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An online near-infrared (NIR) spectroscopy platform system for real-time powder blending monitoring and blend endpoint determination was tested for a phenytoin sodium formulation. The study utilized robust experimental design and multiple sensors to investigate multivariate data acquisition, model development, and model scale-up from lab to manufacturing. The impact of the selection of various blend endpoint algorithms on predicted blend endpoint (i.e., mixing time) was explored. Spectral data collected at two process scales using two NIR spectrometers was incorporated in a single (global) calibration model. Unique endpoints were obtained with different algorithms based on standard deviation, average, and distributions of concentration prediction for major components of the formulation. Control over phenytoin sodium's distribution was considered critical due to its narrow therapeutic index nature. It was found that algorithms sensitive to deviation from target concentration offered the simplest interpretation and consistent trends. In contrast, algorithms sensitive to global homogeneity of active and excipients yielded the longest mixing time to achieve blending endpoint. However, they were potentially more sensitive to subtle uniformity variations. Qualitative algorithms using principal component analysis (PCA) of spectral data yielded the prediction of shortest mixing time for blending endpoint. The hybrid approach of combining NIR data from different scales presents several advantages. It enables simplifying the chemometrics model building process and reduces the cost of model building compared to the approach of using data solely from commercial scale. Success of such a hybrid approach depends on the spectroscopic variability captured at different scales and their relative contributions in the final NIR model.

In this work, the effect of water on the packing and flow properties of cohesive powders was experimentally investigated. Two cohesive powders with small particle sizes were studied with weight per...

Direct compression (DC) remains the most preferred technique to produce tablets, and its effectiveness is directly influenced by raw material attributes. Therefore, the selection of specific grades of excipients to achieve desirable powder flow and compression properties is of importance. Shifting toward continuous manufacturing requires even more enhanced performance, quality, and consistency directly from the starting ingredients. Starch, as a well-known excipient with good compression characteristics in its native state, is poorly flowing and highly sensitive to lubrication. The objective of this study was to characterize the flow properties of different starch products and investigate the suitability of modified starch products for continuous manufacturing. The proposed methodology included techniques to characterize material flow properties and their feeding performance in a loss-in-weight feeder. Principal component analysis (PCA) was then applied to project material properties onto reduced dimensions and compare the flow behavior of starch products with other common excipients. A newly developed starch product, StarTab®, was characterized and compared with two other starch products in terms of their flow behavior. StarTab showed desirable flowability and packing behavior, demonstrated by shear cell and compressibility results. During feeder refill studies, StarTab exhibited a low total deviation of material fed in excess of setpoint compared with the other two starch products. Additionally, it was observed that StarTab flow behavior was not sensitive to its moisture level. StarTab showed suitable characteristics compared with other starch products making it a potential candidate to design and develop pharmaceutical continuous direct compression (PCDC) formulations.

Loss-in-weight feeders play a vital role in assuring blend and content uniformity as well as lot-to-lot powder traceability in continuous manufacturing. Irregular flow from the feeders propagates through the system, potentially resulting in out-of-specification product. Powder properties such as density, cohesion, and adhesion can cause large variability in the flow rate of ingredients from powder feeders. Feeding of active pharmaceutical ingredients (API) can be difficult because of inherently poor flow, low density, high cohesion, and adhesion. API was chosen due to known adhesion behavior inside feeders during continuous operations. The selected APIs were blended with nanosized silica, in a V-blender, to provide separation between the API particles and reducing the effect of interparticle forces. The material was characterized by standard pharmaceutical techniques to identify bulk changes between the as-received and the blended API. The coated API was then fed using a Coperion K-Tron KT-20 pharmaceutical loss-in-weight feeder. The material was dispensed onto a catch scale, recording mass versus time, and analyzed for relative standard deviation and deviation from the set point. Additionally, the mass of the remaining API in the feeder after the run ended was compared between the API and silica-blended API. Blending APIs with nanosized silica successfully improved API feeding performance despite their intrinsic highly adhesive and cohesive behavior. Both the pure and silica-blended APIs were fed using a twin screw powder feeder. When feeding unsilicated APIs, large variability in flow rate and screw speed were observed, and large amounts of material coated the inside of the hopper and screws. When the adhesive APIs were blended with nanosized silica and fed through a loss-in-weight feeder, there was significantly less adhesion to the feeder, and the material was dispensed with significant reduction in flow rate and screw speed variability. This work provides a means to increase the applicability of continuous manufacturing by enabling the practitioner to manage adverse impact of cohesive material properties on feeder performance. Blending highly adhesive API with silica reduces the adhesion of the API to the feeder hopper and screws, while also improving the deviation in mass flow rate exiting the feeder.

