Computational Modeling Approaches for Optimizing Microencapsulation Processes: From Molecular Dynamics to CFD and FEM Techniques

Polymer-drug interactions play a pivotal role in the design and optimization of drug delivery systems. Thermodynamic principles, such as enthalpy, entropy and free energy, govern these interactions and significantly influence the stability, efficacy and release profiles of the drug-polymer complexes. In this review we explore the role of thermodynamics in drug-polymer binding, focusing on non-covalent interactions, including van der Waals forces, hydrogen bonding, ionic interactions and hydrophobic effects. These interactions affect the drug encapsulation efficiency, release kinetics and the overall stability of the delivery system. Understanding the balance between the various thermodynamic forces helps in the rational design of advanced drug delivery platforms, such as nanoparticles, micelles and hydrogels, which rely on optimized drug-polymer affinity. Our review further delves into the experimental techniques and modeling approaches used to characterize these interactions, such as isothermal titration calorimetry (ITC), molecular dynamics (MD) simulations and surface plasmon resonance (SPR). By examining these thermodynamic foundations, we believe this article provides insights into developing more efficient and stable drug delivery systems that ensure targeted release, enhanced bioavailability and reduced side effects.

This study focuses on science teachers’ first encounter with computational modeling in professional development workshops. It examines the factors shaping the teachers’ self-efficacy and attitudes towards integrating computational modeling within inquiry-based learning modules for 9th grade physics. The learning modules introduce phenomena, the analysis of measurement data, and offer a method for coordinating the experimental findings with a theory-based computational model. Teachers’ attitudes and self-efficacy were studied using survey questions and workshop activity transcripts. As expected, prior experience in physics teaching was related to teachers’ self-efficacy in teaching physics in 9th grade. Also, teachers’ prior experience with programming was strongly related to their self-efficacy regarding the programming component of model construction. Surprisingly, the short interaction with computational modeling increased the group’s self-efficacy, and the average rating of understanding and enjoyment was similar among teachers with and without prior programming experience. Qualitative data provides additional insights into teachers’ predispositions towards the integration of computational modeling into the physics teaching.Electronic supplementary materialThe online version of this article (10.1007/s10956-020-09855-3) contains supplementary material, which is available to authorized users.

A comprehensive, falsifiable, theory of volition necessitates quantitative computational models. Ambiguous word models are insufficient to understand volition. The desiderata for computational models, and hence for understanding volition, include (1) explaining and predicting how neurons give rise to volitional decisions, as well as (2) explaining and predicting behavior from the activity of such neurons. Efforts toward building computational models of volitional decisions remain in their infancy. Initial and preliminary sketches of models rely on gradual integration toward a threshold that originates the decision. The integration of computational models with state-of-the-art experimental observations in the field constitutes an essential goal to pave the way toward understanding volition.

Nicotine is a fat-soluble substance that is easily absorbed through the skin and mucosal tissues of the human body. However, its properties, such as light exposure, heat decomposition, and volatilization, restrict its development and application in external preparations. This study focused on the preparation of stable nicotine-encapsulated ethosomes. During their preparation, two water-phase miscible osmotic promoters, ethanol and propylene glycol (PG), were added to obtain a stable transdermal delivery system. Skin nicotine delivery was enhanced through the synergistic action of osmotic promoters and phosphatidylcholine in binary ethosomes. Various characteristics of the binary ethosomes were measured, including the vesicle size, particle size distribution, and zeta potential. In order to optimize the ratio of ethanol and PG, the skin permeability test was performed on mice in vitro in a Franz diffusion cell to compare cumulative skin permeabilities. The penetration depth and fluorescence intensity of rhodamine-B-entrapped vesicles in isolated mouse skin samples were observed using laser confocal scanning microscopy. When ethanol:PG was used in a ratio of 5:5 (w/w), binary ethosomes were found to be the most stable, had the highest encapsulation rate (86.13 ± 1.40), smallest particle size (106.0 ± 11.0) nm, maximum transdermal depth (180 μm), and maximum fluorescence intensity (160 AU). Nicotineencapsulated ethosomes (ethanol: PG = 5:5, w/w) were an efficient and stable transdermal delivery system. The nicotine-encapsulated ethosomes containing ethanol and PG are considered to be safe and reliable as a transdermal administration agent, which does not irritate the skin.

