Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Modeling the Solubility of Phenolic Acids in Aqueous Media at 37 °C.

ABSTRACT: In this work, the solubility of vanillic, gallic, syringic, p-coumaric, ferulic and caffeic acids was determined at 37 °C under different conditions, namely pure water and two different ionic media, NaCl(aq) and NaClO4(aq), at different ionic strengths (i.e., 0.16, 0.50, 1.0, 2.0 and 3.0 M). The solubility in water of all the acids was found to be higher than that in both of the ionic media. Moreover, the solubility of hydroxycinnamic acids was lower than that of hydroxybenzoic acids. The activity coefficients of neutral species were calculated from these data; this knowledge is necessary when modeling the dependence of equilibrium constants on the ionic strength. Results obtained in this work can be useful for further studies regarding complex formation equilibria between these ligands and bioavailable metal cations.

SUBMITTER: Furia E

PROVIDER: S-EPMC8587167 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Modeling the Solubility of Phenolic Acids in Aqueous Media at 37 °C.

Furia Emilia E Beneduci Amerigo A Malacaria Luana L Fazio Alessia A La Torre Chiara C Plastina Pierluigi P

Molecules (Basel, Switzerland) 20211028 21

In this work, the solubility of vanillic, gallic, syringic, <i>p</i>-coumaric, ferulic and caffeic acids was determined at 37 °C under different conditions, namely pure water and two different ionic media, NaCl(aq) and NaClO<sub>4</sub>(aq), at different ionic strengths (i.e., 0.16, 0.50, 1.0, 2.0 and 3.0 M). The solubility in water of all the acids was found to be higher than that in both of the ionic media. Moreover, the solubility of hydroxycinnamic acids was lower than that of hydroxybenzoic ...[more]

PMID: 34770909

Similar Datasets

Exposure to phenolic acids

Project description:Exposure of Erwinia chrysanthemi 3937 to a combination of phenolic acids (salicylic, benzoic and t-cinnamic) each at a concentration of 0.078 mM resulted in activation of genes encoding efflux pumps and those involved in oxidative stress resistance. Keywords: phenolic acid exposure gene response

2006-06-13 | GSE4558 | GEO

Improved understanding of aqueous solubility modeling through topological data analysis.

Project description:Topological data analysis is a family of recent mathematical techniques seeking to understand the 'shape' of data, and has been used to understand the structure of the descriptor space produced from a standard chemical informatics software from the point of view of solubility. We have used the mapper algorithm, a TDA method that creates low-dimensional representations of data, to create a network visualization of the solubility space. While descriptors with clear chemical implications are prominent features in this space, reflecting their importance to the chemical properties, an unexpected and interesting correlation between chlorine content and rings and their implication for solubility prediction is revealed. A parallel representation of the chemical space was generated using persistent homology applied to molecular graphs. Links between this chemical space and the descriptor space were shown to be in agreement with chemical heuristics. The use of persistent homology on molecular graphs, extended by the use of norms on the associated persistence landscapes allow the conversion of discrete shape descriptors to continuous ones, and a perspective of the application of these descriptors to quantitative structure property relations is presented.

| S-EPMC6755597 | biostudies-literature

Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances.

Project description:Intrinsic aqueous solubility is a foundational property for understanding the chemical, technological, pharmaceutical, and environmental behavior of drug substances. Despite years of solubility research, molecular structure-based prediction of the intrinsic aqueous solubility of drug substances is still under active investigation. This paper describes the authors' systematic data-driven modelling in which two fit-for-purpose training data sets for intrinsic aqueous solubility were collected and curated, and three quantitative structure-property relationships were derived to make predictions for the most recent solubility challenge. All three models perform well individually, while being mechanistically transparent and easy to understand. Molecular descriptors involved in the models are related to the following key steps in the solubility process: dissociation of the molecule from the crystal, formation of a cavity in the solvent, and insertion of the molecule into the solvent. A consensus modeling approach with these models remarkably improved prediction capability and reduced the number of strong outliers by more than two times. The performance and outliers of the second solubility challenge predictions were analyzed retrospectively. All developed models have been published in the QsarDB.org repository according to FAIR principles and can be used without restrictions for exploring, downloading, and making predictions.

| S-EPMC9611068 | biostudies-literature

Photoprotective Effects of Selected Amino Acids on Naproxen Photodegradation in Aqueous Media.

