Continuous Powder Blending Research Articles

Real-time release testing of in vitro dissolution and blend uniformity in a continuous powder blending process by NIR spectroscopy and machine vision

Continuous manufacturing is gaining increasing interest in the pharmaceutical industry, also requiring real-time and non-destructive quality monitoring. Multiple studies have already addressed the possibility of surrogate in vitro dissolution testing, but the utilization has rarely been demonstrated in real-time. Therefore, in this work, the in-line applicability of an artificial intelligence-based dissolution surrogate model is developed the first time. NIR spectroscopy-based partial least squares regression and artificial neural networks were developed and tested in-line and at-line to assess the blend uniformity and dissolution of encapsulated acetylsalicylic acid (ASA) – microcrystalline cellulose (MCC) powder blends in a continuous blending process. The studied blend is related to a previously published end-to-end manufacturing line, where the varying size of the ASA crystals obtained from a continuous crystallization significantly affected the dissolution of the final product. The in-line monitoring was suitable for detecting the variations in the ASA content and dissolution caused by the feeding of ASA with different particle sizes, and the at-line predictions agreed well with the measured validation dissolution curves (f2 = 80.5). The results were further validated using machine vision-based particle size analysis. Consequently, this work could contribute to the advancement of RTRT in continuous end-to-end processes.

Manufacturing process transfer to a 30 kg/h continuous direct compression line with real-time composition monitoring

Transferring an existing marketed pharmaceutical product from batch to continuous manufacturing (CM) without changes in regulatory registration is a challenging task in the pharmaceutical industry. Continuous manufacturing can provide an increased production rate and better equipment utilisation while retaining key quality attributes of the final product. Continuous manufacturing necessitates the monitoring of critical quality attributes in real time by appropriate process analytical tools such as near infra-red (NIR) probes. The present work reports a successful transfer of an existing drug product from batch to continuous manufacturing process without changing the formulation. A key step was continuous powder blending, whose design and operating parameters including weir type, agitation rate, dynamic hold-up and residence time were systematically investigated with respect to process repeatability. A NIR-based multivariate data model for in-line composition monitoring has been developed and validated against an existing quality control method for measuring tablet content uniformity. A continuous manufacturing long-run with a throughput of 30 kg/h (approx. 128,000 tablets per hour), uninterrupted for 320 min, has been performed to test and validate the multivariate data model as well as the batch to continuous process transfer. The final disintegration and dissolution properties of tablets manufactured by the continuous process were found to be equivalent to those manufactured by the original batch process.

Selection of an appropriate tracer to measure the residence time distribution (RTD) of continuous powder blending operations

Residence time distributions (RTDs) are quickly becoming an important component of the quality control strategy for continuous manufacturing (CM) of pharmaceutical products. However, methodologies for determining RTDs, including appropriate selection of tracers and proper processing of RTD data, have not been fully developed, and there is potential to improve current methods.This work aims to implement protocols for tracer selection and establish a methodology for the determination and quantitative comparison of RTD curves obtained by the pulse method. We used an extensive material property data library and multivariate analyses to select appropriate tracers for active pharmaceutical ingredient (API) of interest. Tracer performance was evaluated in two continuous blenders using pulse input experiments. The equivalence of RTDs obtained from the API and the tracer was examined for formulations with widely different blend flow properties.For the most suitable tracer candidate, material properties were determined for mixtures with different API concentrations to ensure that blend properties containing varying amounts of tracer along the RTD profile remained similar to those of the target formulation. It was observed that even small changes in the physical properties of the target formulation would result in a different tracer RTD. This observation emphasized the importance of preserving the physical properties of the blend when characterizing the dynamical behavior of a unit operation.Further, the RTD of the tracer and the API in the blender were characterized under three different base flow properties. The outcome of these experiments enabled a fair side-by-side comparison of RTDs obtained using pulses of either API or tracer for powders with a varying range of properties. In finding an appropriate tracer, RTD profiles were compared quantitively using different methods. Multivariate analysis of variance (MANOVA) was shown to be a suitable tool for RTD profile comparisons.

