Microfluidic Manufacturing of Liposomes: Development and Optimization by Design of Experiment and Machine Learning
- PMID: 36001743
- DOI: 10.1021/acsami.2c06627
Microfluidic Manufacturing of Liposomes: Development and Optimization by Design of Experiment and Machine Learning
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.
Keywords: Liposomes; artificial neural network; design of experiment; lipid nanoparticles; machine learning; microfluidics; nanomaterial; nanomedicine.
Similar articles
-
Microfluidic manufacturing of tioconazole loaded keratin nanocarriers: Development and optimization by design of experiments.Int J Pharm. 2023 Nov 25;647:123489. doi: 10.1016/j.ijpharm.2023.123489. Epub 2023 Oct 5. Int J Pharm. 2023. PMID: 37805150
-
Rapid optimization of liposome characteristics using a combined microfluidics and design-of-experiment approach.Drug Deliv Transl Res. 2019 Feb;9(1):404-413. doi: 10.1007/s13346-018-0587-4. Drug Deliv Transl Res. 2019. PMID: 30306459
-
Microfluidics: a transformational tool for nanomedicine development and production.J Drug Target. 2016 Nov;24(9):821-835. doi: 10.1080/1061186X.2016.1198354. Epub 2016 Aug 5. J Drug Target. 2016. PMID: 27492254 Review.
-
High-throughput manufacturing of size-tuned liposomes by a new microfluidics method using enhanced statistical tools for characterization.Int J Pharm. 2014 Dec 30;477(1-2):361-8. doi: 10.1016/j.ijpharm.2014.10.030. Epub 2014 Oct 14. Int J Pharm. 2014. PMID: 25455778
-
Microfluidic and lab-on-a-chip preparation routes for organic nanoparticles and vesicular systems for nanomedicine applications.Adv Drug Deliv Rev. 2013 Nov;65(11-12):1496-532. doi: 10.1016/j.addr.2013.08.002. Epub 2013 Aug 8. Adv Drug Deliv Rev. 2013. PMID: 23933616 Review.
Cited by
-
Microfluidic Fabricated Liposomes for Nutlin-3a Ocular Delivery as Potential Candidate for Proliferative Vitreoretinal Diseases Treatment.Int J Nanomedicine. 2024 Apr 11;19:3513-3536. doi: 10.2147/IJN.S452134. eCollection 2024. Int J Nanomedicine. 2024. PMID: 38623081 Free PMC article.
-
Exploiting Pharma 4.0 Technologies in the Non-Biological Complex Drugs Manufacturing: Innovations and Implications.Pharmaceutics. 2023 Oct 28;15(11):2545. doi: 10.3390/pharmaceutics15112545. Pharmaceutics. 2023. PMID: 38004525 Free PMC article. Review.
-
Microsystem Advances through Integration with Artificial Intelligence.Micromachines (Basel). 2023 Apr 8;14(4):826. doi: 10.3390/mi14040826. Micromachines (Basel). 2023. PMID: 37421059 Free PMC article. Review.
-
Evaluation of Adjuvant Activity and Bio-Distribution of Archaeosomes Prepared Using Microfluidic Technology.Pharmaceutics. 2022 Oct 26;14(11):2291. doi: 10.3390/pharmaceutics14112291. Pharmaceutics. 2022. PMID: 36365110 Free PMC article.
MeSH terms
Substances
LinkOut - more resources
Full Text Sources