A strategic approach for efficient cryo-EM grid optimization using design of experiments.

In recent years, cryo-electron microscopy (cryo-EM) has become a practical and effective method of determining structures at previously unattainable resolutions due to advances in detection, automation, and data processing. However, sample preparation remains a major bottleneck in the cryo-EM workflow. Even after the arduous process of biochemical sample optimization, it often takes several iterations of grid vitrification and screening to determine the optimal grid freezing parameters that yield suitable ice thickness and particle distribution for data collection. Since a high-quality sample is imperative for high-resolution structure determination, grid optimization is a vital step. For researchers who rely on cryo-EM facilities for grid screening, each iteration of this optimization process may delay research progress by a matter of months. Therefore, a more strategic and efficient approach should be taken to ensure that the grid optimization process can be completed in as few iterations as possible. Here, we present an implementation of Design of Experiments (DOE) to expedite and strategize the grid optimization process. A Fractional Factorial Design (FFD) guides the determination of a limited set of experimental conditions which can model the full parameter space of interest. Grids are frozen with these conditions and screened for particle distribution and ice thickness. Quantitative scores are assigned to each of these grid characteristics based on a qualitative rubric. Input conditions and response scores are used to generate a least-squares regression model of the parameter space in JMP, which is used to determine the conditions which should, in theory, yield optimal grids. Upon testing this approach on apoferritin and L-glutamate dehydrogenase on both the Vitrobot Mark IV and the Leica GP2 plunge freezers, the resulting grid conditions reliably yielded grids with high-quality ice and particle distribution that were suitable for collecting large overnight datasets on a Krios. We conclude that a DOE-based approach is a cost-effective and time-saving tool for cryo-EM grid preparation.

Designing optimal experiments in metabolomics.

BACKGROUND: Metabolomics data is often complex due to the high number of metabolites, chemical diversity, and dependence on sample preparation. This makes it challenging to detect significant differences between factor levels and to obtain accurate and reliable data. To address these challenges, the use of Design of Experiments (DoE) techniques in the setup of metabolomic experiments is crucial. DoE techniques can be used to optimize the experimental design space, ensuring that the maximum amount of information is obtained from a limited sample space. AIM OF REVIEW: This review aims at providing a baseline workflow for applying DoE when generating metabolomics data. KEY SCIENTIFIC CONCEPTS OF REVIEW: The review provides insights into the theory of DoE. The review showcases the theory being put into practice by highlighting different examples DoE being applied in metabolomics throughout the literature, considering both targeted and untargeted metabolomic studies in which the data was acquired using both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry techniques. In addition, the review presents DoE concepts not currently being applied in metabolomics, highlighting these as potential future prospects.

Itaconic acid production by co-feeding of Ustilago maydis: A combined approach of experimental data, design of experiments, and metabolic modeling.

Itaconic acid is a platform chemical with a range of applications in polymer synthesis and is also discussed for biofuel production. While produced in industry from glucose or sucrose, co-feeding of glucose and acetate was recently discussed to increase itaconic acid production by the smut fungus Ustilago maydis. In this study, we investigate the optimal co-feeding conditions by interlocking experimental and computational methods. Flux balance analysis indicates that acetate improves the itaconic acid yield up to a share of 40% acetate on a carbon molar basis. A design of experiment results in the maximum yield of 0.14 itaconic acid per carbon source from 100 g L - 1 $\,\text{g L}{}^{-1}$ glucose and 12 g L - 1 $\,\text{g L}{}^{-1}$ acetate. The yield is improved by around 22% when compared to feeding of glucose as sole carbon source. To further improve the yield, gene deletion targets are discussed that were identified using the metabolic optimization tool OptKnock. The study contributes ideas to reduce land use for biotechnology by incorporating acetate as co-substrate, a C2-carbon source that is potentially derived from carbon dioxide.

In Vitro Simulation of the Fasted Gastric Conditions and Their Variability to Elucidate Contrasting Properties of the Marketed Dabigatran Etexilate Pellet-Filled Capsules and Loose Pellets.

