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.

Enhancing the Design of Experiments on the Fatigue Life Characterisation of Fibre-Reinforced Plastics by Incorporating Artificial Neural Networks.

Fatigue life testing is a complex and costly matter, especially in the case of fibre-reinforced thermoplastics, where other parameters in addition to force alone must be taken into account. The number of tests required therefore increases significantly, especially if the influence of different fibre orientations is to be taken into account. It is therefore important to gain the greatest possible amount of knowledge from the limited number of available tests. In order to achieve this, this study aims to utilise adaptive sampling, which is used in numerous areas of computational engineering, for the design of experiments on fatigue life testing. Artificial neural networks (ANNs) are therefore trained on data for the short-fibre-reinforced material PBT GF30, and their areas of greatest model uncertainty are queried. This was undertaken with ANNs from various numbers of hidden layers, which were analysed for their performance. The ideal case turned out to be four hidden layers, for which a squared error as small as 1 × 10-3 was recorded. Locally resolved, the ANN was used to identify the region of greatest uncertainty for samples of vertical orientation and small numbers of cycles. With information such as this, additional data can be obtained in such uncertain regions in order to improve the model prediction-almost halving the recorded error to only 0.55 × 10-3. In this way, a model of comparable value can be found with less experimental effort, or a model of better quality can be set up with the same experimental effort.

Application of experimental design as a statistical approach to recover bioactive peptides from different food sources.

Bioactive peptides (BAPs) derived from samples of animals and plants have been widely recommended and consumed for their beneficial properties to human health and to control several diseases. This work presents the applications of experimental designs (DoE) used to perform factor screening and/or optimization focused on finding the ideal hydrolysis condition to obtain BAPs with specific biological activities. The collection and discussion of articles revealed that Box Behnken Desing and Central Composite Design were the most used. The main parameters evaluated were pH, time, temperature and enzyme/substrate ratio. Among vegetable protein sources, soy was the most used in the generation of BAPs, and among animal proteins, milk and shrimp stood out as the most explored sources. The degree of hydrolysis and antioxidant activity were the most investigated responses in obtaining BAPs. This review brings new information that helps researchers apply these DoE to obtain high-quality BAPs with the desired biological activities.

Understanding the influence of surgical parameters on craniofacial surgery outcomes: a computational study.

Sagittal craniosynostosis (SC) is a congenital condition whereby the newborn skull develops abnormally owing to the premature ossification of the sagittal suture. Spring-assisted cranioplasty (SAC) is a minimally invasive surgical technique to treat SC, where metallic distractors are used to reshape the newborn's head. Although safe and effective, SAC outcomes remain uncertain owing to the limited understanding of skull-distractor interaction and the limited information provided by the analysis of single surgical cases. In this work, an SC population-averaged skull model was created and used to simulate spring insertion by means of the finite-element analysis using a previously developed modelling framework. Surgical parameters were varied to assess the effect of osteotomy and spring positioning, as well as distractor combinations, on the final skull dimensions. Simulation trends were compared with retrospective measurements from clinical imaging (X-ray and three-dimensional photogrammetry scans). It was found that the on-table post-implantation head shape change is more sensitive to spring stiffness than to the other surgical parameters. However, the overall end-of-treatment head shape is more sensitive to spring positioning and osteotomy size parameters. The results of this work suggest that SAC surgical planning should be performed in view of long-term results, rather than immediate on-table reshaping outcomes.

Exploring the synergistic effect of carboxymethyl cellulose and chitosan in enhancing thermal stability of polyurethanes through statistical mixture design approach.

