Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles

Johnsen, Okay. B., Burkhart, A., Thomsen, L. B., Andresen, T. L. & Moos, T. Targeting the transferrin receptor for mind drug supply. Prog. Neurobiol. 181, 101665. https://doi.org/10.1016/j.pneurobio.2019.101665 (2019).Article 
CAS 
PubMed 

Google Scholar 
Langer, R. New strategies of drug supply. Science 249(4976), 1527–1533. https://doi.org/10.1126/science.2218494 (1990).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Cipolla, D. Will pulmonary drug supply for systemic software ever fulfill its wealthy promise?. Expert Opin. Drug Deliv. 13(10), 1337–1340. https://doi.org/10.1080/17425247.2016.1218466 (2016).Article 
PubMed 

Google Scholar 
Jain, Okay. Okay. An overview of drug supply techniques. Methods Mol. Biol. 2059, 1–54. https://doi.org/10.1007/978-1-4939-9798-5_1 (2020).Article 
CAS 
PubMed 

Google Scholar 
Karthikeyan, A., Senthil, N. & Min, T. Nanocurcumin: A promising candidate for therapeutic purposes. Front. Pharmacol. 11, 487. https://doi.org/10.3389/fphar.2020.00487 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, J. et al. Amikacin liposome inhalation suspension (ALIS) penetrates non-tuberculous mycobacterial biofilms and enhances Amikacin uptake into macrophages. Front. Microbiol. 9, 915. https://doi.org/10.3389/fmicb.2018.00915 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Khatib, I., Chow, M. Y. T., Ruan, J., Cipolla, D. & Chan, H. Okay. Modeling of a sprig drying technique to produce ciprofloxacin nanocrystals inside the liposomes using a response floor methodology: Box-Behnken experimental design. Int. J. Pharm. 597, 120277. https://doi.org/10.1016/j.ijpharm.2021.120277 (2021).Article 
CAS 
PubMed 

Google Scholar 
Chen, Okay. J., Plaunt, A. J., Leifer, F. G., Kang, J. Y. & Cipolla, D. Recent advances in prodrug-based nanoparticle therapeutics. Eur. J. Pharm. Biopharm. 165, 219–243. https://doi.org/10.1016/j.ejpb.2021.04.025 (2021).Article 
CAS 
PubMed 

Google Scholar 
Hatamipour, M., Sahebkar, A., Alavizadeh, S. H., Dorri, M. & Jaafari, M. R. Novel nanomicelle formulation to improve bioavailability and stability of curcuminoids. Iran. J. Basic Med. Sci. 22(3), 282–289. https://doi.org/10.22038/ijbms.2019.32873.7852 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Chen, Y., Lu, Y., Lee, R. J. & Xiang, G. Nano encapsulated Curcumin: And its potential for biomedical purposes. Int. J. Nanomed. 15, 3099–3120. https://doi.org/10.2147/ijn.S210320 (2020).Article 
CAS 

Google Scholar 
Adepu, S. & Ramakrishna, S. Controlled drug supply techniques: Current standing and future instructions. Molecules https://doi.org/10.3390/molecules26195905 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Nik, M. E. et al. Targeted-nanoliposomal combretastatin A4 (CA-4) as an environment friendly antivascular candidate in the metastatic most cancers remedy. J. Cell. Physiol. https://doi.org/10.1002/jcp.28230 (2019).Article 
PubMed 

Google Scholar 
Nikpoor, A. R. et al. Nanoliposome-mediated concentrating on of antibodies to tumors: IVIG antibodies as a mannequin. Int. J. Pharm. 495(1), 162–170. https://doi.org/10.1016/j.ijpharm.2015.08.048 (2015).Article 
CAS 
PubMed 

Google Scholar 
Khatib, I. et al. Formation of ciprofloxacin nanocrystals inside liposomes by spray drying for managed launch by way of inhalation. Int. J. Pharm. 578, 119045. https://doi.org/10.1016/j.ijpharm.2020.119045 (2020).Article 
CAS 
PubMed 

Google Scholar 
He, C., Yin, L., Tang, C. & Yin, C. Size-dependent absorption mechanism of polymeric nanoparticles for oral supply of protein medication. Biomaterials 33(33), 8569–8578. https://doi.org/10.1016/j.biomaterials.2012.07.063 (2012).Article 
CAS 
PubMed 

