Investigating the Effects of Mixing Dynamics on Twin-Screw Granule Quality Attributes via the Development of a Physics-Based Process Map.

Twin-screw granulation (TSG) is an emerging continuous wet granulation technique that has not been widely applied in the industry due to a poor mechanistic understanding of the process. This study focuses on improving this mechanistic understanding by analyzing the effects of the mixing dynamics on the granule quality attributes (PSD, content uniformity, and microstructure). Mixing is an important dynamic process that simultaneously occurs along with the granulation rate mechanisms during the wet granulation process. An improved mechanistic understanding was achieved by identifying and quantifying the physically relevant intermediate parameters that affect the mixing dynamics in TSG, and then their effects on the granule attributes were analyzed by investigating their effects on the granulation rate mechanisms. The fill level, granule liquid saturation, extent of nucleation, and powder wettability were found to be the key physically relevant intermediate parameters that affect the mixing inside the twin-screw granulator. An improved geometrical model for the fill level was developed and validated against existing experimental data. Finally, a process map was developed to depict the effects of mixing on the temporal and spatial evolution of the materials inside the twin-screw granulator. This process map illustrates the mechanism of nucleation and the growth of the granules based on the fundamental material properties of the primary powders (solubility and wettability), liquid binders (viscosity), and mixing dynamics present in the system. Furthermore, it was shown that the process map can be used to predict the granule product quality based on the granule growth mechanism.

Optimizing Energy Efficiency of a Twin-Screw Granulation Process in Real-Time Using a Long Short-Term Memory (LSTM) Network.

Traditional pharmaceutical manufacturing processes for solid oral dosage forms can be inefficient and have been known to produce a large amount of undesired product. With the progressing trend of achieving carbon neutrality, there is an impetus to increase the energy efficiency of these manufacturing processes while maintaining the critical quality attributes of the product. One of the important steps in downstream pharmaceutical manufacturing is wet granulation, and within that, twin screw granulation (TSG) is a popular continuous manufacturing technique. In this study, the energy efficiency of the TSG process was maximized by combining a long-term memory (LSTM) model with an optimization algorithm. The LSTM model was trained on time-series process data obtained from the TSG experimental runs. The optimization process, with the objective of maximizing energy efficiency, was performed using a stochastic optimization algorithm, and constraints were enforced on the process parameter design space. Experimental runs at the optimal process parameters were conducted on the TSG equipment with updates occurring at predefined intervals depending on the optimization scenarios. The purpose of these experimental runs was to validate the capability of increasing the overall process energy efficiency when operating at the optimized process parameters. A maximum increase of 27% was obtained between two tested optimization scenarios while maintaining the yield of the granules at the end of the twin-screw granulation process.

Multi-dimensional population balance model development using a breakage mode probability kernel for prediction of multiple granule attributes.

Milling affects not only particle size distributions but also other important granule quality attributes, such as API content and porosity, which can have a significant impact on the quality of the final drug form. The ability to understand and predict the effects of milling conditions on these attributes is crucial. A hybrid population balance model (PBM) was developed to model the Comil, which was validated using experimental results with an R2 of above 0.9. This presented model is dependent on the process conditions, material properties and equipment geometry, such as the classification screen size. In order to incorporate the effects of different quality attributes in the model physics, the dimensionality of the PBM was increased to account for changes in API content and porosity, which also produced predictions for these attributes in the results. Additionally, a breakage mode probability kernel was used to introduce dynamic breakage modes by predicting the probability of attrition and impact mode, which are dependent on the process conditions and feed properties at each timestep.

End-point determination of heterogeneous formulations using inline torque measurements for a high-shear wet granulation process.

In this study, the torque profiles of heterogeneous granulation formulations with varying powder properties in terms of particle size, solubility, deformability, and wettability, were studied, and the feasibility of identifying the end-point of the granulation process for each formulation based on the torque profiles was evaluated. Dynamic median particle size (d50) and porosity were correlated to the torque measurements to understand the relationship between torque and granule properties, and to validate distinction between different granulation stages based on the torque profiles made in previous studies. Generally, the torque curves obtained from the different granulation runs in this experimental design could be categorized into two different types of torque profiles. The primary factor influencing the likelihood of producing each profile was the binder type used in the formulation. A lower viscosity, higher solubility binder resulted in a type 1 profile. Other contributing factors that affected the torque profiles include API type and impeller speed. Material properties such as the deformability and solubility of the blend formulation and the binder were identified as important factors affecting both granule growth and the type of torque profiles observed. By correlating dynamic granule properties with torque values, it was possible to determine the granulation end-point based on a pre-determined target median particle size (d50) range which corresponded to specific markers identified in the torque profiles. In type 1 torque profiles, the end-point markers corresponded to the plateau phase, whereas in type 2 torque profiles the markers were indicated by the inflection point where the slope gradient changes. Additionally, we proposed an alternative method of identification by using the first derivative of the torque values, which facilitates an easier identification of the system approaching the end-point. Overall, this study identified the effects of different variations in formulation parameters on torque profiles and granule properties and implemented an improved method of identification of granulation end-point that is not dependent on the different types of torque profiles observed.

