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.

Fighting antibiotic resistance in the local management of bovine mastitis.

Bovine mastitis is a widespread infectious disease with a significant economic burden, accounting for 80 % of the antibiotic usage in dairy animals. In recent years, extensive research has focused on using biomimetic approaches such as probiotics, bacteriocins, bacteriophages, or phytochemicals as potential alternatives to antibiotics. The local administration of therapeutic molecules through the intramammary route is one of the most commonly strategies to manage bovine mastitis. This review highlights the most important findings in this field and discusses their local application in mastitis therapy. In contrast to antibiotics, the proposed alternatives are not limited to promote bacterial death but consider other factors associated to the host microenvironments. To this end, the proposed biomimetic strategies can modulate different stages of infection by modifying the local microbiota, preventing oxidative stress, reducing bacterial adhesion to epithelial cells, modulating the immune response, or mediating the inflammatory process. Numerous in vitro studies support the antimicrobial, antibiofilm or antioxidant properties of these alternatives. However, in vivo studies incorporating these components within pharmaceutical formulations with potential clinical application are limited. The development of secure, stable, and effective drug delivery systems based on the proposed options is necessary to achieve real alternatives to antibiotics in the clinic.

Vancomycin-Loaded 3D-Printed Polylactic Acid-Hydroxyapatite Scaffolds for Bone Tissue Engineering.

The regeneration of bone remains one of the main challenges in the biomedical field, with the need to provide more personalized and multifunctional solutions. The other persistent challenge is related to the local prevention of infections after implantation surgery. To fulfill the first one and provide customized scaffolds with complex geometries, 3D printing is being investigated, with polylactic acid (PLA) as the biomaterial mostly used, given its thermoplastic properties. The 3D printing of PLA in combination with hydroxyapatite (HA) is also under research, to mimic the native mechanical and biological properties, providing more functional scaffolds. Finally, to fulfill the second one, antibacterial drugs locally incorporated into biodegradable scaffolds are also under investigation. This work aims to develop vancomycin-loaded 3D-printed PLA-HA scaffolds offering a dual functionality: local prevention of infections and personalized biodegradable scaffolds with osseointegrative properties. For this, the antibacterial drug vancomycin was incorporated into 3D-printed PLA-HA scaffolds using three loading methodologies: (1) dip coating, (2) drop coating, and (3) direct incorporation in the 3D printing with PLA and HA. A systematic characterization was performed, including release kinetics, Staphylococcus aureus antibacterial/antibiofilm activities and cytocompatibility. The results demonstrated the feasibility of the vancomycin-loaded 3D-printed PLA-HA scaffolds as drug-releasing vehicles with significant antibacterial effects for the three methodologies. In relation to the drug release kinetics, the (1) dip- and (2) drop-coating methodologies achieved burst release (first 60 min) of around 80-90% of the loaded vancomycin, followed by a slower release of the remaining drug for up to 48 h, while the (3) 3D printing presented an extended release beyond 7 days as the polymer degraded. The cytocompatibility of the vancomycin-loaded scaffolds was also confirmed.

Aerogels as Carriers for Oral Administration of Drugs: An Approach towards Colonic Delivery.

Polysaccharide aerogels have emerged as a highly promising technology in the field of oral drug delivery. These nanoporous, ultralight materials, derived from natural polysaccharides such as cellulose, starch, or chitin, have significant potential in colonic drug delivery due to their unique properties. The particular degradability of polysaccharide-based materials by the colonic microbiota makes them attractive to produce systems to load, protect, and release drugs in a controlled manner, with the capability to precisely target the colon. This would allow the local treatment of gastrointestinal pathologies such as colon cancer or inflammatory bowel diseases. Despite their great potential, these applications of polysaccharide aerogels have not been widely explored. This review aims to consolidate the available knowledge on the use of polysaccharides for oral drug delivery and their performance, the production methods for polysaccharide-based aerogels, the drug loading possibilities, and the capacity of these nanostructured systems to target colonic regions.

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.

