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[Display omitted] Near infrared (NIR) spectroscopy is a valuable analytical technique for monitoring chemical composition of powder blends in continuous pharmaceutical processes. However, the variation in density captured by NIR during spectral collection of dynamic powder streams at different flow rates often reduces the performance and robustness of NIR models. To overcome this challenge, quantitative NIR measurements are commonly collected across all potential manufacturing conditions, including multiple flow rates to account for the physical variations. The utility of this approach is limited by the considerable quantity of resources required to run and analyze an extensive calibration design at variable flow rates in a continuous manufacturing (CM) process. It is hypothesized that the primary variation introduced to NIR spectra from changing flow rates is a change in the density of the powder from which NIR spectra are collected. In this work, powder stream density was used as an efficient surrogate for flow rate in developing a quantitative NIR method with enhanced robustness against process rate variation. A density design space of two process parameters was generated to determine the conditions required to encompass the apparent density and spectral variance from increases in process rate. This apparent density variance was included in calibration at a constant low flow rate to enable the development of a density-insensitive NIR quantitative model with limited consumption of materials. The density-insensitive NIR model demonstrated comparable prediction performance and flow rate robustness to a traditional NIR model including flow rate variation ("gold standard" model) when applied to monitoring drug content in continuous runs at varying flow rates. The proposed platform for the development of in-line density-insensitive NIR methods is expected to facilitate robust analytical model performance across variable continuous manufacturing production scales while improving the material efficiency over traditional robust modeling approaches for calibration development. [ABSTRACT FROM AUTHOR]

The Process Analytical Technology (PAT) initiative, undertaken by the Food and Drug Administration (FDA), paves the way for improvement of drug manufacturing through real-time measurements that allow better process understanding. This study is the third and final Part in a series of studies that represent an integrated approach for real-time blend uniformity assessment using near-infrared (NIR) technology. In this study, the development of a quantitative NIR model for prediction of blending end point is presented. Process signature was built into NIR calibration models by using blend samples that were collected from actual blend experiments under different processing conditions. Evaluation of various calibration algorithms including principal component regression (PCR), partial least squares (PLS), and multi-term linear regression (MLR) was performed. It was found that linear regression, using a single wavelength, yielded optimum calibration and prediction results. The blending profiles predicted by the NIR quantitative model correlated well to those determined by the UV reference analytical method. Characterization of intra-shell versus inter-shell powder mixing kinetics and its implication in sensor positioning was also performed and will be discussed. © 2005 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 95:422–434, 2006 [ABSTRACT FROM AUTHOR]

The development of pharmaceutical nanoformulations has accelerated over the past decade. However, the nano-sized drug carriers continue to meet substantial regulatory and clinical translation challenges. In order to address some of these key challenges in early development, we adopted a quality by design approach to develop robust predictive mathematical models for microemulsion formulation, manufacturing, and scale-up. The presented approach combined risk management, design of experiments, multiple linear regression (MLR), and logistic regression to identify a design space in which microemulsion colloidal properties were dependent solely upon microemulsion composition, thus facilitating scale-up operations. Developed MLR models predicted microemulsion diameter, polydispersity index (PDI), and diameter change over 30 days storage, while logistic regression models predicted the probability of a microemulsion passing quality control testing. A stable microemulsion formulation was identified and successfully scaled up tenfold to 1L without impacting droplet diameter, PDI, or stability. [ABSTRACT FROM AUTHOR]

Refractive index is an important optical parameter that can be used to characterize the physicochemical properties of pharmaceutical solids. The complexity of most drugs and solid oral dosage systems introduces challenges for refractive index measurement methods. These challenges are highlighted, and different types of measurement methods are discussed in this review article. These measurements provide pharmaceutical scientists the opportunity to improve the drug-development process and enhance product quality. Pharmaceutical applications range from identification and quantification of drug crystallinity and polymorphism to mechanical strength assessment of tablets. This review article surveys the literature and evaluates the current and potential future characterization of pharmaceutical solids using refractive index measurements. [ABSTRACT FROM AUTHOR]

Process analytical technology (PAT) has shown great potential for in-line tableting process monitoring. The study focuses on the development and validation of an in-line near-infrared (NIR) spectroscopic method for the determination of content uniformity of blends in a tablet feed frame. An in-line NIR method was developed after careful evaluation of the impact of potential experimental factors on the robustness and model accuracy and precision. The NIR method was validated according to the principles outlined in International Conference on Harmonization–Q2 for validation of analytical procedures and was demonstrated to be suitable for monitoring blend content for the formulation under evaluation. Reliable measurements of blend homogeneity rely on representative sampling. To reach the appropriate scale of scrutiny for a unit dose, the study assessed factors that influence the effective sample size measured by NIR. Spectral averaging, integration time, and feed frame paddle wheel speed were found to influence the effective sample size measured by the NIR probe. The effective sampling size was also estimated by comparing the distribution of predicted values with the reference values. The development of a robust, in-line PAT method was facilitated by thorough understanding of the sensitivity of PAT sensors to factors affecting pharmaceutical processes and products. [ABSTRACT FROM AUTHOR]