Google Scholar 
Peer, D. et al. Nanocarriers as an rising platform for most cancers remedy. Nat. Nanotechnol. 2(12), 751–760. https://doi.org/10.1038/nnano.2007.387 (2007).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Dunning H. Size determines how nanoparticles have an effect on organic membranes Imperial College London2020. https://www.imperial.ac.uk/news/204433/size-determines-nanoparticles-affect-biological-membranes/#:~:text=The%20research%20findings%20also%20have,easily%20drawn%20into%20the%20cell (Accessed 18 February 2023).Wu, L., Zhang, J. & Watanabe, W. Physical and chemical stability of drug nanoparticles. Adv. Drug Deliv. Rev. 63(6), 456–469. https://doi.org/10.1016/j.addr.2011.02.001 (2011).Article 
CAS 
PubMed 

Google Scholar 
Patravale, V., Date, A. A. & Kulkarni, R. Nanosuspensions: A promising drug supply technique. J. Pharm. Pharmacol. 56(7), 827–840 (2004).Article 
CAS 
PubMed 

Google Scholar 
Chen, Y. et al. Preparation of Curcumin-loaded liposomes and analysis of their pores and skin permeation and pharmacodynamics. Molecules 17(5), 5972–5987. https://doi.org/10.3390/molecules17055972 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
De Leo, V. et al. Encapsulation of Curcumin-loaded liposomes for colonic drug supply in a pH-responsive polymer cluster utilizing a pH-driven and natural solvent-free course of. Molecules https://doi.org/10.3390/molecules23040739 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Tai, Okay., Rappolt, M., Mao, L., Gao, Y. & Yuan, F. Stability and launch efficiency of curcumin-loaded liposomes with various content material of hydrogenated phospholipids. Food Chem. 326, 126973. https://doi.org/10.1016/j.foodchem.2020.126973 (2020).Article 
CAS 
PubMed 

Google Scholar 
Wu, Y. et al. Curcumin-loaded liposomes ready from bovine milk and krill phospholipids: Effects of chemical composition on storage stability, in-vitro digestibility and anti-hyperglycemic properties. Food Res. Int. 136, 109301. https://doi.org/10.1016/j.foodres.2020.109301 (2020).Article 
CAS 
PubMed 

Google Scholar 
Karimi, M. et al. Preparation and characterization of secure nanoliposomal formulations of curcumin with excessive loading efficacy: In vitro and in vivo anti-tumor examine. Int. J. Pharm. 580, 119211. https://doi.org/10.1016/j.ijpharm.2020.119211 (2020).Article 
CAS 
PubMed 

Google Scholar 
Rabima, R. & Sari, M. P. Entrapment effectivity and drug loading of curcumin nanostructured lipid provider (NLC) system. Pharmaciana 9(2), 299–306 (2019).Article 

Google Scholar 
Esmaeilzadeh-Gharedaghi, E. et al. Effects of processing parameters on particle size of ultrasound ready chitosan nanoparticles: An synthetic neural networks examine. Pharm. Dev. Technol. 17(5), 638–647. https://doi.org/10.3109/10837450.2012.696269 (2012).Article 
CAS 
PubMed 

Google Scholar 
Baharifar, H. & Amani, A. Size, loading effectivity, and cytotoxicity of albumin-loaded chitosan nanoparticles: An synthetic neural networks examine. J. Pharm. Sci. 106(1), 411–417. https://doi.org/10.1016/j.xphs.2016.10.013 (2017).Article 
CAS 
PubMed 

Google Scholar 
Sansare, S. et al. Artificial neural networks in tandem with molecular descriptors as predictive instruments for steady liposome manufacturing. Int. J. Pharm. 603, 120713. https://doi.org/10.1016/j.ijpharm.2021.120713 (2021).Article 
CAS 
PubMed 

Google Scholar 
Huang, S. M., Kuo, C. H., Chen, C. A., Liu, Y. C. & Shieh, C. J. RSM and ANN modeling-based optimization approach for the improvement of ultrasound-assisted liposome encapsulation of piceid. Ultrason. Sonochem. 36, 112–122. https://doi.org/10.1016/j.ultsonch.2016.11.016 (2017).Article 
CAS 
PubMed 

