Automated digital design for 3D-printed individualized therapies

Research output: Contribution to journalJournal articleResearchpeer-review

  • Georgios K. Eleftheriadis
  • Efthymios Kantarelis
  • Paraskevi Kyriaki Monou
  • Eleftherios G. Andriotis
  • Nikolaos Bouropoulos
  • Emmanouil K. Tzimtzimis
  • Dimitrios Tzetzis
  • Rantanen, Jukka
  • Dimitrios G. Fatouros

Customization of pharmaceutical products is a central requirement for personalized medicines. However, the existing processing and supply chain solutions do not support such manufacturing-on-demand approaches. In order to solve this challenge, three-dimensional (3D) printing has been applied for customization of not only the dose and release characteristics, but also appearance of the product (e.g., size and shape). A solution for customization can be realized via non-expert-guided processing of digital designs and drug dose. This study presents a proof-of-concept computational algorithm which calculates the optimal dimensions of grid-like orodispersible films (ODFs), considering the recommended dose. Further, the algorithm exports a digital design file which contains the required ODF configuration. Cannabidiol (CBD) was incorporated in the ODFs, considering the simple correspondence between the recommended dose and the patient's weight. The ODFs were 3D-printed and characterized for their physicochemical, mechanical, disintegration and drug release properties. The algorithm was evaluated for its accuracy on dose estimation, highlighting the reproducibility of individualized ODFs. The in vitro performance was principally affected by the thickness and volume of the grid-like structures. The concept provides an alternative approach that promotes automation in the manufacturing of personalized medications in distributed points of care, such as hospitals and local pharmacies.

Original languageEnglish
Article number120437
JournalInternational Journal of Pharmaceutics
Number of pages12
Publication statusPublished - 2021

    Research areas

  • 3D printing, Fused deposition modeling, Digital health, Algorithm, Personalization, Orodispersible films, Cannabidiol

ID: 262799574