, OPUSGRAN^®, a novel granulation technology, was investigated by in-line NIR monitoring. NIR spectra were collected using a NIR diffuse reflectance fiber optic probe immersed in a high-shear granulator while simultaneously examining the morphology, particle size, density, strength, and Raman images of the mixture during granulation. The NIR baseline shift pattern was found to be characteristic of the OPUSGRAN^® technology and was attributed to variations in the light transmittance, reflection, and scattering resulting from changes in the physicochemical properties of the samples during granulation. The baseline shift also exhibited an inflection point around the completion of granulation, and therefore may be used to determine the endpoint of the process. These results suggest that a specific pattern of NIR baseline shifts are associated with the unique OPUSGRAN^® granulation mechanism and can be applied to monitor the manufacturing process and determine the endpoint.     

Monitoring of a Hot Melt Coating Process via a Novel Multipoint Near-Infrared Spectrometer

The aim of the present work was to develop a PAT strategy for the supervision of hot melt coating processes. Optical fibers were placed at various positions in the process chamber of a fluid bed device. Experiments were performed to determine the most suitable position for in-line process monitoring, taking into account such requirements as a good signal to noise ratio, the mitigation of dead zones, the ability to monitor the product over the entire process, and reproducibility. The experimental evidence suggested that the position at medium fluid bed height, looking towards the center, i.e., normal to particle movement, proved to be the most reliable position. In this study, the advantages of multipoint monitoring are shown, and an in-line-implementation was created. This enabled the real-time supervision of the process, including the fast detection of inhomogeneities and disturbances in the process chamber, and the compensation of sensor malfunction. In addition, a model for estimating the particle size distribution via NIR was successfully created. This ensures that the quality of the product and the endpoint of the coating process can be determined correctly.     

Multivariate statistical monitoring of batch processes: an industrial case study of fermentation supervision

This article describes the development of Multivariate Statistical Process Control (MSPC) procedures for monitoring batch processes and demonstrates its application with respect to industrial tylosin biosynthesis. Currently, the main fermentation phase is monitored using univariate statistical process control principles implemented within the G2 real-time expert system package. This development addresses integrating various process stages into a monitoring system and observing interactions among individual variables through the use of multivariate projection methods. The benefits of this approach will be discussed from an industrial perspective.     

Pellet manufacturing by extrusion-spheronization using process analytical technology

The aim of this study was to investigate the phase transitions occurring in nitrofurantoin and theophylline formulations during pelletization by extrusion-spheronization. An at-line process analytical technology (PAT) approach was used to increase the understanding of the solid-state behavior of the active pharmaceutical ingredients (APIs) during pelletization. Raman spectroscopy, near-infrared (NIR) spectroscopy, and X-ray powder diffraction (XRPD) were used in the characterization of polymorphic changes during the process. Samples were collected at the end of each processing stage (blending, granulation, extrusion, spheronization, and drying). Batches were dried at 3 temperature levels (60°C, 100°C, and 135°C). Water induced a hydrate formation in both model formulations during processing. NIR spectroscopy gave valuable real-time data about the state of water in the system, but it was not able to detect the hydrate formation in the theophylline and nitrofurantoin formulations during the granulation, extrusion, and spheronization stages because of the saturation of the water signal. Raman and XRPD measurement results confirmed the expected pseudopolymorphic changes of the APIs in the wet process stages. The relatively low level of Raman signal with the theophylline formulation complicated the interpretation. The drying temperature had a significant effect on dehydration. For a channel hydrate (theophylline), dehydration occurred at lower drying temperatures. In the case of isolated site hydrate (nitrofurantoin), dehydration was observed at higher temperatures. To reach an understanding of the process and to find the critical process parameters, the use of complementary analytical techniques are absolutely necessary when signals from APIs and different excipients overlap each other. Published: September 30, 2005     

Investigation of the Variability of NIR In-line Monitoring of Roller Compaction Process by Using Fast Fourier Transform (FFT) Analysis