Process intensification using a semi-continuous mini-blender to support continuous direct compression processing

The production of pharmaceutical tablets is shifting towards continuous manufacturing. Therefore, individual unit operations such as powder blending need to be re-designed. Continuous powder blending has been implemented successfully, but for low-dose formulations this is challenging due to difficulties with continuously dosing small amounts of powder. To overcome such challenges, a semi-continuous mini-blending operation combined with batch-wise dosing can be introduced in a continuous process. The objective of this study is to investigate key factors that affect blending performance of a novel, semi-continuous mini-blending process. A design of experiments is performed, varying both process settings and blend formulations. This reveals that blending performance of the mini-blending process is largely controlled by process settings. These results show that the mini-blender design, with high mixing intensity and short mixing times, supports process intensification of powder blending. The results obtained can be used for Quality by Design-based formulation development for continuous direct compression.

Residence Time Distribution-Based Smith Predictor: an Advanced Feedback Control for Dead Time–Dominated Continuous Powder Blending Process

PurposeIn continuous manufacturing (CM), the material traceability and process dynamics can be investigated by residence time distribution (RTD). Many of the unit operations used in the pharma industry were characterized by dead time–dominated RTD. Even though feasible and proper feedback control is one of the many advantages of CM, its application is challenging in these cases. This study aims to develop a feedback control, implementing the RTD in a Smith predictor control structure in a continuous powder blender line.MethodsContinuous powder blending was investigated with near-infrared spectroscopy (NIR), and the blending was controlled through a volumetric feeder. A MATLAB GUI was developed to calculate and control the concentration of the API based on the chemometric evaluation of the spectra. The programmed GUI changed the feeding rate based on the proportional integral derivative (PID) and the Smith predictor, which implemented the RTD of the system. The control structures were compared even on a system with amplified dead time.ResultsIn this work, the control structure of the Smith control was devised by utilizing the RTD of the system. The Smith control was compared to a classic PI control structure on the normal system and on an increased dead time system. The Smith predictor was able to reduce the response time for various disturbances by up to 50%, and the dead time had a lower effect on the control.ConclusionsImplementing the RTD models in the control structure improved the process design and further expanded the wide range of applications of the RTD models. Both control structures were able to reduce the effect of disturbances on the system; however, the Smith predictor presented more reliable and faster control, with a wider space for control tuning.

Assessing residence time distributions and hold-up mass in continuous powder blending using discrete element method

Continuous manufacturing of pharmaceutical products can improve process efficiency and quality assurance. The propagation of material through continuous manufacturing processes can be characterized by residence time distributions (RTDs). Conventional parameter-fitted RTD models rely extensively on experimental measurements that are often expensive and limited to available observation methods and instruments. This limitation can be compensated by discrete element method (DEM) simulations. This work evaluates the predictive capabilities of DEM modeling for a continuous powder mixing process. First, the developed DEM model was validated using experimentally measured RTDs. Then, the effects of the mass flow rate, blender element pattern, and blender speed on mean residence time and hold-up mass were studied. The simulation results showed that the mean residence time increased linearly with increasing number of mixing elements but decreased with increasing mass flow rate and blender speed. The hold-up mass increased with increasing mass flow rate and number of mixing elements but decreased with increasing blender speed. The work successfully demonstrated the capability of the DEM model in improving our understanding regarding the impacts of the blender operating conditions on the RTD and hold-up mass as well as in supporting the assessment of control strategies for continuous blending processes.

Investigation of granular dynamics in a continuous blender using the GPU-enhanced discrete element method

Continuous powder blending is an essential operation during continuous pharmaceutical manufacturing. However, the complex granular dynamics in the blender is still poorly understood. This study employs a graphic processor unit (GPU) enhanced discrete element method (DEM) to analyse the granular dynamics in a continuous blender. Numerical results indicate that only a small fraction of powder distributes in the upper region of the blender, while most of that distributes in the middle and lower regions. Besides, a higher impeller speed leads to a smaller hold-up mass and a shorter mean residence time. Interestingly, the maximum number of blade passes is achieved at an intermediate impeller speed. There are two distinct regimes during continuous blending: i) a shearing regime at low impeller speeds; and ii) a dynamic regime at high impeller speeds. This study demonstrates that the GPU-enhanced DEM can be a robust tool for analysing powder flow during continuous pharmaceutical manufacturing. • GPU-enhanced DEM analysis on the full-scale continuous blending is performed. • Impeller speed has a significant impact on the powder flow in the inclined blender. • At steady-state, powder mainly distributes in the middle and bottom zones of blender. • Maximum number of blade passes can be achieved at an intermediate impeller speed.