Neurofuzzy logic predicts a fine-tuning metabolic reprogramming on elicited Bryophyllum PCSCs guided by salicylic acid.

Novel approaches to the characterization of medicinal plants as biofactories have lately increased in the field of biotechnology. In this work, a multifaceted approach based on plant tissue culture, metabolomics, and machine learning was applied to decipher and further characterize the biosynthesis of phenolic compounds by eliciting cell suspension cultures from medicinal plants belonging to the Bryophyllum subgenus. The application of untargeted metabolomics provided a total of 460 phenolic compounds. The biosynthesis of 164 of them was significantly modulated by elicitation. The application of neurofuzzy logic as a machine learning tool allowed for deciphering the critical factors involved in the response to elicitation, predicting their influence and interactions on plant cell growth and the biosynthesis of several polyphenols subfamilies. The results indicate that salicylic acid plays a definitive genotype-dependent role in the elicitation of Bryophyllum cell cultures, while methyl jasmonate was revealed as a secondary factor. The knowledge provided by this approach opens a wide perspective on the research of medicinal plants and facilitates their biotechnological exploitation as biofactories in the food, cosmetic and pharmaceutical fields.

Using machine learning to link the influence of transferred Agrobacterium rhizogenes genes to the hormone profile and morphological traits in Centella asiatica hairy roots.

Hairy roots are made after the integration of a small set of genes from Agrobacterium rhizogenes in the plant genome. Little is known about how this small set is linked to their hormone profile, which determines development, morphology, and levels of secondary metabolite production. We used C. asiatica hairy root line cultures to determine the putative links between the rol and aux gene expressions with morphological traits, a hormone profile, and centelloside production. The results obtained after 14 and 28 days of culture were processed via multivariate analysis and machine-learning processes such as random forest, supported vector machines, linear discriminant analysis, and neural networks. This allowed us to obtain models capable of discriminating highly productive root lines from their levels of genetic expression (rol and aux genes) or from their hormone profile. In total, 12 hormones were evaluated, resulting in 10 being satisfactorily detected. Within this set of hormones, abscisic acid (ABA) and cytokinin isopentenyl adenosine (IPA) were found to be critical in defining the morphological traits and centelloside content. The results showed that IPA brings more benefits to the biotechnological platform. Additionally, we determined the degree of influence of each of the evaluated genes on the individual hormone profile, finding that aux1 has a significant influence on the IPA profile, while the rol genes are closely linked to the ABA profile. Finally, we effectively verified the gene influence on these two specific hormones through feeding experiments that aimed to reverse the effect on root morphology and centelloside content.

Artificial Neural Networks Elucidated the Essential Role of Mineral Nutrients versus Vitamins and Plant Growth Regulators in Achieving Healthy Micropropagated Plants.

The design of an adequate culture medium is an essential step in the micropropagation process of plant species. Adjustment and balance of medium components involve the interaction of several factors, such as mineral nutrients, vitamins, and plant growth regulators (PGRs). This work aimed to shed light on the role of these three components on the plant growth and quality of micropropagated woody plants, using Actinidia arguta as a plant model. Two experiments using a five-dimensional experimental design space were defined using the Design of Experiments (DoE) method, to study the effect of five mineral factors (NH4NO3, KNO3, Mesos, Micros, and Iron) and five vitamins (Myo-inositol, thiamine, nicotinic acid, pyridoxine, and vitamin E). A third experiment, using 20 combinations of two PGRs: BAP (6-benzylaminopurine) and GA3 (gibberellic acid) was performed. Artificial Neural Networks (ANNs) algorithms were used to build models with the whole database to determine the effect of those components on several growth and quality parameters. Neurofuzzy logic allowed us to decipher and generate new knowledge on the hierarchy of some minerals as essential components of the culture media over vitamins and PRGs, suggesting rules about how MS basal media formulation could be modified to assess the quality of micropropagated woody plants.

Sublingual Boosting with a Novel Mucoadhesive Thermogelling Hydrogel Following Parenteral CAF01 Priming as a Strategy Against Chlamydia trachomatis.

