Two-step machine learning-assisted label-free surface-enhanced Raman spectroscopy for reliable prediction of dissolved furfural in transformer oil.

Accurate detection of dissolved furfural in transformer oil is crucial for real-time monitoring of the aging state of transformer oil-paper insulation. While label-free surface-enhanced Raman spectroscopy (SERS) has demonstrated high sensitivity for dissolved furfural in transformer oil, challenges persist due to poor substrate consistency and low quantitative reliability. Herein, machine learning (ML) algorithms were employed in both substrate fabrication and spectral analysis of label-free SERS. Initially, a high-consistency Ag@Au substrate was prepared through a combination of experiments, particle swarm optimization-neural network (PSO-NN), and a hybrid strategy of particle swarm optimization and genetic algorithm (Hybrid PSO-GA). Notably, a two-step ML framework was proposed, whose operational mechanism is classification followed by quantification. The framework adopts a hierarchical modeling strategy, incorporating simple algorithms such as kernel support vector machine (Kernel-SVM), k-nearest neighbors (KNN), etc., to independently establish lightweight regression models on each cluster, which allows each model to focus more effectively on fitting the data within its cluster. The classification model achieved an accuracy of 100%, while the regression models exhibited an average correlation coefficient (R2) of 0.9953 and the root mean square errors (RMSE) consistently below 10-2. Thus, this ML framework emerges as a rapid and reliable method for detecting dissolved furfural in transformer oil, even in the presence of different interfering substances, which may also have potentiality for other complex mixture monitoring systems.

A Split-Plot Experimentation Strategy for Making Causal Inferences in Advanced Materials: Auxetic Polyurethane Foam Manufacturing and Processing Analysis.

Development of advanced materials is often time consuming and expensive because of the large number of variables involved and experiments needed. An effective experimentation strategy would accelerate development by reducing the required amount of experiments without sacrificing the obtainable information. In this paper, the development of auxetic polyurethane (PU) foams was discussed as a case study. Auxetic materials are materials with a negative Poisson's ratio and have potential in many structural and functional applications. Auxetic PU foams are the most studied auxetic materials, and their manufacturing and properties are affected by many processing and environmental factors. This paper introduces a sophisticated design of experimental methodology to help reduce the experimental effort while effectively screening these factors. This methodology is then applied in an experiment to illustrate its utility and distinct advantages that greatly facilitate material development.

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.

Amiodarone chewable gels as a potential appproach for paediatric congenital cardiopathies treatment: Comparison between animal and vegetal gelling agents.

The difficulty in swallowing is a frequent problem when oral solid dosage forms (conventional tablets or capsules) are administered to paediatric population or patients with dysphagia. An interesting alternative to overcome these problems are non-conventional formulations like chewable gels, commonly known as 'gummies'. Therefore, this work addresses the design, development and characterization of gummies using gelatine and pectin, for the vehiculization of the antiarrhythmic amiodarone (AMIO). Applying a Design of Experiments (DoE) approach, four gelatine (GG1-GG4) and eight pectin formulations (PG1-PG8) were developed. Considering the obtained results for responses during DoE evaluation (i.e., volume, syneresis, hardness, and gumminess), GG3 and PG8 were selected for complete characterization. Water activity, pH, drug content, texture parameters (adhesiveness, springiness, cohesiveness, and fracturability), disintegration time, in vitro dissolution, and microbiological features were evaluated. The obtained results were within the expected values for this type of formulation. The dissolution profiles showed a 94 % - 99 % of the AMIO content released for GG3 and PG8, respectively, so they could be considered suitable as immediate release dosage forms. In conclusion, the chewable gels were successfully developed and characterised, suggesting a potential means to accomplish a final prototype for the improvement of congenital cardiopathies treatment.

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.

Influence of Extrusion Screw Speed and CNT Concentration on the Mechanical and EMI Properties of PC/ABS Based Nanocomposites.

This study investigates the effect of extrusion screw speed and carbon nanotube (CNT) concentration on the thermal, mechanical, and electromagnetic interference shielding effectiveness (EMI SE) properties of Polycarbonate (PC)/acrylonitrile-butadiene-styrene (ABS) and its polymer nanocomposites (PNCs) by means of design of experiments (DoE) approach. A masterbatch method was employed to obtain the best dispersion of the CNTs throughout the polymer matrix. This study evaluates the thermo-mechanical characterisation of the polymers and PNCs at varying screw speeds to assess filler matrix bonding. The results highlight that CNT concentration has a significant effect on all mechanical properties, while screw speed only affects the Charpy impact strength and flexural properties of the samples. Compounding at 200 rpm has the best flexural and tensile strength, which is attributed to the best filler matrix bonding (highest storage modulus) of the PNCs. The best EMI SE results were obtained at 10 wt.% CNTs. This research contributes valuable insights into the effect of CNT concentration and extrusion screw speed on the mechanical, thermal and EMI SE properties of PC/ABS and its PNCs.

