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.

Greedy design space construction based on regression and latent space extraction for pharmaceutical development.

Implementation of the design space (DS) is a scientific concept for ensuring quality to be submitted as a part of the regulatory filing of a drug product for approval to market. An empirical approach is constructing the DS based on the regression model whose inputs are process parameters and material attributes over the different unit operations, i.e., a high-dimensional statistical model. While the high-dimensional model assures quality and process flexibility through a comprehensive process understanding, it has difficulty visualizing the feasible range of input parameters, i.e., DS. Therefore, this study proposes a greedy approach to constructing the extensive and flexible low-dimensional DS based on the high-dimensional statistical model and the observed internal representations that satisfies both comprehensive process understanding and the DS visualization capability. Introducing the observed correlation structure enabled the dimensionality reduction of the DS. The non-critical controllable parameters were fixed to the target values in visualizing the low-dimensional DS as a function of critical parameters. The expected variation of non-critical non-controllable parameters was considered the source of variation in prediction. The case study demonstrated the proposed approach's usefulness for developing the pharmaceutical manufacturing process.

Gray-box Soft Sensor for Water Content Monitoring in Fluidized Bed Granulation.

Soft sensors are powerful tools for the implementation of process analytical technology (PAT). They are categorized into white-box (first-principle), black-box (statistical), and gray-box models. Gray-box models integrate white-box and black-box models to address each drawback, i.e., prediction accuracy and intuitiveness. Although they have been applied to various industrial processes, their applicability to water content monitoring in fluidized bed granulation has not been reported. In this study, we evaluated three types of gray-box models, i.e., parallel, serial, and combined gray-box models, in terms of prediction accuracy using real operating data on a commercial scale with two formulations. The gray-box models were constructed by integrating the heat and mass balance model (white-box model) and locally weighted partial least squares regression (LW-PLSR) model (black-box model). LW-PLSR was utilized to cope with collinearity and nonlinearity. In the serial gray-box models, LW-PLSR models adjusted the fitting parameters of the white-box model depending on the process parameters for each query. In the parallel gray-box or combined gray-box models, LW-PLSR models compensated for the output error of the white-box or serial gray-box models, respectively. The results demonstrated that all three types of gray-box models improved the prediction accuracy of the white-box models regardless of the formulation. Besides, we proposed the assessment method based on Hotelling's T2 and Q residual for gray-box models using LW-PLSR, which contributes decision support to select gray-box or white-box model. The accurate and descriptive gray-box models are expected to enhance process understanding and precise quality control in fluidized bed granulation.

Prediction Performance and Economic Efficiency of Soft Sensors for in-Line Water Content Monitoring in Fluidized Bed Granulation: PP-Based Model vs. NIRS-Based Model.

Soft sensors play a crucial role as process analytical technology (PAT) tools. They are classified into physical models, statistical models, and their hybrid models. In general, statistical models are better estimators than physical models. In this study, two types of standard statistical models using process parameters (PPs) and near-infrared spectroscopy (NIRS) were investigated in terms of prediction accuracy and development cost. Locally weighted partial least squares regression (LW-PLSR), a type of nonlinear regression method, was utilized. Development cost was defined as the cost of goods required to construct an accurate model of commercial-scale equipment. Eleven granulation lots consisting of three laboratory-scale, two pilot-scale, and six commercial-scale lots were prepared. Three commercial-scale granulation lots were selected as a validation dataset, and the remaining eight granulation lots were utilized as calibration datasets. The results demonstrated that the PP-based and NIRS-based LW-PLSR models achieved high prediction accuracy without using the commercial-scale data in the calibration dataset. This practical case study clarified that the construction of accurate LW-PLSR models requires the calibration samples with the following two features: 1) located near the validation samples on the subspace spanned by principal components (PCs), and 2) having a wide range of variations in PC scores. In addition, it was confirmed that the reduction in cost and mass fraction of active pharmaceutical ingredient (API) made the PP-based models more cost-effective than the NIRS-based models. The present work supports to build accurate models efficiently and save the development cost of PAT.

Scale-Free Soft Sensor for Monitoring of Water Content in Fluid Bed Granulation Process.

In-line monitoring of granule water content during fluid bed granulation is important to control drug product qualities. In this study, a practical scale-free soft sensor to predict water content was proposed to cope with the manufacturing scale changes in drug product development. The proposed method exploits two key ideas to construct a scale-free soft sensor. First, to accommodate the changes in the manufacturing scale, the process parameters (PPs) that are critical to water content at different manufacturing scales were selected as input variables. Second, to construct an accurate statistical model, locally weighted partial least squares regression (LW-PLSR), which can cope with collinearity and nonlinearity, was utilized. The soft sensor was developed using both laboratory (approx. 4 kg) data and pilot (approx. 25 kg) scale data, and the prediction accuracy in the commercial (approx. 100 kg) scale was evaluated based on the assumption that the process was scaled-up from the pilot scale to the commercial scale. The developed soft sensor exhibited a high prediction accuracy, which was equivalent to the commonly used near-infrared (NIR) spectra-based method. The proposed method requires only standard instruments; therefore, it is expected to be a cost-effective alternative to the NIR spectra-based method.