Chlamydia trachomatis is the most prevalent sexually transmitted disease of bacterial origin. The high number of asymptomatic cases makes it difficult to stop the transmission, requiring vaccine development. Herein, a strategy is proposed to obtain local genital tract immunity against C. trachomatis through parenteral prime and sublingual boost. Subcutaneous administration of chlamydia CTH522 subunit vaccine loaded in the adjuvant CAF01 is combined with sublingual administration of CTH522 loaded in a novel thermosensitive and mucoadhesive hydrogel. Briefly, a ternary optimized hydrogel (OGEL) with desirable biological and physicochemical properties is obtained using artificial intelligence techniques. This formulation exhibits a high gel strength and a strong mucoadhesive, adhesive and cohesive nature. The thermosensitive properties of the hydrogel facilitate application under the tongue. Meanwhile the fast gelation at body temperature together with rapid antigen release should avoid CTH522 leakage by swallowing and increase the contact with sublingual tissue, thus promoting absorption. In vivo studies demonstrate that parenteral-sublingual prime-boost immunization, using CAF01 and OGEL as CTH522 vaccine carriers, shows a tendency to increase cellular (Th1/Th17) immune responses when compared to mucosal or parenteral vaccination alone. Furthermore, parenteral prime with CAF01/CTH522 followed by sublingual boosting with OGEL/CTH522 elicits a local IgA response in the genital tract.

Computer-Based Tools Unmask Critical Mineral Nutrient Interactions in Hoagland Solution for Healthy Kiwiberry Plant Acclimatization.

The aim of this study was to better understand the response of ex vitro acclimatized plants grown to a set of mineral nutrient combinations based on Hoagland solution. To reach that, two computer-based tools were used: the design of experiments (DOE) and a hybrid artificial intelligence technology that combines artificial neural networks with fuzzy logic. DOE was employed to create a five-dimensional IV-design space by categorizing all macroelements and one microelement (copper) of Hoagland mineral solution, reducing the experimental design space from 243 (35) to 19 treatments. Typical growth parameters included hardening efficiency (Hard), newly formed shoot length (SL), total leaf number (TLN), leaf chlorophyll content (LCC), and leaf area (LA). Moreover, three physiological disorders, namely, leaf necrosis (LN), leaf spot (LS), and curled leaf (CL), were evaluated for each treatment (mineral formulation). All the growth parameters plus LN were successfully modeled using neuro-fuzzy logic with a high train set R 2 between experimental and predicted values (72.67 < R 2 < 98.79). The model deciphered new insights using different sets of "IF-THEN" rules, pinpointing the positive role of Mg2+ and Ca2+ to improve Hard, SL, TLN, and LA and alleviate LN but with opposite influences on LCC. On the contrary, TLN and LCC were negatively affected by the addition of NO3 - into the media, while NH4 + in complex interaction with Cu2+ or Mg2+ positively enhanced SL, TLN, LCC, and LA. In our opinion, the approach and results achieved in this work are extremely fruitful to understand the effect of Hoagland mineral nutrients on the healthy growth of ex vitro acclimatized plants, through identifying key factors, which favor growth and limit physiological abnormalities.

The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in Bryophyllum Medicinal Plants (Genus Kalanchoe).

Phenolic compounds constitute an important family of natural bioactive compounds responsible for the medicinal properties attributed to Bryophyllum plants (genus Kalanchoe, Crassulaceae), but their production by these medicinal plants has not been characterized to date. In this work, a combinatorial approach including plant tissue culture, untargeted metabolomics, and machine learning is proposed to unravel the critical factors behind the biosynthesis of phenolic compounds in these species. The untargeted metabolomics revealed 485 annotated compounds that were produced by three Bryophyllum species cultured in vitro in a genotype and organ-dependent manner. Neurofuzzy logic (NFL) predictive models assessed the significant influence of genotypes and organs and identified the key nutrients from culture media formulations involved in phenolic compound biosynthesis. Sulfate played a critical role in tyrosol and lignan biosynthesis, copper in phenolic acid biosynthesis, calcium in stilbene biosynthesis, and magnesium in flavanol biosynthesis. Flavonol and anthocyanin biosynthesis was not significantly affected by mineral components. As a result, a predictive biosynthetic model for all the Bryophyllum genotypes was proposed. The combination of untargeted metabolomics with machine learning provided a robust approach to achieve the phytochemical characterization of the previously unexplored species belonging to the Bryophyllum subgenus, facilitating their biotechnological exploitation as a promising source of bioactive compounds.

