Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2023 May 18;28(10):4169.
doi: 10.3390/molecules28104169.

The Power of Field-Flow Fractionation in Characterization of Nanoparticles in Drug Delivery

Juan Bian  1 , Nemal Gobalasingham  2 , Anatolii Purchel  2 , Jessica Lin  1
Affiliations
Review

The Power of Field-Flow Fractionation in Characterization of Nanoparticles in Drug Delivery

Juan Bian et al. Molecules. .

Abstract

Asymmetric-flow field-flow fractionation (AF4) is a gentle, flexible, and powerful separation technique that is widely utilized for fractionating nanometer-sized analytes, which extend to many emerging nanocarriers for drug delivery, including lipid-, virus-, and polymer-based nanoparticles. To ascertain quality attributes and suitability of these nanostructures as drug delivery systems, including particle size distributions, shape, morphology, composition, and stability, it is imperative that comprehensive analytical tools be used to characterize the native properties of these nanoparticles. The capacity for AF4 to be readily coupled to multiple online detectors (MD-AF4) or non-destructively fractionated and analyzed offline make this technique broadly compatible with a multitude of characterization strategies, which can provide insight on size, mass, shape, dispersity, and many other critical quality attributes. This review will critically investigate MD-AF4 reports for characterizing nanoparticles in drug delivery, especially those reported in the last 10-15 years that characterize multiple attributes simultaneously downstream from fractionation.

Keywords: asymmetrical flow field-flow fractionation; light scattering detection; multi-attribute characterization; nanoparticle drug delivery systems.

