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

Rebeca Martinez-Borrajo1, Patricia Diaz-Rodriguez2, Mariana Landin1

  • 1Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, Grupo I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Instituto de Materiais da Universidade de Santiago de Compostela (iMATUS), Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain.

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|May 29, 2024
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Summary
This summary is machine-generated.

Artificial intelligence optimizes mannosylated nanostructured lipid carriers (NLCs) for targeted drug delivery. This approach enhances macrophage uptake, improving treatments for inflammatory diseases.

Keywords:
Artificial intelligenceArtificial neural networksCarbohydrate surface functionalizationGenetic algorithmsMannosylation optimizationNanostructured lipid carriersNeurofuzzy logicQuality by design

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Area of Science:

  • Biomaterials Science
  • Nanotechnology
  • Drug Delivery

Background:

  • Nanostructured lipid carriers (NLCs) are promising drug delivery systems (DDS) due to their size and drug-loading capacity.
  • Surface functionalization, like mannosylation, enhances nanoparticle interaction with macrophages for targeted delivery.
  • Developing functionalized NLCs is complex, requiring optimization of multiple variables and lacking functionalization efficiency evaluation.

Purpose of the Study:

  • To employ hybrid Artificial Intelligence (AI) technologies for designing mannosylated drug-loaded NLCs.
  • To optimize the complex functionalization process of NLCs for improved drug delivery.
  • To quantify and optimize functionalization efficiency for the first time using chemically modified mannose.

Main Methods:

  • Utilized Artificial Neural Networks combined with fuzzy logic or genetic algorithms to model NLC formation.
  • Optimized variable combinations for various functionalization steps using AI.
  • Chemically modified mannose to enable functionalization efficiency quantification and optimization.

Main Results:

  • Developed a robust sequential methodology for stable mannosylated NLCs.
  • Achieved small particle size (<100 nm) with uniform distribution and high positive zeta potential (>20 mV).
  • Reached over 85% functionalization efficiency for mannose incorporation on NLC surfaces.

Conclusions:

  • AI-driven design provides a robust procedure for creating optimized mannosylated NLCs.
  • High functionalization efficiency enhances macrophage recognition and internalization of NLCs.
  • This approach facilitates targeted drug delivery for treating chronic inflammatory diseases.