A Traffic Light System to Maximize Carbohydrate Cryoprotectants' Effectivity in Nanostructured Lipid Carriers' Lyophilization.

Lyophilization is often employed to transform nanoparticle suspensions to stable solid forms. This work proposed Neurofuzzy Logic (NFL) to better understand the lyophilization process of Nanostructured Lipid Carriers' (NLCs) dispersions and the carbohydrate cryoprotectants' (CPs) performance in these processes. NLCs were produced by hot homogenization, frozen at different speeds, and lyophilized using several CPs at variable concentrations. NLCs were characterized, and results were expressed as increase in particle size (Δ size), polydispersity (Δ PdI), and zeta potential (Δ ZP) of lyophilized powders (LP) regarding initial dispersions. CPs were classified according to their molecular weights (MW), and the osmolarities (Π) of CPs solutions were also determined. Databases obtained were finally modelled through FormRules® (Intelligensys Ltd., Kirkwall, Scotland, UK), an NFL software. NFL models revealed that CPs' MW determines the optimal freezing conditions and CPs' proportions. The knowledge generated allowed the establishment of a traffic light system intended to successfully select and apply sugars for nanoparticles lyophilization.

Targeting joint inflammation for osteoarthritis management through stimulus-sensitive hyaluronic acid based intra-articular hydrogels.

Numerous therapeutic strategies have been developed for osteoarthritis (OA) management, including intra-articular (IA) injections. The ideal IA formulation should control cartilage degradation and restore synovial fluid viscosity. To this end, we propose to combine thermo-sensitive polymers (poloxamers) with hyaluronic acid (HA) to develop suitable beta-lapachone (ßLap) loaded IA formulations. The development of IA formulations with these components entails several difficulties: low ßLap solubility, unknown ßLap therapeutic dose and the bonded commitment of easy administration and viscosupplementation. An optimized formulation was designed using artificial intelligence tools based on the experimental results of a wide variety of hydrogels and its therapeutic capacity was evaluated on an ex vivo OA model. The formulation presented excellent rheological properties and significantly decreased the secretion of degradative (MMP13) and pro-inflammatory (CXCL8) molecules. Therefore, the developed formulation is a promising candidate for OA treatment restoring the synovial fluid rheological properties while decreasing inflammation and cartilage degradation.

Tailored Hydrogels as Delivery Platforms for Conditioned Medium from Mesenchymal Stem Cells in a Model of Acute Colitis in Mice.

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is increasingly prevalent and current therapies are not completely effective. Mesenchymal stem cells are emerging as a promising therapeutic option. Here, the effect of local hydrogel application loaded with conditioned medium (CM) from human uterine cervical stem cells (hUCESC-CM) in an experimental acute colitis mice model has been evaluated. Colitis induction was carried out in C57BL/6 mice by dissolving dextran sulfate sodium (DSS) in drinking water for nine days. Ulcers were treated by rectal administration of either mesalazine (as positive control) or a mucoadhesive and thermosensitive hydrogel loaded with hUCESC-CM (H-hUCESC-CM). Body weight changes, colon length, and histopathological analysis were evaluated. In addition, pro-inflammatory TNF-α, IL-6, and IFN-γ mRNA levels were measured by qPCR. Treatment with H-hUCESC-CM inhibited body weight loss and colon shortening and induced a significant decrease in colon mucosa degeneration, as well as TNF-α, IFN-γ, and IL-6 mRNA levels. Results indicate that H-hUCESC-CM effectively alleviated DSS-induced colitis in mice, suggesting that H-hUCESC-CM may represent an attractive cell-free therapy for local treatment of IBD.

