Developing a machine learning enabled integrated formulation and process design framework for a pharmaceutical dropwise additive manufacturing printer.

The pharmaceutical manufacturing sector needs to rapidly evolve to absorb the next wave of disruptive industrial innovations - Industry 4.0. This involves incorporating technologies like artificial intelligence, smart factories and 3D printing to automate, miniaturize and personalize the production processes. The goal of this study is to build a formulation and process design (FPD) framework for a pharmaceutical 3D printing technique called drop-on-demand (DoD) printing. FPD can automate the determination of formulation properties and printing conditions (input conditions) for DoD operation that can guarantee production of drug products with desired functional attributes. This study proposes to build the FPD framework in two parts: the first part involves building a machine learning model to simulate the forward problem - predicting DoD operation based on input conditions and the second part seeks to solve and experimentally validate the inverse problem - predicting input conditions that can yield desired DoD operation.