The thermal stability of polyurethanes, known for its limitations, was addressed in this research by seeking improvement through the introduction of carbohydrate-based chain extenders. In this research paper, we systematically sought to improve the thermal resistance of polyurethanes by incorporating carboxymethyl cellulose and chitosan, representing a pioneering application of the mixture design approach in their preparation. In this synthesis, hydroxyl-terminated polybutadiene and isophorone diisocyanate (IPDI) were reacted to prepare -NCO terminated prepolymer, which was subsequently reacted with varying mole ratios of CMC and CSN to develop a series of five PU samples. The prepared PU samples were characterized using the Fourier-transformed infrared spectroscopic technique. Thermal pyrolysis of PU samples was examined using thermal gravimetric analysis (TGA). It was observed that, among all the samples, PUS-3 showed remarkable thermal stability over a wide temperature range. A comprehensive statistical analysis was conducted to substantiate the experimental findings. It was estimated that CMC and CSN significantly enhance the thermal stability of the samples when involved in an interaction fashion. The ANOVA Table for the mixture design demonstrates that over 90 % of the total variation in thermal stability is explained by the mixture model across a wide temperature range. Moreover, PSU-3 exhibited 4 % more thermal stability over a wide range of temperatures on average, as compared to contemporary samples.

Optimizing N,N,N-trimethyl chitosan synthesis: A design of experiments (DoE) approach.

This study aimed to optimize the synthesis of trimethyl chitosan (TMC) with a high degree of N,N,N-trimethylation (DTM) through a one-step procedure, minimizing reagent use, reaction time, and avoiding O-methylation, using the Design of Experiments (DoE) approach. Initially, sequential designs were done. Following the determination of the initial conditions a Fractional Factorial Design was used, investigating methyl iodide (MeI) and NaHCO3 molar ratios, temperature, and reaction time on DTM. MeI and NaHCO3 molar ratios were found to be significant (p-values equal to 0.02 and 0.02, respectively), the reaction temperature (p = 0.04) displayed a non-linear effect, while the reaction time was found to be non-significant (p = 0.93). Finally, a Full Factorial Design was done to optimize temperature and base addition methods. Incremental addition of the base was determined to be feasible without affecting the DTM, thereby preventing any viscosity-related problems. DTM was achieved up to 72 % in a one-step procedure, with no O-methylation. These optimized conditions offer a cost-effective, one-step synthesis method for TMC production, holding significant promise for industrial applications by avoiding multistep reactions, ensuring minimal reagent use, and preventing O-methylation. The findings mark a substantial advancement in TMC synthesis, presenting a streamlined and efficient approach with substantial practical implications for process development.

Proceedings of the 2023 Viral Clearance Symposium, Session 5: Viral Clearance Strategy and Process Understanding.

Session 5 of the 2023 Viral Clearance Symposium reviewed the strategy and process understanding of viral clearance testing. Topics included learnings from the past, leveraging surrogate-based methodologies, cleaning agents that inactivate enveloped baculoviruses, segregation, and retrovirus-like particles both in continuous process and in-use as spiking viruses. Overall, there were discussions over a wide array of viral clearance determinants.

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 (DoE). 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 for specific consumer communities, opening the way to follow this approach with other probiotics of impact for human health.

DoE-based medium optimization for improved biosurfactant production with Aureobasidium pullulans.

Polyol lipids (a.k.a. liamocins) produced by the polyextremotolerant, yeast-like fungus Aureobasidium pullulans are amphiphilic molecules with high potential to serve as biosurfactants. So far, cultivations of A. pullulans have been performed in media with complex components, which complicates further process optimization due to their undefined composition. In this study, we developed and optimized a minimal medium, focusing on biosurfactant production. Firstly, we replaced yeast extract and peptone in the best-performing polyol lipid production medium to date with a vitamin solution, a trace-element solution, and a nitrogen source. We employed a design of experiments approach with a factor screening using a two-level-factorial design, followed by a central composite design. The polyol lipid titer was increased by 56% to 48 g L-1, and the space-time yield from 0.13 to 0.20 g L-1 h-1 in microtiter plate cultivations. This was followed by a successful transfer to a 1 L bioreactor, reaching a polyol lipid concentration of 41 g L-1. The final minimal medium allows the investigation of alternative carbon sources and the metabolic pathways involved, to pinpoint targets for genetic modifications. The results are discussed in the context of the industrial applicability of this robust and versatile fungus.

Taguchi optimisation of the synthesis of vine-pruning-waste hydrochar as potential adsorbent for pesticides in water.

