This study employed a Quality by Design (QbD) approach to spray dry amorphousclotrimazole nanosuspension (CLT-NS) consisting of Soluplus® and microcrystallinecellulose. Using the Box-Behnken Design, a systematic evaluation was conducted toanalyze the impact of inlet temperature, % aspiration, and feed rate on the criticalquality attributes (CQAs) of the clotrimazole spray-dried nanosuspension (CLT-SDNS). In this study, regression analysis and ANOVA were employed to detect significantfactors and interactions, enabling the development of a predictive model for the spraydrying process. Following optimization, the CLT-SD-NS underwent analysis using Xraypowder diffraction (XRPD), Fourier transform infrared spectroscopy (FTIR), Dynamic Scanning Calorimetry (DSC), and in vitro dissolution studies. The resultsshowed significant variables, including inlet temperature, feed rate, and aspiration rate,affecting yield, redispersibility index (RDI), and moisture content of the final product. The models created for critical quality attributes (CQAs) showed statistical significanceat a p-value of 0.05. XRPD and DSC confirmed the amorphous state of CLT in theCLT-SD-NS, and FTIR indicated no interactions between CLT and excipients. In vitrodissolution studies showed improved dissolution rates for the CLT-SD-NS (3.12-foldincrease in DI water and 5.88-fold increase at pH 7.2 dissolution media), attributed torapidly redispersing nanosized amorphous CLT particles. The well-designed studyutilizing the Design of Experiments (DoE) methodology.

Successful therapeutic delivery of siRNA with polymeric nanoparticles seems to be a promising but not vastly understood and complicated goal to achieve. Despite years of research, no polymer-based delivery system has been approved for clinical use. Polymers, as a delivery system, exhibit considerable complexity and variability, making their consistent production a challenging endeavor. However, a better understanding of the polymerization process of polymer excipients may improve the reproducibility and material quality for more efficient use in drug products. Here, we present a combination of Design of Experiment and Python-scripted data science to establish a prediction model, from which important parameters can be extracted that influence the synthesis results of polybeta-amino esters (PBAEs), a common type of polymer used preclinically for nucleic acid delivery. We synthesized a library of 27 polymers, each one at different temperatures with different reaction times and educt ratios using an orthogonal central composite (CCO-) design. This design allowed a detailed characterization of factor importance and interactions using a very limited number of experiments. We characterized the polymers by analyzing the resulting composition by 1H-NMR and the size distribution by GPC measurements. To further understand the complex mechanism of block polymerization in a one-pot synthesis, we developed a Python script that helps us to understand possible step-growth steps. We successfully developed and validated a predictive response surface and gathered a deeper understanding of the synthesis of polyspermine-based amphiphilic PBAEs.

BACKGROUND: Stem cell-derived therapies hold the potential for treatment of regenerative clinical indications. Static culture has a limited ability to scale up thus restricting its use. Suspension culturing can be used to produce target cells in large quantities, but also presents challenges related to stress and aggregation stability.
METHODS: Utilizing a design of experiments (DoE) approach in vertical wheel bioreactors, we evaluated media additives that have versatile properties. The additives evaluated are Heparin sodium salt (HS), polyethylene glycol (PEG), poly (vinyl alcohol) (PVA), Pluronic F68 and dextran sulfate (DS). Multiple response variables were chosen to assess cell growth, pluripotency maintenance and aggregate stability in response to the additive inputs, and mathematical models were generated and tuned for maximal predictive power.
RESULTS: Expansion of iPSCs using 100 ml vertical wheel bioreactor assay for 4 days on 19 different media combinations resulted in models that can optimize pluripotency, stability, and expansion. The expansion optimization resulted in the combination of PA, PVA and PEG with E8. This mixture resulted in an expansion doubling time that was 40% shorter than that of E8 alone. Pluripotency optimizer highlighted the importance of adding 1% PEG to the E8 medium. Aggregate stability optimization that minimizes aggregate fusion in 3D culture indicated that the interaction of both Heparin and PEG can limit aggregation as well as increase the maintenance capacity and expansion of hiPSCs, suggesting that controlling fusion is a critical parameter for expansion and maintenance. Validation of optimized solution on two cell lines in bioreactors with decreased speed of 40 RPM, showed consistency and prolonged control over aggregates that have high frequency of pluripotency markers of OCT4 and SOX2 (> 90%). A doubling time of around 1-1.4 days was maintained after passaging as clumps in the optimized medium. Controlling aggregate fusion allowed for a decrease in bioreactor speed and therefore shear stress exerted on the cells in a large-scale expansion.
CONCLUSIONS: This study resulted in a control of aggregate size within suspension cultures, while informing about concomitant state control of the iPSC state. Wider application of this approach can address media optimization complexity and bioreactor scale-up challenges.

