T-shaped partial least squares for high-dosed new active pharmaceutical ingredients in continuous twin-screw wet granulation: Granule size prediction with limited material information

Hybrid modeling of T-shaped partial least squares regression and transfer learning for formulation and manufacturing process development of new drug products

T-shaped partial least squares regression (T-PLSR) is a valuable machine learning technique for the formulation and manufacturing process development of new drug products. An accurate T-PLSR model requires experimental data with multiple formulations and process conditions. However, it is usually challenging to collect comprehensive experimental data using large-scale manufacturing equipment because of the cost, time, and large consumption of raw materials. This study proposes a hybrid modeling of T-PLSR and transfer learning (TL) to enhance the prediction performance of a T-PLSR model for large-scale manufacturing data by exploiting a large amount of small-scale manufacturing data for model building. The proposed method of T-PLSR+TL was applied to a practical case study focusing on scaling up the tableting process from an experienced compaction simulator to a less-experienced rotary tablet press. The T-PLSR+TL models achieved significantly better prediction performance for tablet quality attributes of new drug products than T-PLSR models without using large-scale manufacturing data with new drug products. The results demonstrated that T-PLSR+TL is more capable of addressing new drug products than T-PLSR by using small-scale manufacturing data to cover a scarcity of large-scale manufacturing data. Furthermore, T-PLSR+TL holds the potential to streamline formulation and manufacturing process development activities for new drug products using an extensive database.

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.

Validation of model-based design of experiments for continuous wet granulation and drying

This paper presents an application case of model-based design of experiments for the continuous twin-screw wet granulation and fluid-bed drying sequence. The proposed framework consists of three previously developed models. Here, we are testing the applicability of previously published unit operation models in this specific part of the production line to a new active pharmaceutical ingredient. Firstly, a T-shaped partial least squares regression model predicts d-values of granules after wet granulation with different process settings. Then, a high-resolution full granule size distribution is computed by a hybrid population balance and partial least squares regression model. Lastly, a mechanistic model of fluid-bed drying simulates drying time and energy efficiency, using the outputs of the first two models as a part of the inputs. In the application case, good operating conditions were calculated based on material and formulation properties as well as the developed process models. The framework was validated by comparing the simulation results with three experimental results. Overall, the proposed framework enables a process designer to find appropriate process settings with a less experimental workload. The framework combined with process knowledge reduced 73.2% of material consumption and 72.3% of time, especially in the early process development phase.