2020
DOI: 10.1002/ange.201913539
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Stable Polymer Nanoparticles with Exceptionally High Drug Loading by Sequential Nanoprecipitation

Abstract: Poor solubility often leads to low drug efficacy. Encapsulation of water‐insoluble drugs in polymeric nanoparticles offers a solution. However, low drug loading remains a critical challenge. Now, a simple and robust sequential nanoprecipitation technology is used to produce stable drug‐core polymer‐shell nanoparticles with high drug loading (up to 58.5 %) from a wide range of polymers and drugs. This technology is based on tuning the precipitation time of drugs and polymers using a solvent system comprising mu… Show more

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Cited by 55 publications
(42 citation statements)
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“…The decrease of interfacial tension between the two phases, caused by rapid diffusion of solvent into non-solvent phase, results in the increase of surface area and leads to the formation of nanoparticles. As Liu et al reported in 2020 [35], simple and robust sequential nanoprecipitation is a suitable method for enhancing drug loading and produces a stable drug-core polymer shell PNPs with high amount of loaded drug (up to 58.5%).…”
Section: Preparation Structure and Properties Of Polymer Nanoparticlesmentioning
confidence: 99%
“…The decrease of interfacial tension between the two phases, caused by rapid diffusion of solvent into non-solvent phase, results in the increase of surface area and leads to the formation of nanoparticles. As Liu et al reported in 2020 [35], simple and robust sequential nanoprecipitation is a suitable method for enhancing drug loading and produces a stable drug-core polymer shell PNPs with high amount of loaded drug (up to 58.5%).…”
Section: Preparation Structure and Properties Of Polymer Nanoparticlesmentioning
confidence: 99%
“…The results of the ANOVA test (Table 4) showed that the P-value was lower than 0.05 for all responses and P for the lack of fit was greater than 0.05 for all responses, confirming that the proposed model is adequate, reliable, and has a good predictive power. (1) Regression coefficient; (2) predictive power of the model; (3) degrees of freedom; (4) sum of squares; (5) mean of square (variance); (6) Fisher's ratio; (7) probability; (8) particle size (µm); (9) particle size distribution (%); (10) drug loading (%); (11) entrapment efficiency (%); (12) yield (%); (13)(14)(15)(16)(17) percentage of curcumin released after 2 h, 4 h, 6 h, 12 h, and 24 h (%).…”
Section: Summary Of Fitmentioning
confidence: 99%
“…CQA (1) Predicted (1) Critical quality attribute; (2) particle size (%); (3) particle size distribution (%); (4) drug loading (%); (5) entrapment efficiency (%); (6)(7)(8)(9) percentage of curcumin released after 2 h, 4 h, 6 h, and 12 h (%).…”
Section: Establishment Of the Design Space And Validation Of The Modelmentioning
confidence: 99%
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“…The LDBCs, coded as PEG 2k -b-dxDAP (where x is the number of peripheral DAP moieties), have been synthesized by clicking two performed blocks and using the first four generations of bis-MPA dendrons (G = 1, 2, 3, or 4) with x = 2, 4, 8, or 16 DAP units, respectively (Scheme 1). Together with their characterization, the self-assembly properties of the LDBCs using two different methodologies, nanoprecipitation [42][43][44][45] and microfluidics, are described. Microfludics provide controlled reaction conditions and then an excellent control on the assembly of organic nanomaterials due to their remarkable heat and mass transfer enhancement [46].…”
Section: Introductionmentioning
confidence: 99%