Nanoparticle-mediated mRNA delivery has emerged as a promising therapeutic modality, but its growth is still limited by the discovery and optimization of effective and well-tolerated delivery strategies. Lipid nanoparticles containing charged or ionizable lipids are an emerging standard for in vivo mRNA delivery, so creating facile, tunable strategies to synthesize these key lipid-like molecules is essential to advance the field. Here, we generate a library of N-substituted glycine oligomers, peptoids, and undertake a multistage down-selection process to identify lead candidate peptoids as the ionizable component in our Nutshell nanoparticle platform. First, we identify a promising peptoid structural motif by clustering a library of >200 molecules based on predicted physical properties and evaluate members of each cluster for reporter gene expression in vivo. Then, the lead peptoid motif is optimized using design of experiments methodology to explore variations on the charged and lipophilic portions of the peptoid, facilitating the discovery of trends between structural elements and nanoparticle properties. We further demonstrate that peptoid-based Nutshells leads to expression of therapeutically relevant levels of an anti-respiratory syncytial virus antibody in mice with minimal tolerability concerns or induced immune responses compared to benchmark ionizable lipid, DLin-MC3-DMA. Through this work, we present peptoid-based nanoparticles as a tunable delivery platform that can be optimized toward a range of therapeutic programs.

Capillary electrophoresis-sodium dodecyl sulfate (CE-SDS) is a common analytical procedure used to quantitate critical quality attributes relating to the purity and glycosylation of monoclonal antibodies (mAbs). In this study, the application of an Analytical Quality by Design framework incorporating Design of Experiments was used to develop and validate both non-reduced (CE-NR) and reduced (CE-R) versions of this analytical procedure. Formal risk assessments were used to identify critical method attributes for optimization based on their potential impacts to performance criteria outlined in an analytical target profile. The resulting response surfaces connecting these critical factors to method performance were then utilized to generate a harmonized procedure to reduce execution risk across CE-R and CE-NR applications. Validation of these procedures according to regulatory guidelines support that they meet their required performance criteria, and a multivariate assessment of procedure robustness indicates that method parameters are in a sufficient state of control to ensure appropriate quantitation of mAb quality. Overall, this study demonstrates the utility of adopting an Analytical Quality by Design framework to leverage multidimensional knowledge from multiple critical method parameters to ensure an analytical procedure is fit-for-purpose.

UNASSIGNED: This work aims to present a Quality-by-Design (QbD) step-by-step methodology to formulate anti-ulcer and gastro-protective oral suspensions.
UNASSIGNED: Sucralfate was used as a drug model. The Quality Target Product Profile was established early during preformulation. Viscosity, resuspendability, pH, and density were assessed through the screening of several suspension platforms based on different prototype compositions. A compatibility study between the active pharmaceutical ingredient and the excipients was performed by thermal analysis and infrared spectroscopy. An Ishikawa fishbone diagram and Failure Mode and Effect Analysis were employed to identify the Critical Material Attributes (CMAs), Critical Process Parameters (CPPs), and Critical Quality Attributes (CQAs). CMAs\' and CPPs\' impact on identified CQAs was further assessed through a 22 full factorial experimental design at normal conditions after manufacture and one month at super-accelerated stress conditions. Results: The lead prototype exhibited no physicochemical incompatibilities. The risk assessment tools revealed that the concentration of the wetting agent and the total concentration of thickening agents represented critical factors for the quality profile of the preparation in terms of viscosity. The optimized formulation comprising 1.125 w/v% total concentration of Natrosol 250 HX and Avicel RC 591 exhibited an enhanced performance according to the established profile.
UNASSIGNED: The analytical and physicochemical tests showed the robustness and compliance of the final preparation with the quality profile. The proposed step-by-step methodology based on QbD, Design of Experiments, and Quality Risk Management presented in our research holds practical implications for local industries and formulation scientists involved in the development of oral suspensions.

This study focuses on the design, characterization, and optimization of nanostructured lipid carriers (NLCs) loaded with docetaxel for the treatment of skin cancer. Employing a systematic formulation development process guided by Design of Experiments (DoE) principles, key parameters such as particle size, polydispersity index (PDI), zeta potential, and entrapment efficiency were optimized to ensure the stability and drug-loading efficacy of the NLCs. Combined XRD and cryo-TEM analysis were employed for NLC nanostructure evaluation, confirming the formation of well-defined nanostructures. In vitro kinetics studies demonstrated controlled and sustained docetaxel release over 48 h, emphasizing the potential for prolonged therapeutic effects. Cytotoxicity assays on human umbilical vein endothelial cells (HUVEC) and SK-MEL-24 melanoma cell line revealed enhanced efficacy against cancer cells, with significant selective cytotoxicity and minimal impact on normal cells. This multidimensional approach, encompassing formulation optimization and comprehensive characterization, positions the docetaxel-loaded NLCs as promising candidates for advanced skin cancer therapy. The findings underscore the potential translational impact of these nanocarriers, paving the way for future preclinical investigations and clinical applications in skin cancer treatment.

