Preparation of Berberin Proliposomes by Film Deposition on Carrier Surface Method
Bibliographic record
Abstract
This study aims to formulate berberin (BBR) proliposomes by film-deposition on carrier surface to increase BBR’s solubility and permeability through biological membranes. Proliposomes were hydrated in water to form BBR liposomes for determining the size and distribution of the vesicles. Differential thermal analysis was used to evaluate the BBR proliposomes. The study results show that berberin proliposomes prepared with hydrogenated soy phosphatidylcholine: cholesterol: berberin with a molar ratio of 9:1:6 using sorbitol as carrier with a weight ratio to lipid of 10:1. The obtained BBR proliposomes in the form of a dry yellowish powder were hydrated in water to form BBR liposomes with an average diameter of about 8.41μm. The results of the differential thermal analysis show that BBR was dispersed in molecular form into proliposomes. Keywords: Berberin, proliposomes, sorbitol, film-deposition on the carrier method. References [1] J. Plessis, C. Ramachandran, N. Weiner, D.G Müller, The influence of lipid composition and lamellarity of liposomes on the physical stability of liposomes upon storage, International Journal of Pharmaceutics, 2 (1996) 273-278. https://doi.org/10.1016/0378-5173(95)04281-4[2] A.V. Yadav, M.S. Murthy, Stability Aspects of Liposomes, Indian Journal of Pharmaceutical Education and Research 24 (2011) 402413 – 43. [3] V. Nekkanti, N. Venkatesan, G.V. Betageri, Proliposomes for Oral Delivery: Progress and Challenges, Current Pharmaceutical Biotechnology, 16(2015) 303-312. 10.2174/1389201016666150118134256[4] M. Khayam, S. Umar, Berberine nanoparticles with enhanced in vitro bioavailability: characterization and antimicrobial activity, Drug Design, Development and Therapy, 12 (2018) 303-312. https://doi.org/10.2147/DDDT.S156123[5] S.J. Jia, G.Y. Ningning, L. Zhang, Y. Zhao, Release-controlled curcumin proliposome produced by ultrasound-assisted supercritical antisolvent method, Journal of Supercritical Fluids, 113 (2016) 150-157. https://doi.org/10.1016/j.supflu.2016.03.026.[6] K.G.B. Sharan, R.V. Prabhakar, Formulation, evaluation, and pharmacokinetics of isradipine proliposomes for oral delivery, Journal of liposome research, 4(2012) 285-294. https://doi.org/10.3109/08982104.2012.697067.[7] Q. Fu, H.L. Fu, L. Huan, Preparation of cefquinome sulfate proliposome and its pharmacokinetics in rabbit, Iranian journal of pharmaceutical research, 4 (2013) 611-21. [8] T.T. H. Yen, T.T. Loan, D.T. Thuan, P.T.M. Hue, Preparation of berberin liposomes by ethanol injection method, Pharmaceutical journal, 59 (2019) 54-58 (in Vietnamese). [9] P. Elahehnaz, R. Marzieh, K. Maryam, Design and development of vitamin C-encapsulated proliposome with improved in-vitro and ex-vivo antioxidant efficacy, Journal of microemulsion 3(2018) 301 – 311. https://doi.org/10.1080/02652048.2018.1477845[10] I. Khan, S. Yousaf, S. Subramanian, Proliposome tablets manufactured using a slurry-driven lipid-enriched powders: Development, characterization and stability evaluation, J Int Pharm 1-2 (2018) 250 – 262. doi: 10.1016/j.ijpharm.2017.12.049[11] N.I. Payne, P. Timmins, V.A. Cheryl, Proliposomes: A Novel Solution to an Old Problem, Journal of Pharmaceutical Sciences 4 (1986) 325-329. https://doi.org/10.1002/jps.2600750402.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".