Nanoethosomes for transdermal delivery of tropisetron HCl: multi-factorial predictive modeling, characterization, and ex vivo skin permeation

Abstract

Objective: The aim of the present work is to exclusively optimize and model the effect of phospholipid type either egg phosphatidylcholine (EPC) or soybean phosphatidylcholine (SPC), together with other formulation variables, on the development of nano-ethosomal systems for transdermal delivery of a water-soluble antiemetic drug. Tropisetron HCl (TRO) is available as hard gelatin capsules and IV injections. The transdermal delivery of TRO is considered as a novel alternative route supposing to improve BAV as well as patient convenience.

Methods: TRO-loaded ethanolic vesicular systems were prepared by hot technique. The effect of formulation variables were optimized through a response surface methodology using 3 × 2²-level full factorial design. The concentrations of both PC (A) and ethanol (B) and PC type (C) were the factors, while entrapment efficiency (Y₁), vesicle size (Y₂), polydispersity index (Y₃), and zeta potential (Y₄) were the responses. The drug permeation across rat skin from selected formulae was studied. Particle morphology, drug–excipient interactions, and vesicle stability were also investigated.

Results: The results proved the critical role of all formulation variables on ethosomal characteristics. The suggested models for all responses showed good predictability. Only the concentration of phospholipid, irrespective to PC type, had a significant effect on the transdermal flux (p < 0.01). The ethosomal vesicles were unilamellar with a nearly spherical shape. EPC-based ethosomes proved good stability.

Conclusion: The study suggests the applicability of statistical modeling as a promising tool for prediction of ethosomal characteristics. The ethanolic vesicles were considered as novel potential nanocarriers for accentuated transdermal TRO delivery.

Keywords:

• Ethosomes
• tropisetron
• lecithin
• response surface methodology
• egg yolk
• soybean
• prediction
• optimization

Disclosure statement

The authors declare that there is no conflict of interest.