Engineering mannose-functionalized nanostructured lipid carriers by sequential design using hybrid artificial intelligence tools

Abstract

Nanostructured lipid carriers (NLCs) hold significant promise as drug delivery systems (DDS) owing to their small size and efficient drug-loading capabilities. Surface functionalization of NLCs can facilitate interaction with specific cell receptors, enabling targeted cell delivery. Mannosylation has emerged as a valuable tool for increasing the ability of nanoparticles to be recognized and internalized by macrophages. Nevertheless, the design and development of functionalized NLC is a complex task that entails the optimization of numerous variables and steps, making the process challenging and time-consuming. Moreover, no previous studies have been focused on evaluating the functionalization efficiency. In this work, hybrid Artificial Intelligence technologies are used to help in the design of mannosylated drug loaded NLCs. Artificial neural networks combined with fuzzy logic or genetic algorithms were employed to understand the particle formation processes and optimize the combinations of variables for the different steps in the functionalization process. Mannose was chemically modified to allow, for the first time, functionalization efficiency quantification and optimization. The proposed sequential methodology has enabled the design of a robust procedure for obtaining stable mannosylated NLCs with a uniform particle size distribution, small particle size (< 100 nm), and a substantial positive zeta potential (> 20mV). The incorporation of mannose on the surfaces of these DDS following the established protocols achieved > 85% of functionalization efficiency. This high effectiveness should enhance NLC recognition and internalization by macrophages, thereby facilitating the treatment of chronic inflammatory diseases