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Four-dimensional (4D) printing, as a newly evolving technology to formulate drug delivery devices, displays distinctive advantages that can autonomously monitor drug release according to the actual physiological circumstances. In this work, we reported our earlier synthesized novel thermo-responsive self-folding feedstock for possible SSE-mediated 3D printing to form a 4D printed construct deploying machine learning (ML) modeling to determine its shape recovery behavior followed by its potential drug delivery applications. Therefore, in the present study, we converted our earlier synthesized temperature-responsive self-folding (both placebo and drug-loaded) feedstock into 4D printed constructs using SSE-mediated 3D printing technology. Further, the shape memory programming of the printed 4D construct was achieved at 50 °C followed by shape fixation at 4 °C. The shape recovery was achieved at 37 °C, and the obtained data were used to train and ML algorithms for batch optimization. The optimized batch showed a shape recovery ratio of 97.41. Further, the optimized batch was used for the drug delivery application using paracetamol (PCM) as a model drug. The % entrapment efficiency of the PCM-loaded 4D construct was found to be 98.11 ± 1.5%. In addition, the in vitro release of PCM from this programmed 4D printed construct confirms temperature-responsive shrinkage/swelling properties via releasing almost 100% ± 4.19 of PCM within 4.0 h. at gastric pH medium. In summary, the proposed 4D printing strategy pioneers the paradigm that can independently control drug release with respect to the actual physiological environment.

Four-Dimensional Printed Construct from Temperature-Responsive Self-Folding Feedstock for Pharmaceutical Applications with Machine Learning Modeling