Variability of the gastrointestinal tract is rarely reflected in in vitro test protocols but often turns out to be crucial for the oral dosage form performance. In this study, we present a generation method of dissolution profiles accounting for the variability of fasted gastric conditions. The workflow featured 20 biopredictive tests within the physiological variability. The experimental array was constructed with the use of the design of experiments, based on three parameters: gastric pH and timings of the intragastric stress event and gastric emptying. Then, the resulting dissolution profiles served as a training data set for the dissolution process modeling with the machine learning algorithms. This allowed us to generate individual dissolution profiles under a customizable gastric pH and motility patterns. For the first time ever, we used the method to successfully elucidate dissolution properties of two dosage forms: pellet-filled capsules and bare pellets of the marketed dabigatran etexilate product Pradaxa. We showed that the dissolution of capsules was triggered by mechanical stresses and thus was characterized by higher variability and a longer dissolution onset than observed for pellets. Hence, we proved the applicability of the method for the in vitro and in silico characterization of immediate-release dosage forms and, potentially, for the improvement of in vitro-in vivo extrapolation.

The development and optimisation of an HPLC-based in vitro serum stability assay for a calcitonin gene-related peptide receptor antagonist peptide.

Evaluation of the stability of peptide drug candidates in biological fluids, such as blood serum, is of high importance during the lead optimisation phase. Here, we describe the optimisation and validation of a method for the evaluation of the stability of a lead calcitonin gene-related peptide antagonist peptide (P006) in blood serum. After initially determining appropriate peptide and human serum concentrations and selection of the quenching reagent, the HPLC method optimisation used two experimental designs, Plackett-Burman design and Taguchi design. The analytical method was validated as complying with the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use guidelines. The optimised method allowed the successful resolution of the parent peptide from its metabolites using RP-HPLC and identification of the major metabolites of P006 by mass spectrometry. This paradigm may be widely adopted as a robust early-stage platform for screening peptide stability to rule out candidates with low in vitro stability, which would likely translate into poor in vivo pharmacokinetics.

Examining functional group-dependent effects on the ionization of lignin monomers using supercritical fluid chromatography/electrospray ionization mass spectrometry.

The chemical and biological conversion of biomass-derived lignin is a promising pathway for producing valuable low molecular weight aromatic chemicals, such as vanillin or guaiacol, known as lignin monomers (LMs). Various methods employing chromatography and electrospray ionization-mass spectrometry (ESI-MS) have been developed for LM analysis, but the impact of LM chemical properties on analytical performance remains unclear. This study systematically optimized ESI efficiency for 24 selected LMs, categorized by functionality. Fractional factorial designs were employed for each LM to assess ESI parameter effects on ionization efficiency using ultra-high-performance supercritical fluid chromatography/ESI-MS (UHPSFC/ESI-MS). Molecular descriptors were also investigated to explain variations in ESI parameter responses and chromatographic retention among the LMs. Structural differences among LMs led to complex optimal ESI settings. Notably, LMs with two methoxy groups benefited from higher gas and sheath gas temperatures, likely due to their lower log P and higher desolvation energy requirements. Similarly, vinyl acids and ketones showed advantages at elevated gas temperatures. The retention in UHPSFC using a diol stationary phase was correlated with the number of hydrogen bond donors. In summary, this study elucidates structural features influencing chromatographic retention and ESI efficiency in LMs. The findings can aid in developing analytical methods for specific technical lignins. However, the absence of an adequate number of LM standards limits the prediction of LM structures solely based on ESI performance data.

Polar licit and illicit ingredients in dietary supplements: chemometric optimization of extraction and HILIC-MS/MS analysis.

Many dietary supplements claim the ability to enhance sports performance and to improve the fitness of the consumers. Occasionally, along with legal ingredients, illicit compounds may be added without being labelled, leading to unintended doping. Hence, the aim of this study was to develop an analytical method to determine a set of 12 polar (logDpH=7 from -2.0 to +0.3) compounds including diuretics, stimulants, ß2-agonists, methylxanthines, and sweeteners. Hydrophilic interaction liquid chromatography was chosen as separation strategy, coupled with tandem mass spectrometry. The instrumental method was optimized using a two-step design of experiments (DoE). Firstly, a Plackett-Burman (PB) DoE was performed to identify the more influencing variables affecting peak areas and chromatographic resolution among temperature, water percentage in the mobile phase, and flow rate, as well as type and concentration of buffers. Secondly, a D-optimal DoE was set, considering only the most significant variables from the PB-DoE results, achieving a deeper understanding of the retention mechanism. Sample processing by salt-assisted liquid-liquid extraction was studied through DoE as well, and the whole method showed recoveries in the range 40-107% and procedural precision ≤11% for all analytes. Finally, it was applied to real samples, in which the four methylxanthines and two artificial sweeteners were detected and quantified in the range of 0.02-192 mg g-1. These values were compared to the quantities declared on the DS labels, when possible. Furthermore, a sequence of MS/MS scans allowed detection of a signal in one of the samples, structurally similar to the ß2-agonist clenbuterol.

Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.

Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3-20), and it reduced the parameter variance metric by 33-37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.

A quality-by-design evaluated liquid chromatography method development and validation for the separation and quantification of nitroxoline and its impurities.

A novel, isocratic, sensitive, stability-indicating high-performance liquid chromatography method was developed for the separation and quantification of related substances in nitroxoline (NTL). The chromatographic separation has been achieved on Inertsil ODS-3 V, (250 × 4.6 mm, 5 µm) at 240 nm using ethylenediamine tetraacetic acid buffer and methanol in the ratio of 60:40 v/v as mobile phase. The performance of the method has been checked as per the International Conference on Harmonization guidelines for specificity, linearity, accuracy, precision, and robustness. Regression analysis showed a correlation coefficient value greater than 0.99 for NTL and its three impurities. The detection limit of impurities was in the range of 0.01% (0.05 µg/mL)-0.22% (1.1 µg/mL) indicating the sensitivity of the newly developed method. The accuracy of the method was established based on the recovery obtained between 94.7% and 104.1% for all the impurities. The percentage relative standard deviation obtained for the repeatability was less than 4.0% at the specification level for all impurities. Forced degradation was performed to establish the stability-indicating nature of the method and to know about the degradation products, the quality of a drug substance changes with time under the influence of stress conditions. Thus, the proposed method was validated and found to be specific, sensitive, linear, accurate, precise, reproducible, and beneficial for routine usage.

Retention mechanisms of amitriptyline and its impurities on amide, amino, diol, and silica columns in hydrophilic interaction liquid chromatography.

Hydrophilic interaction liquid chromatography (HILIC) has been widely applied to challenging analysis in biomedical and pharmaceutical fields, bridging the gap between normal-phase high-performance liquid chromatography and reversed-phase high-performance liquid chromatography (RP-HPLC). This paper comprehensively explores the retention mechanisms of amitriptyline and its impurities A, B, C, D, F, and G on amide, amino, diol, and silica columns. Dual HILIC/RP-HPLC retention mechanisms were developed, and transitional points between HILIC and RP-HPLC mechanisms were calculated on amide, diol, and silica columns. Adsorption and partition contributions to overall retention mechanisms were evaluated using Python software in HILIC and RP-HPLC regions. The cation exchange mechanism dominates overall retention for ionized analytes in the silica column (R2 > 0.995), whereas the retention of ionized analytes increases with pH. Impacts of acetonitrile content, buffer ionic strength, and pH, along with their interactions on the retention of ionized analytes in the silica column, were determined using the chemometric approach. Acetonitrile content showed the most significant impact on the retention mechanisms. These findings highlight that a detailed investigation into retention mechanisms provides notable insights into factors influencing analyte retention and separation, promising valuable guidance for future analysis.

Using design of experiments to guide genetic optimization of engineered metabolic pathways.

Design of experiments (DoE) is a term used to describe the application of statistical approaches to interrogate the impact of many variables on the performance of a multivariate system. It is commonly used for process optimization in fields such as chemical engineering and material science. Recent advances in the ability to quantitatively control the expression of genes in biological systems open up the possibility to apply DoE for genetic optimization. In this review targeted to genetic and metabolic engineers, we introduce several approaches in DoE at a high level and describe instances wherein these were applied to interrogate or optimize engineered genetic systems. We discuss the challenges of applying DoE and propose strategies to mitigate these challenges. ONE-SENTENCE SUMMARY: This is a review of literature related to applying Design of Experiments for genetic optimization.

Vegan grade medium component screening and concentration optimization for the fermentation of the probiotic strain Lactobacillus paracasei IMC 502® using Design of Experiments.

