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As pharmaceutical manufacturing is transitioning from traditional batch to continuous manufacturing (CM), there is a lack of tools for CM design and development, which can integrate drug substance and drug product unit operations for overall evaluation. Recently, a Python-based PharmaPy framework was proposed to advance the design, simulation, and analysis of continuous pharmaceutical processes. However, the initial library of models only addressed upstream drug substance processing. In this work, new capabilities, including drug product unit operations such as feeder, blender, and tablet press, have been added to the PharmaPy framework, enabling end-to-end study and optimizing the effects of material properties and process conditions on solid oral dosage products. The platform supports computational efficiency and model accuracy by allowing the development of different mechanistic and semi-mechanistic models. Sensitivity analysis is performed on the integrated end-to-end simulator to identify critical input variables influencing product quality and control strategies. The analysis lowers the complexity of the model by ranking significant input variables. Finally, feasibility studies are conducted on extracted influential input variables to characterize the process design space and achieve desirable output. The enhanced PharmaPy package can now support decision-making from early research and development stages through manufacturing. 

The determination of the design space (DS) in a pharmaceutical process is a crucial aspect of the quality-by-design (QbD) initiative which promotes quality built into the desired product. This is achieved through a deep understanding of how the critical quality attributes (CQAs) and process parameters (CPPs) interact that have been demonstrated to provide quality assurance. For computational inexpensive models, the original process model can be directly deployed to identify the design space. One such crucial process is the Tablet Press (TP), which directly compresses the powder blend into individual units of the final product or adds dry or wet granulation to meet specific formulation needs. In this work, we identify the design space of input variables in a TP such that there is a (probabilistic) guarantee that the tablets meet the quality constraints under a set of operating conditions. A reduced-order model of TP is assigned for this purpose where the effects of lubricants and glidants are used to characterize the design space to achieve the desired tablet CQAs. The probabilistic design space, which takes into account interactions between crucial process parameters and important quality characteristics including model uncertainty, is also approximated because of the high cost associated with the comprehensive experiments.