Investigation of comminution in a Wiley Mill: Experiments and DEM Simulations

A Multi-variate Mathematical Model for Simulating the Granule Size Distribution in Roller Compaction-Milling Process

Granule size distribution (GSD) is one of the critical quality attributes in the roller compaction (RC) process. Determination of GSD for newly developed pharmaceutical compounds with unknown ribbon breakage behaviors at the RC milling step requires a quantitative insight into process parameters and ribbon attributes. Despite its pivotal role in mapping the process operating conditions to achieve desired granule size, limited work has been presented in literature with a focus on RC-milling modeling. In this study, a multi-variate mathematical model is presented to simulate the full size-distribution of granulated ribbons as a function of ribbon mechanical properties. Experimental data with a lab-scale oscillating milling apparatus were generated using ribbons made of various powder compositions. Model parameters were determined by fitting it to experimental data sets. Parameters obtained from the first step were correlated to ribbon Young's modulus. The model was validated by predicting GSD of data that were excluded in model development step. Predictive capabilities of the developed model were further explored by simulating GSD profiles of a granulated pharmaceutical excipient obtained at three different conditions of a real-scale Gerteis RC system. While maintaining the milling operating conditions similar to the lab-scale apparatus (i.e., screen size and spacing, and low rotor speed), the proposed modeling approach successfully predicted the GSD of roller compacted MCC powder as the model compound. This model can be alternatively utilized in conjunction with an RC model in order to facilitate the process understanding to obtain granule attributes as part of Quality-by-Design paradigm.

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.

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.

Quantifying Dry Milling in Pharmaceutical Processing: A Review on Experimental and Modeling Approaches

Particle size reduction by mechanical means is an important unit operation in the pharmaceutical industry, used to improve flow, solubility, and in amorphization of drugs. It is usually achieved by the fracturing of particles under the action of applied energy. Despite being pervasive in the pharmaceutical field, it is one of the least understood processes owing to the complexity of material and process variables involved during milling. To comprehend the process, efforts should be focused on techniques that measure the particle size as well as the control the process. With the ongoing initiative of US FDA to encourage design in quality, the review is focused on some process analytical tools to characterize particle size distribution as well as process modeling tools to simulate particle size reduction. Additionally, an overview of some fundamental aspects related to milling is provided. To this end, the review is limited, mainly concentrating on some of experimental and modeling approaches used to quantify and understand the physics behind the process of dry milling.