Cohesive effects in powder mixing in a tumbling blender

Optimization of Pregelatinized Taro Boloso-I Starch as a Direct Compression Tablet Excipient

Background. Tablets are still the most preferred means of drug delivery. The search for new and improved direct compression tablet excipients is an area of research focus. This is because the direct compression method overcomes the drawbacks of granulation methods of tablet production. It exempts several treatment steps associated with the granulation methods. The requirements for the powders to be directly compressible include flowability, low friction tendency, compressibility, and fast disintegration capacity. Taro Boloso-I is a new variety of Colocasia esculenta (L. Schott) yielding 67% more than a previously reported variety (Godare) in Ethiopia. This study is aimed at enhancing the flowability while keeping the compressibility and compactibility of the pregelatinized Taro Boloso-I starch. Methods. Central composite design was used for the optimization of two factors which were the temperature and duration of pregelatinization against 4 responses. The responses were angle of repose, Hausner’s ratio, Heckel’s yield pressure, and tablet breaking force. Results and Discussions. An increase in the temperature resulted in decrease in both the angle of repose and the Hausner ratio and that of time decreased angle of repose as well. The Heckel yield pressure was observed to increase with increasing levels of both temperature and time. The pregelatinized starch prepared by heating 15% slurry of Taro Boloso-I starch at the pregelatinization temperature of 66.22°C for 20 min showed desired flow property and compressibility. Conclusions. Pregelatinized Taro Boloso-I starch could be regarded as a potential direct compression excipient in terms of flowability, compressibility, and compactibility. The PGTBIS could perform better as filler and binder in direct compression tablets than the Starch 1500® in terms of compactibility.

Twin-Screw Continuous Mixing Can Produce Dry Powder Inhalation Mixtures for Pulmonary Delivery

The feasibility of twin-screw corotating extruder as a continuous process mixer to prepare dry powder inhalation (DPI) powders was investigated. Interactive mixtures of 1% micronized budesonide, 0.3% magnesium stearate and 98.7% alpha-lactose monohydrate were manufactured using a Leistritz Nano-16 extruder at various processing conditions. One set of GFM (grooved mixing) elements were included in the screw profile to provide distributive mixing of conveyed powders with the goal of resulting in a homogeneous mixture. Residence time in the twin-screw mixer was modelled to quantify mixing efficiency. Comparative powders were also prepared using either low or high-shear batch mixing to compare the effect of mixing methods on the properties of the budesonide dry powder inhalation formulation. Twin screw mixing results in homogeneous mixtures with aerosol performance comparable to that of high-shear batch mixing. Scanning electron microscopy confirmed that twin screw mixing produces particles with morphology like that of low and high-shear batch mixing. X-ray diffraction (XRD) analysis verified that there was no form change of the drug due to twin-screw processing. Statistical regression was used to probe the relationship between twin screw mixing process parameters such as screw speed and feed rate and aerosol performance. The twin screw mixing process was found to be robust, as no significant differences in aerosol performance were found for various processing parameters.

Recent developments in lactose blend formulations for carrier-based dry powder inhalation

Lactose is the most commonly used excipient in carrier-based dry powder inhalation (DPI) formulations. Numerous inhalation therapies have been developed using lactose as a carrier material. Several theories have described the role of carriers in DPI formulations. Although these theories are valuable, each DPI formulation is unique and are not described by any single theory. For each new formulation, a specific development trajectory is required, and the versatility of lactose can be exploited to optimize each formulation. In this review, recent developments in lactose-based DPI formulations are discussed. The effects of varying the material properties of lactose carrier particles, such as particle size, shape, and morphology are reviewed. Owing to the complex interactions between the particles in a formulation, processing adhesive mixtures of lactose with the active ingredient is crucial. Therefore, blending and filling processes for DPI formulations are also reviewed. While the role of ternary agents, such as magnesium stearate, has increased, lactose remains the excipient of choice in carrier-based DPI formulations. Therefore, new developments in lactose-based DPI formulations are crucial in the optimization of inhalable medicine performance.