The quality of pharmaceutical products is currently evaluated through a series of tests that do not explicitly communicate the clinical consequences of product variability. A previously published risk simulation platform was used to generate quantitative estimates of inefficacy and toxicity for 288 uniform lots of extended-release theophylline tablets displaying various levels of content uniformity and dissolution variability. These data were used to evaluate the univariate specifications utilized in the United States Pharmacopeia (USP) <711> and <905>. Simulation revealed that the specifications are too lenient for content uniformity, both in terms of inefficacy and toxicity, whereas the criteria for dissolution testing are too strict for inefficacy and inaccurate for toxicity. The USP tests also failed to pinpoint the clinical interaction between content uniformity and dissolution variability. Additionally, the simulation platform was used to define the underlying relationship between product quality attributes and clinical performance. Here, content uniformity and Weibull dissolution time constants were used as inputs to the design spaces, which were conditioned on quantitative estimates of inefficacy and toxicity. This methodology enhances the information content of the design space by omitting quality surrogates (e.g., dissolution, moisture content) that are utilized in current design space practices. © 2010 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 100:1566-1575, 2011 [ABSTRACT FROM AUTHOR]

The successful implementation of near-infrared spectroscopy (NIRS) in process control of powder blending requires constructing an inclusive spectral database that reflects the anticipated voluntary or involuntary changes in processing conditions, thereby minimizing bias in prediction of blending behavior. In this study, experimental design was utilized as an efficient way of generating blend experiments conducted under varying processing conditions such as humidity, blender speed and component concentration. NIR spectral data, collected from different blending experiments, was used to build qualitative models for prediction of blend homogeneity. Two pattern recognition algorithms: Soft Independent Modeling of Class Analogies (SIMCA) and Principal Component Modified Bootstrap Error-adjusted Single-sample Technique (PC-MBEST) were evaluated for qualitative analysis of NIR blending data. Optimization of NIR models, for the two algorithms, was achieved by proper selection of spectral processing, and training set samples. The models developed were successful in predicting blend homogeneity of independent blend samples under different processing conditions. © 2005 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 95:407–421, 2006 [ABSTRACT FROM AUTHOR]

Experimental design, multivariate data acquisition, and analysis in addition to real time monitoring and control through process analyzers, represent an integrated approach for implementation of Process Analytical Technology (PAT) in the pharmaceutical industry. This study, which is the first in a series of three parts, uses an experimental design approach to identify critical factors affecting powder blending. Powder mixtures composed of salicylic acid and lactose were mixed in an 8 qt. V-blender. D-optimal design was employed to characterize the blending process, by studying the effect of humidity, component concentration, and blender speed on mixing end point. Additionally, changes in particle size and density of powder mixtures were examined. A near-infrared (NIR) fiber-optic probe was used to monitor mixing, through multiple optical ports on the blender. Humidity, component concentration, and blender speed were shown to have a significant impact on the blending process. Furthermore, humidity and concentration had a significant effect on particle size and density of powder mixtures. NIRS was sensitive to changes in physicochemical properties of the mixtures, resulting from process variables. Proper selection of NIR spectral preprocessing is of ultimate importance for successful implementation of this technology in the monitoring and control of powder blending and is discussed. © 2005 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 95:392–406, 2006 [ABSTRACT FROM AUTHOR]

Film coating of nifedipine tablets is commonly performed to reduce photo-degradation. The coating thickness of these tablets is a primary dictating factor of photo-stability. Terahertz spectroscopy enables accurate measurement of coating thickness. This study identifies a method to determine an end-point of a photo-protective coating process by using coating thickness measurements from terahertz time of flight spectroscopy (THz-TOF). For this method, nifedipine tablets, at different coating thicknesses, were placed in a photostability chamber. The illumination conditions of the coated tablets were adjusted based on the time duration of these tablets inside the chamber. A multiple linear regression model was developed with the coating thickness estimates from THz-TOF and illumination conditions information to predict the amount of drug remaining after photo-degradation (percent label claim). The prediction error of this model was 1.03% label claim in the range of 88.4–100.6% label claim. According to this model, acceptable levels of photo-protection in illumination conditions of up to approximately 700,000 lx hours was achieved at the end of the coating process (approximately 50 µm coating thickness) performed in this study. These results suggest THz-TOF as a viable process analytical technology tool for process understanding and end-point determination of a photo-protective coating process. [ABSTRACT FROM AUTHOR]