This research aimed to synthesise an effective hydrochar adsorbent from vineyard pruning wastes to remove emerging contaminants as a potential valorisation product. The adsorption capacity of the hydrochar was optimised using the Taguchi method. Four synthesis variables were evaluated: hydrothermal reaction temperature, use of H3PO4 as a catalyst, number of acetone washes, and type of chemical cold activation. The simultaneous adsorption of five model pesticides (clothianidin (CTD), acetamiprid (ACE), 2,4-D, metalaxyl (MET), and atrazine (ATZ)) at an initial pH of 7 was studied. At optimum conditions, the hydrochar presented a total adsorption capacity of 22.7 µmol/g, representing a 2.7-fold improvement with respect to pristine hydrochar performance. High percentage removals were achieved for all pollutants (85 % CTD, 94 % ACE, 86 % MET, and 95 % ATZ) except for 2,4-D (4 %). This research provides a valuable reference for developing hydrochar adsorbents for pollution control and the valorisation of biomass wastes.

Effect of in vitro transcription conditions on yield of high quality messenger and self-amplifying RNA.

Messenger RNA (mRNA) and self-amplifying RNA (saRNA) vaccines against SARS-CoV-2 produced using in vitro transcription (IVT) were clinically approved in 2020 and 2022, respectively. While the industrial production of mRNA using IVT has been extensively optimized, the optimal conditions for saRNA have been explored to a lesser extent. Most T7 polymerase IVT protocols have been specifically optimized for mRNA which is ∼5-10-fold smaller than saRNA and may have profound effects on both the quality and yield of longer transcripts. Here, we optimized IVT conditions for simultaneously increasing the yield of full-length transcripts and reducing dsRNA formation through Design of Experiments. Using a definitive screening approach, we found that the key parameters are temperature and magnesium in the outcome of RNA quality (% full length transcript) and yield in small scale synthesis. The most important parameter for reducing dsRNA formation for both mRNA and saRNA was Mg2+ concentration (10 mM). We observed that a lower temperature was vital for production of high quality saRNA transcripts. mRNA quality was optimal at higher Mg2+ concentration (>40 mM). High quality transcripts correspond to significantly reduced product yield for saRNA, but not for mRNA. The differences between mRNA and saRNA requirements for high quality product and the relationship between high quality large saRNA molecules and low temperature synthesis have not been reported previously. These findings are key for informing future IVT parameters design and optimization for smaller and larger RNA transcripts.

Eco-friendly extraction of antibacterial compounds from enriched olive pomace: a design-of-experiments approach to sustainability.

The increasing interest in utilizing olive pomace bioactive molecules to advance functional elements and produce antioxidant and antimicrobial additives underscores the need for eco-friendly extraction and purification methods. This study aims to develop an eco-friendly extraction method to evaluate the effect of extraction parameters on the recovery of bioactive molecules from enriched olive pomace. The effects were identified based on total phenolic and flavonoid contents and antioxidant activity, employing a design of experimental methodology. The positive and the negative simultaneous effects showed that among the tested enrichments, those incorporating Nigella Sativa, dates, and coffee demonstrated superior results in terms of the measured responses. Furthermore, chromatographic analysis unveiled the existence of intriguing compounds such as hydroxytyrosol, tyrosol, and squalene in distinct proportions. Beyond this, our study delved into the structural composition of the enriched pomace through FTIR analysis, providing valuable insights into the functional groups and chemical bonds present. Concurrently, antimicrobial assays demonstrated the potent inhibitory effects of these enriched extracts against various microorganisms, underscoring their potential applications in food preservation and safety. These findings highlight enriched olive pomace as a valuable reservoir of bioactive molecules for food products since they can enhance their anti-oxidative activity and contribute to a sustainable circular economy model for olive oil industries.

Chemometric Optimization of SPE for the Determination of Multiclass Pesticides in Portable Water Samples by UHPLC-MS/MS.

