Twin-screw Granulation Process Research Articles

Towards the prediction of barrel fill level in twin-screw wet granulation

The barrel fill level is defined as the fraction of the free available volume for a given screw configuration that is occupied by the wet material and is an interplay of the material throughput, screw speed, screw setup, barrel length of the twin-screw granulator used and the properties of the starting material. The fill level has a major impact on mixing and densification of the wetted mass and thus on the granules produced. It influences the twin-screw granulation process accordingly.In the current study, a model has been developed which is predictive in terms of material hold-ups in the barrel at various process settings by considering the geometries of the different screw elements in a configuration and the conveying velocity of the wet mass through the barrel. The model was checked on two granulators of different dimensions with various screw configurations, different materials and at different process settings.The model represents a step forward in predicting the barrel fill level but further research with a broader spectrum of materials, screw configurations and process settings is still needed and additional twin-screw granulators of other dimensions must be investigated.

Process Simulation of Twin-Screw Granulation: A Review.

Twin-screw granulation has emerged as a key process in powder processing industries and in the pharmaceutical sector to produce granules with controlled properties. This comprehensive review provides an overview of the simulation techniques and approaches that have been employed in the study of twin-screw granulation processes. This review discusses the major aspects of the twin-screw granulation process which include the fundamental principles of twin-screw granulation, equipment design, process parameters, and simulation methodologies. It highlights the importance of operating conditions and formulation designs in powder flow dynamics, mixing behaviour, and particle interactions within the twin-screw granulator for enhancing product quality and process efficiency. Simulation techniques such as the population balance model (PBM), computational fluid dynamics (CFD), the discrete element method (DEM), process modelling software (PMS), and other coupled techniques are critically discussed with a focus on simulating twin-screw granulation processes. This paper examines the challenges and limitations associated with each simulation approach and provides insights into future research directions. Overall, this article serves as a valuable resource for researchers who intend to develop their understanding of twin-screw granulation and provides insights into the various techniques and approaches available for simulating the twin-screw granulation process.

A novel combination of machine learning models and metaheuristic algorithm to predict important parameters of twin screw wet granulation process

Twin screw granulation (TSG) has recently been emerged as a novel approach for the continuous wet granulation of fine particles (i.e., powders) in the pharmaceutical industry. The presence of brilliant advantages like the ability of operation at very low liquid concentrations and excellent product consistency has made this technique promising. Except positive points, the existence of major challenges like scalability and flexibility in the processing regimes has enhanced the importance of deeper investigations towards true recognition of this process. The central aim of this theoretical article is to develop the modeling process of TSG employing four machine learning models and one metaheuristic algorithms in a hybrid approach. Screw speeds, material throughputs, liquid binder (water)-to-solid ratios, and screw configurations are known as important parameters of TSG process, which were validated via their comparison with the obtained experimental data. GBR, SGD, and SVR were finally selected for 3 targets with their best combinations of hyper-parameters employing FA. The output is based on d-values (d10, d50, d90) for the granulate particle size distribution (PSD). Final models have R2 scores of 0.919, 0.960, and 0.877 for d10, d50, d90 outputs, respectively.

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.

Continuous manufacturing of vitamin D3 and iron enriched granules by means of a novel twin-screw dry granulation process

A multi-disciplinary approach involving continuous manufacturing (CM) of thermally stable food supplements containing vitamin D3 (VitD3) and iron (Fe ++ ) via a single-step twin-screw granulation (TSG) process was tested in this work. Two selected excipients, namely Neusilin® US2 and corn starch, were used in the work. The particle size distribution and morphology of pre-mixed and post-extruded granules were investigated with a laser diffraction particle size analyzer, sieve analysis, scanning electron microscopy and energy dispersive X-ray (SEM-EDX), X-ray powder diffraction (PXRD), and polarized light microscopy (PLM). The thermal stability of the granules was assessed through differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The SEM visualized a good distribution of all ingredients without the forming of any crystals on the extrudate surface, indicating good mixing during the high shear granulation inside the extruder barrel. The FT-IR analysis demonstrates that characteristic vibration peaks of VitD3 were present in all physical mixtures as well as the extruded granules, indicating that VitD3 was stable in the formulations despite the high shear mixing during the TSG. The mean volumetric diameter (Dv50) of the formulation containing Neusilin increased significantly ( P < 0.05) to 90.8-μm when compared with neat Neusilin (71.3-μm) and VitD3 (33.8 μm) confirming granulation, however, no improvement was observed for the corn starch blend. In addition, the calculated Hausner ratio (HR) and Carr's index (CI) suggest similar inter-particulate friction of VitD3 and Neusilin, whereas corn starch showed a difference. The surface elemental analysis confirmed the presence of Fe ++ with a homogenous distribution of the metal in the granules. Overall, for the first time, the blend of VitD3 and iron has been prepared using the CM platform, which has great potential for the intervention of VitD3 and iron deficiencies. • Twin screw dry granulation is employed to develop vitamin D3 and iron granules. • Neusilin and corn starch are used as excipients for the blend. • Particle size distributions and morphology confirm the formation of granules. • Vitamin D3 remained almost constant in the TSG process.

