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Machine learning enabled integrated formulation and process design framework for a pharmaceutical 3D printing platform |
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| Authors: | Varun Sundarkumar Zoltan K. Nagy Gintaras V. Reklaitis |
| Affiliation: | 1. Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA;2. Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA Contribution: Conceptualization (equal), Funding acquisition (equal), Project administration (equal), Resources (equal), Supervision (equal), Writing - review & editing (equal) |
| Abstract: | 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 and 3D printing (3DP) to automate and personalize the drug production processes. This study aims to build a formulation and process design (FPD) framework for a pharmaceutical 3DP platform that recommends operating (formulation and process) conditions at which consistent drop printing can be obtained. The platform used in this study is a displacement-based drop-on-demand 3D printer that manufactures dosages by additively depositing the drug formulation as droplets on a substrate. The FPD framework is built in two parts: the first part involves building a machine learning model to simulate the forward problem—predicting printer operation for given operating conditions and the second part seeks to solve and experimentally validate the inverse problem—predicting operating conditions that can yield desired printer operation. |
| Keywords: | 3D printing additive manufacturing artificial neural networks formulation and process design Industry 4.0 machine learning Pharma 4.0 pharmaceutical manufacturing pharmaceutical product process design |