An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants.

The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. In this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.

Optimization of key energy and performance metrics for drug product manufacturing.

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors (Sampat et al., 2022), this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. The results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

Development of a Granule Growth Regime Map for Twin Screw Wet Granulation Process via Data Imputation Techniques.

Twin screw granulation (TSG) is a continuous wet granulation technique that is used widely across different solid manufacturing industries. The TSG has been recognized to have numerous advantages due to its modular design and continuous manufacturing capabilities, including processing a wide range of formulations. However, it is still not widely employed at the commercial scale because of the lack of holistic understanding of the process. This study addresses that problem via. the mechanistic development of a regime map that considers the complex interactions between process, material, and design parameters, which together affect the final granule quality. The advantage of this regime map is that it describes a more widely applicable quantitative technique that can predict the granule growth behavior in a TSG. To develop a robust regime map, a database of various input parameters along with the resultant final granule quality attributes was created using previously published literature experiments. Missing data for several quality attributes was imputed using various data completion techniques while maintaining physical significance. Mechanistically relevant non-dimensional X and Y axis that quantify the physical phenomena occurring during the granulation were developed to improve the applicability and predictability of the regime map. The developed regime map was studied based on process outcomes and granule quality attributes to identify and create regime boundaries for different granule growth regimes. In doing so breakage-dominant growth was incorporated into the regime map, which is very important for TSG. The developed regime map was able to accurately explain the granule growth regimes for more than 90% of the studied experimental points. These experimental were generated at vastly different material, design, and process parameters across various studies in the literature, this further increases the confidence in the developed regime map.

Risk Assessment for a Twin-Screw Granulation Process Using a Supervised Physics-Constrained Auto-encoder and Support Vector Machine Framework.

Quality risk management is an important task when it pertains to the pharmaceutical industry, as this is directly related to product performance. With the ICH Q9 guidelines, several regulatory bodies have encouraged the pharmaceutical industry to implement risk management plans using scientific and systemic approaches such as quality-by-design to asses product quality. However, the implementation of such methods has been challenging as assessment of risks requires accurate quantitative models to predict changes in quality when variations occur. This study describes a framework that quantitatively assesses risk for a twin screw wet granulation process. This framework consists of a physics-constrained autoencoder system, whose outputs are constrained using physics-based boundary conditions. The latent variables obtained from the auto-encoder are used in a support vector machine-based classifier to understand the granule growth behavior occurring within the system. This framework is able to predict the process outcomes with 86% accuracy and classify the granule growth regimes with a true positive rate of 0.73. Based on the classification the risk associated with the process can be estimated.

Assessment of Dentist Participation in Public Insurance Programs for Children in the US.

Importance: Evaluating the availability of dentists to provide dental care services to children is important for identifying interventions for improving access. Objective: To assess dental care availability for children in the US by public insurance participation, rural-urban setting, and dentist taxonomy (general, pediatric, or specialized). Design, Setting, and Participants: This cross-sectional study analyzed the availability of dentists from matching 3 data sets: the 2020 National Plan and Provider Enumeration System, the 2019-2020 State Board of Dentistry information acquired from each state, and the 2019 InsureKidsNow.org database. Data on active dentists in most states (including the District of Columbia [combined hereinafter with states] and excluding Hawaii and Washington) were included in the analysis. The study was conducted from January 2019 to March 2022. Main Outcomes and Measures: The number and percentage of dentists participating in public insurance programs (Medicaid and/or Children's Health Insurance Program [CHIP]) were aggregated at the dental office and stratified by the rurality of their practice and taxonomy. State-level comparisons were derived between this study and reports from the Health Policy Institute of the American Dental Association, along with maps and summary statistics disseminated through a data portal and state reports. Results: Among 204 279 active dentists, participation in public insurance varied widely across states, especially for the states that manage the Medicaid and CHIP programs separately. Participation rates in Medicaid and CHIP varied substantially from those of the Health Policy Institute of the American Dental Association. Participation in Medicaid and CHIP was lowest among urban dentists (Medicaid, 26%; CHIP, 29%) and highest among rural dentists (Medicaid, 39%; CHIP, 40%), while urban dentists accounted for most of the dentist population (urban, 84%; rural, 5%). Similarly, participation in Medicaid and CHIP was substantially lower among general dentists (Medicaid, 28%; CHIP, 29%) vs pediatric dentists (57% in both programs), while each state's dentist population consisted of notably more general (84%) than pediatric (3%) dentists. Nearly half of the states revealed wide variations in Medicaid and CHIP participation between counties, ranging from no participation (21 states) to full participation (22 states). Conclusions and Relevance: The findings of this study suggest that disparities in the availability of dentists for pediatric dental care are extensive, particularly for Medicaid- and CHIP-insured children, those living in rural communities, and those receiving specialized care. Lack of dentist availability for Medicaid- and CHIP-insured children appears to deter access to receiving dental care.