A better understanding of a pharmaceutical tablet’s microstructure has the potential to unlock the black box between material attributes, process parameters and the critical quality attributes. Microstructure determination requires measuring the spatial-, particle size-distributions (absolute and relative) of the ingredients, and the void space, which is the overt goal of chemical Imaging (CI). Reliable quantitative results can be obtained by imaging multiple layers per tablet, with each layer having a minimal surface roughness. This study utilized scanning electron microscopy/energy dispersive X-ray spectroscopy (SEM/EDX) and Raman chemical imaging (RCI) to provide a comparative discussion of results obtained when determining the microstructure of commercial zinc sulfate tablets, using three methods of tablet surface preparation: scoring & hand-fracturing, microtoming, and grating. A description of the working principles of the measurement and surface preparation methods is followed by a comparison of microstructure (particle size distribution and homogeneity of distribution) using chemical images. A comparison of the general advantages and disadvantages of SEM/EDX and RCI and the common errors in analyzing microstructure are also discussed. The results indicate that in addition to selecting the correct tablet surface preparation method, chemical imaging method, and the subsequent microstructural analyses method, correct problem formulation is also critical.

Over the past decade, continuous wet granulation has been emerging as a promising technology in drug product development. In this paper, the continuous high-shear mixer granulator, Lӧdige CoriMix® CM5, was investigated using a low-dose formulation with acetaminophen as the model drug. Design of experiments was deployed in conjunction with multivariate data analysis to explore the granulator design space and comprehensively understand the interrelation between process parameters and critical attributes of granules and tablets. Moreover, several complementary imaging techniques were implemented to unveil the underlying mechanisms of physical and chemical microstructure in affecting the tablet performance. The results indicated that L/S ratio and impeller speed outweighed materials feeding rate in modifying the granule and tablet properties. Increasing the degree of liquid saturation and mechanical shear input in the granulation system principally produced granules of larger size, smaller porosity, improved flowability and enhanced sphericity, which after compression generated tablets with slower disintegration process and drug release kinetics due to highly consolidated physical microstructure. Besides, in comparison to batch mixing, continuous mixing integrated with a conical mill enabled better powder de-agglomeration effect, thus accelerating the drug dissolution with increased surface area.

Powder properties, such as density and adhesion, can cause large variability in the flow rate of ingredients fed from feeders, which can propagate through the system. Knowing an ideal range of operation and correlating powder properties to process performance can result in faster optimization of performance, which saves time and money. A K-Tron KT-20 pharmaceutical loss-in-weight feeder was evaluated with multiple screw types, feed rates, and materials. The material was dispensed onto a catch scale, recording mass versus time, and analyzed for relative standard deviation and deviation from the setpoint. The materials used in this work were characterized by particle size, packing, and flow properties. Linear relationships between the feeder’s throughput capacity and material bulk density were identified for four different types of screws. Analyzing the feeder data resulted in a region where the RSD was at a minimum across a range of screw speeds. This was identified between 20–90% screw speed for coarse concave, fine concave, and coarse auger screws and 40–90% screw speed for fine auger screws. An optimal range of operation for K-Tron KT20 feeders was identified, as well as a linear correlation between material conditioned bulk density and maximum throughput for a given material and screw type. Using both the predictable maximum throughput and the optimal screw range, an optimal design space has been identified that will save development time and money.

This study describes how near infrared (NIR) spectroscopy can be used to predict the dissolution of bilayer tablets as a non-destructive approach. Tablets in this study consist of two active pharmaceutical ingredients (APIs) physically separated in layers and manufactured under three levels of hardness. NIR spectra were individually acquired for both layers in diffuse reflectance mode. Reference dissolution profile values were obtained using dissolution apparatus & HPLC. A multivariate partial least squares (PLS) calibration model was developed for each API relating its dissolution profile to spectral data. This calibration model was used to predict dissolution profiles of an independent test set and results of the prediction were compared using model free approaches i.e. dissimilarity (f1) & similarity (f2) factors to assure similarity in dissolution performance.

Process analytical technologies (PAT) are identified as an essential element in the Quality by Design framework, providing the cornerstone to implement continuous pharmaceutical manufacturing. This study is concerned with employing three in-line PATs: Eyecon™ 3D imaging system, Near-infrared spectroscopy (NIRS) and Raman spectroscopy (RS), in a continuous twin-screw granulation process to enable real-time monitoring and prediction of critical quality attributes of granules. The Thermo Scientific™ Pharma 11 twin-screw granulator was used to manufacture granules from a low-dose formulation with caffeine anhydrous as the model drug. A 30-run full factorial design including three critical process parameters (liquid to solid ratio, barrel temperature and throughput) was conducted to evaluate the performance of each analytical tool. Eyecon™ successfully captured the granule size and shape variation from different experimental conditions and demonstrated sufficient sensitivity to the fluctuation of size parameter D10 in the presence of process perturbations. The partial least square regression (PLSR) models developed using NIRS showed small relative standard error of prediction values (less than 5%) for most granule physical properties. In contrast, the RS-based PLSR models revealed higher prediction errors towards granule drug concentration, potentially due to the inhomogeneous premixing of raw materials during calibration model development.

