Effect of speed, loading and spray pattern on coating variability in a pan coater

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.

Comparison of video analysis and simulations of a drum coating process

Tablet coating is a common unit operation in the pharmaceutical industry. To improve currently established processes, it is important to understand the influence of the process parameters on the coating quality. One of the critical parameters is the tablet velocity. In this work, numerical results are compared to results obtained experimentally. Tablet movement in the drums was simulated using the Discrete Element Method (DEM). The simulation parameters were adapted to fit the simulation to the experimental data. A comparison of the experimental and simulation results showed that the simulation correctly represents the real tablet velocity. A change in the velocity over time and its dependence on the rotation rates and the baffle position in the simulation were similar to the experimental results. In summary, simulations can improve the understanding of tablet coating processes and will thus provide insights into the underlying process mechanics, which cannot be obtained via ordinary experiments.

The Future of Pharmaceutical Manufacturing Sciences

The entire pharmaceutical sector is in an urgent need of both innovative technological solutions and fundamental scientific work, enabling the production of highly engineered drug products. Commercial-scale manufacturing of complex drug delivery systems (DDSs) using the existing technologies is challenging. This review covers important elements of manufacturing sciences, beginning with risk management strategies and design of experiments (DoE) techniques. Experimental techniques should, where possible, be supported by computational approaches. With that regard, state-of-art mechanistic process modeling techniques are described in detail. Implementation of materials science tools paves the way to molecular-based processing of future DDSs. A snapshot of some of the existing tools is presented. Additionally, general engineering principles are discussed covering process measurement and process control solutions. Last part of the review addresses future manufacturing solutions, covering continuous processing and, specifically, hot-melt processing and printing-based technologies. Finally, challenges related to implementing these technologies as a part of future health care systems are discussed.

Experimental analysis of tablet properties for discrete element modeling of an active coating process

Coating of solid dosage forms is an important unit operation in the pharmaceutical industry. In recent years, numerical simulations of drug manufacturing processes have been gaining interest as process analytical technology tools. The discrete element method (DEM) in particular is suitable to model tablet-coating processes. For the development of accurate simulations, information on the material properties of the tablets is required. In this study, the mechanical parameters Young's modulus, coefficient of restitution (CoR), and coefficients of friction (CoF) of gastrointestinal therapeutic systems (GITS) and of active-coated GITS were measured experimentally. The dynamic angle of repose of these tablets in a drum coater was investigated to revise the CoF. The resulting values were used as input data in DEM simulations to compare simulation and experiment. A mean value of Young's modulus of 31.9 MPa was determined by the uniaxial compression test. The CoR was found to be 0.78. For both tablet-steel and tablet-tablet friction, active-coated GITS showed a higher CoF compared with GITS. According to the values of the dynamic angle of repose, the CoF was adjusted to obtain consistent tablet motion in the simulation and in the experiment. On the basis of this experimental characterization, mechanical parameters are integrated into DEM simulation programs to perform numerical analysis of coating processes.

Improvement of tablet coating uniformity using a quality by design approach

A combination of analytical and statistical methods is used to improve a tablet coating process guided by quality by design (QbD) principles. A solid dosage form product was found to intermittently exhibit bad taste. A suspected cause was the variability in coating thickness which could lead to the subject tasting the active ingredient in some tablets. A number of samples were analyzed using a laser-induced breakdown spectroscopy (LIBS)-based analytical method, and it was found that the main variability component was the tablet-to-tablet variability within a lot. Hence, it was inferred that the coating process (performed in a perforated rotating pan) required optimization. A set of designed experiments along with response surface modeling and kriging method were used to arrive at an optimal set of operating conditions. Effects of the amount of coating imparted, spray rate, pan rotation speed, and spray temperature were characterized. The results were quantified in terms of the relative standard deviation of tablet-averaged LIBS score and a coating variability index which was the ratio of the standard deviation of the tablet-averaged LIBS score and the weight gain of the tablets. The data-driven models developed based on the designed experiments predicted that the minimum value of this index would be obtained for a 6% weight gain for a pan operating at the highest speed at the maximum fill level while using the lowest spraying rate and temperature from the chosen parametric space. This systematic application of the QbD-based method resulted in an enhanced process understanding and reducing the coating variability by more than half.