D-optimal design-based optimization of etoricoxib cubogel for management of arthritis; in vitro and in vivo evaluation

Etoricoxib (Et) is selective COX-2 inhibitor with several drawbacks after oral administration. Current study focused on formulating targeted Et cubogel for osteoarthritis management. Interaction between formulation factors; (glyceryl-monooleate (GMO) and Poloxamer407 (Px)) concentrations and process parameters (melting/solvent-evaporation preparation methods) was investigated using D-optimal design. Considered levels were 3, 5 and 7% for GMO and 0.5,0.75 and 1% for Px. Effect of selected variables on particle size (PS) and entrapment efficiency (EE) of Et cubosomes was studied using Design Expert software. Optimized formulation was studied for zeta potential, TEM, and Et release. Optimum formula was loaded into gel formulations and subjected to physical characterization and in-vitro Et release. Selected cubogel was evaluated for ex-vivo permeation, and anti-inflammatory activity using carrageenan-induced edema model. Optimum formulation (6.5% GMO,1% Px , melting preparation method) had PS of 58.6 ± 0.51 nm, EE of 96.1 ± 1.5%, zeta potential of -26.6 ± 0.66 mV and cubic structure as indicated by TEM. Formulated cubogels had acceptable physical properties with sustained release depending on gelling agent type and concentration. Ex-vivo permeation confirmed higher permeability for Et cubogel than Etgel. Anti-inflammatory study confirmed enhanced (<i>p</i> < 0.05) anti-inflammatory activity of Et cubogel as compared to Et gel. Hence, the present study presents Et cubgel formulation as anti-inflammatory remedy.

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Publication Details

Journal
Pharmaceutical Development and Technology
Published
2025-09-04
DOI
https://doi.org/10.1080/10837450.2025.2556062
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.50
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article

D-optimal design-based optimization of etoricoxib cubogel for management of arthritis; in vitro and in vivo evaluation

Heba A. Eassa, Manal K. Darwish, Marwa A. Abd El‐Fattah, Marwa H. Abdo
1 citations
Pharmaceutical Development and Technology
Computational Drug Discovery Methods
1.50
article

D-optimal design-based optimization of etoricoxib cubogel for management of arthritis; in vitro and in vivo evaluation

Heba A. Eassa, Manal K. Darwish, Marwa A. Abd El‐Fattah, Marwa H. Abdo
article en
1 citations

Abstract

Etoricoxib (Et) is selective COX-2 inhibitor with several drawbacks after oral administration. Current study focused on formulating targeted Et cubogel for osteoarthritis management. Interaction between formulation factors; (glyceryl-monooleate (GMO) and Poloxamer407 (Px)) concentrations and process parameters (melting/solvent-evaporation preparation methods) was investigated using D-optimal design. Considered levels were 3, 5 and 7% for GMO and 0.5,0.75 and 1% for Px. Effect of selected variables on particle size (PS) and entrapment efficiency (EE) of Et cubosomes was studied using Design Expert software. Optimized formulation was studied for zeta potential, TEM, and Et release. Optimum formula was loaded into gel formulations and subjected to physical characterization and in-vitro Et release. Selected cubogel was evaluated for ex-vivo permeation, and anti-inflammatory activity using carrageenan-induced edema model. Optimum formulation (6.5% GMO,1% Px , melting preparation method) had PS of 58.6 ± 0.51 nm, EE of 96.1 ± 1.5%, zeta potential of -26.6 ± 0.66 mV and cubic structure as indicated by TEM. Formulated cubogels had acceptable physical properties with sustained release depending on gelling agent type and concentration. Ex-vivo permeation confirmed higher permeability for Et cubogel than Etgel. Anti-inflammatory study confirmed enhanced (<i>p</i> < 0.05) anti-inflammatory activity of Et cubogel as compared to Et gel. Hence, the present study presents Et cubgel formulation as anti-inflammatory remedy.

Pharmaceutical Development and TechnologyVol. 30(8)
University of Saint Joseph (US), Al-Azhar University (ID)
Openalex Percentile: Top 13%
Computational Drug Discovery Methods
1.50
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