ArticlePharmaceutical nanotechnology2026
Optimization of Levomilnacipran Loaded Nanostructured Lipid Carrier Using Response Surface Methodology.
Article in Pharmaceutical nanotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
aimThe study employed Response Surface Methodology (RSM) with a Central Composite Rotatable Design (CCRD) model to optimise the formulations of Levomilnacipran nanostructured lipid carriers (LEV-NLC).
methodsThis study utilised a CCRD (Central Composite Rotatable Design) with a three-factor factorial design and three levels. It examined the particle size, zeta potential, and entrapment efficiency of LEV-NLC in relation to three independent variables: the ratio of aqueous to organic phase (X1), the ratio of drug to lipid (X2), and the concentration of surfactant (X3).
resultsThe results demonstrated that the most favourable composition could be achieved using Response Surface Methodology (RSM). The most effective composition for LEV-NLC consisted of a 1:1 ratio of aqueous to organic phase (X1), a 1:7 ratio of drug to lipid (X2), and a surfactant concentration (X3) of 0.5%. Under the optimised conditions, the LEV-NLC formulation resulted in a particle size of 148 nm, a zeta potential of 36 mV, and an entrapment efficiency of 88%. The optimised LEV-NLC was examined using Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), which revealed the presence of spherical particles. The total percentage of Levomilnacipran released from the NLC was 77% at pH 7.4 and 76% at pH 6.0 over 24 hours, exhibiting a sustained release profile that could enhance the therapeutic benefits of the drug. DISCUSSION: The optimised formulation demonstrated that RSM effectively predicts formulation variables and their interactions, leading to improved stability, high drug encapsulation, and sustained release suitable for enhanced therapeutic performance.
conclusionThis study demonstrated the effective application of RSM-CCRD for modelling LEVNLC.
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