Residence time distribution (RTD) is a probability density function that describes the time materials spend inside a system. It is a promising tool for mixing behavior characterization, material traceability, and real-time quality control in pharmaceutical manufacturing. However, RTD measurements are accompanied with some degree of uncertainties because of process fluctuation and variation, measurement error, and experimental variation among different replicates. Due to the strict quality control requirements of drug manufacturing, it is essential to consider RTD uncertainty and characterize its effects on RTD-based predictions and applications. Towards this end, two approaches were developed in this work, namely model-based and data-based approaches. The model-based approach characterizes the RTD uncertainty via RTD model parameters and uses Monte Carlo sampling to propagate and analyze the effects on downstream processes. To avoid bias and possible reduction of uncertainty during model fitting, the data-based approach characterizes RTD uncertainty using the raw experimental data and utilizes interval arithmetic for uncertainty propagation. A constrained optimization approach was also proposed to overcome the drawback of interval arithmetic in the data-based approach. Results depict probability intervals around the upstream disturbance tracking profile and the funnel plot, facilitating better decision-making for quality control under uncertainty.

Process analytical technology in the pharmaceutical industry requires the monitoring of critical quality attributes (CQA) through calibrated models. However, the development, implementation, and maintenance of these quantitative models are both resource and time-intensive. This study proposes the implementation of a non-linear iterative optimization technology (IOT) to study the magnitude of analytical errors when the calibration tablet used to extract the λ vector deviates physically and chemically from the test samples. IOT is based on mathematical optimization of excess spectral absorbance. It requires minimum calibration effort and allows simultaneous prediction of the entire formulation instead of only the active pharmaceutical ingredient (API), with just one standard and pure component spectral data. Unlike Partial Least Squares (PLS), which requires the development of standards to incorporate variations in the process, this non-destructive methodology minimizes significant calibration effort by developing a mathematical model that uses only one standard and spectral information of pure powders present in the tablet. The method described in this study allows a fast re-calculation to include factors such as change of spectroscopic instruments, variations in raw materials, environmental conditions, and methods of tablet preparation. The robustness of the proposed approach for variation in compaction (physical changes) and variation in composition (chemical changes) was evaluated for correlated and uncorrelated formulations. For uncorrelated formulation a PLS model was also constructed to compare the robustness of the proposed methodology. The RMSEP of API in target formulation predicted using non-linear IOT method was varied from 0.17 to 1.50 depends on compaction of tablet chosen to compute λ vector. On the other hand, the RMSEP of API in target formulation predicted using PLS-based model was varied from 0.13 to 0.57 depending on compaction of tablet. The additional accuracy achieved in PLS based model required significant calibration effort of preparing 84 tablets compared to just one in proposed non-linear IOT method.

While continuous manufacturing (CM) of pharmaceutical solid-based drug products has been shown to be advantageous for improving the product quality and process efficiency in alignment with FDA’s support of the quality-by-design paradigm (Lee, 2015; Ierapetritou et al., 2016; Plumb, 2005; Schaber, 2011), it is critical to enable full utilization of CM technology for robust production and commercialization (Schaber, 2011; Byrn, 2015 ). To do so, an important prerequisite is to obtain a detailed understanding of overall process characteristics to develop cost-effective and accurate predictive models for unit operations and process flowsheets. These models are utilized to predict product quality and maintain desired manufacturing efficiency (Ierapetritou et al., 2016). Residence time distribution (RTD) has been a widely used tool to characterize the extent of mixing in pharmaceutical unit operations ( Vanhoorne, 2020 , Rogers and Ierapetritou, 2015 , Tezyk et al., 2015 ) and manufacturing lines and develop computationally cheap predictive models. These models developed using RTD have been demonstrated to be crucial for various flowsheet applications ( Kruisz, 2017 , Martinetz, 2018 , Tian, 2021 ). Though extensively used in the literature (Gao et al., 2012), the implementation, execution, evaluation, and assessment of RTD studies has not been standardized by regulatory agencies and can thus lead to ambiguity regarding their accurate implementation. To address this issue and subsequently prevent unforeseen errors in RTD implementation, the presented article aims to aid in developing standardized guidelines through a detailed review and critical discussion of RTD studies in the pharmaceutical manufacturing literature. The review article is divided into two main sections – 1) determination of RTD including different steps for RTD evaluation including experimental approach, data acquisition and pre-treatment, RTD modeling, and RTD metrics and, 2) applications of RTD for solid dose manufacturing. Critical considerations, pertaining to the limitations of RTDs for solid dose manufacturing, are also examined along with a perspective discussion of future avenues of improvement.

In-line measurements of low dose blends in the feed frame of a tablet press were performed for API concentration levels as low as 0.10% w/w. The proposed methodology utilizes the advanced sampling capabilities of a Spatially Resolved Near-Infrared (SR-NIR) probe to develop Partial Least-Squares calibration models. The fast acquisition speed of multipoint spectra allowed the evaluation of different numbers of co-adds and feed frame paddle speeds to establish the optimum conditions of data collection to predict low potency blends. The interaction of the feed frame paddles with the SR-NIR probe was captured with high resolution and allowed the implementation of a spectral data selection criterion to remove the effect of the paddles from the calibration and testing process. The method demonstrated accuracy and robustness when predicting drug concentrations across different feed frame paddle speeds.

