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Machine learning enabled integrated formulation and process design framework for a pharmaceutical 3D printing platform
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DOI：10.1002/aic.17990.png)
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Abstract
En 中文
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
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