Screening of critical variables in fabricating polycaprolactone nanoparticles using Neuro Fuzzy Logic.

In this work, we used the artificial intelligence tool known as neurofuzzy logic (NFL) for fabricating uniform nanoparticles of polycaprolactone by the nanoprecipitation method with a focus on stabilizer selection. The adaptability of NFL assisted the decision-making on different manufacturing and formulation conditions. The nanoprecipitation method can be summarized as mixing a poorly water-soluble polymer solution with water and its consequent precipitation. Although nanoprecipitation seems simple, the process is highly variable to even slight modifications, leading to polydispersity and nanoparticle aggregation. Here, the NFL model established relationships between mixing conditions, different stabilizers and solvents, among other parameters. Seven parameters measured by dynamic light scattering and laser doppler electrophoresis were modelized with high predictability using NFL tool, as a function of the raw materials and operation conditions. The model allowed the principal component analysis to be carried out, showing that the selection of a stabilizer is the most critical parameter for avoiding nanoparticle aggregation. Then, inputs related to fluid dynamics were relevant to tune the characteristics of the stabilized nanoparticles even further. NFL model showed great potential to support pharmaceutical research by finding subtle relationships between several variables, even from incomplete or fragmented data, which is common in pharmaceutical development.

Tailor-made oligonucleotide-loaded lipid-polymer nanosystems designed for bone gene therapy.

Gene therapy has emerged as a tool for the treatment of systemic metabolic disorders as osteoporosis (OP). However, the design of a suitable vehicle able to efficiently load and release the genetic material on the target cells is still a challenge. Moreover, the internalization pathway of nanosystems has been described to be dependent on their surface characteristics and the cell type evaluated. In this study, we aim at obtaining PEGylated lipid-PLGA nanoparticles (NPs) with variable surface charge able to incorporate GapmeRs (single-strand antisense oligonucleotides) for OP treatment. Nanoparticles showing negative, positive, and neutral surface charge were obtained by modulating the lipid composition. All formulations showed a remarkably low polydispersity index with adequate size. NPs were loaded with GapmeRs showing a high encapsulation efficiency and a surface charge-independent oligonucleotide loading. All the formulations were adequately internalized by MSCs. Future experiments will be devoted to use the developed formulations to clarify if the intracellular distribution of hybrid NPs on mesenchymal stem cells (MSCs) is dependent on surface charge. This portfolio of NPs will serve as a tool to analyze the effect of NP surface charge on gene therapy efficiency.

Design of novel orotransmucosal vaccine-delivery platforms using artificial intelligence.

The linings of the oral cavity are excellent needle-free vaccination sites, able to induce immune responses at distal sites and confer systemic protection. However, owing to the mucosal tissues' intrinsic characteristics, the design of effective antigen-delivery systems is not an easy task. In the present work, we propose to develop and characterize thermosensitive and mucoadhesive hydrogels for orotransmucosal vaccination taking advantage of artificial intelligence tools (AIT). Hydrogels of variable composition were obtained combining Pluronic® F127 (PF127), Hybrane® S1200 (HS1200) and Gantrez® AN119 (AN119) or S97 (S97). Systems were characterized in terms of physicochemical properties, adhesion capacity to mucosal tissues and antigen-like microspheres release. Additionally, polymers biocompatibility and their immune-stimulation capacity was assessed in human macrophages. Interestingly, cells treated with HS1200 exhibited a significant proliferation enhancement compared to control. The use of AIT allowed to determine the effect of each polymer on formulations properties. The proportions of PF127 and Gantrez® are mainly the factors controlling gelation temperature, mucoadhesion, adhesion work and gel strength. Meanwhile, cohesion and short-term microsphere release are dependent on the PF127 concentration. However, long-term microsphere release varies depending on the Gantrez® variety and the PF127 concentration used. Hydrogels prepared with S97 showed slower microsphere release. The use of AIT allowed to establish the conditions able to produce ternary hydrogels with immune-stimulatory properties together with adequate mucoadhesion capacity and antigen-like microspheres release.

