Next-generation skin cancer treatment: A quality by design perspective on artificial neural network-optimized cationic ethosomes with bleomycin sulphate

Abstract

Skin cancer, exacerbated by environmental factors like ozone layer depletion, poses a global health concern. Topical administration of chemotherapy, a promising approach, minimizes systemic toxicity. Bleomycin Sulphate (BLM), effective intravenously, faces challenges due to limited skin penetration. Cationic ethosomes, known for superior skin penetration, address this by enhancing electrostatic interactions with cancer cells. This ensures localized drug delivery, preventing systemic circulation. Employing thin film hydration, we prepared cationic ethosomes with BLM, optimizing through Artificial Neural Network (ANN) and Response Surface Methodology (RSM). Characterization included entrapment efficiency, vesicle size, morphology, and zeta potential. In-vitro drug release and various kinetic models were analyzed. Ex-vivo evaluations, like dermatokinetic studies and fluorescence microscopy, and in-vivo skin sensitivity testing were conducted. Results showed the optimized formulation with 27.1 % entrapment efficiency, 144.3 nm vesicle size, smooth surface morphology, and positive zeta potential. Ex-vivo assessments indicated favorable skin retention and in-vivo tests suggested suitability for topical administration, highlighting its potential in skin cancer treatment. This research offers a promising strategy for localized and effective skin cancer therapy, addressing challenges associated with systemic toxicity and limited drug penetration.