The Heck reaction is widely employed to build a variety of biologically relevant scaffolds and has been successfully implemented in the production of active pharmaceutical ingredients (APIs). Typically, the reaction with terminal alkenes gives high yields and stereoselectivity toward the trans-substituted alkenes product, and many green variants of the original protocol have been developed for such substrates. However, these methodologies may not be applied with the same efficiency to reactions with challenging substrates, such as internal olefins, providing trisubstituted alkenes. In the present work, we have implemented a Heck reaction protocol under green conditions to access trisubstituted alkenes as final products or key intermediates of pharmaceutical interest. A set of preliminary experiments performed on a model reaction led to selecting a simple and green setup based on a design of experiments (DoE) study. In such a way, the best experimental conditions (catalyst loading, equivalents of alkene, base and tetraalkylammonium salt, composition, and amount of solvent) have been identified. Then, a second set of experiments were performed, bringing the reaction to completion and considering additional factors. The protocol thus defined involves using EtOH as the solvent, microwave (mw) irradiation to achieve short reaction times, and the supported catalyst Pd EnCat®40, which affords an easier recovery and reuse. These conditions were tested on different aryl bromides and internal olefines to evaluate the substrate scope. Furthermore, with the aim to limit as much as possible the production of waste, a simple isomerization procedure was developed to convert the isomeric byproducts into the desired conjugated E alkene, which is also the thermodynamically favoured product. The approach herein disclosed represents a green, efficient, and easy-to-use handle towards different trisubstituted alkenes via the Heck reaction.

The absorption of acidic gases in the oil and gas industries is important due to their toxicity and corrosive effects. Recently, the application of nanofluids based on aqueous or organic solvents as absorbents has been examined by a variety of researchers. In this study, a single bubble column was exploited to study the effect of water-based nanofluids on the absorption processes of SO2 and CO2 using response surface methodology (RSM) based on Box-Behnken three-level experiment design. With this in mind, CO2 and SO2 are separately injected at the bottom of a bubble column filled with one of the nanofluids: Al2O3-water, SiO2-water, or ZnO-water for each experiment. Then, the rate of SO2 or CO2 absorption in the nanofluids has been elucidated. The effect of important parameters including the weight fraction of the nanoparticles (NPs) (0.01, 0.055, and 0.1 wt.%), gas-liquid contact time (150, 300, and 450 s), and the diameter of nozzle for gas injection (0.46, 0.57, and 0.68 mm) have been studied. Results revealed that the maximum molar flux of both gases was observed in the ZnO-water nanofluid, followed by the SiO2-water nanofluid. In addition, increasing the nanoparticle mass fraction and the bubble size causes the molar flux to rise. However, increasing the gas-liquid contact time causes the molar flux of the mentioned gases to decrease. Finally, a set of the accurate equations has been proposed to predict the molar flux of SO2 and CO2 in the various nanofluids assessed in this work.

The design and development of pharmaceutical products require specific knowledge, time, and investment. Response surface methodology (RSM) is a widely used technique in the design of experiments (DoE) to optimize various processes and products. The aim of this study was to model and produce experimental emulgels containing 1% ciclopirox olamine and to evaluate their physical, rheological, and mechanical properties and their ability to release ciclopirox olamine. The objective was to optimize the composition of the experimental emulgel containing 1% ciclopirox olamine by applying a central composite design based on selected criteria. The surfactant (polysorbate 80) had the greatest influence on the physical, rheological, and mechanical properties of the emulgels, as well as on the release of ciclopirox olamine from these systems. During the optimization process, an emulgel of optimal composition was generated containing 38.27% mineral oil, 6.56% polysorbate 80, and 55.17% hydrogel containing 1% ciclopirox olamine, meeting specified criteria (dependent variables) including the maximum flux of ciclopirox olamine, the minimum sol-gel transition temperature (Tsol/gel), and the minimum particle size of the oil phase. The oil phase particle size (D50) of this emulgel was determined to be 0.337 µm, the system Tsol/gel was 9.1 °C, and the flux of ciclopirox olamine from this gel matrix was calculated to be 1.44 mg/cm2. This emulgel of optimal composition could be used to treat fungal skin diseases.

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.

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.

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.