Digital twin of low dosage continuous powder blending – Artificial neural networks and residence time distribution models

In this paper we present a thorough description of the digital twin development for a continuous pharmaceutical powder blending process in accordance with the Process Analytical Technologies (PAT) and Quality by Design (QbD) guidelines. A low-dosage system of caffeine active pharmaceutical ingredient (API) and dextrose excipient was examined via continuous blending experiments. Near infrared (NIR) spectroscopy–based process analytics were applied; quantitative evaluation of spectra was achieved using multivariate data analysis. The blending system was represented with mechanistic residence time distribution (RTD) models and two types of recurrent artificial neural networks (ANN), experimental datasets were used for model training or fitting and validation. Detailed comparison of the two modelling approaches, the optimization of the model-based digital twin, and efficiency of the soft sensor-based process monitoring is presented through several validating simulations. Both RTD models and nonlinear autoregressive neural networks demonstrated excellent predictive power for the low dosage blending process. RTD models can prove to be more advantageous in industrial development as they are less resource-intensive to develop and prediction accuracy on low concentration levels lacks dependency from the precision of chemometric calibration. Reduced material costs and limited development timeframe render the digital twin an efficient tool in technological development.

Impact of excipients on batch and continuous powder blending

Stimulated by Pharma 4.0, the pharmaceutical industry is changing from batch-wise to continuous manufacturing. Therefore, individual processing steps such as blending of the active pharmaceutical ingredient (API) with excipients have to be re-designed to continuous operations. In this study, batch and continuous powder blending are directly compared for API with three different excipients with varying material properties. Content uniformity analysis of the blends shows that the continuous process results in more homogeneous blends for all excipients, independent of API dosage. The type of excipient used, however does impact the consistency of the continuous blending process over time. The results presented here reveal the impact of the physical material properties of excipients on blending with API in batch and continuous processes.

Continuous blending monitored and feedback controlled by machine vision-based PAT tool

In a continuous powder blending process machine vision is utilized as a Process Analytical Technology (PAT) tool. While near-infrared (NIR) and Raman spectroscopy are reliable methods in this field, measurements become challenging when concentrations below 2 w/w% are quantified. However, an active pharmaceutical ingredient (API) with an intense color might be quantified in even lower quantities by images recorded with a digital camera. Riboflavin (RI) was used as a model API with orange color, its Limit of Detection was found to be 0.015 w/w% and the Limit of Quantification was 0.046 w/w% using a calibration based on the pixel value of images. A calibration for in-line measurement of RI concentration was prepared in the range of 0.2−0.45 w/w%, validation with UV/VIS spectrometry showed great accuracy with a relative error of 2.53 %. The developed method was then utilized for a residence time distribution (RTD) measurement in order to characterize the dynamics of the blending process. Lastly, the technique was applied in real-time feedback control of a continuous powder blending process. Machine vision based direct or indirect API concentration determination is a promising and fast method with a great potential for monitoring and control of continuous pharmaceutical processes.

Process Design of Continuous Powder Blending Using Residence Time Distribution and Feeding Models.

The present paper reports a thorough continuous powder blending process design of acetylsalicylic acid (ASA) and microcrystalline cellulose (MCC) based on the Process Analytical Technology (PAT) guideline. A NIR-based method was applied using multivariate data analysis to achieve in-line process monitoring. The process dynamics were described with residence time distribution (RTD) models to achieve deep process understanding. The RTD was determined using the active pharmaceutical ingredient (API) as a tracer with multiple designs of experiment (DoE) studies to determine the effect of critical process parameters (CPPs) on the process dynamics. To achieve quality control through material diversion from feeding data, soft sensor-based process control tools were designed using the RTD model. The operation block model of the system was designed to select feasible experimental setups using the RTD model, and feeder characterizations as digital twins, therefore visualizing the output of theoretical setups. The concept significantly reduces the material and instrumental costs of process design and implementation.

Effect of tracer material properties on the residence time distribution (RTD) of continuous powder blending operations. Part I of II: Experimental evaluation

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.