Optimization of screw design for continuous wet granulation: A case study of metoprolol succinate ER tablets.

This study aimed at understanding the effect of screw design on the critical characteristics of granules and tablets of an extended-release (ER) formulation for twin screw granulation process. The screw design parameters assessed included number of kneading elements (KEs) per kneading zone, distance separating kneading zones, staggering angle (SA) of kneading elements and number of sizing elements (SEs). These input variables were varied using a design of experiment (DoE) approach to manufacture granules. Particle size distribution (PSD), flow and bulk properties of the granules, breaking strength and dissolution of tablets manufactured using these granules were characterized. The results of least square fitting showed that KEs, SA, and SEs of the screws significantly (p -values < 0.05) affected the PSD, cohesion, compressibility (CPS), conditioned bulk density (CBD) and permeability of the granules. The KEs and SEs significantly (p -value < 0.05) affected the dissolution, which was attributed to their effects on CPS and CBD of the granules. The distance between kneading zones had no significant effect on granules and tablet characteristics. These results may be used to further study the interaction of the identified critical screw design parameters with other processing parameters for continuous manufacturing of this ER matrix-based tablet formulation.

Switching from batch to continuous granulation: A case study of metoprolol succinate ER tablets.

Continuous manufacturing (CM) has been used to produce several immediate release drug products. No extended-release (ER) product manufactured employing CM technology has been approved yet. This study investigated the critical aspects of switching from the batch mode of high shear granulation to the continuous operation of twin-screw granulation for extended-release tablets. Metoprolol succinate ER tablets was used as a model ER formulation for this purpose. A central composite design (CCD) was employed to determine the effects of high shear granulator (HSG) parameters, namely impeller speed, granulation time, and binder liquid feeding rate, on the critical granulation characteristics important for product performance. These critical granulation characteristics served as a guide for switching from the batch processing to the continuous operation for achieving the same breaking strength and dissolution for this ER metoprolol tablets. The granulation time was the most critical factor affecting the bulk properties of granules which contributed to tablet dissolution. The higher density and lower compressibility of granules were attained at the longest granulation time of 5.4 min with the fastest liquid feeding rate of 75 g/min. The granules' density was the primary factor negatively affecting the dissolution of metoprolol tablets. However, the breaking strength of tablets confounded the effect of granules density on metoprolol dissolution. Switching the processing parameters of high shear granulation to twin-screw granulation achieved similar dissolution profiles (F2 greater than 50). The screw speed was not found to affect bulk properties of granules. The root cause of granulation failures in twin-screw granulation, such as premature consolidation, excessive swelling, poor cohesion, inconsistent shearing effects, and formation of deformed agglomerates, were identified. In conclusion, the use of critical granulation characteristics through a performance-based approach of ER tablets facilitated the switching of manufacturing of an ER formulation form batch to continuous operation.

Prediction of entire tablet formulations from pure powder components' spectra via a two-step non-linear optimization methodology.

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.

A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation Process.

In this study, a hybrid modeling framework was developed for predicting size distribution and content uniformity of granules in a bi-component wet granulation system with components of differing hydrophobicities. Two bi-component formulations, (1) ibuprofen-USP and micro-crystalline cellulose and (2) micronized acetaminophen and micro-crystalline cellulose, were used in this study. First, a random forest method was used for predicting the probability of nucleation mechanism (immersion and solid spread), depending upon the formulation hydrophobicity. The predicted nucleation mechanism probability is used to determine the aggregation rate as well as the initial particle distribution in the population balance model. The aggregation process was modeled as Type-I: Sticking aggregation and Type-II: Deformation driven aggregation. In Type-I, the capillary force dominant aggregation mechanism is represented by the particles sticking together without deformation. In the case of Type-II, the particle deformation causes an increase in the contact area, representing a viscous force dominant aggregation mechanism. The choice between Type-I and II aggregation is determined based on the difference in nucleation mechanism that is predicted using the random forest method. The model was optimized and validated using the granule content uniformity data and size distribution data obtained from the experimental studies. The proposed framework predicted content non-uniform behavior for formulations that favored immersion nucleation and uniform behavior for formulations that favored solid-spreading nucleation.