The purpose of this research was to investigate the variability of the roller compaction process while monitoring in-line with near-infrared (NIR) spectroscopy. In this paper, a pragmatic method in determining this variability of in-line NIR monitoring roller compaction process was developed and the variability limits were established. Fast Fourier Transform (FFT) analysis was used to study the source of the systematic fluctuations of the NIR spectra. An off-line variability analysis method was developed as well to simulate the in-line monitoring process in order to determine the variability limits of the roller compaction process. For this study, a binary formulation was prepared composed of acetaminophen and microcrystalline cellulose. Different roller compaction parameters such as roll speed and feeding rates were investigated to understand the variability of the process. The best-fit line slope of NIR spectra exhibited frequency dependence only on the roll speed regardless of the feeding rates. The eccentricity of the rolling motion of rollers was identified as the major source of variability and correlated with the fluctuations of the slopes of NIR spectra. The off-line static and dynamic analyses of the compacts defined two different variability of the roller compaction; the variability limits were established. These findings were proved critical in the optimization of the experimental setup of the roller compaction process by minimizing the variability of NIR in-line monitoring.     

Discriminating pulmonary hypertension caused by monocrotaline toxicity from chronic hypoxia by near-infrared spectroscopy and multivariate methods of analysis

A new method has been developed for the determination of tissue pathology caused by chronic hypoxia and monocrotaline toxicity. The method is based on the use of near-infrared (NIR) spectrophotometry to measure spectra of lung tissue from normal chronic hypoxia (CH) and monocrotaline (MCT) models of pulmonary hypertension (PH), followed by analysis using multivariate methods, that is, principal component analysis (PCA) and partial least squares (PLS). Synergistic use of NIR with the PCA/PLS method makes it possible, for the first time, not only to divide different lung tissue samples into their respective groups (normal, CH, and MCT) but also to gain insight into mechanisms of PH caused by CH and MCT toxicity. Specifically, MCT metabolites and other hypertensive conditions are known to produce subtle and minor chemical changes in the compositions of tissue (e.g., proteins, carbohydrates, lipids). Although these changes were detected by the NIR technique, they were too small to be discerned through visual inspection of the spectra. However, they can be accurately classified and properly assigned by the PCA/PLS method. The fact that different tissue types can be accurately divided into their corresponding groups by the NIR and PCA/PLS methods suggests that chemical alterations and mechanisms of pulmonary vascular remodeling and PH induced by MCT are different from those induced by CH.     

Controlled Porosity Solubility Modulated Osmotic Pump Tablets of Gliclazide

A system that can deliver drug at a controlled rate is very important for the treatment of various chronic diseases such as diabetes, asthma, and heart disease. Poorly water-soluble drug with pH-dependent solubility such as gliclazide (GLZ) offers challenges in the controlled-release formulation because of low dissolution rate and poor bioavailability. Solid dispersion (SD) of GLZ consisted of hydroxypropyl cellulose (HPC-SSL) as a polymeric solubilizer was manufactured by hot melt extrusion (HME) technology. Then, controlled porosity osmotic pump (CPOP) tablet of gliclazide was designed to deliver drug in a controlled manner up to 16 h. The developed formulation was optimized for type and level of pore former and coating weight gain. The optimized formulation was found to exhibit zero order kinetics independent of pH and agitation speed but depends on osmotic pressure of dissolution media indicated that mechanism of drug release was osmotic pressure. The in vivo performance prediction of developed formulation using convolution approach revealed that the developed formulation was superior to the existing marketed extended-release formulation in terms of attaining steady state plasma levels and indicated adequate exposure in translating hypoglycemic response. The prototype solubilization method combined with controlled porosity osmotic pump based technique could provide a unique way to increase dissolution rate and bioavailability of many poorly water-soluble, narrow therapeutic index drugs used in diabetes, cardiovascular diseases, etc.KEY WORDS: convolution approach, gliclazide, hot melt extrusion (HME), hydroxypropyl cellulose, solid dispersion