Model driven design for integrated twin screw granulator and fluid bed dryer via flowsheet modelling

This paper presents a flowsheet modelling of an integrated twin screw granulation (TSG) and fluid bed dryer (FBD) process using a Model Driven Design (MDD) approach. The MDD approach is featured by appropriate process models and efficient model calibration workflow to ensure the product quality. The design space exploration is driven by the physics of the process instead of extensive experimental trials. By means of MDD, the mechanistic-based process kernels are first defined for the TSG and FBD processes. With the awareness of the underlying physics, the complementary experiments are carried out with relevance to the kinetic parameters in the defined models. As a result, the experiments are specifically purposeful for model calibration and validation. The L/S ratio (liquid to solid ratio) and inlet air temperature are selected as the Critical Process Parameters (CPPs) in TSG and FBD for model validation, respectively. Global System Analysis (GSA) is further performed to assess the uncertainty of CPPs imposed on the Critical Quality Attributes (CQAs), which provides significant insights to the exploration of the design space considering both TSG and FBD process parameters.

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.

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.

Model driven design for twin screw granulation using mechanistic-based population balance model

This paper presents a generic framework of Model Driven Design (MDD) with its application for a twin screw granulation process using a mechanistic-based population balance model (PBM). The process kernels including nucleation, breakage, layering and consolidation are defined in the PBM. A recently developed breakage kernel is used with key physics incorporated in the model formulation. Prior to granulation experiments, sensitivity analysis of PBM parameters is performed to investigate the variation of model outputs given the input parameter variance. The significance of liquid to solid ratio (L/S ratio), nucleation and breakage parameters is identified by sensitivity analysis. The sensitivity analysis dramatically reduces the number of fitting parameters in PBM and only nine granulation experiments are required for model calibration and validation. A model validation flowchart is proposed to elucidate the evolution of kinetic rate parameters associated with L/S ratio and screw element geometry. The presented MDD framework for sensitivity analysis, parameter estimation, model verification and validation can be generalized and applied for any particulate process.

TPLS as predictive platform for twin-screw wet granulation process and formulation development

In recent years, the interest in continuous manufacturing techniques, such as twin-screw wet granulation, has increased. However, the understanding of the influence of the combination of raw material properties and process settings upon the granule quality attributes is still limited. In this study, a T-shaped partial least squares (TPLS) model was developed to link raw material properties, the ratios in which these raw materials were combined and the applied process parameters for the twin-screw wet granulation process with the granule quality attributes. In addition, the predictive ability of the TPLS model was used to find a suitable combination of formulation composition and twin-screw granulation process settings for a new API leading to desired granule quality attributes. Overall, this study helped to better understand the link between raw material properties, formulation composition and process settings on granule quality attributes. Moreover, as TPLS can provide a reasonable starting point for formulation and process development for new APIs, it can reduce the experimental development efforts and consequently the consumption of expensive (and often limited available) new API.

Improvement of a 1D Population Balance Model for Twin-Screw Wet Granulation by Using Identifiability Analysis.

Recently, the pharmaceutical industry has undergone changes in the production of solid oral dosages from traditional inefficient and expensive batch production to continuous manufacturing. The latest advancements include increased use of continuous twin-screw wet granulation and application of advanced modeling tools such as Population Balance Models (PBMs). However, improved understanding of the physical process within the granulator and improvement of current population balance models are necessary for the continuous production process to be successful in practice. In this study, an existing compartmental one-dimensional PBM of a twin-screw granulation process was improved by altering the original aggregation kernel in the wetting zone as a result of an identifiability analysis. In addition, a strategy was successfully applied to reduce the number of model parameters to be calibrated in both the wetting zone and kneading zones. It was found that the new aggregation kernel in the wetting zone is capable of reproducing the particle size distribution that is experimentally observed at different process conditions as well as different types of formulations, varying in hydrophilicity and API concentration. Finally, it was observed that model parameters could be linked not only to the material properties but also to the liquid to solid ratio, paving the way to create a generic PBM to predict the particle size distribution of a new formulation.

GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator

During twin screw granulation (TSG), small particles, which generally have irregular shapes, agglomerate together to form larger granules with improved properties. However, how particle shape impacts the conveying characteristics during TSG is not explored nor well understood. In this study, a graphic processor units (GPUs) enhanced discrete element method (DEM) is adopted to examine the effect of particle shape on the conveying characteristics in a full scale twin screw granulator for the first time. It is found that TSG with spherical particles has the smallest particle retention number, mean residence time, and power consumption; while for TSG with hexagonal prism (Hexp) shaped particles the largest particle retention number is obtained, and TSG with cubic particles requires the highest power consumption. Furthermore, spherical particles exhibit a flow pattern closer to an ideal plug flow, while cubic particles present a flow pattern approaching a perfect mixing. It is demonstrated that the GPU-enhanced DEM is capable of simulating the complex TSG process in a full-scale twin screw granulator with non-spherical particles.

DEM analysis of residence time distribution during twin screw granulation

Twin screw granulation (TSG) is increasingly used to produce granules in various industries, such as food, pharmaceutical, and fine chemicals. However, there is a large parametric space in terms of screw designs, formulation properties and operating conditions, so how to maximise the production throughput while maintaining consistent product quality is not a trivial task and still needs further investigation. In this study, the TSG process was systematically analysed using a discrete element method (DEM) based on the graphics processor unit (GPU) architectures that can provide not only macroscopic information but also microscopic insights into the complicated TSG process. In particular, the particle flow profiles and residence time distributions were obtained from the simulations and analysed in details. The effects of particle size and screw speed on flow behaviour of particles in TSG were also explored. It was shown that the mean residence time and its variance in the granulator decreased with increasing particle size and screw speed. The E-curves of the residence time with larger particle size at higher screw speed had a narrower spread, implying that particles with a larger size had similar residence time in the twin screw granulator. In addition, the cumulative distribution function, the F-curves, showed a higher increasing rate for larger particles and higher screw speeds, indicating a faster conveying efficiency.

Development and Scale-Up of Diversion Strategy for Twin Screw Granulation in Continuous Manufacturing

Successful implementation of Continuous Manufacturing technology requires real time product quality monitoring that can result into rejection strategies for material manufactured outside process control limits. In a twin screw granulation process, parameters like water content, powder feed rate, and granulator screw speed can influence granule quality. Deviations in any of these parameters from the set-point may affect granule quality. Having a sound diversion strategy in place can help divert these implicated granules to waste. Residence time distribution experiments were conducted on a 16-mm Thermo Fisher twin screw granulator (TSG) for a range of process parameters, and the data was modelled to predict the needed diversion time as a function of process parameters. Scale-up from the 16-mm to 24-mm granulator was evaluated and data was found to scale based on mass per unit volume of granulator (channel fill), thus enabling 16-mm data to scale to 24-mm. The diversion strategy proposed is based on utilizing a wash out curve derived from residence time distribution to quantify the maximum concentration of implicated material that could be present in the next downstream unit operation(s) (e.g. a fluid bed dryer) and ensuring it is less than a suitable threshold to prevent product quality impact.

Switch of tablet manufacturing from high shear granulation to twin-screw granulation using quality by design approach.

This study aimed to transfer a high shear granulation (HSG) process to a twin-screw granulation (TSG) process while maintaining equivalent dissolution profiles. Ibuprofen (IBP) was used as poorly soluble model drug. Granules were obtained by HSG or TSG according to a full factorial design. The liquid-to-solid ratio and wet massing time (HSG) or powder throughput (TSG) were selected as factors. The granules were compressed to tablets with immediate release and a drug load of 50% (w/w). Quality attributes (QAs) of the granules, especially the granule strength (GS), and the resulting tablets were evaluated. The effect of process parameters on the QAs was statistically analyzed. The comparison of HSG tablets with TSG tablets revealed that TSG tablets showed higher tensile strength and lower ejection force than HSG tablets. The dissolution profiles of the tablets in different pH media were also evaluated. Equivalent dissolution profiles in all four media (e.g., f2 values≥54 in pH5.5) were obtained by adjusting process parameters. It was concluded that the GS was the most important QA for dissolution.

