3D-Printed Network Structures as Controlled-Release Drug Delivery Systems: Dose Adjustment, API Release Analysis and Prediction


3D printing evolved as a promising technique to improve individualization of drug therapy. In particular, when printing sustained release solid dosage forms, as for instance implants, inserts, and also tablets, estimation of the drug release profile in vivo is necessary. In most cases, corresponding analyses cannot be performed at hospital or community pharmacies. Therefore, the present study aimed to develop a sustained release drug delivery system produced via 3D printing, which allows dose adaption and estimation of drug release at the same time. Filaments as feedstock for the printer were produced via hot-melt extrusion and consisted of Eudragit® RL as sustained release polymer, 30% theophylline as model active pharmaceutical ingredient, and stearic acid as solid plasticizer. Assuming that the surface/mass ratio was constant, network structures of different densities were printed as novel solid dosage form. Their weight (263 to 668 mg), thereby their dose, and surface area, determined using X-ray microcomputed tomography, showed a linear correlation with the fill density. The specific surface area of the network hardly varied with changing fill density. Dissolution studies showed a slower drug release for dosage forms with a denser network. Higuchi’s model was used for prediction of drug release and showed limited applicability due to different release kinetics for different fill densities. However, using linear interpolation for the prediction resulted in good RMSEP values between 1.4 and 3.7%. These findings might be useful to enable customized production of sustained release solid dosage forms via 3D printing in hospital and community pharmacies in the future.

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Active pharmaceutical ingredient


Computed tomography


Differential scanning calorimetry


Ethyl cellulose


Fused deposition modeling


Hot-melt extrusion


Hydroxypropyl methylcellulose


Root mean square error of prediction


Specific surface area

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The authors would like to thank Dorothee Eikeler for her support in the content determination of the matrices, Stefan Stich for preparing in-house-built sinkers, and Karin Matthée for conduction of DSC measurements. Further, Raphael Wiedey is acknowledged for his support in the conduction and evaluation of measurements using X-ray microcomputed tomography.

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Correspondence to Julian Quodbach.

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Guest Editors: Niklas Sandler and Jukka Rantanen

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Korte, C., Quodbach, J. 3D-Printed Network Structures as Controlled-Release Drug Delivery Systems: Dose Adjustment, API Release Analysis and Prediction. AAPS PharmSciTech 19, 3333–3342 (2018). https://doi.org/10.1208/s12249-018-1017-0

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Key words

  • 3D printing
  • dissolution analysis and prediction
  • extended release matrix
  • personalized medicine
  • advanced drug delivery system