From Ethnomedicine to Plant Biotechnology and Machine Learning: The Valorization of the Medicinal Plant Bryophyllum sp.

The subgenus Bryophyllum includes about 25 plant species native to Madagascar, and is widely used in traditional medicine worldwide. Different formulations from Bryophyllum have been employed for the treatment of several ailments, including infections, gynecological disorders, and chronic diseases, such as diabetes, neurological and neoplastic diseases. Two major families of secondary metabolites have been reported as responsible for these bioactivities: phenolic compounds and bufadienolides. These compounds are found in limited amounts in plants because they are biosynthesized in response to different biotic and abiotic stresses. Therefore, novel approaches should be undertaken with the aim of achieving the phytochemical valorization of Bryophyllum sp., allowing a sustainable production that prevents from a massive exploitation of wild plant resources. This review focuses on the study of phytoconstituents reported on Bryophyllum sp.; the application of plant tissue culture methodology as a reliable tool for the valorization of bioactive compounds; and the application of machine learning technology to model and optimize the full phytochemical potential of Bryophyllum sp. As a result, Bryophyllum species can be considered as a promising source of plant bioactive compounds, with enormous antioxidant and anticancer potential, which could be used for their large-scale biotechnological exploitation in cosmetic, food, and pharmaceutical industries.

Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro.

Plant nutrition is a crucial factor that is usually underestimated when designing plant in vitro culture protocols of unexploited plants. As a complex multifactorial process, the study of nutritional imbalances requires the use of time-consuming experimental designs and appropriate statistical and multiple regression analysis for the determination of critical parameters, whose results may be difficult to interpret when the number of variables is large. The use of machine learning (ML) supposes a cutting-edge approach to investigate multifactorial processes, with the aim of detecting non-linear relationships and critical factors affecting a determined response and their concealed interactions. Thus, in this work we applied artificial neural networks coupled to fuzzy logic, known as neurofuzzy logic, to determine the critical factors affecting the mineral nutrition of medicinal plants belonging to Bryophyllum subgenus cultured in vitro. The application of neurofuzzy logic algorithms facilitate the interpretation of the results, as the technology is able to generate useful and understandable "IF-THEN" rules, that provide information about the factor(s) involved in a certain response. In this sense, ammonium, sulfate, molybdenum, copper and sodium were the most important nutrients that explain the variation in the in vitro culture establishment of the medicinal plants in a species-dependent manner. Thus, our results indicate that Bryophyllum spp. display a fine-tuning regulation of mineral nutrition, that was reported for the first time under in vitro conditions. Overall, neurofuzzy model was able to predict and identify masked interactions among such factors, providing a source of knowledge (helpful information) from the experimental data (non-informative per se), in order to make the exploitation and valorization of medicinal plants with high phytochemical potential easier.

Modeling of the Production of Lipid Microparticles Using PGSS® Technique.

Solid lipid microparticles (SLMPs) are attractive carriers as delivery systems as they are stable, easy to manufacture and can provide controlled release of bioactive agents and increase their efficacy and/or safety. Particles from Gas-Saturated Solutions (PGSS®) technique is a solvent-free technology to produce SLMPs, which involves the use of supercritical CO2 (scCO2) at mild pressures and temperatures for the melting of lipids and atomization into particles. The determination of the key processing variables is crucial in PGSS® technique to obtain reliable and reproducible microparticles, therefore the modelling of SLMPs production process and variables control are of great interest to obtain quality therapeutic systems. In this work, the melting point depression of a commercial lipid (glyceryl monostearate, GMS) under compressed CO2 was studied using view cell experiments. Based on an unconstrained D-optimal design for three variables (nozzle diameter, temperature and pressure), SLMPs were produced using the PGSS® technique. The yield of production was registered and the particles characterized in terms of particle size distribution. Variable modeling was carried out using artificial neural networks and fuzzy logic integrated into neurofuzzy software. Modeling results highlight the main effect of temperature to tune the mean diameter SLMPs, whereas the pressure-nozzle diameter interaction is the main responsible in the SLMPs size distribution and in the PGSS® production yield.