Lactobacillus paracasei IMC502® is a commercially successful probiotic strain. However, there are no reports that investigate growth medium composition in relation to improved biomass production for this strain. The major outcome of the present study is the design and optimization of a growth medium based on vegan components to be used in the cultivation of Lactobacillus paracasei IMC502®, by using Design of Experiments. Besides comparing different carbon sources, the use of plant-based peptones as nitrogen sources was considered. In particular, the use of guar peptone as the main nitrogen source, in the optimization of fermentation media for the production of probiotics, could replace other plant peptones (e.g. potato, rice, wheat, and soy) which are part of the human diet, thereby avoiding an increase in product and process prices. A model with R2 and adjusted R2 values higher than 95% was obtained. Model accuracy was equal to 94.11%. The vegan-optimized culture medium described in this study increased biomass production by about 65% compared to growth on De Man-Rogosa-Sharpe (MRS) medium. Moreover, this approach showed that most of the salts and trace elements generally present in MRS are not affecting biomass production, thus a simplified medium preparation can be proposed with higher probiotic biomass yield and titer. The possibility to obtain viable lactic acid bacteria at high density from vegetable derived nutrients will be of great interest to specific consumer communities, opening the way to follow this approach with other probiotics of impact for human health.

Monitoring Flow-Forming Processes Using Design of Experiments and a Machine Learning Approach Based on Randomized-Supervised Time Series Forest and Recursive Feature Elimination.

The machines of WF Maschinenbau process metal blanks into various workpieces using so-called flow-forming processes. The quality of these workpieces depends largely on the quality of the blanks and the condition of the machine. This creates an urgent need for automated monitoring of the forming processes and the condition of the machine. Since the complexity of the flow-forming processes makes physical modeling impossible, the present work deals with data-driven modeling using machine learning algorithms. The main contributions of this work lie in showcasing the feasibility of utilizing machine learning and sensor data to monitor flow-forming processes, along with developing a practical approach for this purpose. The approach includes an experimental design capable of providing the necessary data, as well as a procedure for preprocessing the data and extracting features that capture the information needed by the machine learning models to detect defects in the blank and the machine. To make efficient use of the small number of experiments available, the experimental design is generated using Design of Experiments methods. They consist of two parts. In the first part, a pre-selection of influencing variables relevant to the forming process is performed. In the second part of the design, the selected variables are investigated in more detail. The preprocessing procedure consists of feature engineering, feature extraction and feature selection. In the feature engineering step, the data set is augmented with time series variables that are meaningful in the domain. For feature extraction, an algorithm was developed based on the mechanisms of the r-STSF, a state-of-the-art algorithm for time series classification, extending them for multivariate time series and metric target variables. This feature extraction algorithm itself can be seen as an additional contribution of this work, because it is not tied to the application domain of monitoring flow-forming processes, but can be used as a feature extraction algorithm for multivariate time series classification in general. For feature selection, a Recursive Feature Elimination is employed. With the resulting features, random forests are trained to detect several quality features of the blank and defects of the machine. The trained models achieve good prediction accuracy for most of the target variables. This shows that the application of machine learning is a promising approach for the monitoring of flow-forming processes, which requires further investigation for confirmation.

Development of alginate beads for precise environmental release applications: A design of experiment based approach and analysis.

Controlled release of active ingredients are important for drug delivery and more recently environmental applications including modulated dosing of chemical and biological controls. This study demonstrates the importance of investigating various material science factors that can influence the diffusion rates of alginate beads to improve and tune their performance for marine environmental applications. This investigation aimed to design a rational workflow to aid in leveraging alginate bead use as a carrier matrix for releasing a specific active agent into water. Experiments were conducted to focus on the narrow a large list of relevant material formulation parameters, which included chitosan molecular weight, chitosan concentration, calcium concentration, drop height, and bead size. Once the most relevant material preparation methods were screened, a more robust statistic Design of Experiments approach was performed and results determined the important (and unimportant) factors for increasing dye release kinetics in marine water. The process was further streamlined by narrowing the critical experimental factors to a three-level based on the prior analysis: chitosan MW, chitosan concentration, and bead size. Analysis of the collected data indicated that while chitosan MW had a negligible impact (Fstatistic = 0.22), bead size (Fstatistic = 60.33) significantly influenced the diffusion rates based on surface area. However, chitosan MW had minor effects where lower chitosan MW enabled higher product release rates. This case investigation was a novel application of the design of experiment approach towards environmental applications to understand differences in release rates to marine waters for the first time and the workflow provided also serve as the basis for researchers to optimize other environmental applications requiring optimization when it is unknown how a large number of formulation variables will impact performance in different environmental scenarios.