An interpretable fuzzy logic based data-driven model for the twin screw granulation process

In this research, a new framework based on fuzzy logic is proposed to model the twin screw granulation (TSG) process. First, various fuzzy logic systems (FLSs) having different structures are developed to define various rule bases. The extracted fuzzy rules are assessed and reduced accordingly into a single rule base by utilizing the singular value decomposition-QR factorization (SVD-QR) approach. The resulted reduced FLS is, then, implemented to describe the TSG process mathematically and linguistically via simple to understand IF-THEN rules. The linguistic output provides an accessible framework to increase the understanding of this complex process within an industrial context. Validated on laboratory-scale experiments, it is shown that the newly proposed model successfully predicts the granule size and enhances the understanding of the TSG process. Furthermore, the proposed framework outperforms the standard FLS and the Artificial Neural Network (ANN), with an overall improvement of approximately 16% and 29% in R2, respectively.

Continuous twin screw granulation: Impact of the starting material properties and various process parameters

The purpose of this study is to confirm the correlation between the formulation factors and the process parameters using multivariate analysis (MVA) during twin screw granulation. Various grades of lactose and MCC were used as starting materials and the characteristics of final granules were investigated by varying the L/S ratio and feed rate. The size distribution, friability, and various flow properties of granules using powder rheometer were evaluated. When the effect of formulation factors and process parameters were investigated, it was confirmed that the characteristics of granules change largely according to L/S ratio. MVA also showed that the influence of formulation factors on characteristics of granules was significant as L/S ratio. The feed rate had few effects on the characteristics of the granules. MVA is a useful tool to confirm the correlations of various factors. The properties of the starting materials are important in the continuous twin screw granulation process.

Managing API raw material variability during continuous twin-screw wet granulation

Very few studies have investigated the impact of raw material variability upon the granule critical quality attributes (CQAs) produced via twin-screw wet granulation (i.e., granule size distribution, density, flowability). In this study, the impact of the raw material variability of an active pharmaceutical ingredient (API) in a high dose formulation on the twin-screw wet granulation process and on the resulting granule quality attributes was investigated. In a previous study (Stauffer et al., 2018), eight API batches were characterized to determine the API batch-to-batch variability. Principal component analysis (PCA) was then used to analyse the raw material property differences between the API batches and to determine the causes of the batch-to-batch variability. In current study, the three principal components from that PCA model were used as factors together with twin-screw granulation process parameters (i.e., screw speed and liquid-to-solid ratio) in a D-optimal screening design of experiments to understand the influence of these factors upon the granule CQAs. It was found that the API particle size distribution and related properties (e.g., density, agglomeration profile) were critical for the granule CQAs. In a next step, the significant factors from the screening design results were used to determine the design space of the twin-screw granulation process for the studied formulation via a D-optimal optimisation design, herewith controlling the risk of failure for the potential API raw material variability. The possibility to obtain suitable granule CQAs with a risk of failure of 1% for all API batches was demonstrated. It was thus possible to identify a combination of process parameters that can manage the API batch-to-batch variability leading to granules with pre-defined suitable CQAs.

Advanced process design and understanding of continuous twin-screw granulation via implementation of in-line process analytical technologies

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

Effect of formulation and process variables on lipid based sustained release tablets via continuous twin screw granulation: A comparative study

The current study's aim is to prepare lipid based sustained release tablets via a twin-screw granulation technique and compare those dosage forms with conventional techniques, namely wet granulation and direct compression. The granules were successfully manufactured in a single-step, continuous twin-screw granulation process with a low proportion of binder (Klucel™ EF, HPC SSL) using Compritol® 888 ATO, Precirol® ATO 5 and Geleol™ as sustained release agents. The granules prepared showed good flow characteristics and compaction properties. DSC and XRD studies were conducted to characterize the granules prepared via a twin-screw granulation method and the results demonstrated the crystalline nature of lipids within the granules. FTIR data indicated that there were no interactions with the formulation components investigated. The formulations developed by all three methods were compressed into tablets with a mechanical strength of 14–16 KP. The tablets formulated were characterized for physicochemical properties, in vitro drug release studies, water uptake and erosion studies. These results showed that the drug was not completely released after 24 h for tablets developed by the wet granulation process using all three lipids. The tablets prepared by the direct compression method demonstrated a burst release within 8 to 10 h from Precirol ATO 5® and Geleol™ formulations compared to Compritol® 888 ATO. However, tablets prepared using twin-screw granulation exhibited sustained release of the drug over 24 h and the water uptake and erosion results were in accordance with dissolution data. Stability data for 45 days at accelerated conditions (40 °C/75% RH) showed similar release profiles with ƒ2 values above 50 for all of the twin screw granulation formulations, indicating the suitability of the process for formulating sustained release tablets. These findings of a single-step, continuous twin-screw granulation process are novel and demonstrate new opportunities for development of sustained release tablets.