Google Scholar 
Cardoso-Daodu, I. M., Ilomuanya, M. O., Amenaghawon, A. N. & Azubuike, C. P. Artificial neural community for optimizing the formulation of curcumin-loaded liposomes from statistically designed experiments. Prog. Biomater. 11(1), 55–65. https://doi.org/10.1007/s40204-022-00179-6 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Liao, Y. et al. Using convolutional neural community as a statistical algorithm to discover the therapeutic impact of insulin liposomes on corneal irritation. Comput. Intell. Neurosci. 2022, 1169438. https://doi.org/10.1155/2022/1169438 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhao, F. et al. Comparison of response floor methodology and synthetic neural community to optimize novel ophthalmic versatile nano-liposomes: Characterization, analysis, in vivo pharmacokinetics and molecular dynamics simulation. Colloids Surf. B Biointerfaces 172, 288–297. https://doi.org/10.1016/j.colsurfb.2018.08.046 (2018).Article 
CAS 
PubMed 

Google Scholar 
Honary, S., Ebrahimi, P. & Hadianamrei, R. Optimization of particle size and encapsulation effectivity of vancomycin nanoparticles by response floor methodology. Pharm. Dev. Technol. 19(8), 987–998. https://doi.org/10.3109/10837450.2013.846375 (2014).Article 
CAS 
PubMed 

Google Scholar 
Hashad, R. A., Ishak, R. A. H., Fahmy, S., Mansour, S. & Geneidi, A. S. Chitosan-tripolyphosphate nanoparticles: Optimization of formulation parameters for enhancing course of yield at a novel pH utilizing synthetic neural networks. Int. J. Biol. Macromol. 86, 50–58. https://doi.org/10.1016/j.ijbiomac.2016.01.042 (2016).Article 
CAS 
PubMed 

Google Scholar 
Shalaby, Okay. S. et al. Determination of factors controlling the particle size and entrapment effectivity of noscapine in PEG/PLA nanoparticles utilizing synthetic neural networks. Int. J. Nanomed. 9, 4953–4964. https://doi.org/10.2147/ijn.S68737 (2014).Article 
CAS 

Google Scholar 
Reker, D. et al. Computationally guided high-throughput design of self-assembling drug nanoparticles. Nat. Nanotechnol. 16(6), 725–733. https://doi.org/10.1038/s41565-021-00870-y (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
León Blanco, J. M. et al. Artificial neural networks as different instrument for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations. Drug Dev. Ind. Pharm. 44(1), 135–143. https://doi.org/10.1080/03639045.2017.1386201 (2018).Article 
CAS 
PubMed 

Google Scholar 
Santos, M. et al. Artificial neural networks for qualitative and quantitative evaluation of goal proteins with polymerized liposome vesicles. Anal. Biochem. 361(1), 109–119. https://doi.org/10.1016/j.ab.2006.11.019 (2007).Article 
CAS 
PubMed 

Google Scholar 
Moussa, H. G., Husseini, G. A., Abel-Jabbar, N. & Ahmad, S. E. Use of mannequin predictive management and synthetic neural networks to optimize the ultrasonic launch of a mannequin drug from liposomes. IEEE Trans. Nanobiosci. 16(3), 149–156. https://doi.org/10.1109/tnb.2017.2661322 (2017).Article 

Google Scholar 
Hathout, R. M., Gad, H. A. & Metwally, A. A. Gelatinized-core liposomes: Toward a extra strong provider for hydrophilic molecules. J. Biomed. Mater. Res. A 105(11), 3086–3092. https://doi.org/10.1002/jbm.a.36175 (2017).Article 
CAS 
PubMed 

Google Scholar 
Dayhoff, J. E. & DeLeo, J. M. Artificial neural networks: Opening the black field. Cancer 91(8 Suppl), 1615–1635. https://doi.org/10.1002/1097-0142(20010415)91:8+%3c1615::aid-cncr1175%3e3.0.co;2-l (2001).Article 
CAS 
PubMed 

Google Scholar 
Li, Y., Abbaspour, M. R., Grootendorst, P. V., Rauth, A. M. & Wu, X. Y. Optimization of managed launch nanoparticle formulation of verapamil hydrochloride utilizing synthetic neural networks with genetic algorithm and response floor methodology. Eur. J. Pharm. Biopharm. 94, 170–179. https://doi.org/10.1016/j.ejpb.2015.04.028 (2015).Article 
CAS 
PubMed 