Bedaquiline (BQ) solid lipid nanoparticles (SLNs), which have previously been formulated for parenteral administration, have a risk of patient non-compliance in treating tuberculosis. This research presents a strategy to develop BQ SLNs for oral delivery to improve patient adherence, The upper and lower levels for the formulation excipients were generated from screening experiments. Using 4 input factors (BQ, lecithin, Tween 80, and PEG), a full factorial design from 3 × 2x2 × 2 experiments was randomly arranged to investigate 3 response variables: Particle size distribution (PSD), polydispersity index (PdI), and zeta potential (ZP). High shear homogenization was used to mix the solvent and aqueous phases, with 15% sucrose as a cryoprotectant. The response variables were assessed using a zeta sizer while TEM micrographs confirmed the PSD data. Solid-state assessments were conducted using powdered X-ray diffraction and scanning electron microscopy (SEM) imaging. A comparative invitro assessment was used to determine drug release from an equivalent dose of BQ free base powder and BQ-SLN, both packed in hard gelatin capsules. The sonicated formulations obtained significant effects for PSD, PdI, and ZP. The p-values (0.0001 for PdI, 0.0091 for PSD) for BQ as an independent variable in the sonicated formulation were notably higher than those in the unsonicated formulation (0.1336 for PdI, 0.0117 for PSD). The SEM images were between 100 - 400 nm and delineated nanocrystals of BQ embedded in the lipid matrix. The SLN formulation provides higher drug levels over the drug\'s free base; a similarity factor (f2 = 18.3) was estimated from the dissolution profiles.

Current research studies devoted to cutting forces in drilling are oriented toward predictive model development, however, in the case of mechanistic models, the material effect on the drilling process itself is mostly not considered. This research study aims to experimentally analyze how the machined material affects the feed force (Ff) during drilling, alongside developing predictive mathematical-statistical models to understand the main effects and interactions of the considered technological and tool factors on Ff. By conducting experiments involving six factors (feed, cutting speed, drill diameter, point angle, lip relief angle, and helix angle) at five levels, the drilling process of stainless steel AISI1045 and case-hardened steel 16MnCr5 is executed to validate the numerical accuracy of the established prediction models (AdjR = 99.600% for C45 and AdjR = 97.912% for 16MnCr5). The statistical evaluation (ANOVA, RSM, and Lack of Fit) of the data proves that the drilled material affects the Ff value at the level of 17.600% (p < 0.000). The effect of feed represents 44.867% in C45 and 34.087% in 16MnCr5; the cutting speed is significant when machining C45 steel only (9.109%). When machining 16MnCr5 compared to C45 steel, the influence of the point angle (lip relief angle) is lower by 49.198% (by 22.509%). The effect of the helix angle is 163.060% higher when machining 16MnCr5.

Fused Deposition Modelling (FDM) is one of the layer-based technologies that fall under the umbrella term \"Additive Manufacturing\", where the desired part is created through the successive layer-by-layer addition process with high accuracy using computer-aided design data. Additive manufacturing technology, or as it is commonly known, 3D (three-dimensional) printing, is a rapidly growing sector of manufacturing that is incorporated in automotive, aerospace, biomedical, and many other fields. This work explores the impact of the Additive Manufacturing process on the mechanical proprieties of the fabricated part. To conduct this study, the 3D printed tensile specimens are designed according to the ASTM D638 standards and printed from a digital template file using the FDM 3D printer Raise3D N2. The material chosen for this 3D printing parameter optimization is Polylactic acid (PLA). The FDM process parameters that were studied in this work are the infill pattern, the infill density, and the infill cell orientation. These factors\' effects on the tensile behavior of printed parts were analyzed by the design of experiments method, using the statistical software MINITAB2020.