Experimental Modeling, Statistical Analysis, and Optimization of the Laser-Cutting Process of Hardox 400 Steel.

Fiber laser cutting machines are widely used in industry for cutting various sheet metals. Hardox steel is widely used in the construction of machinery and equipment that are subjected to wear and impact due to its anti-wear properties and good impact resistance. In this experimental study, the effect of input parameters including laser output power (LOP), laser-cutting speed (LCS), and focal point position (FPP) of fiber laser on the surface roughness and kerf width of Hardox 400 steel sheets are studied. In addition, the optimization of input parameters to achieve the desired surface roughness and kerf width are investigated and analyzed using the response surface methodology (RSM). The experiments are performed using a 4 kW fiber laser-cutting machine and the output results including surface roughness and kerf width are measured using roughness meters and optical microscope. The results of the analysis of variance (ANOVA) for surface roughness and kerf width show that the FPP and LCS are the most significant process parameters affecting the surface roughness and kerf width. With a positive focal point, the surface roughness decreases while the kerf width increases. With increasing the laser-cutting speed, both the surface roughness and kerf width decrease.

Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design.

Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.

QbD-Based Development and Evaluation of Pazopanib Hydrochloride Extrudates Prepared by Hot-Melt Extrusion Technique: In Vitro and In Vivo Evaluation.

BACKGROUND: Pazopanib hydrochloride (PZB) is a protein kinase inhibitor approved by the United States Food and Drug Administration and European agencies for the treatment of renal cell carcinoma and other renal malignancies. However, it exhibits poor aqueous solubility and inconsistent oral drug absorption. In this regard, the current research work entails the development and evaluation of the extrudates of pazopanib hydrochloride by the hot-melt extrusion (HME) technique for solubility enhancement and augmenting oral bioavailability. RESULTS: Solid dispersion of the drug was prepared using polymers such as Kollidon VA64, hydroxypropylmethylcellulose (HPMC), Eudragit EPO, and Affinisol 15LV in a 1:2 ratio by the HME process through a lab-scale 18 mm extruder. Systematic optimization of the formulation variables was carried out with the help of custom screening design (JMP Software by SAS, Version 14.0) to study the impact of polymer type and plasticizer level on the quality of extrudate processability by measuring the torque value, appearance, and disintegration time as the responses. The polymer blends containing Kollidon VA64 and Affinisol 15LV resulted in respective clear transparent extrudates, while Eudragit EPO and HPMC extrudates were found to be opaque white and brownish, respectively. Furthermore, evaluation of the impact of process parameters such as screw rpm and barrel temperature was measured using a definitive screening design on the extrude appearance, torque, disintegration time, and dissolution profile. Based on the statistical outcomes, it can be concluded that barrel temperature has a significant impact on torque, disintegration time, and dissolution at 30 min, while screw speed has an insignificant impact on the response variables. Affinisol extrudates showed less moisture uptake and faster dissolution in comparison to Kollidon VA64 extrudates. Affinisol extrudates were evaluated for polymorphic stability up to a 3-month accelerated condition and found no recrystallization. PZB-Extrudates using the Affinisol polymer (Test formulation A) revealed significantly higher bioavailability (AUC) in comparison to the free Pazopanib drug and marketed formulation.

Identification and Characterization of Critical Processing Parameters in the Fabrication of Double-Emulsion Poly(lactic-co-glycolic) Acid Microparticles.

In the past several decades, polymeric microparticles (MPs) have emerged as viable solutions to address the limitations of standard pharmaceuticals and their corresponding delivery methods. While there are many preclinical studies that utilize polymeric MPs as a delivery vehicle, there are limited FDA-approved products. One potential barrier to the clinical translation of these technologies is a lack of understanding with regard to the manufacturing process, hindering batch scale-up. To address this knowledge gap, we sought to first identify critical processing parameters in the manufacturing process of blank (no therapeutic drug) and protein-loaded double-emulsion poly(lactic-co-glycolic) acid MPs through a quality by design approach. We then utilized the design of experiments as a tool to systematically investigate the impact of these parameters on critical quality attributes (e.g., size, surface morphology, release kinetics, inner occlusion size, etc.) of blank and protein-loaded MPs. Our results elucidate that some of the most significant CPPs impacting many CQAs of double-emulsion MPs are those within the primary or single-emulsion process (e.g., inner aqueous phase volume, solvent volume, etc.) and their interactions. Furthermore, our results indicate that microparticle internal structure (e.g., inner occlusion size, interconnectivity, etc.) can heavily influence protein release kinetics from double-emulsion MPs, suggesting it is a crucial CQA to understand. Altogether, this study identifies several important considerations in the manufacturing and characterization of double-emulsion MPs, potentially enhancing their translation.

