Pharmaceutical tablet manufacturers lack reliable computer models to predict whether a tablet will actually work as intended—dissolve at the right speed, release the drug properly—before they make it. Current prediction methods fall into two unsatisfactory camps. Simple empirical models only work for narrow sets of ingredients and conditions. Complex physics-based models are accurate but so computationally demanding that industry rarely uses them. The result is that manufacturers must rely on trial-and-error testing, which slows development and raises costs. This project builds a hybrid model that combines mechanistic equations describing the physical processes inside a tablet with machine learning to handle the computational heavy lifting. The model would link how a tablet is made (process parameters) to its internal structure and then to how it performs—for example, how quickly it disintegrates. An inverse optimisation step would then let engineers work backwards: specify the desired performance and calculate the exact formulation and process settings needed to achieve it. If successful, this could let pharmaceutical companies design tablets with specific, reliable properties on a computer rather than through physical experimentation. The approach could also extend to other manufactured structured products where performance depends on internal architecture.
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The manufacturing process for pharmaceutical tablets involves a series of process units, designed to create a structured product from often complicated mixtures of pharmaceutical ingredients. The bulk of research in the field has concentrated on creating process models to predict tablet properties, but there's a notable absence of dependable models which depict tablet functionality, known as product performance models, or simply product models. Existing product performance models tend to be either 1) highly empirical, focusing on specific formulation properties or process conditions, or 2) complex and computationally demanding models, which provide excellent detail but are impractical to use extensively in industry. There's a growing demand for mechanistic models which can accurately describe and predict the key rate processes involved in product performance. In order to reduce computational burden, there is an additional need to hybridise these models with machine learning techniques. Ideally, these models would facilitate the optimization of critical process parameters (CPPs) based on critical quality attributes (CQAs), such as achieving specific disintegration times. This could be accomplished by establishing a connection between the process model and the product performance model through the intermediate stage of tablet structure. Employing an appropriate inverse optimization approach would then allow for the determination of the necessary formulation and process parameters to achieve the desired outcomes. The aim of this research is to develop a coupled mechanistic and machine learning model for the performance of pharmaceutical tablets.
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