Google Scholar 
Zaki, M. R., Varshosaz, J. & Fathi, M. Preparation of agar nanospheres: Comparison of response floor and synthetic neural community modeling by a genetic algorithm approach. Carbohydr. Polym. 122, 314–320. https://doi.org/10.1016/j.carbpol.2014.12.031 (2015).Article 
CAS 
PubMed 

Google Scholar 
Tu, J. V. Advantages and disadvantages of utilizing synthetic neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 49(11), 1225–1231. https://doi.org/10.1016/s0895-4356(96)00002-9 (1996).Article 
CAS 
PubMed 

Google Scholar 
Abdalla, Y. et al. Machine learning utilizing multi-modal knowledge predicts the manufacturing of selective laser sintered 3D printed drug merchandise. Int. J. Pharm. 633, 122628. https://doi.org/10.1016/j.ijpharm.2023.122628 (2023).Article 
CAS 
PubMed 

Google Scholar 
Hayashi, Y. et al. Application of machine learning to a fabric library for modeling of relationships between materials properties and pill properties. Int. J. Pharm. 609, 121158. https://doi.org/10.1016/j.ijpharm.2021.121158 (2021).Article 
CAS 
PubMed 

Google Scholar 
Jiang, J. et al. The purposes of machine learning (ML) in designing dry powder for inhalation by utilizing thin-film-freezing expertise. Int. J. Pharm. 626, 122179. https://doi.org/10.1016/j.ijpharm.2022.122179 (2022).Article 
CAS 
PubMed 

Google Scholar 
Galata, D. L. et al. Real-time launch testing of dissolution primarily based on surrogate fashions developed by machine learning algorithms utilizing NIR spectra, compression drive and particle size distribution as enter knowledge. Int. J. Pharm. 597, 120338. https://doi.org/10.1016/j.ijpharm.2021.120338 (2021).Article 
CAS 
PubMed 

Google Scholar 
Džeroski, S., Panov, P. & Ženko, B. Machine learning, ensemble strategies. In Encyclopedia of Complexity and Systems Science (ed. Meyers, R. A.) 5317–25 (Springer New York, 2009).Chapter 

Google Scholar 
Neumann, D., Merkwirth, C. & Lamprecht, A. Nanoparticle design characterised by in silico preparation parameter prediction utilizing ensemble fashions. J. Pharm. Sci. 99(4), 1982–1996. https://doi.org/10.1002/jps.21941 (2010).Article 
CAS 
PubMed 

Google Scholar 
Li, L., Braiteh, F. S. & Kurzrock, R. Liposome-encapsulated curcumin. Cancer 104(6), 1322–1331 (2005).Article 
CAS 
PubMed 

Google Scholar 
Cipolla, D., Wu, H., Gonda, I. & Chan, H. Okay. Aerosol efficiency and stability of liposomes containing ciprofloxacin nanocrystals. J. Aerosol. Med. Pulm. Drug Deliv. 28(6), 411–422. https://doi.org/10.1089/jamp.2015.1241 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Akbarzadeh, A. et al. Liposome: Classification, preparation, and purposes. Nanoscale Res. Lett. 8(1), 102. https://doi.org/10.1186/1556-276x-8-102 (2013).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Liu, P., Chen, G. & Zhang, J. A evaluation of liposomes as a drug supply system: Current standing of authorised merchandise, regulatory environments, and future views. Molecules https://doi.org/10.3390/molecules27041372 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Ding, T., Li, T., Wang, Z. & Li, J. Curcumin liposomes intrude with quorum sensing system of Aeromonas sobria and in silico evaluation. Sci. Rep. 7(1), 8612. https://doi.org/10.1038/s41598-017-08986-9 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Angmo, S., Rana, S., Yadav, Okay., Sandhir, R. & Singhal, N. Okay. Novel liposome eencapsulated guanosine DI phosphate primarily based therapeutic goal towards anemia of irritation. Sci. Rep. 8(1), 17684. https://doi.org/10.1038/s41598-018-35992-2 (2018).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Khatib, I., Ke, W. R., Cipolla, D. & Chan, H. Okay. Storage stability of inhalable, controlled-release powder formulations of ciprofloxacin nanocrystal-containing liposomes. Int. J. Pharm. 605, 120809. https://doi.org/10.1016/j.ijpharm.2021.120809 (2021).Article 
CAS 
PubMed 

