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

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.

Harnessing extracellular vesicle membrane for gene therapy: EVs-biomimetic nanoparticles.

One of the main concerns in oligonucleotide-based therapeutics is achieving a successful cell targeting while avoiding drug degradation and clearance. Nanoparticulated drug delivery systems have emerged as a way of overcoming these issues. Among them, membrane-coated nanoparticles are of increasing relevance mainly due to their enhanced cellular uptake, immune evasion and biocompatibility. In this study, we designed and elaborated a simple and highly tuneable biomimetic drug delivery nanosystem based on a polymeric core surrounded by extracellular vesicles (EVs)-derived membranes. This strategy should allow the nanosystems to benefit from the properties conferred by the membrane proteins present in EVs membrane, key paracrine mediators. The developed systems were able to successfully encapsulate the required oligonucleotides. Also, their characterisation through already well standardised methods (dynamic light scattering, transmission electron microscopy and nanoparticle tracking analysis) and by fluorescence cross-correlation spectroscopy (FCCS) showed the desired core-shell structure. The cellular uptake using different cell types further confirmed the coating though an enhancement in cell internalisation of the developed biomimetic nanoparticles. This study brings up new possibilities for GapmeR delivery as it might be a base for the development of new delivery systems for gene therapy.

Rationalized design to explore the full potential of PLGA microspheres as drug delivery systems.

Polymeric microparticles are widely used as drug delivery platforms either alone or embedded in more complex structures for regenerative medicine. Emulsion-solvent evaporation is the most extensively used technique for microparticles preparation. Despite the apparent simplicity of this method, there is no general procedure for producing microparticles of predictable characteristics (particle size, size distribution, encapsulation efficiency, and drug loading). Hybrid systems such as neurofuzzy logic allow identifying relationships between inputs and outputs, expressing the generated mathematical models through rules in linguistic format. In this work, the relationships between the variables involved in the emulsion-solvent evaporation process and the quality parameters of PLGA microparticles as drug delivery systems were established. Neurofuzzy logic software was able to generate models of high predictability (> 85%) for the microspheres properties namely particle size, size distribution, encapsulation efficiency and drug loading. Moreover, the generated sets of IF-THEN rules allowed to dictate general guidelines to better select the PLGA microparticles formulation parameters. This approach would be of great interest as a starting point to set-up protocols for the development of PLGA microparticles obtained by emulsion-solvent evaporation for many applications.