Numerical modeling of particle deposition in a realistic respiratory airway using CFD-DPM and genetic algorithm.

In this study, a realistic model of the respiratory tract obtained from CT medical images was used to solve the flow field and particle motion using the Eulerian-Lagrangian approach to obtain the maximum particle deposition in the bronchial tree for the main purpose of optimizing the performance of drug delivery devices. The effects of different parameters, including particle diameter, particle shape factor, and air velocity, on the airflow field and particle deposition pattern in different zones of the lung were investigated. In addition, a genetic algorithm was employed to obtain the maximum particle deposition in the bronchial tree and the effect of the aforementioned parameters on particle deposition. Reverse flow, vortex formation, and laryngeal jet all affect the airflow structure and particle deposition pattern. The mouth-throat region had the highest deposition fraction at various flow rates. A change in the deposition pattern with an increased deposition fraction in the throat was observed owing to the increased diameter and shape factor of the particles, resulting from the higher inertia and drag force, respectively. The particle deposition analysis showed that three parameters, shape factor, diameter, and velocity, are directly related to particle deposition, and the diameter is the most effective parameter for particle deposition, with an effect of 60% compared to the shape factor and velocity. Finally, the prediction of the genetic algorithm reported a maximum particle deposition in the bronchial tree of 17%, whereas, based on the numerical results, the maximum particle deposition was reported to be 16%. Therefore, there is a 1% difference between the prediction of the genetic algorithm and the numerical results, which indicates the high accuracy of the prediction of the genetic algorithm.

Statistical optimization of guest uptake in crystalline sponges: grading structural outcomes.

Investigation of the analyte soaking conditions on the crystalline sponge {[(ZnI2)3(tpt)2·x(solvent)]n} method using a statistical design of experiments model has provided fundamental insights into the influence of experimental variables. This approach focuses on a single analyte tested via 60 experiments (20 unique conditions) to identify the main effects for success and overall guest structure quality. This is employed as a basis for the development of a novel molecular structure grading system that enables the quantification of guest exchange quality.

Integrated approach of white analytical chemistry and design of experiments to microwave-assisted sensitive and eco-friendly spectrofluorimetric estimation of mirabegron using 4-chloro-7-nitrobezofuran as biosensing fluorescent probe.

The USFDA recently approved mirabegron, a novel once-daily ß-3 adrenoceptor agonist for oral administration, as a transformative treatment for overactive bladder. Despite the existence of numerous analytical methods for the assay and bioanalysis of mirabegron, it's perplexing that none have explored the domain of microwave-assisted sensitive spectrofluorimetric method for mirabegron estimation, even after extensive literature review. Adding to the enigma is the insistence of current analytical methods on using expensive and harmful organic solvents, posing a threat to marine life and the broader environment. Recently, the white analytical chemistry approach has been introduced to develop analytical methods that are cost-effective, environmentally friendly, and user-friendly. Consequently, a white analytical chemistry-based, sensitive, and eco-friendly spectrofluorimetric estimation of mirabegron has been initiated, using 4-Chloro-7-nitrobenzofurazan as a fluorescent biosensing probe. The development of this robust method involved a series of experiments designed to minimize solvent and time wastage. Through a combination of fractional factorial and Box-Behnken designs, researchers identified the critical variables and optimized the method to perfection. This method was validated according to the stringent ICH Q2 (R2) and USFDA guidelines, ensuring its reliability and accuracy. Once approved, this sensitive spectrofluorimetric method was tested, accurately estimating mirabegron levels in commercial formulations and rat plasma samples. To further enrich the study, a comprehensive evaluation of existing analytical methods was conducted alongside the proposed spectrofluorimetric method, using advanced tools like the AGREE calculator, GAPI software, and RGB model to assess their eco-friendliness and effectiveness in mirabegron estimation.

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.

A case study application of AQbD to the re-development and validation of an affinity chromatography analytical procedure for mAb titer quantitation.

