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.

Multi-layer Raman chemical mapping to investigate the effect of API particle size and blending shear rate on API domain sizes in pharmaceutical tablets.

While macromixing (gross uniformity) has received a lot of attention in pharmaceutical powder blending, micromixing (particularly, particle-level aggregation) has been significantly less studied. This study investigated the impact of active pharmaceutical ingredient (API) particle size (D50: 11, 28, and 70 µm) and blending shear rate (low and high) that was caused by tumbling blending (specifically, a V-blender) on micro-mixing. The effect on micro-mixing (API domain sizes) was assessed in direct compression tablets using high-resolution Raman chemical mapping. Analyses of multiple layers within tablets enabled a more reliable understanding of the variability in API domain sizes with respect to the independent variables. The relationship between API domain sizes and the manufactured tablets' content uniformity (CU) was also investigated using near-infrared transmission spectroscopy. Generally, at low shear, as the API particle size decreased, the frequency and size of API agglomerates increased, resulting in poor CU. However, in all cases, API domain sizes drastically reduced at high shear, resulting in an acceptable CU. The results of this work clearly demonstrated the utility of a multi-layer, multi-tablet, and high-resolution Raman chemical mapping as an off-line process analytical technology (PAT) system, to enable quality-by-design driven formulation and process development.

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.

Residence time distribution as a traceability method for lot changes in a pharmaceutical continuous manufacturing system.

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.

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.

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.

Residence time distribution modelling and in line monitoring of drug concentration in a tablet press feed frame containing dead zones.

The presence of a 'significant dead zone' in any continuous manufacturing equipment may affect the product quality and need to be investigated systematically. Dead zone will affect the residence time distribution (RTD) of continuous manufacturing and thus the mixing and product quality. Tablet press (feed frame) is one of unit operations that directly influence the critical quality attributes (CQA's). However, currently no systematic methods and tools are available to characterize and model the feed frame dead zone. In this manuscript, the RTD of the tablet press feed frame containing dead zone is investigated. Step-change experiments revealed that the feed frame could be expressed as a traditional continuous stirred tank model. The volume fractions of the dead zones are determined experimentally as well as using RTD model. In addition, an in-line NIR method for drug concentration monitoring inside the feed frame is also developed. The developed NIR calibration model enables to monitor the drug concentration precisely and detect the variation immediately with the probe positioned right above the left paddle. It is also found that the feed frame paddle speed slightly affects the predictive accuracy of NIR, while the die disc speed has no significant effect.

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).

Method transfer of a near-infrared spectroscopic method for blend uniformity in a poorly flowing and hygroscopic blend.

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.

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.

Classical Least Squares-Assisted Mid-Infrared (MIR) Laser Spectroscopy Detection of High Explosives on Fabrics.

Mid-infrared (MIR) laser spectroscopy was used to detect the presence of residues of high explosives (HEs) on fabrics. The discrimination of the vibrational signals of HEs from a highly MIR-absorbing substrate was achieved by a simple and fast spectral evaluation without preparation of standards using the classical least squares (CLS) algorithm. Classical least squares focuses on minimizing the differences between the spectral features of the actual spectra acquired using MIR spectroscopy and the spectral features of calculated spectra modeled from linear combinations of the spectra of neat components: HEs, fabrics, and bias. Samples in several combinations of cotton fabrics/HEs were used to validate the methodology. Several experiments were performed focusing on binary, ternary, and quaternary mixtures of TNT, RDX, PETN, and fabrics. The parameters obtained from linear combinations of the calculated spectra were used to perform discrimination analyses and to determine the sensitivity and selectivity of HEs with respect to the substrates and to each other. However, discrimination analysis was not necessary to achieve successful detection of HEs on cotton fabric substrates. The RDX signals ( mRDX > 0.02 mg) on cotton were used to calculate the limit of detection (LOD). The signal-to-noise ratios (S/N) calculated from the spectra of cotton dosed with decreasing masses of RDX until S/N ≈ 3 resulted in a LOD of 15-33 µg, depending on the vibrational band used. Linear fits generated by comparing the mass dosed RDX with the fraction predicted were also used to calculate the LOD based on the uncertainty of the blank and the slope. This procedure resulted in a LOD of 58 µg. Probably the most representative value of the method LOD was calculated using an interpolation of a threshold determined using the predicted average value for the blank plus 3.28 times the standard deviations ( p-value threshold) for low surface dosages of RDX (LOD = 40 µg). The contribution demonstrates that to achieve HE detection on fabrics using the proposed algorithm, i.e., determining the presence/absence of HEs on the substrates, the library must contain the spectra of HEs, substrates, and potential interferents or that these spectra be added to the models in the field. If the model does not contain the spectra of the fabric components, there is a high probability of finding false positives for clean samples (no HEs) and a low probability for failed detection in samples with HEs. More work will be required to demonstrate that these new approaches to HE detection work on real-world samples and when contaminating materials are present in the samples.