The integration of computational modelling in science classrooms provides a unique opportunity to promote key 21st century skills including computational thinking (CT) and collaboration. The open-ended, problem-solving nature of the task requires groups to grapple with the combination of two domains (science and computing) as they collaboratively construct computational models. While this approach has produced significant learning gains for students in both science and CT in K–12 settings, the collaborative learning processes students use, including learner regulation, are not well understood. In this paper, we present a systematic analysis framework that combines natural language processing (NLP) of collaborative dialogue, log file analyses of students’ model-building actions, and final model scores. This analysis is used to better understand students’ regulation of collaborative problem solving (CPS) processes over a series of computational modelling tasks of varying complexity. The results suggest that the computational modelling challenges afford opportunities for students to a) explore resource-intensive processes, such as trial and error, to more systematic processes, such as debugging model errors by leveraging data tools, and b) learn from each other using socially shared regulation (SSR) and productive collaboration. The use of such SSR processes correlated positively with their model-building scores. Our paper aims to advance our understanding of collaborative, computational modelling in K–12 science to better inform classroom applications.

This Special Issue is based on presentations at the Workshop on “Action, Language and Neuroinformatics” held in July of 2011. It contributes to the view that neuroinformatics must include the informatics of computational modeling of neural systems as well as the development and linkage of database resources for both models and empirical data. The papers introduce key results in empirical research, computational modeling, and neuroinformatics for two areas of neuroscience–neurolinguistics and the study of neural mechanisms underlying manual action and its recognition– and assess ways in which the study of language processing can benefit from models of action production. Because the two areas span the spectrum from animal studies to human studies, and from basic sensorimotor processes to cognition, they provide a setting for assessing the diverse challenges of creating computational models for neuroscience, for providing databases for the very different forms of empirical data now exploited by neuroscience, and for linkage of data and models in systems and cognitive neuroscience generally, not just within our two focal areas. The papers in this Special Issue are divided into four parts: Part 1, Databasing the Brain, presents three approaches to the development of neuroinformatics databases, including a new methodology for linking data and models in systems and cognitive neuroscience, tools for federating online neuroinformatics databases, and tools to link gene expression data to cognitive brain systems. Part 2, Action, Imitation and Gesture, provides two cases studies linking research on monkeys, apes, humans and machines, exemplifying neuroinformatics in the wide sense that embraces computational modeling as well as database construction. Part 3, Language, develops this story in relation to the uniquely human capacity for language, offering models of human syntactic encoding and decoding, actor-based language comprehension and the linkage of visual scenes to language via template construction grammar, in each case considering how to test the models against data from human behavior and brain imaging. Finally, Part 4 builds upon a series of intense discussions held at the Workshop on the present and future of neuroinformatics, with especial emphasis on the integration of computational models with empirical data.

Cornus macrophylla has been traditionally recognized for its medicinal properties, particularly in managing inflammatory conditions. However, a scientific understanding of its bioactive constituents and mechanisms remains underexplored. This study aimed to isolate and characterize bioactive compounds from the bark of C. macrophylla and evaluate their anti-inflammatory potential through in silico and in vitro analyses. A total of ten compounds, including an ellagic acid derivative and nine steroids and triterpenes, were isolated. Comprehensive analyses integrating molecular docking, ADMET profiling, and density functional theory (DFT) calculations were conducted to elucidate the molecular and anti-inflammatory properties. Experimental validation was performed to confirm the findings. 1,2,3-trimethoxychromeno[5,4,3-cde][1,3]dioxolo[4,5-h]chromene-5,11-dione (1) emerged as the most active compound among those tested, demonstrating moderate inhibition of lipoxygenase (LOX), exhibiting an IC50 value of 78.1 ± 0.03µM. It also exhibited measurable suppression of respiratory burst activity in human neutrophils, achieving an IC50 of 298.21 ± 0.037µM, comparable to the benchmark anti-inflammatory agent, Indomethacin (IC50: 271.14 ± 0.032µM). Molecular docking studies revealed that compound 1, strongly interacts with 15-LOX, demonstrating a binding affinity of -7.038kcal/mol and forming stable interactions with key active site residues. ADMET profiling and DFT analysis indicated its favourable drug-like properties, reinforcing its potential as a therapeutic candidate. The findings highlight compound (1) as a potent natural inhibitor of LOX, with significant anti-inflammatory activity validated through both experimental and computational approaches. Its efficacy and drug-likeness underscore its therapeutic potential for developing novel anti-inflammatory agents. Further studies are warranted to explore its clinical applicability.

