Optimizing Environmental Sustainability in Pharmaceutical 3D Printing through Machine Learning.

3D Printing (3DP) of pharmaceuticals could drastically transform the manufacturing of medicines and facilitate the widespread availability of personalised healthcare. However, with increasing awareness of the environmental damage of manufacturing, 3DP must be eco-friendly, especially when it comes to carbon emissions. This study investigated the environmental effects of pharmaceutical 3DP. Using Design of Experiments (DoE) and Machine Learning (ML), we looked at energy use in pharmaceutical Fused Deposition Modeling (FDM). From 136 experimental runs across four common dosage forms, we identified several key parameters that contributed to energy consumption, and consequently CO emission. These parameters, identified by both DoE and ML, were the number of objects printed, build plate temperature, nozzle temperature, and layer height. Our analysis revealed that minimizing trial-and-error by being more efficient in R&D and reducing the build plate temperature can significantly decrease CO emissions. Furthermore, we demonstrated that only the ML pipeline could accurately predict CO emissions, suggesting ML could be a powerful tool in in the development of more sustainable manufacturing processes. The models were validated experimentally on new dosage forms of varying geometric complexities and were found to maintain high accuracy across all three dosage forms. The study underscores the potential of merging sustainability and digitalization in the pharmaceutical sector, aligning with the principles of Industry 5.0. It highlights the comparable learning traits between DoE and ML, indicating a promising pathway for wider adoption of ML in pharmaceutical manufacturing. Through focused efforts to reduce wasteful practices and optimize printing parameters, we can pave the way for a more environmentally sustainable future in pharmaceutical 3DP.