In a pharmaceutical manufacturing process, Critical Quality Attributes (CQAs) need to be monitored not only for the final product but also for intermediates. Blend uniformity of powders is one such attribute that needs to be measured to ensure the quality of the final product. Multiple in-line sensors were implemented within a Direct Compaction (DC) continuous tablet manufacturing line to monitor the blend content of the powders. In most cases, since the primary ingredient of interest is the active pharmaceutical ingredient (API), the concentration (potency) of the API was monitored/predicted over the course of manufacturing. For the calibration model building process, a unique setup involving dynamic powder spectral acquisition method was used. This setup was aimed at mimicking the powder flow characteristics within the manufacturing line, while at the same time utilizing a relatively small amount of powder. A Raman probe and a portable NIR were used concurrently at the exit of the blending process before the tableting stage. The performance of the two sensors and their respective models were evaluated in terms of accuracy, precision, operating range, measurement frequency, placement, reliability, robustness, and compared to predictions using gravimetric feed rates. Additionally, their performances were validated by off-line traditional analytical measurements.

Residence time distribution (RTD) modeling can aid the understanding and characterization of macro-mixing in continuous powder processing unit operations by relating observed behavior to quantitative model parameters. This article is the second part of the work done to characterize the effect of material properties on the measurement of RTDs in continuous powder processing operations. The goal of this paper is to examine the behavior of the RTD given different sets of tracer material properties. Tracer addition methods are discussed within the framework of their mathematical representation. The two most widely used RTD models in powder systems in the literature, the axial dispersion and the tank-in-series model, are presented and used to describe the experimental data. The RTD model parameters (e.g., Peclet number, number of tanks in series, and residence times) were regressed from the experimental data and compared using one-way ANOVA to determine the effects of materials properties on RTD. A model independent approach using a Multivariate Analysis of Variance (MANOVA) was also applied to compare the results with the model dependent method. Lastly, examples of how the RTD models can aid process design and understanding were described using both continuous and discrete convolution. The RTD models and their regressed coefficients were used to predict the mixing outputs of a semi-random input and the impact of disturbances on the process.

Residence time distribution (RTD) models are essential to understand process dynamics and support process monitoring and control in continuous manufacturing systems. RTD models can also be used to monitor material traceability and to isolate intermediate materials or finished products when specifications are not met. However, while pharmaceutical companies are currently making extensive use of RTD approaches, standard methods for conducting, interpreting, and using RTD results in continuous pharmaceutical manufacturing have not yet been established by regulatory authorities. This paper seeks to facilitate generating such standards. We discuss in detail the assumptions and conditions that are relevant to the proper selection of tracers for RTD experiments, and demonstrate that tracer selection can have substantial impact on RTD results. We selected seven materials with a wide range of properties as tracers and a single material as our base “blend”. The experimental results led to two major conclusions: (1) materials with different mechanical properties have dissimilar mean residence times (MRT) inside the systems and (2) blend ingredients with different mechanical properties travel at different speed inside of continuous blending systems. Results further indicated there were two critical mean residence times (MRTs): that of the tracer and that of the bulk. Matching of material properties between tracers is key in order to obtain similar MRTs using a given tracer. Differences between selected tracer and bulk material properties were found to lead to differences between the bulk space time and the tracer MRT. A set of recommendations on how to select tracer materials that would help characterize accurately the RTD of a continuous flow system are presented.

The rotary kiln is an essential device in chemical and metallurgical industries, with applications in a wide range of solids manufacturing processes. In particular, in the preparation of industrial chemical catalysts, the kiln has become a popular reactor for continuous calcination of catalysts ranging from millimeter-sized extrudates to micron-sized powders. As granular and powder flow behaviors are difficult to characterize, the design and scale-up of rotary calcination processes are often performed empirically. The goal of this research is to improve the fundamental understanding of powder flow in rotary kilns to aid in optimization of the continuous calcination process. For successful calcination to occur, the residence time of the particles must exceed the time required for heating and subsequent treatment. For uniform treatment of the feed, the particles must also exhibit low axial dispersion. In this work, the mean residence time and axial dispersion coefficient for a cohesive fluid catalytic cracking powder were determined in a pilot plant kiln by measuring the residence time distribution. This study utilized a pulse test developed by Danckwerts. Results were fit to the Taylor solution of the axial dispersion model and compared to the Sullivan prediction for mean residence time. It was found that the mean residence time decreases as the feed rate, kiln incline, and rotation rate increase. It was also found that the axial dispersion coefficient increases with speed of rotation and angle of incline. However, the axial dispersion coefficient decreases as the feed rate is increased.

