Process control of a dropwise additive manufacturing system for pharmaceuticals using polynomial chaos expansion based surrogate model

• A dropwise additive manufacturing process for pharmaceutical products is described.
• A process control framework is developed for this process.
• A data driven model based on polynomial chaos expansion is developed.
• The surrogate model is used to relate critical process parameters to product quality.
• The model is used to optimize the temperature profile to achieve desired bioavailability.

The paper presents a dropwise additive manufacturing process for pharmaceutical products (DAMPP) as an alternative to conventional methods. This mini manufacturing process for the production of personalized pharmaceutical products utilizes drop-on-demand (DoD) printing technology for the deposition of active pharmaceutical ingredient (API) onto edible substrates. Here we present a process control framework for DAMPP, including on-line monitoring, automation and closed loop control, in order to produce individual dosage forms with the desired critical quality attributes, including formulation composition, drop size, deposit morphology and dissolution performance. In order to achieve desired product morphology, a surrogate model based on polynomial chaos expansion is developed to relate the critical process parameters to deposit morphology using dissolution data of the active pharmaceutical ingredient. The proposed process control strategy can effectively mitigate variations in the dissolution profiles due to variable dosage amounts and enable the application of the DoD system for the production of individualized dosage regimens.