Design and assessment of amino acid-based gemini catanionic niosomes for dual-drug delivery of anticancer drug...

Niosomes are versatile nanocarriers capable of enabling targeted delivery and controlled release of anticancer agents. Incorporation of ionic surfactants offers an effective strategy to functionalize these systems. In particular, amino acid-derived gemini catanionic niosomes, composed of multifunctional surfactants, represent a promising platform for advanced drug delivery applications. An integrated computational framework combining molecular modeling with design of experiments (DoE) was employed to investigate key properties of amino acid-based gemini catanionic niosomal bilayer. An arginine-derived gemini surfactant was selected as the cationic component, while sodium laurate was used as the anionic surfactant. Two clinically approved anticancer drugs with distinct intracellular targets, niraparib and lapatinib, were evaluated. The findings demonstrate that the bilayer composition and structure strongly influence drug transport across the niosomal membrane. The ratio of diffusion coefficients of the two drugs was identified as a critical performance metric. Among the evaluated formulations, F7 exhibited the most favorable ratio (5.46). Numerical optimization of the diffusion responses yielded an optimized formulation with an improved ratio of 10.28. The corresponding diffusion coefficients for lapatinib and niraparib were 8.52 × 10- 12 and 8.28 × 10- 13 m²/s, respectively. Lapatinib diffusion was enhanced through synergistic interactions between surfactant components, whereas niraparib diffusion followed an additive trend predominantly governed by sodium laurate concentration. These results highlight the capability of molecular modeling integrated with experimental design to guide the rational optimization of nanocarrier systems. Moreover, amino acid-based gemini catanionic niosomal bilayer offer tunable properties that can be exploited for controlled and site-specific delivery of anticancer drug combinations.