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Liposomes: The Brave Old World. Int J Mol Sci 2023; 24:ijms24054343. [PMID: 36901770 PMCID: PMC10002030 DOI: 10.3390/ijms24054343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 02/03/2023] [Indexed: 02/24/2023] Open
Abstract
Liposomes have been known of for about 60 years, since they were discovered by A [...].
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Rebollo R, Oyoun F, Corvis Y, El-Hammadi MM, Saubamea B, Andrieux K, Mignet N, Alhareth K. Microfluidic Manufacturing of Liposomes: Development and Optimization by Design of Experiment and Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2022; 14:39736-39745. [PMID: 36001743 DOI: 10.1021/acsami.2c06627] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Liposomes constitute the most exploited drug-nanocarrier with several liposomal drugs on the market. Microfluidic-based preparation methods stand up as a promising approach with high reproducibility and the ability to scale up. In this study, liposomes composed of DOPC, cholesterol, and DSPE-PEG 2000 with different molar ratios were fabricated using a microfluidic system. Process and conditions were optimized by applying design of experiments (DoE) principles. Furthermore, data were used to build an artificial neural network (ANN) model, to predict size and polydispersity index (PDI). Sets of runs were designed by DoE and performed on a micromixer microfluidic chip. Lipids' molar ratio and the process parameters, i.e. total flow rate (TFR) and flow rate ratio (FRR), were found to be the most influential factors on the formation of vesicles with target size and PDI under 100 nm and lower than 0.2, respectively. Size and PDI were predicted by the ANN model for 3 preparations with defined experimental conditions. The results showed no significant difference in size and PDI between the preparations and their values calculated with the ANN. In conclusion, production of optimized liposomes with high reproducibility was achieved by the application of microfluidic manufacturing processes, DoE, and Artificial Intelligence (AI). Microfluidic-based preparation methods assisted by computational tools would enable a faster development and clinical transfer of nanobased medications.
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Affiliation(s)
- René Rebollo
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Feras Oyoun
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Yohann Corvis
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Mazen M El-Hammadi
- Department of Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy, University of Seville, c/Prof. García González n◦2, 41012Seville, Spain
| | - Bruno Saubamea
- Université Paris Cité, US25 INSERM, UMS3612 CNRS, Plateforme Imagerie Cellulaire et Moléculaire, 75006Paris, France
| | - Karine Andrieux
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Nathalie Mignet
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
| | - Khair Alhareth
- Université Paris Cité, CNRS, INSERM, UTCBS (Chemical and Biological Technologies for Health Group), 4 avenue de l'observatoire, 75006Paris, France
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