This study utilized multiple modeling approaches to predict immediate release tablet dissolution profiles of 2 model drugs: theophylline and carbamazepine. Two sets of designs of experiments were applied based on individual drug characteristics to build in adequate dissolution variability. The tablets were scanned using a near-infrared (NIR) spectrometer and then subjected to in vitro dissolution test at critical time points. Because of the inherent difference in dissolution profiles, a hierarchical modeling approach was applied for theophylline data, whereas global models were constructed from carbamazepine data. The partial least squares models were trained using 3 predictor sets including (1) formulation, material, and process variables, (2) NIR spectra, and (3) a combination of both. The dependent variables of the models were the dissolution profiles, which were presented either as parameters of Weibull fitting curves or raw data. The comparison among the predictive models revealed that the incorporation of NIR spectral information in calibration reduced prediction error in the carbamazepine case but undermined the performance of theophylline models. It suggests that the modeling strategy for dissolution prediction of pharmaceutical tablets should not be universal but on a case-by-case basis. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Raman spectral variations of the pharmaceutical materials were observed due to the presence of water. • Spectral baseline change caused spectral inconsistency across calibration and test samples which degraded model performance. • Including moisture variations into the calibration set significantly improved Raman spectroscopic model performance. Abstract To develop a robust quantitative calibration model for spectroscopy, different sources of variability that are not directly related to the components of interest should be included in the calibration samples; this variability should be similar to that which is anticipated during validation and routine operation. Moisture content of pharmaceutical samples can vary as a function of supplier, storage conditions, geographic origin or seasonal variation. Additionally, some pharmaceutical operations (e. g., wet granulation) cause exposure of excipients and API to water. Although water is a weak Raman scatterer, moisture variability has an indirect effect on analytical model performance. Because many pharmaceutical components have intrinsic fluorescent characteristics (with broad spectral features), moisture variability may cause spectral artifacts in the form of baseline variation associated with fluorescence quenching. This work investigates the deleterious effects of water quenching on quantitative prediction accuracy of a multivariate calibration algorithm for Raman spectroscopy. To demonstrate this, a formulation composed of acetaminophen, lactose, microcrystalline cellulose, HPMC and magnesium stearate was used. Tablets were manufactured using laboratory scale equipment. A full-factorial design was used to vary acetaminophen (5 levels), and excipient ratios (3 levels) to generate tablets for calibration and testing. Tablet moisture variation was introduced by placing samples in different humidity chambers. Significant spectral effects arising from fluorescence were identified in the Raman spectra and due to moisture variation: the fluorescence related spectral variability caused substantial degradation of the prediction performance for API. The work demonstrated that accounting for moisture variation during method development reduced the prediction error of the multivariate prediction model. [ABSTRACT FROM AUTHOR]

Abstract Accurate assessment of tablet content uniformity is critical for narrow therapeutic index drugs such as phenytoin sodium. This work presents a near-infrared (NIR)-based analytical method for rapid prediction of content uniformity based on a large number of phenytoin sodium formulation tablets. Calibration tablets were generated through an integrated experimental design by varying formulation and process parameters, and scale of manufacturing. A partial least squares model for individual tablet content was developed based on tablet NIR spectra. The tablet content was obtained from a modified United States Pharmacopeia phenytoin sodium high-performance liquid chromatography assay method. The partial least squares model with 4 latent variables explained 92% of the composition variability and yielded a root mean square error of prediction of 0.48% w/w. The resultant NIR model successfully assayed the composition of tablets manufactured at the pilot scale. For one such batch, bootstrapping was applied to calculate the confidence intervals on the mean, acceptance value, and relative SD for different sample sizes, n = 10, 30, and 100. As the bootstrap sample size increased, the confidence interval on the mean, acceptance value, and relative SD became narrower and symmetric. Such a 'large N' NIR-based process analytical technology method can increase reliability of quality assessments in solid dosage manufacturing. [ABSTRACT FROM AUTHOR]