Sustainable and Biomimetic Methodology for Extraction of High-Value-Added Compounds in Almond Hulls.

Almond trees are the most cultivated nut tree in the world. The production of almonds generates large amounts of by-products, much of which goes unused. Herein, this study aimed to develop a green chemistry approach to identify and extract potentially valuable compounds from almond by-products. Initially, a screening was performed with 10 different Natural Deep Eutectic Solvents (NADESs). The mixture lactic acid/glycerol, with a molar ratio 1:1 (1:50 plant material to NADES (w/v) with 20% v/v of water) was identified as the best extraction solvent for catechin, caffeoylquinic acid, and condensed tannins in almond hulls. Subsequently, a method was optimized by a Design of Experiment (DoE) protocol using a miniaturized extraction technique, Microwave-Assisted Extraction (MAE), in conjunction with the chosen NADESs. The optimal conditions were found to be 70 °C with 15 min irradiation time. The optimal extraction conditions determined by the DoE were confirmed experimentally and compared to methods already established in the literature. With these conditions, the extraction of metabolites was 2.4 times higher, according to the increase in total peak area, than the established literature methods used. Additionally, by applying the multiparameter Analytical Greenness Metric (AGREE) and Green Analytical Process Index (GAPI) metrics, it was possible to conclude that the developed method was greener than the established literature methods as it includes various principles of green analytical chemistry.

The Effect of Ultrasound on the Extraction and Functionality of Proteins from Duckweed (Lemna minor).

The inclusion of protein in the regular human diet is important for the prevention of several chronic diseases. In the search for novel alternative protein sources, plant-based proteins are widely explored from a sustainable and ecological point of view. Duckweed (Lemna minor), also known as water lentil, is an aquatic plant with potential applications for human consumption due to its protein content and carbohydrate contents. Among all the conventional and novel protein extraction methods, the utilization of ultrasound has attracted the attention of scientists because of its effects on improving protein extraction and its functionalities. In this work, a Box-Behnken experimental design was proposed to optimize the alkaline extraction of protein from duckweed. In addition, an exploration of the effects of ultrasound on the morphological, structural, and functional properties of the extracted protein was also addressed. The optimal extraction parameters were a pH of 11.5 and an ultrasound amplitude and processing time of 60% and 20 min, respectively. These process conditions doubled the protein content extracted in comparison to the value from the initial duckweed sample. Furthermore, the application of ultrasound during the extraction of protein generated changes in the FTIR spectra, color, and structure of the duckweed protein, which resulted in improvements in its solubility, emulsifying properties, and foaming capacity.

Green Method Comparison and Optimization of Anthocyanin Recovery from "Sangiovese" Grape Pomace: A Critical Evaluation of the Design of Experiments Approach.

Grape pomace is the main by-product obtained from wine production that is still enriched in bioactive compounds. Within a framework of waste/by-product reuse through a sustainable approach, various green methods were utilized in this work to recover anthocyanins from the pomace resulting from "Sangiovese" grape vinification. Ultrasound- and Microwave-Assisted Extractions (UAE and MAE) were coupled with the use of green solvents, such as acidified water, an ethanol/water mixture, and Natural Deep Eutectic Solvents (NaDES), and their efficacy was compared with that of a conventional method based on a methanol/acidified water mixture. The Total Anthocyanin Index ranged from 36.9 to 75.2 mg/g DW for UAE, and from 54.4 to 99.6 mg/g DW for MAE, while resulting in 47.1 mg/g DW for conventional extraction. A Design of Experiments (DoE) approach was applied to MAE, the most efficient technique. Temperature, time, and the solid-to-liquid ratio were set as X variables, while malvidin-3-O-glucoside content and antioxidant activity were used as response variables, measured by High-Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD) and 2,2-Diphenyl-1-picrylhydrazyl (DPPH) assay, respectively. The correlation between temperature and time and the antioxidant activity of the extract was positive, while it was found to be negative when considering malvidin-3-O-glucoside concentration as a response variable. Thus, the optimal conditions in temperature, time and solid-to-liquid ratio were different depending on the chosen variable. The results underline the importance of selecting an accurate response when using the response surface methodology approach.

Application of Quality by Design in the Development of Hydrogen Sulfide Donor Loaded Polymeric Microparticles.