4D study of liquid binder penetration dynamics in pharmaceutical powders using synchrotron X-ray micro computed tomography

The properties of pharmaceutical powders, and the liquid binder, directly influence the penetration behavior in the wet granulation process of the pharmaceutical industry. Conventional methods encounter challenges in understanding this fast process. In this work, an emerging synchrotron-based X-ray imaging technique (having fast imaging capability) was employed to investigate the internal process from 2D and 3D to real-time (in-situ with ms time intervals) 3D (also considered 4D) perspectives. Two commonly used excipients (lactose monohydrate (LMH) and microcrystalline cellulose (MCC)) were used to make binary mixtures with acetaminophen (APAP) as the active pharmaceutical ingredient (API). Isopropanol and water were employed as liquid binders in the single droplet impact method. Results showed that for most of the mixtures, the porosity increased at higher fractions of APAP. MCC mixtures experienced less agglomeration and more uniform pore distribution than LMH ones, resulting in a faster droplet penetration with isopropanol. Moreover, the imbibition-spreading studies showed that isopropanol penetration in MCC powders followed more unidirectional vertical movement than horizontal spreading. Our results also demonstrated that simultaneous granulation of LMH with water resulted in much slower penetration. This study revealed that synchrotron X-ray imaging can investigate 3D internal pore structures and how they affect the quantitively real-time internal penetration dynamics.

Assessment of Resonant Acoustic Mixing for Low-Dose Pharmaceutical Powder Blends

Obtaining a homogeneous low-dose pharmaceutical powder blend without multi-step processing remains a challenge. One promising technology to address this risk is resonant acoustic mixing (RAM). In this study, the performance of a laboratory resonant acoustic mixer (LabRAM) was studied at low active pharmaceutical ingredient (API) concentrations (0.1-0.5% w/w), using three commercial grades of a model API (Acetaminophen) and diluents with varying physical properties. The performance was assessed by evaluating blend uniformity (BU) and capsule content uniformity (CU) as a function of mixing time. Overall, the LabRAM achieved acceptable BU in a single step even at 0.1% w/w drug loading. A reduction in API primary particle size led to improved mixing efficiency and uniformity. Moreover, the presence of surface cavities in the diluents used appeared to have led to improved uniformity. The results demonstrated that RAM could achieve uniform powder blends without multi-step processing, for low-dose formulations.

A comprehensive review of the application of DEM in the investigation of batch solid mixers

Powder mixing is a vital operation in a wide range of industries, such as food, pharmaceutical, and cosmetics. Despite the common use of mixing systems in various industries, often due to the complex nature of mixing systems, the effects of operating and design parameters on the mixers’ performance and final blend are not fully known, and therefore optimal parameters are selected through experience or trial and error. Experimental and numerical techniques have been widely used to analyze mixing systems and to gain a detailed understanding of mixing processes. The limitations associated with experimental techniques, however, have made discrete element method (DEM) a valuable complementary tool to obtain comprehensive particle level information about mixing systems. In the present study, the fundamentals of solid-solid mixing, segregation, and characteristics of different types of batch solid mixers are briefly reviewed. Previously published papers related to the application of DEM in studying mixing quality and assessing the influence of operating and design parameters on the mixing performance of various batch mixing systems are summarized in detail. The challenges with regards to the DEM simulation of mixing systems, the available solutions to address those challenges and our recommendations for future simulations of solid mixing are also presented and discussed.

Direct Compaction Drug Product Process Modeling

Most challenges during the development of solid dosage forms are related to the impact of any variations in raw material properties, batch size, or equipment scales on the product quality and the control of the manufacturing process. With the ever pertinent restrictions on time and resource availability versus heightened expectations to develop, optimize, and troubleshoot manufacturing processes, targeted and robust science-based process modeling platforms are essential. This review focuses on the modeling of unit operations and practices involved in batch manufacturing of solid dosage forms by direct compaction. An effort is made to highlight the key advances in the past five years, and to propose potentially beneficial future study directions.

