Nanoporous smartPearls for dermal application – Identification of optimal silica types and a scalable production process as prerequisites for marketed products

smartPearls are a dermal delivery system for poorly soluble active agents, consisting of nanoporous silica particles loaded with a long-term stable, amorphous active agent in its mesopores (2–50 nm). The amorphous state of the active agent is known to increase dermal bioavailability. For use in marketed products, optimal silica types were identified from commercially available, regulatory accepted silica. In addition, a scalable production process was demonstrated. The loading of the particles was performed by applying the immersion–evaporation method. The antioxidant rutin was used as a model active agent and ethanol was applied as the solvent. Various silica particles (Syloid®, Davisil®) differing in particle size (7–50 µm), pore diameter (3–25 nm) and pore volume (0.4–1.75 mL/g) were investigated regarding their ease of processing. The evaporation from the silica–ethanol suspensions was performed in a rotary evaporator. The finest powders were obtained with larger-sized silica. The maximum loading staying amorphous was achieved between 10% and 25% (w/w), depending on the silica type. A loading mechanism was also proposed. The most suitable processing occurred with the large-sized Syloid® XDP 3050 silica with a 50 µm particle size and a pore diameter of 25 nm, resulting in 18% (w/w) maximum loading. Based on a 10% (w/w) loading and the amorphous solubility of the active agent, for a 100 kg dermal formulation, about 500 g of loaded particles were required. This corresponds to production of 5 kg of loaded smartPearls for a formulation batch size of a ton. The production of 5 kg (i.e., about 25 L of solvent removal) can be industrially realized in a commercial 50 L rotary evaporator

Solubility of Calcined Kaolinite, Montmorillonite, and Illite in High Molar NaOH and Suitability as Precursors for Geopolymers

Clays and clay minerals dissolve over a broad pH range, such as during sediment diagenesis and in a variety of applications, including nuclear waste storage, landfills, and geopolymer binders in the construction industry. The solubility depends on process parameters (pH, temperature, pressure, etc.) and material properties (phase content, clay mineral composition, particle size, etc.). Pretreatments such as calcination or severe grinding change the material properties and could enhance solubility, which is called activation. The aim of the current study was to determine the solubility of three different clay minerals after calcination (metakaolinite, metamontmorillonite, and metaillite) in high molar alkaline solutions (NaOH) up to 10.79 mol/L and pH = 14.73. Furthermore, the solubility of an Al(OH)3 powder in alkaline solution (NaOH) was analyzed, as it can be used to adjust the Si:Al ratio of geopolymer precursors. The residues of the clay minerals after the alkaline treatment were investigated to disclose potential alterations in their phase contents. Based on the results of the thermal and alkaline activation, conclusions about the suitability as geopolymer precursors were made. All clay minerals showed an increase in solubility proportional to the concentration of the alkaline solution. The solubility decreased in the order metakaolinite > metamontmorillonite > metaillite. Thereby, dissolution was incomplete for all three clay minerals (<90%) after 7 days and congruent for metakaolinite and metaillite but incongruent for metamontmorillonite

Comprehensive examination of dehydroxylation of kaolinite, disordered kaolinite, and dickite: Experimental studies and density functional theory

Kaolins and clays are important rawmaterials for production of supplementary cementitious materials and geopolymer precursors through thermal activation by calcination beyond dehydroxylation (DHX). Both types of clay contain different polytypes and disordered structures of kaolinite but little is known about the impact of the layer stacking of dioctahedral 1:1 layer silicates on optimum thermal activation conditions and following reactivity with alkaline solutions. The objective of the present study was to improve understanding of the impact of layer stacking in dioctahedral 1:1 layer silicates on the thermal activation by investigating the atomic structure after dehydroxylation. Heating experiments by simultaneous thermal analysis (STA) followed by characterization of the dehydroxylated materials by nuclear magnetic resonance spectroscopy (NMR) and scanning electron microscopy (SEM) together with first-principles calculations were performed. Density functional theory (DFT) was utilized for correlation of geometry-optimized structures to thermodynamic stability. The resulting volumes of unit cells were compared with data from dilatometry studies. The local structure changes were correlated with experimental results of increasing DHX temperature in the following order: disordered kaolinite, kaolinite, and dickite, whereupon dickite showed two dehydroxylation steps. Intermediate structures were found that were thermodynamically stable and partially dehydroxylated to a degree of DHX of 75% for kaolinite, 25% for disordered kaolinite, and 50% for dickite. These thermodynamically stable, partially dehydroxylated intermediates contained AlV while metakaolinite and metadickite contained only AlIV with a strongly distorted coordination shell. These results indicate strongly the necessity for characterization of the structure of dioctahedral 1:1 layer silicates in kaolins and clays as a key parameter to predict optimized calcination conditions and resulting reactivity

A Learnable Prior Improves Inverse Tumor Growth Modeling

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Specificity Effects of Amino Acid Substitutions in Promiscuous Hydrolases: Context-Dependence of Catalytic Residue Contributions to Local Fitness Landscapes in Nearby Sequence Space

Catalytic promiscuity can facilitate evolution of enzyme functions-a multifunctional catalyst may act as a springboard for efficient functional adaptation. We test the effect of single mutations on multiple activities in two groups of promiscuous AP superfamily members to probe this hypothesis. We quantify the effect of site-saturating mutagenesis of an analogous, nucleophile-flanking residue in two superfamily members: an arylsulfatase (AS) and a phosphonate monoester hydrolase (PMH). Statistical analysis suggests that no one physicochemical characteristic alone explains the mutational effects. Instead, these effects appear to be dominated by their structural context. Likewise, the effect of changing the catalytic nucleophile itself is not reaction-type-specific. Mapping of "fitness landscapes" of four activities onto the possible variation of a chosen sequence position revealed tremendous potential for respecialization of AP superfamily members through single-point mutations, highlighting catalytic promiscuity as a powerful predictor of adaptive potential.This research was funded by the Biological and Biotechnological Research Council (BBSRC) and the Human Frontiers Science Programme. C.D.B. was supported by a BBSRC studentship and the Cambridge European Trust

Ergebnisse des Workshops vom 26.11.2021 des Forschungsschwerpunkts Vernetzte intelligente Infrastrukturen und mobile Systeme (VIMS)

Kölner Beiträge zur technischen Informatik - Cologne Contributions to Computer Engineering Ergebnisse des Workshops vom 26.11.2021 des Forschungsschwerpunkts Vernetzte intelligente Infrastrukturen und mobile Systeme (VIMS