Protein A (ProA) high-performance liquid chromatography (HPLC) is a common analytical procedure for measuring monoclonal antibody (mAb) titers due to its high specificity and efficiency. Accurate and reliable results of this procedure are imperative, as the quantitation of the total mAb present for in-process samples directly impacts downstream purification steps related to the removal of process-related impurities. This study aimed to improve a platform ProA HPLC analytical procedure which was previously developed using traditional approaches and was not always reliable. By retrospectively applying Analytical Quality by Design (AQbD) principles and statistical assessments of performance, a bias in the calibration standard due to protein-adsorption to common sample vial materials was identified. The inclusion of Tween® 20 into the mobile phase used as sample diluent was optimized to ensure procedure performance and improve analytical range. The resulting procedure robustness was evaluated using Design of Experiment (DoE) approaches and performance was verified against Analytical Target Profile (ATP) criteria as recommended by regulatory agencies. The resulting linearity displayed R2 values of 1.00 with intercept biases of 1.2 % (analyst 1) and 0.8 % (analyst 2), accuracy across all levels was reported at 99.2 % recovery, and intermediate precision was reported as 3.0 % RSD. Application of this new platform procedure has since reduced development timelines for new mAb products by 50 % and allowed for accurate titer determination to support >5 early phase product-specific process decisions without requiring extensive analytical procedure development. This work demonstrates the utility and relative ease of adopting AQbD concepts, even for established procedures, and supporting them with a lifecycle approach to managing procedure performance.

A hybrid method of system dynamics and design of experiments for investigating the economic and environmental indicators of electricity industry.

Electricity plays a pivotal role in the socio-economic development of nations. However, heavy reliance on fossil fuels for electricity generation, as observed in Iran, poses significant environmental challenges. This study proposes a novel hybrid methodology that combines system dynamics modeling and Design of Experiments (DOE) to examine economic and environmental indicators within Iran's electricity sector. The system dynamics model delineates four key subsystems: consumption, production, CO2 emissions, and power trade. By integrating DOE into this framework, various economic and environmental metrics are assessed for the year 2040. Through a comprehensive analysis of variable impacts on these indicators, optimal levels are identified to achieve favorable outcomes. Notably, variables such as the allocation coefficient of export income to capacity development and electricity export price emerge as critical determinants. Due to economic, environmental, and economic-environmental indicators, the most appropriate level of allocation of export income towards capacity development is estimated at 30, 10, and 20 percent, respectively. The study recommends allocating 80 % of the capacity development budget to renewable energy sources and 20 % to thermal power plants to optimize future conditions. In business as usual, the Export CO2 emission damage to export income index will be 0.19. In implementing the proposed scenario, according to the economic-environmental index, this value will decrease and reach 1.73E-06, which indicates the improvement of electricity export from the economic-environmental dimension. This research underscores the importance of balancing economic prosperity with environmental sustainability in electricity industry planning and policy formulation.

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.

A quality by design approach to optimise disulfide-linked hyaluronic acid hydrogels.

In this study, the disulfide-linked hyaluronic acid (HA) hydrogels were optimised for potential application as a scaffold in tissue engineering through the Quality by Design (QbD) approach. For this purpose, HA was first modified by incorporating the cysteine moiety into the HA backbone, which promoted the formation of disulfide cross-linked HA hydrogel at physiological pH. Utilising a Design of Experiments (DoE) methodology, the critical factors to achieve stable biomaterials, i.e. the degree of HA substitution, HA molecular weight, and coupling agent ratio, were explored. To establish a design space, the DoE was performed with 65 kDa, 138 kDa and 200 kDa HA and variable concentrations of coupling agent to optimise conditions to obtain HA hydrogel with improved rheological properties. Thus, HA hydrogel with a 12 % degree of modification, storage modulus of ≈2321 Pa and loss modulus of ≈15 Pa, was achieved with the optimum ratio of coupling agent. Furthermore, biocompatibility assessments in C28/I2 chondrocyte cells demonstrated the non-toxic nature of the hydrogel, underscoring its potential for tissue regeneration. Our findings highlight the efficacy of the QbD approach in designing HA hydrogels with tailored properties for biomedical applications.

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.

QbD-based co-loading of paclitaxel and imatinib mesylate by protamine-coated PLGA nanoparticles effective on breast cancer cells.

Aim: Paclitaxel and imatinib mesylate are drugs used in the treatment of breast cancer. Conventional drug-delivery systems have limitations in the effective treatment of breast cancer using the drugs. Materials & methods: Combination index studies were used to identify the optimum ratio of both drugs showing maximum synergistic effect. Using a systematic quality-by-design approach, protamine-coated PLGA nanoparticles co-loaded with paclitaxel and imatinib mesylate were formulated. Further characterization and cell line evaluations were performed. Results: Encapsulation efficiency obtained was 92.54% for paclitaxel and 75.12% for imatinib mesylate. A sustained (24 h) and controlled zero-order drug release was obtained. Conclusion: Formulated nanoparticles had a low IC50 value and enhanced cellular uptake.

[Box: see text].