Enhancing flowability of fine cohesive active pharmaceutical ingredients

OBJECTIVE

The effectiveness of pharmaceutical excipients and hydrophobic nano-silica as flow aid/regulator was studied and two active pharmaceutical ingredients, i.e. paracetamol and ibuprofen were used to gauge the effectiveness. Avicel PH-101 and Avicel PH-102 were chosen as pharmaceutical excipients and Aerosil R972 Pharma was used as hydrophobic nano-silica, the API's and the excipients were micro-sized.

SIGNIFICANCE

Fine powders lead to many operational difficulties in the pharmaceutical industry which need to be alleviated. The reliability and repeatability of the Powder Flow Tester need to be established.

METHODS

Flow function, wall friction, and bulk density tests to gauge the said effect were conducted on Brookfield Powder Flow Tester. The samples for testing were prepared by blending (rigorous mixing) to ascertain the capability of inexpensive mixing in comparison to expensive blenders and machines.

RESULTS

The study undertook shows that blending (rigorous mixing) was sufficient in improving the flow of active pharmaceutical ingredient powders. Blending with excipients Avicel PH-101 and Avicel PH-102 improved the flow substantially but the maximum improvement was seen in the nano blended samples.

CONCLUSIONS

The study conducted underlined the efficacy of the blending process by showing significant improvement in flow properties. The Powder Flow Tester confirmed the anticipated results of the powder blends indicative of its reliability. Moreover, repeatable results were obtained which established the repeatability of the instrument.

Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets

The tablet manufacturing process is a complex system, especially in continuous manufacturing (CM). It includes multiple unit operations, such as mixing, granulation, and tableting. In tablet manufacturing, critical quality attributes are influenced by multiple factorial relationships between material properties, process variables, and interactions. Moreover, the variation in raw material attributes and manufacturing processes is an inherent characteristic and seriously affects the quality of pharmaceutical products. To deepen our understanding of the tablet manufacturing process, multivariable modeling techniques can replace univariate analysis to investigate tablet manufacturing. In this review, the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process are discussed. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. To minimize the errors in the process of modeling, good modeling practice (GMoP) was introduced into the pharmaceutical process. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. In this review, information is provided to both researchers and manufacturers to improve tablet quality.

DEM based investigation of powder packing in 3D printing of pharmaceutical tablets

3D printing is emerging as one of the most promising methods to manufacture Pharmaceutical dosage forms as it offers multiple advantages such as personalization of dosage forms, polypill, fabrication of complex dosage forms etc. 3D printing came into existence in 1980s but its use was extended recently to pharmaceutical industry along with the approval of first 3D printed tablet Spritam by FDA in 2015. Spritam was manufactured by Aprecia pharmaceuticals using binder jetting technology. Binder jet 3D printing involves a hopper for powder discharge and printheads for ink jetting. The properties of tablets are highly dependent upon the discharge quality of powder mixture from the hopper and jetting of the ink/binder solution from the printhead nozzle. In this study, numerical models were developed using Discrete element method (DEM) to gain better understanding of the binder jet 3D printing process. The DEM modeling of hopper discharge was performed using in-house DEM code to study the effect of raw material attributes such as powder bed packing density (i.e. particle size, particle density etc) on the printing process, especially during powder bed preparation. This DEM model was further validated experimentally, and the model demonstrated good agreement with experimental results.

A machine learning framework for computationally expensive transient models

Transient simulations of dynamic systems, using physics-based scientific computing tools, are practically limited by availability of computational resources and power. While the promise of machine learning has been explored in a variety of scientific disciplines, its application in creation of a framework for computationally expensive transient models has not been fully explored. Here, we present an ensemble approach where one such computationally expensive tool, discrete element method, is combined with time-series forecasting via auto regressive integrated moving average and machine learning methods to simulate a complex pharmaceutical problem: development of an agitation protocol in an agitated filter dryer to ensure uniform solid bed mixing. This ensemble approach leads to a significant reduction in the computational burden, while retaining model accuracy and performance, practically rendering simulations possible. The developed machine-learning model shows good predictability and agreement with the literature, demonstrating its tremendous potential in scientific computing.