BACKGROUND: Thunbergia laurifolia is used in traditional Thai medicine to reduce fever and treat mouth ulcers. However, the quantitative analysis of chemical markers has not yet been officially defined.
OBJECTIVE: The objective of this study is to develop a high-performance liquid chromatography (HPLC) method using a design of experiment (DoE) for the quantitative analysis of multicomponents by single marker (QAMS) and fingerprinting of the T. laurifolia aqueous extract.
METHODS: Critical variables were screened using a two-level fractional factorial design, followed by the optimization of the selected variables using a central composite design. The validated method was applied for quality assessment based on QAMS and fingerprinting of the extract.
RESULTS: Optimum conditions of DoE for the analysis of caffeic acid, vicenin-2, and rosmarinic acid were determined. The relative correction factors for caffeic acid and vicenin-2 were calculated using rosmarinic acid as an internal reference standard, and their contents in 30 samples were determined. The differences between the external standard method (ESM) and QAMS were compared. No significant difference was observed in the quantitative determination, proving the consistency QAMS and ESM. HPLC fingerprints of T. laurifolia were established with 8 of 12 characteristic peaks that were structurally characterized using HPLC-diode array detection-electrospray ionization/tandem mass spectrometry. The similarity of the fingerprints in all samples was ≥0.74, and the pattern recognition of the characteristic peaks was satisfied.
CONCLUSIONS: The proposed method efficiently detected multiple components of the T. laurifolia extract. Thus, the method is beneficial in providing references for enhancing the quality control of other herbal medicines.

This work is focused on performing a quantitative assessment of the environmental impacts associated with an organic synthesis reaction, optimized using an experimental design approach. A nucleophilic substitution reaction was selected, employing vanillin as the substrate, a phenolic compound widely used in the food industry and of pharmaceutical interest, considering its antioxidant and antitumoral potential. To carry out the reaction, three different solvents have been chosen, namely acetonitrile (ACN), acetone (Ace), and dimethylformamide (DMF). The syntheses were planned with the aid of a multivariate experimental design to estimate the best reaction conditions, which simultaneously allow a high product yield and a reduced environmental impact as computed by Life Cycle Assessment (LCA) methodology. The experimental results highlighted that the reactions carried out in DMF resulted in higher yields with respect to ACN and Ace; these reactions were also the ones with lower environmental impacts. The multilinear regression models allowed us to identify the optimal experimental conditions able to guarantee the highest reaction yields and lowest environmental impacts for the studied reaction. The identified optimal experimental conditions were also validated by experimentally conducting the reaction in those conditions, which indeed led to the highest yield (i.e., 93%) and the lowest environmental impacts among the performed experiments. This work proposes, for the first time, an integrated approach of DoE and LCA applied to an organic reaction with the aim of considering both conventional metrics, such as reaction yield, and unconventional ones, such as environmental impacts, during its lab-scale optimization.

The performance of a Pelton wheel is influenced by the jet created by the nozzle. Therefore, a Computational Fluid Dynamics (CFD) simulation was proposed. In this study, the significant output parameters (outlet velocity, outlet pressure, and tangential force component) and input parameters (different pressure and spear locations) were examined. In addition, the influencing parameters and their contributing percentages to the performance of the Pelton wheel were calculated using different optimisation techniques such as Taguchi Design of Experiments (DoE), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Grey Relational Analysis (GRA) and Criteria Importance Through Intercriteria Correlation (CRITIC). The effect of input factors on the output response was examined with DoE, and the results show that the inlet pressure had the most significant impact (97.38%, 99.18%, and 97.38%, respectively, for all different spear sites with a 99% confidence level). In terms of preference values, the TOPSIS and GRA results are comparable (best ranks for simulation runs #24 and #25 and least ranks for simulations #2 and #3, respectively). The CRITIC results for the pressure parameter are in good agreement with the Taguchi ANOVA analysis. The last spear location (5 mm after the nozzle outlet), with an inlet pressure of 413685 Pa generated the best result when employing the TOPSIS and GRA techniques. The outlet pressure of the nozzle was found to have a significant impact on the flow pattern of the Pelton Wheel based on the analysis of the CRITIC, Taguchi, and CFD results.

In this work, a multivariate approach was utilized for gaining some insights into the processing-structure-properties relationships in polyethylene-based blends. In particular, two high-density polyethylenes (HDPEs) with different molecular weights were melt-compounded using a twin-screw extruder, and the effects of the screw speed, processing temperature and composition on the microstructure of the blends were evaluated based on a Design of Experiment-multilinear regression (DoE-MLR) approach. The results of the thermal characterization, interpreted trough the MLR (multilinear regression) response surfaces, demonstrated that the composition of the blends and the screw rotation speed are the two most important parameters in determining the crystallinity of the materials. Furthermore, the rheological data were examined using a Principal Component Analysis (PCA) multivariate approach, highlighting also in this case the most prominent effect of the weight ratio of the two base polymers and the screw rotation speed.