Near-infrared chemical imaging (NIR-CI) combines spectroscopy with digital imaging, enabling spatially resolved analysis and characterization of pharmaceutical samples. Hardness and relative density are critical quality attributes (CQA) that affect tablet performance. Intra-sample density or hardness variability can reveal deficiencies in formulation design or the tableting process. This study was designed to develop NIR-CI methods to predict spatially resolved tablet density and hardness. The method was implemented using a two-step procedure. First, NIR-CI was used to develop a relative density/solid fraction (SF) prediction method for pure microcrystalline cellulose (MCC) compacts only. A partial least squares (PLS) model for predicting SF was generated by regressing the spectra of certain representative pixels selected from each image against the compact SF. Pixel selection was accomplished with a threshold based on the Euclidean distance from the median tablet spectrum. Second, micro-indentation was performed on the calibration compacts to obtain hardness values. A univariate model was developed by relating the empirical hardness values to the NIR-CI predicted SF at the microindented pixel locations: this model generated spatially resolved hardness predictions for the entire tablet surface. [ABSTRACT FROM AUTHOR]

Polymorph detection is critical for ensuring pharmaceutical product quality in drug substances exhibiting polymorphism. Conventional analytical techniques such as X-ray powder diffraction and solid-state nuclear magnetic resonance are utilized primarily for characterizing the presence and identity of specific polymorphs in a sample. These techniques have encountered challenges in analyzing the constitution of polymorphs in the presence of other components commonly found in pharmaceutical dosage forms. Laborious sample preparation procedures are usually required to achieve satisfactory data interpretability. There is a need for alternative techniques capable of probing pharmaceutical dosage forms rapidly and nondestructively, which is dictated by the practical requirements of applications such as quality monitoring on production lines or when quantifying product shelf lifetime. The sensitivity of transmission Raman spectroscopy for detecting polymorphs in final tablet cores was investigated in this work. Carbamazepine was chosen as a model drug, polymorph form III is the commercial form, whereas form I is an undesired polymorph that requires effective detection. The concentration of form I in a direct compression tablet formulation containing 20% w/w of carbamazepine, 74.00% w/w of fillers (mannitol and microcrystalline cellulose), and 6% w/w of croscarmellose sodium, silicon dioxide, and magnesium stearate was estimated using transmission Raman spectroscopy. Quantitative models were generated and optimized using multivariate regression and data preprocessing. Prediction uncertainty was estimated for each validation sample by accounting for all the main variables contributing to the prediction. Multivariate detection limits were calculated based on statistical hypothesis testing. The transmission Raman spectroscopic model had an absolute prediction error of 0.241% w/w for the independent validation set. The method detection limit was estimated at 1.31% w/w. The results demonstrated that transmission Raman spectroscopy is a sensitive tool for polymorphs detection in pharmaceutical tablets. [ABSTRACT FROM AUTHOR]

The study is aimed at developing a near-infrared (NIR) method for predicting solid fraction (SF) of dry granulated ribbons manufactured with formulation variability. The study investigated the impact of unmodeled chemical variability and regression approaches on method performance. The study utilized an excipient-only formulation system. Calibration compacts were created with chemical and processing variability; followed by collection of NIR spectra. Partial least squares (PLS) and spectral slope algorithms were utilized to model compact SF. Later, the models were deployed to predict SF of test ribbons and compacts containing an API at various concentrations. The risk associated with unmodeled chemical variation manifested itself through generation of new peaks and decreased baseline absorbance in the NIR spectra. The spectral slope was able to better manage this risk, as demonstrated by relatively higher robustness to the increasing load of the active pharmaceutical ingredient (API). The reduced robustness of the PLS approach was attributed to the impact of chemical variability on both spectral baseline and peak absorbance. A prediction error of approximately 5% was observed at 10% drug load using the spectral slope approach. An understanding of the risk associated with unmodeled variability will enable NIR method development as an API sparing technique for low-dose product development. [ABSTRACT FROM AUTHOR]

[Display omitted] A material sparing method for near-infrared (NIR) calibration was developed using an offline apparatus coupled with a calibration transfer method to enable a partial least squares (PLS) model to monitor the concentration of active pharmaceutical ingredients (API) in the feed frame of a rotary tablet press. The offline apparatus was designed to simulate the powder flow dynamic and NIRS measurement environment of a tablet-press feed frame. A comprehensive experimental design, including calibration and testing, was employed to determine blend inhomogeneity. NIR spectra were collected at both the feed frame (inline) conditions and the simulator (offline) conditions. The simulator conditions were designed to mimic the density and powder flow in the feed frame during the actual tableting process. The offline data were pretreated by an orthogonalization-based calibration transfer algorithm, a continuum regression filter (CR filter), before being subjected to PLS modeling. This study demonstrated: (1) calibration for inline application can be generated using an offline apparatus, and (2) the CR filter, as an innovative calibration transfer method, can generalize the offline method for multiple feed frame conditions. [ABSTRACT FROM AUTHOR]