Material properties are known to have a significant impact on pharmaceutical manufacturing performance, particularly for solid product processes. Evaluating the performance of a specific material, for example an active pharmaceutical ingredient or excipient, is critical during development stages in order to determine the impact of material properties on the process. However, materials may be scarce during the early stages of process development due to high cost, unavailability, import restrictions, etc. Furthermore, research on particular active pharmaceutical ingredients may be difficult given unknown exposure limits, which may delay process development and technology transfer. The purpose of this work was to establish a methodology for finding materials with similar behavior during processing using material property measurements so that a surrogate may be found and may replace the scarce material during process development. This work presents several commercially available material property tests and emphasizes the benefits of compiling material property measurements into libraries. Twenty pharmaceutically relevant materials and seven different powder characterization tests were considered as a case study. A total of 32 measurements were collected for each of the 20 materials, leading to a dataset of 640 measurements. The material property library was utilized to find similarities between materials using two multivariate methods: principal component analysis (PCA) and hierarchical clustering. The similarities between materials were evaluated with the performance of materials on powder feeding, refilling, and continuous blending equipment. Material clusters showed similar behavior in the characterized equipment. Moreover, results from the PCA and clustering analysis were further used to evaluate the level of collinearity and similarity between characterization measurements that can be further investigated to reduce the number of measurements that need to be collected. Material property measurement clusters were established based on the collinearity of the metrics.

Continuous manufacturing offers shorter processing times and increased product quality assurance, among several other advantages. This makes it an ever-growing interest among pharmaceutical companies. A suitable efficient control system is however desired for continuous pharmaceutical manufacturing to achieve a consistent predefined end product quality. In order to control product quality more accurately, the effects of input disturbances need to be proactively mitigated. Therefore, it is desired that a combined feedforward/feedback control system integrated with suitable process analytical technology (PAT) be implemented over a traditional feedback-only control system. The feedforward controller measures and takes corrective actions for disturbances proactively before they affect the process and thereby product quality. The feedback controller considers the real-time deviation of control variable from a pre-specified set point and keeps it at a minimum possible value. The deviation of a control variable from the set point could be due to both measurable and unmeasurable disturbances. In this work, a combined control strategy has been developed for a continuous twin screw wet granulation (WG) process. An integrated flowsheet model was developed and simulated in order to evaluate the effect of control loops on critical quality attributes (CQAs). Different strategies of manipulation were evaluated and the best strategy was identified. In silico study on the combined feedforward/feedback control strategy and feedback-only control strategy demonstrates that the combined loop results in diminished variability of the CQAs.

In this study, an active pharmaceutical ingredient (API) was impregnated onto a mesoporous carrier (excipient) in a fluidized bed. Impregnating APIs in porous carriers has the potential to simplify the drug substance development process and leads to the elimination of the blending and granulation unit operations. Impregnation and drying occur simultaneously in fluidized-bed impregnation, and this method precludes several challenges encountered in other impregnation methods. Furthermore, the process ensures a uniform distribution of APIs within the porous carriers. This method also allows for a variety of solvents to be used in the impregnation. Using a batch fluidized-bed dryer, impregnation of acetaminophen (APAP) onto a magnesium/aluminum metasilicate (Neusilin R) was studied. Water and methanol were used as transport solvents to impregnate different loadings (w/w) of APAP, namely, ranging from 24 to 0.1%. Uniformity tests indicated that APAP loadings of all impregnated products are close to their target loadings across carrier size classes. Tests before and after impregnation showed that there were no changes observed in the physical properties of the carrier, such as particle size, compressibility, and flow properties. X-ray diffraction (XRD) and differential scanning calorimetry (DSC) analysis performed on the samples indicated that the low loadings APAP was present in the carrier pores in its amorphous state. At high loadings, we observed that APAP was mainly present in its amorphous state, however, some degree of crystallinity was observed. Lastly, dissolution tests on the impregnated product showed enhanced dissolution rates in acidic media, and comparable dissolution rate in de-ionized (DI) water, compared to the pure drug crystals. Our results show that the method of fluidized-bed impregnation yields a product with high uniformity and overcomes several challenges presented by traditional physical blends.

Impregnation of active pharmaceutical ingredients (APIs) onto porous excipients has numerous benefits for solid dosage formulations. Previous work has successfully demonstrated the manufacturing of pharmaceuticals using fluidized bed (FB) impregnation of APIs onto porous carriers and discussed its advantages (such as easy to implement, improvement of blend uniformity and dissolution kinetics, and stabilization of amorphous APIs). This study aims to develop methods for analysis of the spatial distribution of the impregnated API inside the porous excipient. An understanding of the spatial distribution of the API can be important if one wants to achieve high drug loadings. In addition, the spatial distribution of the API can impact its dissolution rate. The impregnation profile is analyzed using energy dispersive X-ray spectroscopy (EDS). Two formulations are investigated using Fenofibrate and Acetaminophen (model APIs), impregnated onto Neusilin (porous excipient). Several methods are presented for particle embedding and cutting in order to produce cross-sections for analysis. Embedding with carbon-based resins/adhesives produces cross-sections with high quality but the resins contaminate the sample with carbon and reduce the detection of trace elements. Manually cutting particles immobilized on carbon tape or inorganic-based adhesives produces cross-sections with a higher degree of roughness but improves the detection of trace elements and reduces/eliminates carbon contamination in the sample, allowing for API detection by its carbon footprint. EDS analytical results showed that for both Fenofibrate and Acetaminophen formulations examined in this work, the API profile is highly uniform (detected by both carbon and characteristic trace elements).