This study aimed on the development of a SPE-UHPLC-MS/MS method for the simultaneous determination of pesticide residues in drinking water samples. A chemometric approach was applied to optimize the efficiency of the SPE pretreatment procedure. This study involved (i) the application of a Full Factorial Design for the screening of the significant factors, (ii) the application of a Central Composite Design for the determination of the optimal conditions and (iii) the evaluation and validation of the significance of the statistically proposed models. Oasis HLB cartridges were used for the extraction. The optimum sample volume was 300 mL and the elution solvent 3 mL of the mixture of methanol:ethylacetate 70:30 v/v. The method was validated according to the international guidelines. Recoveries were ranged from 63 to 116% and the detection limits were between 0.1 and 1.5 pg mL- 1. The validated method could be used in routine analysis for pesticides screening.

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.

A numerical compass for experiment design in chemical kinetics and molecular property estimation.

Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure-activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package KineticCompass.

Precision Culture Scaling to Establish High-Throughput Vasculogenesis Models.

Hydrogel-based 3D cell cultures can recapitulate (patho)physiological phenomena ex vivo. However, due to their complex multifactorial regulation, adapting these tissue and disease models for high-throughput screening workflows remains challenging. In this study, a new precision culture scaling (PCS-X) methodology combines statistical techniques (design of experiment and multiple linear regression) with automated, parallelized experiments and analyses to customize hydrogel-based vasculogenesis cultures using human umbilical vein endothelial cells and retinal microvascular endothelial cells. Variations of cell density, growth factor supplementation, and media composition are systematically explored to induce vasculogenesis in endothelial mono- and cocultures with mesenchymal stromal cells or retinal microvascular pericytes in 384-well plate formats. The developed cultures are shown to respond to vasculogenesis inhibitors in a compound- and dose-dependent manner, demonstrating the scope and power of PCS-X in creating parallelized tissue and disease models for drug discovery and individualized therapies.

The Effect of Formulation Variables on the Manufacturability of Clopidogrel Tablets via Fluidized Hot-Melt Granulation-From the Lab Scale to the Pilot Scale.

Solid pharmaceutical formulations with class II active pharmaceutical ingredients (APIs) face dissolution challenges due to limited solubility, affecting in vivo behavior. Robust computational tools, via data mining, offer valuable insights into product performance, complementing traditional methods and aiding in scale-up decisions. This study utilizes the design of experiments (DoE) to understand fluidized hot-melt granulation manufacturing technology. Exploratory data analysis (MVDA) highlights similarities and differences in tablet manufacturability and dissolution profiles at both the lab and pilot scales. The study sought to gain insights into the application of multivariate data analysis by identifying variations among batches produced at different manufacturing scales for this technology. DoE and MVDA findings show that the granulation temperature, time, and Macrogol type significantly impact product performance. These factors, by influencing particle size distribution, become key predictors of product quality attributes such as resistance to crushing, disintegration time, and early-stage API dissolution in the profile. Software-aided data mining, with its multivariate and versatile nature, complements the empirical approach, which is reliant on trial and error during product scale-up.

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.

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.

"Full factorial design of experiments dataset for parallel-connected lithium-ion cells imbalanced performance investigation".

This paper shares an experimental dataset of lithium-ion battery parallel-connected modules. The campaign, conducted at the Stanford Energy Control Laboratory, employs a comprehensive full factorial Design of Experiment methodology on ladder-configured parallel strings. A total of 54 test conditions were investigated under various operating temperatures, cell-to-cell interconnection resistance, cell chemistry, and aging levels. The module-level testing procedure involved Constant Current Constant Voltage (CC-CV) charging and Constant Current (CC) discharge. Beyond monitoring total module current and voltage, Hall sensors and thermocouples were employed to measure the signals from each individual cell to quantify both current and temperature distribution within each tested module configuration. Additionally, the dataset contains cell characterization data for every cell (i.e. NCA Samsung INR21700-50E and NMC LG-Chem INR21700-M50T) used in the module-level experiments. This dataset provides valuable resources for developing battery physics-based, empirical, and data-driven models at single cell and module level. Ultimately, it contributes to advance our understanding of how cell-to-cell heterogeneity propagates within the module and how that affects the overall system performance.