A Semi-Mechanistic Prediction of Residence Time Metrics in Twin Screw Granulation

Characterizing a design space for a twin-screw wet granulation process: A case study of extended-release tablets

Twin-screw wet granulation is an emerging continuous manufacturing technology for solid oral dosage forms. This technology has been successfully employed for the commercial manufacture of immediate-released tablets. However, the higher polymer content in extended-release (ER) formulations may present challenges in developing and operating within a desired design space. The work described here used a systematic approach for defining the optimum design space by understanding the effects of the screw design, operating parameters, and their interactions on the critical characteristics of granules and ER tablets. The impacts of screw speed, powder feeding rate, and the number of kneading (KEs) and sizing elements on granules and tablets characteristics were investigated by employing a definitive screening design. A semi-mechanistic model was used to calculate the residence time distribution parameters and validated using the tracers. The results showed that an increase in screw speed decreased the mean residence time of the material within the barrel, while an increase in the powder feeding rate or number of KEs did the opposite and increased the barrel residence time. Screw design and operating parameters affected the flow and bulk characteristics of granules. The screw speed was the most significant factor impacting the tablet's breaking strength. The dissolution profiles revealed that granule characteristics mainly influenced the early phase of drug release. This study demonstrated that a simultaneous optimization of both operating and screw design parameters was beneficial in producing ER granules and tablets of desired performance characteristics while mitigating any failure risks, such as swelling during processing.

Application of I-Optimal Design for Modeling and Optimizing the Operational Parameters of Ibuprofen Granules in Continuous Twin-Screw Wet Granulation

The continuous twin-screw wet granulation (TSWG) process was investigated and optimized with prediction-oriented I-optimal designs. The I-optimal designs can not only obtain a precise estimation of the parameters that describe the effect of five input process parameters, including the screw speed, liquid-to-solid (L/S) ratio, TSWG feed rate, and numbers of the 30° and 60° mixing elements, on the granule quality in a TSWG process, but it can also provide a prediction of the response to determine the optimum operating conditions. Based on the constraints of the desired granule properties, a design space for the TSWG was determined, and the ranges of the operating parameters were defined. An acceptable degree of prediction was confirmed through validation experiments, demonstrating the reliability and effectiveness of using the I-optimal design method to study the TSWG process. The I-optimal design method can accelerate the screening and optimization of the TSWG process.

A Population Balance Methodology Incorporating Semi-Mechanistic Residence Time Metrics for Twin Screw Granulation

This work is concerned with the incorporation of semi-mechanistic residence time metrics into population balance equations for twin screw granulation processes to predict key properties. From the historical residence time and particle size data sourced, process parameters and equipment configuration information were fed into the system of equations where the input flow rates and model compartmentalization varied upon the parameters. Semi-mechanistic relations for the residence time metrics were employed to predict the particle velocities and dispersion coefficients in the axial flow direction of the twin screw granulation. The developed model was then calibrated for several experimental run points in each data-set. The predictions were evaluated quantitatively through the parity plots. The root mean square error (RMSE) was used as a metric to compare the degree of goodness of fit for different data-sets using the developed semi-mechanistic relations. In summary, this paper presents a more mechanistic but simplified approach of feeding residence time metrics into the population balance equations for twin screw granulation processes.