Performance assessment of linear iterative optimization technology (IOT) for Raman chemical mapping of pharmaceutical tablets.

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.

Information flow to increase support for tidal energy development in remote islands of a developing country: agent-based simulation of information flow in Flores Timur Regency, Indonesia.

BACKGROUND: Public awareness is crucial for successful deployment of tidal energy, a renewable energy source that can provide clean electricity to remote islands. However, considering public attitudes on tidal energy are not well known, especially in developing countries, a barrier exists in implementing public engagement strategies. This study aims to contribute by identifying strategies for information provision-the initial step in public engagement-and estimate how these can be engaged to enhance support for tidal energy among the local public in a remote area of a developing country, in this case, Flores Timur Regency, Indonesia, considering their socio-cultural background. METHODS: In this paper, we employ statistical analyses using multinomial probit modelling to identify the key variables that shape information flow. The aptness of the variables is then verified using post-estimation techniques for their use as input parameters for the simulation of the information flow in the field study area. Agent-based simulation (ABS) is employed to replicate the actual conditions in Flores Timur Regency, Indonesia, and simulate the flow of information through the local community. RESULTS: According to the multinomial probit estimations, the people belonging to the top hierarchical group show a higher probability to support tidal energy compared to the members belonging to the lower groups. Understandably, around twice as many information flow cycles are needed to disseminate information to the members of the lowest hierarchical group, compared to the members of the top hierarchical group. The results also show that increasing the amount of available information has a positive impact on information dissemination. CONCLUSIONS: This study demonstrated that information provision is highly effective with propagation of information that specifically highlights the individual benefits, rather than the community benefits of tidal energy. Additionally, savings in terms of costs, time, and efforts can be realized if the most influential members of the local community are targeted initially before including all other stakeholders. The study also indicated that locals absorb more information and increase their support for tidal energy when additional data is made available. Finally, as long-term strategy, information provision becomes most effective when the local population gains higher educational capabilities. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13705-021-00302-8.

A Semi-Mechanistic Prediction of Residence Time Metrics in Twin Screw Granulation.

This work is concerned with the semi-mechanistic prediction of residence time metrics using historical data from mono-component twin screw wet granulation processes. From the data, several key parameters such as powder throughput rate, shafts rotation speed, liquid binder feed ratio, number of kneading elements in the shafts and the stagger angle between the kneading elements were identified and physical factors were developed to translate those varying parameters into expressions affecting the key intermediate phenomena in the equipment, holdup, flow and mixing. The developed relations were then tested across datasets to evaluate the performance of the model, applying a k-fold optimization technique. The semi-mechanistic predictions were evaluated both qualitatively through the main effects plots and quantitatively through the parity plots and correlations between the tuning constants across datasets. The root mean square error (RMSE) was used as a metric to compare the degree of goodness of fit for different datasets using the developed semi-mechanistic relations. In summary this paper presents a new approach at estimating both the residence time metrics in twin screw wet granulation, mean residence time (MRT) and variance through semi-mechanistic relations, the validity of which have been tested for different datasets.

Sampling optimization for blend monitoring of a low dose formulation in a tablet press feed frame using spatially resolved near-infrared spectroscopy.

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.

A moving-boundary model of dissolution from binary drug-excipient granules incorporating microstructure.

Accurate mechanistic in vitro dissolution models can deliver insight into drug release behaviour and guide formulation development. Drug release profiles from drug-excipient granules can be impacted by variation of porosity and drug load within granules, which may arise from inherent variability in granulation processes. Here, we analyse and validate a recent model of drug release from a single spherical granule with a matrix of insoluble excipient, incorporating radial variation of porosity and drug load. The model is presented and specialised to the case where the initial drug load is large compared to the capacity of the granule's pores at solubility. In this limit, the model reduces to a single ordinary differential equation describing depletion of a shrinking, drug-saturated core. Model validation is performed using drug release data from the literature for a granule system consisting of acetaminophen and microcrystalline cellulose. A new extended model to describe dissolution from a polydisperse collection of granules is derived. The performance is compared to single particle models using equivalent spherical diameters. The developed model provides a new tool to explore the dissolution parameter space for these systems and for considering the impact of radial variation of granule porosity and drug load arising from manufacturing processes.

Characterization of NIR interfaces for the feeding and in-line monitoring of a continuous granulation process.

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).

Prediction of dissolution profiles by non-destructive NIR spectroscopy in bilayer tablets.

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.