Elbadawi, M;
Gustaffson, T;
Gaisford, S;
Basit, AW;
(2020)
3D printing tablets: Predicting printability and drug dissolution from rheological data.
International Journal of Pharmaceutics
, 590
, Article 119868. 10.1016/j.ijpharm.2020.119868.
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Abstract
Rheology is an indispensable tool for formulation development, which when harnessed, can both predict a material’s performance and provide valuable insight regarding the material’s macrostructure. However, rheological characterizations are under-utilized in 3D printing of drug formulations. In this study, viscosity measurements were used to establish a mathematical model for predicting the printability of fused deposition modelling 3D printed tablets (Printlets). The formulations were composed of polycaprolactone (PCL) with different amounts of ciprofloxacin and polyethylene glycol (PEG), and different molecular weights of PEG. With all printing parameters kept constant, both binary and ternary blends were found to extrude at nozzle temperatures of 130, 150 and 170 °C. In contrast PCL was unextrudable at 130 and 150 °C. Three standard rheological models were applied to the experimental viscosity measurements, which revealed an operating viscosity window of between 100 and 1000 Pa·s at the apparent shear rate of the nozzle. The drug release profiles of the printlets were experimentally measured over seven days. As a proof-of-concept, machine learning models were developed to predict the dissolution behaviour from the viscosity measurements. The machine learning models were discovered to accurately predict the dissolution profile, with the highest f2 similarity score value of 90.9 recorded. Therefore, the study demonstrated that using only the viscosity measurements can be employed for the simultaneous high-throughput screening of formulations that are printable and with the desired release profile.
| Type: | Article |
|---|---|
| Title: | 3D printing tablets: Predicting printability and drug dissolution from rheological data |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ijpharm.2020.119868 |
| Publisher version: | https://doi.org/10.1016/j.ijpharm.2020.119868 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Three-dimensional printing; 3D Printed drug products; Fused Deposition Modeling (FDM); Oral drug delivery systems; Artificial Intelligence; Machine Learning; Prediction Models. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy > Pharmaceutics |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10111904 |
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