Predictive models of FDM 3D printing using experimental design based on pharmaceutical requirements for tablet production

[Display omitted] •Predictive models for FDM 3D printing were obtained using experimental design.•Drug quality parameters were interdependently affected by printing configurations.•The mechanical language of the printer was translated according to the prescription.•Integrating the responses allowed the printing setup to control the drug release. The present study aimed to analyze how the printing process affects the final state of a printed pharmaceutical product and to establish prediction models for post-printing characteristics according to basic printing settings. To do this, a database was constructed through analysis of products elaborated with a distinct printing framework. The polymers acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), and high-impact polystyrene (HIPS) were tested in a statistically-based experiment to define the most critical printing factors for mass, mass variation, printing time, and porosity. Then, a predictive model equation was established and challenged to determine two different medical prescriptions. The factors of size scale, printlet format, and print temperature influenced printlet mass, while the printing time was impacted by size scale, printing speed, and layer height. Finally, increased printing speed leads to more porous printlets. The prescript-printed tablets showed average mass, mass variations, and porosity close to theoretical values for all filaments, which supports the adequacy of the optimized design of experiments for tablet production. Hence, printing settings can be preselected according to the desired product’s characteristics, resulting in tablets produced with higher precision than usually achieved by compounding pharmacies.