PubMed Disclaimer

Conflict of interest statement

Author Juan Bian and Jessica Lin were employed by the company Genentech Inc. Author Nemal Gobalasingham and Anatolii Purchel were employed by the company Wyatt Technology. The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Representative diagram of relaxation and elution process in AF4: (A) focusing relaxation in conventional AF4; (B) hydrodynamic relaxation in frit inlet or dispersion AF4; (C) elution in the channel, showing the sample migration along with the parabolic flow, where smaller species travel faster than larger species; (D) following AF4 separation, the multi-detector system that is coupled with AF4 enables online characterization towards size-resolved fractions (Purple: population of particles with smaller size, green: population of particles with larger size); (E) critical quality attributes of the nanoparticle drug delivery systems and corresponding analytical techniques; multi-attribute characterization can also be achieved by the multi-detector AF4 (MD-AF4) system. MALS, multi-angle light scattering; DLS, dynamic light scattering; UV, ultraviolet; RI, refractive index; FLD, fluorescence detector; LC, liquid chromatography; NTA, nanoparticle tracking analysis; Cryo-EM, cryogenic electron microscopy.
Figure 2
Figure 2
The mass ratio of DOX and total lipids as a function of the number averaged hydrodynamic diameter of DLF-1. Reprinted from Ref. [53] with permission from Elsevier.
Figure 3
Figure 3
Number-based particle size distribution obtained by NTA and AF4-MALS. Total particle/mL concentration and radius are reported in the table in the graph. Reprinted from Ref. [39] with permission from Elsevier.
Figure 4
Figure 4
(A) UV chromatogram of empty LNP-2E (blue) and RNA-filled LNP-2F (red) showing the significant UV signal from LNP-2E due to the scattering phenomenon in the UV detector. (B) Size-dependent RNA distribution in LNPs. Average of duplicate fractionation and offline RPLC analyses (yellow) vs. data from online analysis (blue for RNA-LNP and green for empty LNP). Reprinted from Ref. [92] with permission from Elsevier.
Figure 5
Figure 5
A scheme showing the convenience of AF4 for separating the unentrapped drug from the entrapped drug for determining drug loading. The free drug is removed via semi-permeable membrane via crossflow, while the entrapped drug elutes and is quantified. Reprinted from Ref. [59] with permission from Elsevier.
Figure 6
Figure 6
Hydrodynamic radius from in-line DLS after AF4 fractionation comparing micelle size distributions at different incubation periods. Reprinted from Ref. [61] with permission from Elsevier.
Figure 7
Figure 7
Ratio of Rg and Rh for spherical BSA-loaded polymersomes extruded using a 100 nm filter. The red line represents the mean value of these ratios. Reprinted from Ref. [62] with permission from American Chemical Society.
Figure 8
Figure 8
Conceptual diagram of executing free, small analyte detection via crossflow pathway detectors (right) and complex fractionation and subsequent detection (left). The molar ratio of RB:PEI-Mal in the complex was achieved by separation of free dye from complex. Reprinted from Ref. [64] with permission from Elsevier.
Figure 9
Figure 9
AF4 fractogram of the VLP with the radius and molar mass distributions of the two different size populations measured by MALS and RI signal. Reprinted from Ref. [45] with permission from Elsevier.
Figure 10
Figure 10
Separation and characterization of EVs using multi-detector asymmetrical flow field-flow fractionation (MD-AF4). (a) A representative AF4 fractionation profile of B16-F10-derived exosomes with UV and QELS (DLS) signals in blue and red separately; black dots illustrate hydrodynamic radius (Rh, nm), showing the particle size distribution over retention time. P1-P5 mark the peaks detected based on UV absorbance. Fractions were pooled for exomeres (hydrodynamic diameter < 50 nm), Exo-S (60–80 nm), and Exo-L (90–120 nm). (b) Representative correlation function in QELS for P3 (t = 25.1 min). (c) TEM imaging analysis of exosome input mixture (pre-fractionation) and fractionated exomere, Exo-S and Exo-L subpopulations. Arrows indicate exomeres (red), Exo-S (blue) and Exo-L (green). Reprint from Ref. [68] with permission from Springer Nature.
Figure 11
Figure 11
Representative workflow of offline coupling of AF4 and CE for separation of extracellular vesicles. (A) Fractograms of injection of HeLa cell medium (red trace) and standard EVs (black trace) to AF4. (B) CE traces of three AF4 fractions were collected from injection of 109 standard EVs, (F1: 20–22 min in green trace, F2: 22–24 min in blue trace, and F3: 24–26 min in red trace). (C) Western-Blot analysis of the CD63 protein, an EV marker in three AF4 fractions collected from injection of a HeLa cell medium. (D) Average diameter of the particles in the AF4 fractions collected from a HeLa cell medium observed in SEM. Reprint from Ref. [70] with permission from ACS publications.
Figure 12
Figure 12
Representative diagram of AF4-MALS-DLS-ICP-MS platform including (A) the AF4-MALS-DLS system with post channel injection and (B) flow injection of calibrant solution. A switch valve allowed A or B to be operational and a separate HPLC pump delivered make-up liquid. (C) AF4-ICP MS fractogram of a mixture of 10, 20, and 60 nm Au NPs (black line) superimposed on a fractogram corresponding to 30 nm NIST Au NPs (light gray line). The signal intensities of post channel injections of 10, 20, and 60 nm Au NPs have been indicated on the secondary y-axis. Reprint from Ref. [73] with permission from ACS publications.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Sharma K., Koirala A., Nicolopoulos K., Chiu C., Wood N., Britton P.N. Vaccines for COVID-19: Where do we stand in 2021? Paediatr. Respir. Rev. 2021;39:22–31. doi: 10.1016/j.prrv.2021.07.001. - DOI - PMC - PubMed
    1. Hussain A., Yang H., Zhang M., Liu Q., Alotaibi G., Irfan M., He H., Chang J., Liang X.J., Weng Y., et al. Mrna vaccines for COVID-19 and diverse diseases. J. Control Release. 2022;345:314–333. doi: 10.1016/j.jconrel.2022.03.032. - DOI - PMC - PubMed
    1. Rauf A., Abu-Izneid T., Khalil A.A., Hafeez N., Olatunde A., Rahman M., Semwal P., Al-Awthan Y.S., Bahattab O.S., Khan I.N., et al. Nanoparticles in clinical trials of COVID-19: An update. Int. J. Surg. 2022;104:106818. doi: 10.1016/j.ijsu.2022.106818. - DOI - PMC - PubMed
    1. Jahangirian H., Lemraski E.G., Webster T.J., Rafiee-Moghaddam R., Abdollahi Y. A review of drug delivery systems based on nanotechnology and green chemistry: Green nanomedicine. Int. J. Nanomed. 2017;12:2957–2978. doi: 10.2147/IJN.S127683. - DOI - PMC - PubMed
    1. Patra J.K., Das G., Fraceto L.F., Campos E.V.R., Rodriguez-Torres M.D.P., Acosta-Torres L.S., Diaz-Torres L.A., Grillo R., Swamy M.K., Sharma S., et al. Nano based drug delivery systems: Recent developments and future prospects. J. Nanobiotechnol. 2018;16:71. doi: 10.1186/s12951-018-0392-8. - DOI - PMC - PubMed

Grants and funding

This research received no external funding.

LinkOut - more resources