Data for: "Scalable manufacturing processes for solid lipid nanoparticles"

Dataset

Description

Background: Solid lipid nanoparticles offer a range of advantages as delivery systems but they are limited by effective manufacturing processes.
Objective: Within this study we outline a high-throughput and scalable manufacturing process for solid lipid nanoparticles.
Method: The solid lipid nanoparticles were formulated from a combination of Tristearin and 1,2-Distearoyl-phosphatidylethanolamine-methyl-polyethyleneglycol conjugate-2000 and manufactured using the M-110P Microfluidizer® processor (Microfluidics Inc, Westwood, Massachusetts, US).
Results: The manufacturing process was optimized in terms of the number of process cycles (1 to 5) and of process pressure change (20,000, 25,000 and 30,000 psi). The solid lipid nanoparticles were purified using tangential flow filtration, and they were characterized in terms of their size, PDI, Z-potential and protein loading. At-line particle size monitoring was also incorporated within the process train. Our results demonstrate that solid lipid nanoparticles can be effectively manufactured using this process at pressures of 20,000 psi with as little as 2 process passes, with purification and removal of non-entrapped protein achieved after 12 diafiltration cycles. Furthermore, the size could be effectively monitored at-line to allow rapid process control monitoring or product validation.
Conclusion: Using this method, protein loaded solid lipid nanoparticles containing a low (1%) and high (16%) Pegylation were manufactured, purified and monitored for particle size using an AT-line system demonstrating a scalable process for the manufacture of these nanoparticles.
All figures were produced in Microsoft excel from raw data collected.
Figure 1. The effect of processing pressure and passes on SLN attributes. A) Size (columns), polydispersity (dots) and B) Z-potential of Tristearin: mPEG-DSPE SLNs obtained with Microfluidizer processor. Sample with pressure from 20000 to 30000 psi as well as cycles number from 1 to 5 had been tested. Results are expressed as the means of three experiments ± S.D.
Figure 2. Purification and removal of non-incorporated protein via TFF. A) Percentage of remained OVA (mg/mL) after 20 washes at initial protein concentrations from 0.1 to 1 mg/mL. B) Minimum number of difiltration cycles required to have a protein remained concentration below 5% (w/w). Results are expressed as the means of three experiments ± S.D.
Figure 3. Production and at-line monitoring of SLNs after production via Microfluidizer processor after concentration via TFF in terms of A) visual appearance, B) Size (columns) and PDI (dots) of Tristearin: PEG SLNs before and after 10 times TFF concentration and C) cryoTEM images of Tristearin: PEG SLNs made by Microfluidizer processor before (on the left) and after (on the right) 10 times TFF concentration. Where appropriate, results are expressed as the means of three experiments ± S.D.
Figure 4. Production and at-line monitoring of SLNs after production via Microfluidizer processor. SLNs were produced, purified via TFF and the particle size measured by circulation between the mixing tank and the homogenizer, until complete detection. Data obtained with al-line and off-line dynamic light scattering were compared.
Figure 5. OVA-loaded solid lipid nanoparticles made by Microfluidizer processor a) Size (columns), PDI (dots) and b) Zeta potential and c) Loading efficiency of Tristearin: mPEG-DSPE SLNs expressed as percentage of the initial protein amount (µg/mL). Results are expressed as the means of three experiments ± S.D.
Figure 6. The effect of PEG on SLN attributes A) size and PDI, B) Zeta Potential, C) Loading and D) release profile. Results are expressed as the means of three experiments ± S.D.
Date made available7 Oct 2019
PublisherUniversity of Strathclyde
Date of data production1 Jan 2016 - 1 Apr 2019

Cite this

Perrie, Y. (Creator), Anderluzzi, G. (Creator). (7 Oct 2019). Data for: "Scalable manufacturing processes for solid lipid nanoparticles". University of Strathclyde. Anderluzzi_et_al_2019_data_set_revised(.xlsx), ReadMe(.rtf). 10.15129/f5bc5050-a88f-4cc6-8aa3-099948689410