Development of near infrared spectroscopic calibration models for in-line determination of low drug concentration, bulk density, and relative specific void volume within a feed frame.

This study describes the development of a near infrared (NIR) calibration model for real time determination of drug concentration, powder density, and porosity or relative specific void volume (RSVV) of 3.00%w/w acetaminophen blends within a feed frame. The NIR calibration model was developed from 1.50 to 4.50%w/w of acetaminophen, using a high variability of major excipients (from 12.92 to 81.95%w/w) which facilitates the prediction of powder density and RSVV based on near infrared calibration spectra. The model using second derivative as spectral preprocessing explained the changes related to acetaminophen concentration in the first latent variable. The second latent variable was related to changes in concentration of microcrystalline cellulose and lactose in the powder blends. NIR calibrations were also developed based on the bulk density and RSVV of the powder blends using the same design as the API model, due to the physical properties of the particles and their effects on the NIR spectra. The RSVV was predicted for the independent set blends with an RSEP(%) below 4% with a significantly low bias (0.04 cm3/g) from reference values of 1.33 to 1.58 cm3/g. The bulk density model also exhibited excellent predictions with RSEP(%) below 2.6% and significantly low bias (0.01 g/cm3) from reference values of 0.45 to 0.51 g/cm3. The excellent results obtained show the potential of near infrared spectroscopic measurements within the feed frame for a Process Analytical Technology method to control the critical properties such as tablet mass, hardness and dissolution in batch and continuous manufacturing processes.

Applications of Quantum Cascade Laser Spectroscopy in the Analysis of Pharmaceutical Formulations.

Quantum cascade laser spectroscopy was used to quantify active pharmaceutical ingredient content in a model formulation. The analyses were conducted in non-contact mode by mid-infrared diffuse reflectance. Measurements were carried out at a distance of 15 cm, covering the spectral range 1000-1600 cm(-1) Calibrations were generated by applying multivariate analysis using partial least squares models. Among the figures of merit of the proposed methodology are the high analytical sensitivity equivalent to 0.05% active pharmaceutical ingredient in the formulation, high repeatability (2.7%), high reproducibility (5.4%), and low limit of detection (1%). The relatively high power of the quantum-cascade-laser-based spectroscopic system resulted in the design of detection and quantification methodologies for pharmaceutical applications with high accuracy and precision that are comparable to those of methodologies based on near-infrared spectroscopy, attenuated total reflection mid-infrared Fourier transform infrared spectroscopy, and Raman spectroscopy.

Near infrared spectroscopic calibration models for real time monitoring of powder density.

Near infrared spectroscopic (NIRS) calibration models for real time prediction of powder density (tap, bulk and consolidated) were developed for a pharmaceutical formulation. Powder density is a critical property in the manufacturing of solid oral dosages, related to critical quality attributes such as tablet mass, hardness and dissolution. The establishment of calibration techniques for powder density is highly desired towards the development of control strategies. Three techniques were evaluated to obtain the required variation in powder density for calibration sets: 1) different tap density levels (for a single component), 2) generating different strain levels in powders blends (and as consequence powder density), through a modified shear Couette Cell, and 3) applying normal forces during a compressibility test with a powder rheometer to a pharmaceutical blend. For each variation in powder density, near infrared spectra were acquired to develop partial least squares (PLS) calibration models. Test samples were predicted with a relative standard error of prediction of 0.38%, 7.65% and 0.93% for tap density (single component), shear and rheometer respectively. Spectra obtained in real time in a continuous manufacturing (CM) plant were compared to the spectra from the three approaches used to vary powder density. The calibration based on the application of different strain levels showed the greatest similarity with the blends produced in the CM plant.