As the pharmaceutical industry modernizes its manufacturing practices and incorporates more efficient processing approaches, it is important to reevaluate which process design elements affect product quality and the means to study these systems. The purpose of this work is to provide insight on a methodology to correlate the effect of raw material properties to equipment and process performance using both data-driven and semi-empirical models. In this work, lubricated blends of pharmaceutically-relevant materials were made using varying levels of magnesium stearate, ranging from 0.25 to 1.5%. Materials characterization (e.g., compressibility, permeability, density, particle size) was performed for all materials and blends. The blends were compressed using a two by three experimental design, varying tablet fill cam depth and tablet thickness, respectively. Tablet properties (e.g., weight, tensile strength, and thickness) were collected for all tablets. Using the collected tablet property results, models coefficients for the semi-empirical Kuentz and Leuenberger equation, which relates the tablet tensile strength to changes in porosity, were regressed. Empirical models were then developed to correlate the values of the Kuentz and Leuenberger equation coefficients to the blend material properties. The empirical models were then used in conjunction with the Kuentz and Leuenberger equation to evaluate the compression design and operational space, accounting for material properties. This proof of concept work aimed at developing correlations between raw material properties and unit operation models can aid process development, especially in design space characterization and robustness analysis.

Residence time distribution (RTD) models were developed to track raw material lots and investigate batch transitions in a continuous manufacturing system. Two raw materials with similar physical properties (granular metformin and lactose) were identified via Principal Component Analysis (PCA) from a library of bulk material properties and used to simulate the switching of lots during production. In-line near-infrared (NIR) spectra were collected with the powder flowing through a chute in a continuous manufacturing system to monitor metformin and lactose concentration in step-change experiments with Partial Least Squares (PLS) models. RTD provided an understanding of raw material propagation through the continuous manufacturing system. Transition times between raw material changes were identified using the results of two multivariate approaches PLS and PCA. The methodology was implemented to discriminate the transition zone in a raw material change, contributing to design control strategies for acceptance and diverting mechanisms.

As the pharmaceutical industry increasingly adopts continuous manufacturing technology, significant attention must be paid to process analytical technology (PAT), process integration, and process control. Published information is no substitute for hands-on comprehensive training, which is critical to implementing and operating continuous pharmaceutical manufacturing systems effectively and efficiently. In this article, an intensive hands-on training course has been developed and implemented at the Engineering Research Center for Structured Organic Particulate Systems (C-SOPS) based on 15 years of experience and several implemented systems. Here, we brought the details of the four integral components in a continuous direct compression manufacturing process: (1) unit operations, (2) PAT, (3) modeling and process controls, and (4) material characterization. Each section built-in classroom lectures with a brief overview on the theoretical aspects of each topic, followed by a hands-on session covering the classroom theory. The training program is described here in sufficient detail to enable creation of similar programs at other institutions.

Raman chemical mapping is an inherently slow analysis tool. Accurate and robust multivariate analysis algorithms, which require least amount of time and effort in method development are desirable. Calibration-free regression and resolution approaches such as classical least squares (CLS) and multivariate curve resolution using alternating least squares (MCR-ALS), respectively, help in reducing the resources required for method development. However, conventional CLS does not consider appropriate constraints, which may result in negative and/or greater than 100 % Raman concentration scores, while MCR-ALS may not always be as accurate as regression-based algorithms. Linear iterative optimization technology (IOT) is another calibration-free algorithm, which with appropriate constraints has previously shown promise in online and offline pharmaceutical mixture composition determination. This paper aims to evaluate the performance of the linear IOT algorithm for Raman chemical mapping of the active pharmaceutical ingredient (API), diluent, and lubricant in pharmaceutical tablets. Two pre-processing strategies were applied to the raw Raman mapping spectra. The results were compared with CLS (current reference method) and MCR-ALS. Special emphasis was given to mapping at low Raman exposure times to enable feasible total acquisition times (5 h). The quality of IOT/CLS/MCR-ALS estimated Raman concentration predictions were assessed by calculating a correlation factor between the spectrum corresponding to the maximum predicted concentration (or resolved spectra) of a component for IOT/CLS (or MCR-ALS) and the pure powder component spectrum. The Raman chemical maps were visualized, and the average Raman concentrations scores were compared. The results demonstrated the utility of IOT in Raman chemical mapping of pharmaceutical tablets. The diluent (lactose) and API (semi-fine APAP) used in this study were reliably estimated by IOT at relatively short Raman exposure times. On the other hand, as expected, the lubricant (magnesium stearate) could not be detected in any of the cases investigated here, irrespective of the algorithm used. Overall, for the API and diluent used in this formulation as well as the chemical mapping conditions, linear IOT seemed to better estimate the pure spectrum intensities and the average Raman scores (closer to CLS) in comparison to MCR-ALS. Moreover, application of appropriate constraints in linear IOT avoided the presence of negative and/or greater than 100 % Raman concentration scores, as observed in CLS-based Raman chemical maps.

