A Novel Semi‐Automated Pipeline for Optimizing 3D‐Printed Drug Formulations

3D printing offers a promising approach to creating personalized medicines. However, costly, expertise‐dependent trial‐and‐error methods hinder efficient drug formulation, posing challenges for tailoring treatments to individual patients. To address this, a novel pipeline is developed for 3D printing using selective laser sintering (SLS), replacing laborious steps with advanced computational methods. A differential evolution‐based optimizer generates formulations for the desired drugs, while a deep learning ensemble predicts the optimal printing parameters along with associated confidence intervals. Manual handling is only required for the final formulation preparation and printing processes. The pipeline successfully generates diverse formulations, composed of a wide variety of materials and with high printability probabilities. This was validated by successfully printing 80% of the generated drug formulations and achieving 92% accuracy in predicting printing parameters. Notably, the time required to develop and print a new drug formulation is decreased to a single day. This study is the first to demonstrate a semiautomated, 3D printing drug formulation design and printing parameter selection pipeline. Furthermore, the pipeline is not limited to SLS printing but can also be adapted for the optimization of other 3D printing technologies or formulation platforms.