Near infrared spectroscopic transmittance measurements for pharmaceutical powder mixtures.

This study describes the development of near infrared (NIR) calibration models using transmittance measurements in powder samples and compares the results obtained with those of tablet transmittance and diffuse reflectance of powders. Transmission near infrared spectroscopy is a method widely used for the analysis of tablets in the evaluation of drug concentration due to the larger sample volume analyzed, but not commonly used for the analysis of powder samples. Diffuse reflection near infrared spectroscopy is a method used in both powder and tablets for the evaluation of quality attributes. In this initial study NIR transmittance measurements were obtained using an off-line spectrometer equipped with a high intensity light source. Spectra were obtained with three different resolutions for the analysis of powder and tablet samples of 7.50-22.50% (w/w) acetaminophen. The Partial Least Squares (PLS) calibration models developed include pretreatments such as Standard Normal Variate (SNV) and first derivative in the region from 9500-7500 cm(-1). Transmittance in powder presented low Root Mean Square Error of Prediction (RMSEP) values that varied from 0.23-1.15% (w/w) APAP with resolution of 64 and 16 cm(-1). The lowest RMSEP values (0.23-0.39% (w/w) APAP) were obtained using a resolution of 64 cm(-1). The RMSEP values for powder transmittance measurements were 2.4-5.6 times lower than the diffuse reflectance measurements of the powder mixtures.

Adequacy and verifiability of pharmaceutical mixtures and dose units by variographic analysis (Theory of Sampling) - A call for a regulatory paradigm shift.

In spite of intense efforts in the last 20 years, the current state of affairs regarding evaluation of adequacy of pharmaceutical mixing is at an impressive standstill, characterized by two draft guidances, one withdrawn, and the other never approved. We here analyze the regulatory, scientific and technological situation and suggest a radical, but logical approach calling for a paradigm shift regarding sampling of pharmaceutical blends. In synergy with QbD/PAT efforts, blend uniformity testing should only be performed with properly designed sampling that can guarantee representativity-in contrast to the current deficient thief sampling. This is necessary for suitable in-process specifications and dosage units meeting desired specifications. The present exposé shows how process sampling based on the Theory of Sampling (TOS) constitutes a new asset for regulatory compliance, providing procedures that suppress hitherto adverse sampling errors. We identify that the optimal sampling location is after emptying the blender, guaranteeing complete characterisation of the residual heterogeneity. TOS includes variographic analysis that decomposes the effective total sampling and analysis error (TSE+TAE) from the variability of the manufacturing process itself. This approach provides reliable in-process characterization allowing independent approval or rejection by the Quality Control unit. The science-based sampling principles presented here will facilitate full control of blending processes, including whether post-blending segregation influences the material stream that reaches the tabletting feed-frame.

Real time monitoring of powder blend bulk density for coupled feed-forward/feed-back control of a continuous direct compaction tablet manufacturing process.

The pharmaceutical industry is strictly regulated, where precise and accurate control of the end product quality is necessary to ensure the effectiveness of the drug products. For such control, the process and raw materials variability ideally need to be fed-forward in real time into an automatic control system so that a proactive action can be taken before it can affect the end product quality. Variations in raw material properties (e.g., particle size), feeder hopper level, amount of lubrication, milling and blending action, applied shear in different processing stages can affect the blend density significantly and thereby tablet weight, hardness and dissolution. Therefore, real time monitoring of powder bulk density variability and its incorporation into the automatic control system so that its effect can be mitigated proactively and efficiently is highly desired. However, real time monitoring of powder bulk density is still a challenging task because of different level of complexities. In this work, powder bulk density which has a significant effect on the critical quality attributes (CQA's) has been monitored in real time in a pilot-plant facility, using a NIR sensor. The sensitivity of the powder bulk density on critical process parameters (CPP's) and CQA's has been analyzed and thereby feed-forward controller has been designed. The measured signal can be used for feed-forward control so that the corrective actions on the density variations can be taken before they can influence the product quality. The coupled feed-forward/feed-back control system demonstrates improved control performance and improvements in the final product quality in the presence of process and raw material variations.