This study investigates the use of twin-screw binder-free melt granulation (BFMG) in the development of high-dose solid dose formulations for low melting point thermally stable drugs. Both ibuprofen and guaifenesin are examined. By granulating pure API powder, it is shown that BFMG can successfully be used to produce granules that contain 100% API. A design of experiments (DoE) response surface methodology was used to establish the design space for the end-product. The effects of the most relevant process variables (barrel operating temperature, powder feed rate, screw speed and screw configuration) on granule properties (outlet temperature, size distribution, morphology, flowability, compressibility, porosity) and tablet attributes (tensile strength and in-vitro dissolution) were thoroughly studied. Barrel temperature (alone or in interactions with the other variables) represented the most significant variable for both drugs since it governs the formation of granules by partial melting and subsequent agglomeration of the fed powder. Interestingly, the shear action originated by screw speed and screw configuration resulted in various significant responses depending on the drug substance, indicating that it can also be affected by the nature of the processed molecule. Flow properties were improved (i.e., lower Hausner ratio) for both drugs after formation of granules. Tabletability was also tested by preparing 600 mg tablets for all samples. Surprisingly, the resulting granules were highly compactible, requiring only 1% lubricant to form strong tablets containing 96% API and 3% disintegrant. The results also showed that tablets become harder as the granule size increased, especially for guaifenesin. As expected, in-vitro dissolution results indicated that tablets and capsules showed slightly slower dissolution rates than the granules.

Purpose Lubrication is critical in pharmaceutical manufacturing of solid dosage forms. The purpose of this paper is to systematically compare and correlate the lubrication effect of two devices, a controlled shear system and a Resonant Acoustic Mixer, on the flow properties of pharmaceutical blends. Method A model formulation was selected. Full factorial designs were conducted to examine the effect of the total strain (or total energy) and the shear rate (or power) on the powder blend flow properties. Analysis of variance (ANOVA) and effect size test using omega-squared statistics were performed. Results Lubrication significantly improved the blend flowability. Mixing without magnesium stearate, or insufficient strain, resulted in more cohesive blends. The statistical analysis suggests that the shear rate had a minimal effect on the blend flow properties. The experimental results also suggest that although the two devices had comparable lubrication effects on the overall blend flowability, the changes of the interparticle forces in the lubricated blends were not identical. Conclusion This study demonstrated a scientific approach to compare different lubrication processes in an objective and reproducible manner. The findings are useful for process design, development, and transfer between different equipment types and process scales.

In this study, the origins of granule content non-uniformity in the high-shear wet granulation of a model two-component pharmaceutical blend were investigated. Using acetaminophen as the active pharmaceutical ingredient (API) and microcrystalline cellulose as the excipient, the distribution of the API across the granule size classes was measured for a range of conditions that differed in the duration of the initial dry mixing stage, the overall composition of the blend and the wet massing time. The coarse granule fractions were found to be systematically sub-potent, while the fines were enriched in the API. The extent of content non-uniformity was found to be dependent on two factors - powder segregation during dry mixing and redistribution of the API between the granule size fractions during the wet massing phase. The latter was demonstrated in an experiment where the excipient was pre-granulated, the API was added later and wet massed. The content non-uniformity in this case was comparable to that obtained when both components were present in the granulator from the beginning. With increasing wet massing time, the extent of content non-uniformity decreased, indicating that longer wet massing times might be a solution for systems with a natural tendency for component segregation.

Wet granulation is widely used in the pharmaceutical industry. This advantageous technology is capable of enhancing compression and powder handling, decreasing ingredient segregation, and promoting blend and content uniformity. Currently, a high level of interest exists in the continuous version of this technology, both by the US Food and Drug Administration (FDA), and by pharmaceutical manufacturers. In this paper, a continuous high shear wet granulation process was examined based on a placebo formulation comprising 70% ∂-lactose monohydrate and 30% microcrystalline cellulose (Avicel® PH101). Granulation was then carried out in a continuous high shear mixer granulator, Glatt GCG 70. The impact of two process variables (rotation speed and liquid/solid (L/S) ratio) and two design parameters (blade configuration and nozzle position) were evaluated via an I-optimal design. Multi-factor analysis of variance (ANOVA) indicated that rotation speed and L/S ratio dominated the granulation process and had the most significant effects on granule size distribution (GSD). The largest granule mass median diameter was obtained at the lowest rotation speed and highest L/S ratio. The granulation mechanism underlying this continuous process was examined using a wetting and nucleation regime map. For the cases studied here, the mechanical dispersion regime controlled the formation of granule nuclei, leading to a broad GSD and a limited growth ratio.

Binary powder mixtures with variable segregation tendencies were “mixed” in a tumbling batch blender and in a continuous convective tubular blender. The degree of homogeneity of the final blend obtained from the continuous blender was found to be much higher than that from the batch V-blender, for mixtures and blender process parameters examined in this study. A direct relationship was observed between the segregation index of mixtures and a new material property metric defined as the ratio of the median particle size and bulk density of the ingredients that constitute the mixture. Moreover, for freely flowing materials, the extent of non-homogeneity at the end of a batch blending process was found to be proportional to the segregation index of the mixture and thus consequently in relation to the material property metric. Results demonstrate the superior capability of continuous blenders compared to batch blenders in their ability to homogenize segregating mixtures.

