Klemen Kreft (Author), Zoran Lavrič (Author), Urška Gradišar Centa (Author), Mohor Mihelčič (Author), Lidija Slemenik Perše (Author), Rok Dreu (Author)

Abstract

Filament formulation for FDM is a challenging and time-consuming process. Several pharmaceutical polymers are not feedable on their own. Due to inadequate filament formulation, 3D printed tablets can also exhibit poor uniformity of tablet attributes. To better understand filament formulation process, 23 filaments were prepared with the polymer mixing approach. To yield processable filaments, brittle and pliable polymers were combined. A 20 % addition of a pliable polymer to a brittle one resulted in filament processability and vice versa. Predictive statistical models for filament processability and uniformity of tablet attributes were established based on the mechanical and rheological properties of filaments. 15 input variables were correlated to 9 responses, which represent filament processability and tablet properties, by using multiple linear regression approach. Filament stiffness, assessed by indentation, and its square term were the only variables that determined the filament’s feedability. However, the resulting model is equipment-specific since different feeding mechanism exert different forces on the filaments. Additional models with good predictive power (R2pred > 0.50) were established for tablet width uniformity, drug release uniformity, tablet disintegration time uniformity and occurrence of disintegration, which are equipment-independent outputs. Therefore, the obtained model outcomes could be used in other research endeavours.

Keywords

fused deposition modelling;personalized medicine;multiple linear regression;tablet quality attributes;correlation;3D printing;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FFA - Faculty of Pharmacy
UDC: 661.12:61-027.552
COBISS: 182047747 Link will open in a new window
ISSN: 0378-5173
Views: 236
Downloads: 11
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: Slovenian
Secondary keywords: modeliranje taljenega nanosa;personalizirana medicina;večkratna linearna regresija;atributi kakovosti tabličnega računalnika;korelacija;3D tiskanje;Personalizirana medicina;Farmacevtska tehnologija;
Type (COBISS): Article
Pages: 14 str.
Issue: ǂVol. ǂ651
Chronology: 2024
DOI: 10.1016/j.ijpharm.2023.123719
ID: 22556984