The effect of rotation speed and particle size distribution variability on mixability: An avalanche rheological and multivariate image analytical approach

The utility of modulating rotation speed in tumble mixing and its mechanistic interplay with particle size distribution (PSD) variability in excipients remain underexplored. They were investigated in this study. For the present purpose, PSD of a commercial grade lactose was modified to reflect commercially relevant variations; and mixed with microcrystalline cellulose and chlorpheniramine in a double-cone blender, at various rotation speeds. Model of mixing was constructed using avalanche rheological properties and was also rendered as quantifiable visual models using avalanche rheological visual metric (ARVM), to uncover mechanistic relationships. ARVM was derived through multivariate image analysis of avalanche flow. It was observed that increasing rotation speed reduced the number of rotations needed to achieve blend homogeneity by 30-33% for PSD variants with 16-20% fines, while increasing the number of rotations by 134% in PSD variants with less than 15% fines (p ≈ 0.00). ARVM successfully modelled (root mean square error of external validation = 2.46%) and revealed the mechanistic interplay. With increased proportion of fines, lactose exhibited quasi-parabolic motion with disaggregation of soft agglomerates and improved mixing. With decreased proportion of fines, lactose flowed as coherent wave-like masses with imperceptible dispersive tendency and increased dilation, which impeded mixing. In conclusion, this study contributes to process understanding and ideas for designing robust mixing operations. It showcases the usefulness of a quantitative visual approach, exemplified by the ARVM, to evaluate material variability and uncover its mechanistic impact on processing.

Multicomponent granular mixing in a Bohle bin Blender-Experiments and simulation

Study of mixing and segregation of granular materials was performed in a Bohle bin blender using both computational modeling and experiments. A multicomponent mixture of pharmaceutical excipients and coated theophylline granules, an active pharmaceutical ingredient (API) was considered as the blend formulation. A DEM (Discrete Element Method) Model was developed to simulate the flow and mixing of the multicomponent blend to compare with the experimental data. DEM is a numerical modeling technique which incorporates all the material properties (such as Particle size, density, elastic modulus, yield strength, Poisson's ratio, work function etc.)to simulate granular flow (such as mixing, conveying) of particles. In simulation, the degree (Relative standard deviation) of mixing in a Bohle bin blender was assessed as a function of critical processing parameters (loading pattern, rotational rate, and fill percentage). Numerical simulation results reveal radial mixing in a Bohle bin blender is faster than axial mixing due to symmetric geometry limitation. This study investigates a numerical model-based approach to study the effect of the critical process parameters on the mixing dynamics in Bohle bin blender for a moderately cohesive pharmaceutical formulation. The DEM model can be used to provide crucial insights to developed optimized mixing protocols to ascertain the best mixing conditions for different formulation. As for example, as we try to develop a mixing protocol for another formulation with different operational parameters such as loading pattern, rotational speed, and fill percentage, one can device an optimized mixing protocol of the formulation with the help of this DEM model.

Neural Image Analysis and Electron Microscopy to Detect and Describe Selected Quality Factors of Fruit and Vegetable Spray-Dried Powders-Case Study: Chokeberry Powder

The study concentrates on researching possibilities of using computer image analysis and neural modeling in order to assess selected quality discriminants of spray-dried chokeberry powder. The aim of the paper is the quality identification of chokeberry powders on account of their highest dying power, the highest bioactivity, as well as technologically satisfying looseness of the powder. The article presents neural models with vision techniques backed up by devices such as digital cameras, as well as an electron microscope. The reduction in size of input variables with PCA has an influence on improving the processes of learning data sets, thus increasing the effectiveness of identifying chokeberry fruit powders included in digital pictures, which is shown in the results of the conducted research. The effectiveness of image recognition is presented by classifying abilities, as well as low Root Mean Square Error (RMSE), for which the best results are achieved with a typology of network type Multi-Layer Perceptron (MLP). The selected networks type MLP are characterized by the highest degree of classification at 0.99 and RMSE at 0.11 at most at the same time.