The challenges in transferring and executing a near-infrared (NIR) spectroscopic method for croscarmellose (disintegrant) in binary blends for a continuous manufacturing (CM) process are presented. This work demonstrates the development of a NIR calibration model and its use to determine the blending parameters needed for binary blends at a development plant and later used to predict CM process blends. The calibration models were developed with laboratory scale powder blends ranging from 4.32%-64.77 (%w/w) of croscarmellose and evaluated using independent test blends. The selected model was then transferred to the continuous manufacturing development site to determine the croscarmellose concentration for spectra collected in real-time. A total of 18 development plant runs were monitored using an in-line NIR spectrometer, however, these spectra showed high baseline variations. The baseline variations were caused by the poor flow of the material within the system. An inconsistent bias which varied from 2.51 to 14.95 (%w/w) was observed in the predictions of croscarmellose. High baseline spectra were eliminated and the bias was significantly reduced by 42-51%. Experiments at lower flow rate speeds did not show significant changes in baseline and bias values showed more consistency. The calibration model was then transferred to two NIR spectrometers installed at-line at the commercial site, where powder samples were collected at the beginning middle and end of each CM plant run. The NIR calibration model predicted disintegrant concentration from the powder samples. Results showed the bias values for the NIR (1) varied from 0.74 to 2.21 (%w/w) and NIR (2) from 0.28 to 3.39 (%w/w). Average concentration values for both equipments were very close to the reference concentration values of 43.18 and 50.98 (%w/w). The study showed the model was able to identify flow issues, identified as baseline shifts, that could be used to alert to problems in the powder bed that may warrant diversion from a production line. These powder flow problems such as air gaps and inconsistent powder bed height affected the NIR spectra collected at the development plant and provided results with high bias. A lower bias was obtained in samples collected at line after blending.

This work describes the characterization of three NIR interfaces intended to monitor a continuous granulation process. Two interfaces (i.e. a barrel interface and a rotating paddle interface) were evaluated to monitor the API concentration at the entrance of the granulator, and a third interface (i.e. an outlet interface), was evaluated to examine the quality of the resulting outlet granules. The barrel interface provided an assessment of the API concentration during the feeding process by scanning the material conveyed by the screws of the loss-in-weight feeder. The rotating paddle interface analyzed discrete amounts of powder upon exiting the feeder via the accumulation of material on the paddles. Partial Least Squares (PLS) calibration models were developed using the same powder blends for the two inlet interfaces and using the outlet granules for the outlet interface. Five independent batches were used to evaluate the prediction performance of each inlet calibration model. The outlet interface produced the lowest error of prediction due to the homogeneity of the granules. The barrel interface produced lower errors of prediction than the rotating paddle interface. However, powder density affected only the barrel interface, producing deviations in the predicted values. Therefore, powder density is a factor that should be considered in the calibration sample design for spectroscopic measurements when using this type of interface. A variographic analysis demonstrated that the continuous 1-dimensional motion in the barrel and outlet interfaces produced representative measurements of each batch during calibration and test experiments, generating a low minimum practical error (MPE).

Continuous manufacturing (CM) has clear potential for manufacturing solid oral dosages. It provides several advantages that may aid the manufacturing and quality of drug products. However, one of the main challenges of this technology is the relatively large amount of knowledge required and the amounts of material needed to develop the process during the early stages of development. Early process development evaluations of continuous manufacturing equipment often require larger amounts of material compared with batch, which hinder CM prospect for drugs during the early stages of process development. In this work, a small-scale evaluation of the mixing process occurring in a continuous mixing system was performed. The evaluation involved the use of a small-scale "mixing cell" which was able to replicate the lubrication process of a continuous mixer. It is worth mentioning that we designed the mixing cell by reconfiguration of an existing continuous tubular blender. The extent of lubrication evaluation was performed for three example formulations and was done by mimicking the amount of shear provided to a formulation by means of matching the number of paddle-passes that a formulation experiences within a continuous blending process in the batch mixing cell. The evaluation showed that the small-scale mixing cell was able to replicate the extent of lubrication-evaluated by measuring the tensile strength of compacts being made with both the continuous and mixing cell experiments-occurring in the continuous mixer using a fraction of the amount of materials needed to perform the same evaluation in the continuous blending process.

This paper provides a method for prediction of weight variability of tablets made in rotary tablet presses as a function of material attributes and processing parameters. The goal was to be able to predict whether or not a formulation is suitable for direct compaction continuous manufacturing using the tablet weight variability as a criterion. The work focused on identifying the significant factors affecting the weight variability in tablets, within the design space studied. A wide range of blends with different powder properties were prepared. It was shown that among powder properties, cohesion, bulk density, and particle size were the most significant and sufficient material attributes to explain tablet weight variability. A response surface model was built and validated with three different blends. The model is not formulation dependent and can be expanded to include other blend properties or processing parameters effects.