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.
摘要：
纳米结构脂质载体（NLC）由于其小尺寸和有效的药物装载能力而具有作为药物递送系统（DDS）的重要前景。NLC的表面功能化可以促进与特定细胞受体的相互作用，实现靶向细胞递送。甘露糖基化已成为增加纳米颗粒被巨噬细胞识别和内化能力的有价值的工具。然而，功能化NLC的设计和开发是一项复杂的任务，需要优化众多变量和步骤，使过程具有挑战性和耗时。此外，以前的研究没有集中在评估功能化效率。在这项工作中,混合人工智能技术被用来帮助设计载有甘露糖基化药物的NLC。结合模糊逻辑或遗传算法的人工神经网络用于理解颗粒形成过程并优化功能化过程中不同步骤的变量组合。甘露糖经过化学修饰，第一次,功能化效率的量化和优化。所提出的顺序方法使设计一个强大的程序，以获得稳定的甘露糖基化NLCs具有均匀的粒度分布，小粒径（<100nm），和相当大的正ζ电位(>20mV)。在建立的方案之后,在这些DDS的表面上掺入甘露糖实现>85%的官能化效率。这种高有效性应该增强巨噬细胞对NLC的识别和内化，从而促进慢性炎性疾病的治疗。