Cohesion-driven mixing and segregation of dry granular media

Granular segregation is a common, yet still puzzling, phenomenon encountered in many natural and engineering processes. Here, we experimentally investigate the effect of particles cohesion on segregation in dry monodisperse and bidisperse systems using a rotating drum mixer. Chemical silanization, glass surface functionalization via a Silane coupling agent, is used to produce cohesive dry glass particles. The cohesive force between the particles is controlled by varying the reaction duration of the silanization process, and is measured using an in-house device specifically designed for this study. The effects of the cohesive force on flow and segregation are then explored and discussed. For monosized particulate systems, while cohesionless particles perfectly mix when tumbled, highly cohesive particles segregate. For bidisperse mixtures of particles, an adequate cohesion-tuning reduces segregation and enhances mixing. Based on these results, a simple scheme is proposed to describe the system’s mixing behaviour with important implications for the control of segregation or mixing in particulate industrial processes.

Application of the Discrete Element Method for Manufacturing Process Simulation in the Pharmaceutical Industry

Process simulation using mathematical modeling tools is becoming more common in the pharmaceutical industry. A mechanistic model is a mathematical modeling tool that can enhance process understanding, reduce experimentation cost and improve product quality. A commonly used mechanistic modeling approach for powder is the discrete element method (DEM). Most pharmaceutical materials have powder or granular material. Therefore, DEM might be widely applied in the pharmaceutical industry. This review focused on the basic elements of DEM and its implementations in pharmaceutical manufacturing simulation. Contact models and input parameters are essential elements in DEM simulation. Contact models computed contact forces acting on the particle-particle and particle-geometry interactions. Input parameters were divided into two types-material properties and interaction parameters. Various calibration methods were presented to define the interaction parameters of pharmaceutical materials. Several applications of DEM simulation in pharmaceutical manufacturing processes, such as milling, blending, granulation and coating, were categorized and summarized. Based on this review, DEM simulation might provide a systematic process understanding and process control to ensure the quality of a drug product.

Scale-Up Strategy in Quality by Design Approach for Pharmaceutical Blending Process with Discrete Element Method Simulation

An approach combining quality by design (QbD) and the discrete element method (DEM) is proposed to establish an effective scale-up strategy for the blending process of an amlodipine formulation prepared by the direct compression method. Critical process parameters (CPPs) for intermediate critical quality attributes (IQAs) were identified using risk assessment (RA) in the QbD approach. A Box–Behnken design was applied to obtain the operating space for a laboratory-scale. A DEM model was developed by the input parameters for the amlodipine formulation; blending was simulated on a laboratory-scale V-blender (3 L) at optimal settings. The efficacy and reliability of the DEM model was validated through a comparison of simulation and experimental results. Change of operating space was evaluated using the validated DEM model when scaled-up to pilot-scale (10 L). Pilot-scale blending was simulated on a V-blender and double-cone blender at the optimal settings derived from the laboratory-scale operating space. Both pilot-scale simulation results suggest that blending time should be lower than the laboratory-scale optimized blending time to meet target values. These results confirm the change of operating space during the scale-up process. Therefore, this study suggests that a QbD-integrated DEM simulation can be a desirable approach for an effective scale-up strategy.

A Comprehensive Approach to Powder Feedstock Characterization for Powder Bed Fusion Additive Manufacturing: A Case Study on AlSi7Mg

In powder bed fusion additive manufacturing, the powder feedstock quality is of paramount importance; as the process relies on thin layers of powder being spread and selectively melted to manufacture 3D metallic components. Conventional powder quality assessments for additive manufacturing are limited to particle morphology, particle size distribution, apparent density and flowability. However, recent studies are highlighting that these techniques may not be the most appropriate. The problem is exacerbated when studying aluminium powders as their complex cohesive behaviors dictate their flowability. The current study compares the properties of three different AlSi7Mg powders, and aims to obtain insights about the minimum required properties for acceptable powder feedstock. In addition to conventional powder characterization assessments, the powder spread density, moisture sorption, surface energy, work of cohesion, and powder rheology, were studied. This work has shown that the presence of fine particles intensifies the pick-up of moisture increasing the total particle surface energy as well as the inter-particle cohesion. This effect hinders powder flow and hence, the spreading of uniform layers needed for optimum printing. When spherical particles larger than 48 µm with a narrow particle distribution are present, the moisture sorption as well as the surface energy and cohesion characteristics are decreased enhancing powder spreadability. This result suggest that by manipulating particle distribution, size and morphology, challenging powder feedstock such as Al, can be optimized for powder bed fusion additive manufacturing.

