Artificial neural network guided optimization of tenofovir and Cyanovirin-N multipurpose preventive hydrogel formulation developed using the Box Behnken design model

Abstract

Tenofovir (TNF) is an antiretroviral drug that has being used as a topical microbicide to prevent human immunodeficiency virus transmission (HIV). Cyanovirin-N (CV-N) is a lectin protein that can bind to the HIV envelope glycoprotein and inhibit viral entry. The combination of TNF and CV-N may have synergistic effects and enhance the efficacy of microbicide. The aim of this study was to develop and optimize composite hydrogel formulations containing 1%TNF and 0.0005% CV-N using Box Behnken Design. A three-factor, three-level Box-Behnken design was employed to investigate the effects of the concentrations of PEG₂₀₀₀, sodium carboxyl methylcellulose (NaCMC), and calcium chloride on the release of Tenofovir, flux, and mucoadhesion. The mucoadhesion was evaluated by measuring the percent mucin adsorption while flux and release kinetics were evaluated using the Franz cell diffusion method. The optimal hydrogel formulation was found to contain 4% NaCMC, 2% PEG2000 and 1% CaCl₂. The hydrogel had a pH of 4.5 ​± ​0.017, a viscosity of 275600 ​± ​0.65 ​cP, flux 9806 μg/cm²/h for TNF with a drug release of 119, 205.6 μg/cm². The TNF gel exhibited a pseudoplastic rheological behavior with 96.3% muco-adhesion. The study successfully developed an optimized TNF/CV-N mucoadhesive hydrogel, highlighting its potential as an on-demand multipurpose prevention technology (MPT) for HIV. The formulation was optimized to ensure good drug release, flux and mucoadhesion. The optimized hydrogel offers a convenient, effective method for preventing sexually transmitted infections (STIs), addressing critical challenges in drug delivery and user adherence while advancing public health strategies for STIs.