Hydrogen sulfide (H2S) is a multifaceted gasotransmitter molecule which has potential applications in many pathological conditions including in lowering intraocular pressure and providing retinal neuroprotection. However, its unique physicochemical properties pose several challenges for developing its efficient and safe delivery method system. This study aims to overcome challenges related to H2S toxicity, gaseous nature, and narrow therapeutic concentrations range by developing polymeric microparticles to sustain the release of H2S for an extended period. Various formulation parameters and their interactions are quantitatively identified using Quality-by-Design (QbD) approach to optimize the microparticle-based H2S donor (HSD) delivery system. Microparticles were prepared using a solvent-evaporation coacervation process by using polycaprolactone (PCL), soy lecithin, dichloromethane, Na2S.9H2O, and silicone oil as polymer, surfactant, solvent, HSD, and dispersion medium, respectively. The microparticles were characterized for size, size distribution, entrapment efficiency, and H2S release profile. A Main Effects Screening (MES) and a Response Surface Design (RSD) model-based Box-Behnken Design (BBD) was developed to establish the relationship between critical process parameters (CPPs) and critical quality attributes (CQAs) qualitatively and quantitatively. The MES model identified polymer to drug ratio and dispersion medium quantity as significant CPPs among others, while the RSD model established their quantitative relationship. Finally, the target product performance was validated by comparing predicted and experimental outcomes. The QbD approach helped in achieving overall desired microparticle characteristics with fewer trials and provided a mathematical relationship between the CPPs and the CQAs useful for further manipulation and optimization of release profile up to at least 30 days.

Prolonged Release Niosomes For Ocular Delivery of Loteprednol: Ocular Distribution Assessment on Dry Eye Disease Induced Rabbit Model.

Loteprednol etabonate (LE) is a topical corticosteroid for the symptomatic management of ocular conditions, encompassing both allergic and infectious etiologies. Owing to the dynamic and static barriers of the eye, LE exhibits significantly low bioavailability, necessitating an increase in the frequency of drug administration. The objective of this study is to overcome the limitations by developing niosomal systems loaded with LE. Design of Experiments (DoE) approach was used for the development of optimal niosome formulation. The optimal formulation was characterized using DLS, FT-IR, and DSC analysis. In vitro and ex vivo release studies were performed to demonstrate drug release patterns. After that HET-CAM evaluation was conducted to determine safety profile. Then, in vivo studies were carried out to determine therapeutic activity of niosomes. Zeta potential (ZP), particle size, polydispersity index (PI), and encapsulation efficacy (EE) were -33.8 mV, 89.22 nm, 0.192, and 89.6%, respectively. Medicated niosomes had a broad distribution within rabbit eye tissues and was absorbed by the aqueous humor of the bovine eye for up to 6 h after treatment. Cumulative permeated drug in the bovine eye and rabbit eye were recorded 52.45% and 54.8%, respectively. No irritation or hemorrhagic situation was observed according to the results of HET-CAM study. Thus, novel LE-loaded niosomal formulations could be considered as a promising treatment option for the dry-eye-disease (DED) due to enhanced bioavailability and decreased side effects.

Sol-gel and Pechini niobium modified: synthesis, characterization and application in the 2,4-D herbicide degradation.

In this work, a comparison was made between the synthesis of niobium-based materials (Nb2O5), both in terms of material characterization and catalytic performance. The methods used were chemical mixtures: modified sol-gel and Pechini. The materials were calcined at different temperatures (753, 873 and 993K) and characterized by the following techniques: photoacousticspectroscopy (PAS), zero charge point (pHPZC), scanning electron microscopy (SEM/EDS), thermogravimetric analysis (TGA/DTG) and X-ray diffraction (XRD). The photocatalytic process was carried out to evaluate the degradation of 2,4-dichlorophenoxyacetic acid (2,4-D) under UV radiation (250 W mercury vapor lamp) and different experimental conditions. In addition, to better understand the influence of parameters such as pH, catalyst concentration (0.2, 0.5 and 0.8 g L-1) and calcination temperature, a Design of Experiments (DoE) was used. The results indicated that despite having similar structures and phases in the XRD analysis, the morphology presents two distinct surfaces, due to the preparation method. Differences in the synthesis method affected the catalytic activity in the parameters studied. Although the zero charge point values are close (6.18-6.36), we observed differences in the band gap depending on the calcination temperature. In the optimal condition studied, the catalyst prepared by the sol-gel method obtained the best results.