Google Scholar 
Hewlings, S. J. & Kalman, D. S. Curcumin: A evaluation of its results on human well being. Foods https://doi.org/10.3390/foods6100092 (2017).Amalraj, A., Pius, A., Gopi, S. & Gopi, S. Biological actions of curcuminoids, different biomolecules from turmeric and their derivatives—A evaluation. J. Tradit. Complement. Med. 7(2), 205–233. https://doi.org/10.1016/j.jtcme.2016.05.005 (2017).Article 
PubMed 

Google Scholar 
Thao, D. T., Nga, N. T., Van, N. A. & Hung, Okay. D. Potential anticancer actions of a mix of Curcumin, Ginger oleoresin, and Rutin stable lipid nanoparticles (Vietlife-Antican) in LLC tumor-bearing mice. Nat. Prod. Commun. 14(6), 1934578X19858461. https://doi.org/10.1177/1934578X19858461 (2019).Article 
CAS 

Google Scholar 
Karimi, M., Mashreghi, M., Shokooh Saremi, S. & Jaafari, M. R. Spectrofluorometric technique improvement and validation for the willpower of Curcumin in nanoliposomes and plasma. J. Fluoresc. 30(5), 1113–1119. https://doi.org/10.1007/s10895-020-02574-3 (2020).Article 
CAS 
PubMed 

Google Scholar 
Wang, M. et al. Potential mechanisms of motion of Curcumin for most cancers prevention: Focus on mobile signaling pathways and miRNAs. Int. J. Biol. Sci. 15(6), 1200–1214. https://doi.org/10.7150/ijbs.33710 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Khezri, Okay., Saeedi, M., Mohammadamini, H. & Zakaryaei, A. S. A complete evaluation of the therapeutic potential of curcumin nanoformulations. Phytother. Res. 35(10), 5527–5563. https://doi.org/10.1002/ptr.7190 (2021).Article 
CAS 
PubMed 

Google Scholar 
Cipolla, D., Blanchard, J. & Gonda, I. Development of liposomal ciprofloxacin to deal with lung infections. Pharmaceutics https://doi.org/10.3390/pharmaceutics8010006 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Tang, W. L. et al. Development of a quickly dissolvable oral pediatric formulation for mefloquine utilizing liposomes. Mol. Pharm. 14(6), 1969–1979. https://doi.org/10.1021/acs.molpharmaceut.7b00077 (2017).Article 
CAS 
PubMed 

Google Scholar 
Nik, M. E. et al. Liposomal formulation of Galbanic acid improved therapeutic efficacy of pegylated liposomal Doxorubicin in mouse colon carcinoma. Sci. Rep. 9(1), 9527. https://doi.org/10.1038/s41598-019-45974-7 (2019).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Matbou Riahi, M., Sahebkar, A., Sadri, Okay., Nikoofal-Sahlabadi, S. & Jaafari, M. R. Stable and sustained launch liposomal formulations of celecoxib: In vitro and in vivo anti-tumor analysis. Int. J. Pharm. 540(1), 89–97. https://doi.org/10.1016/j.ijpharm.2018.01.039 (2018).Article 
CAS 
PubMed 

Google Scholar 
Bartlett, G. R. Phosphorus assay in column chromatography. J. Biol. Chem. 234(3), 466–468. https://doi.org/10.1016/S0021-9258(18)70226-3 (1959).Article 
CAS 
PubMed 

Google Scholar 
Zamani, P. et al. MPL nano-liposomal vaccine containing P5 HER2/neu-derived peptide pulsed PADRE as an efficient vaccine in a mice TUBO mannequin of breast most cancers. J. Control. Release 303, 223–236. https://doi.org/10.1016/j.jconrel.2019.04.019 (2019).Article 
CAS 
PubMed 

Google Scholar 
Alajmi, M. S. & Almeshal, A. M. Least squares boosting ensemble and quantum-behaved particle swarm optimization for predicting the floor roughness in face milling course of of aluminum materials. Appl. Sci. 11(5), 2126. https://doi.org/10.3390/app11052126 (2021).Article 
CAS 