Cellulases are a complex group of enzymes that are fundamental for the degradation of amorphous and crystalline cellulose in lignocellulosic material. Unfortunately, cellulases have a low catalytic efficiency on their substrates when compared to similar enzymes such as amylases, which has led to a strong interest in improving their activities. Thermobifida fusca secretes six cellulose degrading enzymes: two exo- and three endocellulases and an endo/exocellulase Cel9A (formerly called E4). Cel9A shows unique properties because of its endo- and exocellulase characteristics, strong activity on crystalline cellulose, and good synergistic properties. Therefore, it is an excellent target for mutagenesis techniques to improve crystalline cellulose degradation. In this article, we describe research conducted to improve Cel9A catalytic efficiency using a rational design and computer modeling. A computer model of Cel9A was created using the program CHARMM plus its PDB structure and a cellohexose molecule attached to the catalytic site as a starting model. Initially molecular graphics and energy minimization were used to extend the cellulose chain to 18 glucose residues spanning the catalytic domain and cellulose-binding domain (CBD). The interaction between this cellulose chain and conserved CBD residues was determined in the model, and mutations likely to improve the binding properties of the CBD were selected. Site-directed mutations were carried out using the pET vector pET26b, Escherichia coli DH5-alpha, and the QuickChange mutagenesis method. E. coli BL21-DE3 was used for protein production and expression. The purified proteins were assayed for enzymatic activity on filter paper, swollen cellulose, bacterial microcrystalline cellulose, and carboxymethylcellulose (CMC). Mutation of the conserved residue F476 to Y476 gave a 40% improved activity in assays with soluble and amorphous cellulose such as CMC and swollen cellulose.

Uptake of system L amino acid substrates into isolated placental plasma membrane vesicles in the absence of opposing side amino acid (zero-trans uptake) is incompatible with the concept of obligatory exchange, where influx of amino acid is coupled to efflux. We therefore hypothesized that system L amino acid exchange transporters are not fully obligatory and/or that amino acids are initially present inside the vesicles. To address this, we combined computational modeling with vesicle transport assays and transporter localization studies to investigate the mechanisms mediating [14C]l-serine (a system L substrate) transport into human placental microvillous plasma membrane (MVM) vesicles. The carrier model provided a quantitative framework to test the 2 hypotheses that l-serine transport occurs by either obligate exchange or nonobligate exchange coupled with facilitated transport (mixed transport model). The computational model could only account for experimental [14C]l-serine uptake data when the transporter was not exclusively in exchange mode, best described by the mixed transport model. MVM vesicle isolates contained endogenous amino acids allowing for potential contribution to zero-trans uptake. Both L-type amino acid transporter (LAT)1 and LAT2 subtypes of system L were distributed to MVM, with l-serine transport attributed to LAT2. These findings suggest that exchange transporters do not function exclusively as obligate exchangers.—Widdows, K. L., Panitchob, N., Crocker, I. P., Please, C. P., Hanson, M. A., Sibley, C. P., Johnstone, E. D., Sengers, B. G., Lewis, R. M., Glazier, J. D. Integration of computational modeling with membrane transport studies reveals new insights into amino acid exchange transport mechanisms.