Fibrous dosage forms by wet 3D-micro-patterning: Process design, manufacture, and drug release rate

Recently, we have introduced fibrous dosage forms prepared by 3D-micro-patterning of drug-laden viscous melts. Such dosage forms enable predictable microstructures and increased drug release rates, and they can be manufactured continuously. However, melt processing is not applicable if the melting temperature of the formulation is greater than the degradation temperature of the drug or of the excipient. In this work, therefore, a continuous wet micro-patterning process that operates at ambient temperature is presented. The excipient is plasticized by a solvent and the patterned dosage form is solidified by air drying. Process models show that the micro-patterning time is the ratio of the fiber length in the dosage form and the velocity of the fiber stream. It was 1.3 min in the experiments, but can be reduced further. The drying time is limited by the diffusive flux of solvent through the fibers: it was about 3 min for the experimental conditions. Furthermore, models are developed to illustrate the effects of fiber radius, inter-fiber spacing, viscosity of the drug-excipient-solvent mixture, and drying conditions on the microstructure of the dosage form. Both models and experimental results show that for a viscosity of the wet fibers of the order 10³ Pa·s, the patterned microstructure is well preserved and the crossed fibers are well bonded. Finally, the drug release rate by the dosage forms is experimentally determined and theoretically modeled. The results of the experiments validate the models fairly.

An investigation into the effects of excipient particle size, blending techniques and processing parameters on the homogeneity and content uniformity of a blend containing low-dose model drug

Powder blend homogeneity is a critical attribute in formulation development of low dose and potent active pharmaceutical ingredients (API) yet a complex process with multiple contributing factors. Excipient characteristics play key role in efficient blending process and final product quality. In this work the effect of excipient type and properties, blending technique and processing time on content uniformity was investigated. Powder characteristics for three commonly used excipients (starch, pregelatinised starch and microcrystalline cellulose) were initially explored using laser diffraction particle size analyser, angle of repose for flowability, followed by thorough evaluations of surface topography employing scanning electron microscopy and interferometry. Blend homogeneity was evaluated based on content uniformity analysis of the model API, ergocalciferol, using a validated analytical technique. Flowability of powders were directly related to particle size and shape, while surface topography results revealed the relationship between surface roughness and ability of excipient with high surface roughness to lodge fine API particles within surface groves resulting in superior uniformity of content. Of the two blending techniques, geometric blending confirmed the ability to produce homogeneous blends at low dilution when processed for longer durations, whereas manual ordered blending failed to achieve compendial requirement for content uniformity despite mixing for 32 minutes. Employing the novel dry powder hybrid mixer device, developed at Aston University laboratory, results revealed the superiority of the device and enabled the production of homogenous blend irrespective of excipient type and particle size. Lower dilutions of the API (1% and 0.5% w/w) were examined using non-sieved excipients and the dry powder hybrid mixing device enabled the development of successful blends within compendial requirements and low relative standard deviation.

Mechanistic investigation of mixing and segregation of ordered mixtures: experiments and numerical simulations

Pulmonary delivery of cohesive and micronized drugs through dry powder inhalers (DPIs) is traditionally achieved through the formation of ordered mixtures. In order to improve the mechanistic understanding of formation of ordered mixtures, the system consisting of micronized lactose (AZFL, representative of an active pharmaceutical ingredient) and a coarse particle carrier (LH100) is investigated as a function of different process and material variables in a high shear mixer (HSM) and in a low shear double cone (DCN) blender, using both experimental and numerical methods. Process insight is developed using a Discrete Element Method (DEM) based numerical model which could predict the formation of ordered mixtures in the two blenders and was verified against experimental determinations. Spatial and temporal evolution of granular flow are visualized and quantified in silico to reveal distinguishing features of both blenders to aid in rational selection of blenders and process parameters.