Google Scholar 
Ojo, S., Imoize, A. & Alienyi, D. Radial foundation operate neural community path loss prediction mannequin for LTE networks in multitransmitter sign propagation environments. Int. J. Commun. Syst. 34(3), e4680 (2021).Article 

Google Scholar 
Isabona, J., Imoize, A. L. & Kim, Y. Machine learning-based boosted regression ensemble mixed with hyperparameter tuning for optimum adaptive learning. Sensors (Basel) https://doi.org/10.3390/s22103776 (2022).Article 
PubMed 

Google Scholar 
Hothorn, T. & Lausen, B. Double-bagging: Combining classifiers by bootstrap aggregation. Pattern Recogn. 36(6), 1303–1309. https://doi.org/10.1016/S0031-3203(02)00169-3 (2003).Article 
ADS 
MATH 

Google Scholar 
Kashani-Asadi-Jafari, F., Aftab, A. & Ghaemmaghami, S. A machine learning framework for predicting entrapment effectivity in niosomal particles. Int. J. Pharm. 627, 122203. https://doi.org/10.1016/j.ijpharm.2022.122203 (2022).Article 
CAS 
PubMed 

Google Scholar 
Danaei, M. et al. Impact of particle size and polydispersity index on the medical purposes of lipidic nanocarrier techniques. Pharmaceutics https://doi.org/10.3390/pharmaceutics10020057 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Bélteky, P. et al. Are smaller nanoparticles all the time higher? Understanding the organic impact of size-dependent silver nanoparticle aggregation beneath biorelevant circumstances. Int. J. Nanomed. 16, 3021–3040. https://doi.org/10.2147/ijn.S304138 (2021).Article 

Google Scholar 
Ranjan, A. P., Mukerjee, A., Helson, L. & Vishwanatha, J. Okay. Scale up, optimization and stability evaluation of Curcumin C3 complex-loaded nanoparticles for most cancers remedy. J. Nanobiotechnol. 10, 38. https://doi.org/10.1186/1477-3155-10-38 (2012).Article 
CAS 

Google Scholar 
Azhar Shekoufeh Bahari, L. & Hamishehkar, H. The influence of variables on particle size of stable lipid nanoparticles and nanostructured lipid carriers: A comparative literature evaluation. Adv. Pharm. Bull. 6(2), 143–51. https://doi.org/10.15171/apb.2016.021 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Woodbury, D. J., Richardson, E. S., Grigg, A. W., Welling, R. D. & Knudson, B. H. Reducing liposome size with ultrasound: Bimodal size distributions. J. Liposome Res. 16(1), 57–80. https://doi.org/10.1080/08982100500528842 (2006).Article 
CAS 
PubMed 

Google Scholar 
Shaker, S., Gardouh, A. R. & Ghorab, M. M. Factors affecting liposomes particle size ready by ethanol injection technique. Res. Pharm. Sci. 12(5), 346–352. https://doi.org/10.4103/1735-5362.213979 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Nakhaei, P. et al. Liposomes: Structure, biomedical purposes, and stability parameters with emphasis on ldl cholesterol. Front. Bioeng. Biotechnol. https://doi.org/10.3389/fbioe.2021.705886 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Farzaneh, H. et al. A examine on the position of ldl cholesterol and phosphatidylcholine in numerous options of liposomal doxorubicin: From liposomal preparation to remedy. Int. J. Pharm. 551(1–2), 300–308. https://doi.org/10.1016/j.ijpharm.2018.09.047 (2018).Article 
CAS 
PubMed 

Google Scholar 
Lee, S. C., Lee, Okay. E., Kim, J. J. & Lim, S. H. The impact of ldl cholesterol in the liposome bilayer on the stabilization of integrated Retinol. J. Liposome Res. 15(3–4), 157–166. https://doi.org/10.1080/08982100500364131 (2005).Article 
CAS 
PubMed 

Google Scholar 
Briuglia, M.-L., Rotella, C., McFarlane, A. & Lamprou, D. A. Influence of ldl cholesterol on liposome stability and on in vitro drug launch. Drug Deliv. Transl. Res. 5(3), 231–242. https://doi.org/10.1007/s13346-015-0220-8 (2015).Article 
CAS 
PubMed 