Spectroscopic characterization of tablet properties in a continuous powder blending and tableting process

By the advent of continuous pharmaceutical manufacturing, fast and accurate characterization of product quality has become of a major interest. Although it also promotes the real-time release testing approach, so far mainly content uniformity studies were performed by near-infrared (NIR) spectroscopy. This paper proposes the simultaneous application of NIR and Raman spectroscopy to nondestructively analyze the critical quality attributes of continuously produced tablets in a real-time release testing procedure. A face-centered composite design was applied to determine the impact of lubrication and compression force on the properties of a tablet formulation containing caffeine, glucose-monohydrate and magnesium stearate and to provide a systematic comparison of the applicability of spectroscopic methods. Quantitative methods were developed to evaluate different lubrication approaches in a continuous blending and tableting line. The simultaneous application of NIR and Raman spectroscopy revealed that NIR spectroscopy is more suitable to follow the changes of compression force, while Raman spectroscopy could be successfully applied for the detection of overlubrication. The presented approach can be a part of a comprehensive real-time release strategy, where NIR and Raman spectroscopy provide complementary information about multiple critical quality attributes, such as content uniformity, tablet hardness, friability and dissolution.

Modeling and simulation of continuous powder blending applied to a continuous direct compression process

Continuous manufacturing techniques are increasingly being adopted in the pharmaceutical industry and powder blending is a key operation for solid-dosage tablets. A modeling methodology involving axial and radial tanks-in-series flowsheet models is developed to describe the residence time distribution (RTD) and blend uniformity of a commercial powder blending system. Process data for a six-component formulation processed in a continuous direct compression line (GEA Pharma Systems) is used to test the methodology. Impulse tests were used to generate experimental RTDs which are used along with parameter estimation to determine the number of axial tanks in the flowsheet. The weighted residual from the parameter estimation was less than the χ2 value at a 95% confidence indicating a good fit between the model and measured data. In-silico impulse tests showed the tanks-in-series modeling methodology could successfully describe the RTD behavior of the blenders along with blend uniformity through the use of radial tanks. The simulation output for both impulse weight percentage and blend uniformity were within the experimentally observed variance.

Probing pharmaceutical powder blending uniformity with electrostatic charge measurements

Abstract The tribo-electrification of the powder particles is ubiquitous in pharmaceutical powder blending processes. It usually carries a negative connotation as particles may jam and stick inside equipments. Though it is practically important, the tribo-electrification is rarely characterized quantitatively in pharmaceutical areas. The purpose of this research was to examine the relationship between the electrostatic properties of pharmaceutical powders and blend homogeneity. We hypothesized that once the powder mixtures reached the homogeneity, the bulk electrostatic charges of the randomly collected samples at different spatial locations of the powder mixture would converge. Otherwise, the charges would scatter over a wide range due to non-uniformity of the powder mixture. Three prototypic pharmaceutical mixtures with (or without) caffeine as a model active pharmaceutical ingredient, microcrystalline cellulose as a filler/binder (Avicels), Cab-o-sil as a glidant, and Magnesium Stearate as a lubricant, were formulated and mixed in a V-blender. The resulted electrostatic charges were measured with a Faraday Cup connected to an electrometer and a strong correlation between electrostatic charges and blend homogeneity was found. The electrostatic charge monitoring method could be potentially used for assessing the blend uniformity. We also discovered the cyclical blending/deblending phenomena occurred with certain periodicity patterns upon continuous powder blending process, which may help design proper pharmaceutical blending procedures.

Improving Continuous Powder Blending Performance Using Projection to Latent Structures Regression

Purpose There has been increasing interest in the last few years especially within the pharmaceutical industry towards continuous powder blending. In this paper, the effects of different design and operating parameters are investigated, which include blade speed, shaft angle, weir height, fill level, blade angle, and blade width.

Scale-up strategy for continuous powder blending process

Abstract Continuous powder mixing has attracted a lot of interest within the pharmaceutical industry. Much work has been done recently that targets the characterization of continuous powder mixing. In this paper, a quantitative scaling up strategy is introduced that allows the transition from lab to industrial scale. The proposed methodology is based on the variance spectrum analysis, and the residence time distribution, which are key indexes in capturing scale-up of the batch-like mixing, and scale-up of the axial mixing and motion, respectively. Our simulation results are used as preliminary guidance for scaling up different powder mixing cases.

A review on the continuous blending of powders

This review traces the underlying theory and practice of continuous powder blending to provide a foundation for its development. Apart from reviewing the experimental studies reported in literature, some of the monitoring techniques used in blending research with applications in continuous blending also are considered. This review attempts to identify the synergy that can be realized by studying powder behavior in batch blenders and applying similar techniques to continuous blenders. Predictive modeling in the design of continuous blenders is identified as a long-term objective in this growing body of knowledge.