Just an additional hydrogen bond can dramatically reduce the catalytic activity of Bacillus subtilis lipase A I12T mutant: An integration of computational modeling and experimental analysis

The optical control and investigation of neuronal activity can be achieved and carried out with photoswitchable ligands. Such compounds are designed in a modular fashion, combining a known ligand of the target protein and a photochromic group, as well as an additional electrophilic group for tethered ligands. Such a design strategy can be optimized by including structural data. In addition to experimental structures, computational methods (such as homology modeling, molecular docking, molecular dynamics and enhanced sampling techniques) can provide structural insights to guide photoswitch design and to understand the observed light-regulated effects. This review discusses the application of such structure-based computational methods to photoswitchable ligands targeting voltage- and ligand-gated ion channels. Structural mapping may help identify residues near the ligand binding pocket amenable for mutagenesis and covalent attachment. Modeling of the target protein in a complex with the photoswitchable ligand can shed light on the different activities of the two photoswitch isomers and the effect of site-directed mutations on photoswitch binding, as well as ion channel subtype selectivity. The examples presented here show how the integration of computational modeling with experimental data can greatly facilitate photoswitchable ligand design and optimization. Recent advances in structural biology, both experimental and computational, are expected to further strengthen this rational photopharmacology approach.

Proliferative vitreoretinopathy (PVR) is a pathological process characterized by the formation of fibrotic membranes that contract and lead to recurrent retinal detachment. Pars plana vitrectomy (PPV) is the primary treatment, but recurrence rates remain high, as surgery does not address the underlying molecular mechanisms driving fibrosis. Despite several proposed pharmacological interventions, no approved therapies exist, partly due to challenges in conducting preclinical and in vivo studies for ethical and safety reasons. This review explores the potential of computational models and Digital Twins, which are increasingly gaining attention in medicine. These tools could enable the development of progressively complex PVR models, from basic simulations to patient-specific Digital Twins. Nintedanib, a tyrosine kinase inhibitor targeting PDGFR, VEGFR, and FGFR, is presented as a prototype for computational models to simulate its effects on fibrotic pathways in virtual patient cohorts. Although still in its early stages, the integration of computational models and Digital Twins offers promising avenues for improving PVR management through more personalized therapeutic strategies.

We are developing LINDSAY Virtual Human, a 3-dimensional, interactive computer model of male and female anatomy and physiology. LINDSAY is designed to be used for medical education. One key characteristic of LINDSAY is the integration of computational models across a range of spatial and temporal scales. We simulate physiological processes in an integrative fashion: from the body level to the level of organs, tissues, cells, and sub-cellular structures. For use in the classroom, we have built LINDSAY Presenter, a 3D slide-based visualization and exploration environment that presents different scenarios within the simulated human body. We are developing LINDSAY Composer to create complex scenes for demonstration, exploration and investigation of physiological scenarios. At LINDSAY Composer′s core is a graphical programming environment, which facilitates the composition of complex, interactive educational modules around the human body.

Cellulases are a complex group of enzymes that are fundamental for the degradation of amorphous and crystalline cellulose in lignocellulosic material. Unfortunately, cellulases have a low catalytic efficiency on their substrates when compared to similar enzymes such as amylases, which has led to a strong interest in improving their activities. Thermobifida fusca secretes six cellulose degrading enzymes: two exo- and three endocellulases and an endo/exocellulase Ce19A (formerly called E4). Ce19A shows unique properties because of its endo- and exocellulase characteristics, strong activity on crystalline cellulose, and good synergistic properties. Therefore, it is an excellent target for mutagenesis techniques to improve crystalline cellulose degradation. In this article, we describe research conducted to improve Ce19A catalytic efficiency using a rational design and computer modeling. A cornputer model of Ce19A was created using the program CHARMM plus its PDB structure and a cellohexose molecule attached to the catalytic site as a starting model. Initially molecular graphics and energy minimization were used to extend the cellulose chain to 18 glucose residues spanning the catalytic domain and cellulose-binding domain (CBD). The interaction between this cellulose chain and conserved CBD residues was determined in the model, and mutations likely to improve the binding properties of the CBD were selected. Site-directed mutations were carried out using the pET vector pET26b, Escherichia coli DH5-α, and the QuickChange mutagenesis method. E. coli BL21-DE3 was used for protein production and expression. The purfied proteins were assayed for enzymatic activity on filter paper, swollen cellulose, bacterial microcrystalline cellulose, and carboxymethylcellulose (CMC). Mutation of the conserved residue F476 to Y476 gave a 40% improved activity in assays with soluble and amorphous cellulose such as CMC and swollen cellulose.Index EntriesThermonospora fuscaCel9Acellulasesprotein engineeringcomputer modeling

Increasing the Efficiency of Liquid Phase Alkylation of Benzene with Propylene Using the Method of Mathematical Modeling