Google Scholar 
Perumal, V., Banerjee, S., Das, S., Sen, R. Okay. & Mandal, M. Effect of liposomal celecoxib on proliferation of colon most cancers cell and inhibition of DMBA-induced tumor in rat mannequin. Cancer Nanotechnol. 2(1), 67–79. https://doi.org/10.1007/s12645-011-0017-5 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pereira-Lachataignerais, J. et al. Study and formation of vesicle techniques with low polydispersity index by ultrasound technique. Chem. Phys. Lipids 140(1–2), 88–97. https://doi.org/10.1016/j.chemphyslip.2006.01.008 (2006).Article 
CAS 
PubMed 

Google Scholar 
Heurtault, B., Saulnier, P., Pech, B., Proust, J. E. & Benoit, J. P. Physico-chemical stability of colloidal lipid particles. Biomaterials 24(23), 4283–4300. https://doi.org/10.1016/s0142-9612(03)00331-4 (2003).Article 
CAS 
PubMed 

Google Scholar 
Manosroi, A., Podjanasoonthon, Okay. & Manosroi, J. Development of novel topical tranexamic acid liposome formulations. Int. J. Pharm. 235(1–2), 61–70. https://doi.org/10.1016/s0378-5173(01)00980-2 (2002).Article 
CAS 
PubMed 

Google Scholar 
Yamaguchi, T., Nomura, M., Matsuoka, T. & Koda, S. Effects of frequency and energy of ultrasound on the size discount of liposome. Chem. Phys. Lipids 160(1), 58–62. https://doi.org/10.1016/j.chemphyslip.2009.04.002 (2009).Article 
CAS 
PubMed 

Google Scholar 
Yan, F. et al. Paclitaxel-liposome-microbubble complexes as ultrasound-triggered therapeutic drug supply carriers. J Control. Release 166(3), 246–255. https://doi.org/10.1016/j.jconrel.2012.12.025 (2013).Article 
CAS 
PubMed 

Google Scholar 
Abdallah, W. F. et al. Evaluation of ultrasound-assisted thrombolysis utilizing customized liposomes in a mannequin of retinal vein occlusion. Investig. Ophthalmol. Vis. Sci. 53(11), 6920–6927. https://doi.org/10.1167/iovs.12-10389 (2012).Article 

Google Scholar 
Ong, S. G., Chitneni, M., Lee, Okay. S., Ming, L. C. & Yuen, Okay. H. Evaluation of extrusion method for Nanosizing liposomes. Pharmaceutics 8(4), 36. https://doi.org/10.3390/pharmaceutics8040036 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Arulsudar, N., Subramanian, N. & Muthy, R. S. Comparison of synthetic neural community and a number of linear regression in the optimization of formulation parameters of leuprolide acetate loaded liposomes. J. Pharm. Pharm. Sci. 8(2), 243–258 (2005).CAS 
PubMed 

Google Scholar 
Subramanian, N., Yajnik, A. & Murthy, R. S. Artificial neural community as a substitute to a number of regression evaluation in optimizing formulation parameters of cytarabine liposomes. AAPS PharmSciTech. 5(1), E4. https://doi.org/10.1208/pt050104 (2004).Article 
PubMed 

Google Scholar 
Cysewski, P., Jeliński, T., Cymerman, P. & Przybyłek, M. Solvent screening for solubility enhancement of theophylline in neat, binary and ternary NADES solvents: New measurements and ensemble machine learning. Int. J. Mol. Sci. 22(14), 7347. https://doi.org/10.3390/ijms22147347 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoseini B, Jaafari MR, Golabpour A, Momtazi-Borojeni AA, Eslami S. Optimizing nanoliposomal formulations: Assessing factors affecting entrapment effectivity of curcumin-loaded liposomes utilizing machine learning. International Journal of Pharmaceutics. 2023;646:123414. doi: https://doi.org/10.1016/j.ijpharm.2023.123414.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Maeda, H., Wu, J., Sawa, T., Matsumura, Y. & Hori, Okay. Tumor vascular permeability and the EPR impact in macromolecular therapeutics: A evaluation. J. Control. Release 65(1–2), 271–284. https://doi.org/10.1016/s0168-3659(99)00248-5 (2000).Article 
CAS 
PubMed 

Google Scholar 

https://www.nature.com/articles/s41598-023-43689-4

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