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This paper presents a new method for process synthesis and economic assessment for solid drug product manufacturing, considering continuous manufacturing as a prominent process alternative. Of the three phases of drug development, phase II was targeted where the dosage form, formulation, and processing technology are determined. For a comprehensive alternative generation, a superstructure was developed that covered 9452 options for the unit level, which was combined with two options on the formulation strategy. The generated alternative was assessed by a net present value calculation model, which was adapted for dynamic cash flow consideration in the drug lifecycle. The model can incorporate uncertainty in the drug development and manufacturing in the result, and can perform global sensitivity analysis by Monte Carlo simulation. The method was demonstrated in a case study where two different scenarios regarding the price of the active pharmaceutical ingredient and the demand for the product were assumed. The results showed that when the demand and price are both low, the labor-related costs are dominant, and in the opposite case, the material-related costs become relevant. We also introduce the prototype version of the software "SoliDecision," by which the presented method was implemented for industrial application.

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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 pilotscale, 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.