Project description:It is important to develop a photostabilization strategy to ensure the quality of photosensitive compounds, including pharmaceuticals. This study focused on the protective effects of 20 amino acids on the photodegradation of naproxen (NX), a photosensitive pharmaceutical, to clarify the important nature of a good photostabilizer. Our previous report indicated the photodegradability of NX and the protective effects of some antioxidants on its photodegradation, therefore, this compound was used as a model compound. The degradation of NX in aqueous media during ultraviolet light (UV) irradiation and the protective effects of selected amino acids were monitored through high-performance liquid chromatography (HPLC), equipped with a reverse-phase column. Addition of cysteine, tryptophan, and tyrosine induced the significant suppression of NX photodegradation after UV irradiation for 3 h (residual amount of NX; 15.35%, 6.82%, and 15.64%, respectively). Evaluation of the antioxidative activity and UV absorption spectrum showed that cysteine suppressed NX degradation through its antioxidative ability, while tryptophan and tyrosine suppressed it through their UV filtering ability. Furthermore, three amino acids at higher concentrations (more than 100 µmol/L) showed more protective effects on NX photodegradation. For 10 mmol/L, residual amounts of NX with cysteine, tryptophan, and tyrosine were 58.51%, 69.34%, and 82.40%, respectively. These results showed the importance of both photoprotective potencies (antioxidative potency and UV filtering potency) and stability to UV irradiation for a good photostabilizer of photosensitive pharmaceuticals.

| S-EPMC7345999 | biostudies-literature

Pruned Machine Learning Models to Predict Aqueous Solubility.

Project description:Solubility is a key metric for therapeutic compounds. Conversely, insoluble compounds cloud the accuracy of assays at all stages of chemical biology and drug discovery. Herein, we disclose naïve Bayesian classifier models to predict aqueous solubility. Publicly accessible aqueous solubility data were used to create two full, or nonpruned, training sets. These two sets were also combined to create a full fused set, and a training set comprised of a literature collation of solubility data was also considered as a reference. We tested different extents of data pruning on the training sets and constructed machine learning models that were evaluated with two independent, external test sets that contained compounds that were different from the training sets. The best pruned and fused model was significantly more accurate, in comparison to either the full model or the full fused model, with the prediction of these external test sets. By carefully removing data from the training set, less information can be used to create more accurate machine learning models for aqueous solubility. This knowledge and the curated training sets should prove useful to future machine learning approaches.

| S-EPMC7364544 | biostudies-literature

Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction.

Project description:Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications. Despite efforts made over decades, there are still challenges associated with developing a solubility prediction model with satisfactory accuracy for many of these applications. The goals of this study are to assess current deep learning methods for solubility prediction, develop a general model capable of predicting the solubility of a broad range of organic molecules, and to understand the impact of data properties, molecular representation, and modeling architecture on predictive performance. Using the largest currently available solubility data set, we implement deep learning-based models to predict solubility from the molecular structure and explore several different molecular representations including molecular descriptors, simplified molecular-input line-entry system strings, molecular graphs, and three-dimensional atomic coordinates using four different neural network architectures-fully connected neural networks, recurrent neural networks, graph neural networks (GNNs), and SchNet. We find that models using molecular descriptors achieve the best performance, with GNN models also achieving good performance. We perform extensive error analysis to understand the molecular properties that influence model performance, perform feature analysis to understand which information about the molecular structure is most valuable for prediction, and perform a transfer learning and data size study to understand the impact of data availability on model performance.

| S-EPMC9096921 | biostudies-literature

Carbon Dioxide Solubility in Nonionic Deep Eutectic Solvents Containing Phenolic Alcohols.

Project description:Deep eutectic solvents (DES) are a new class of green solvents that have shown unique properties in several process applications. This study evaluates nonionic DES containing phenolic alcohols as solvents for carbon dioxide (CO2) capture applications. Potential phenolic alcohols and the molar ratio between DES constituents were preselected for experimental investigations based on the conductor-like screening model for realistic solvation (COSMO-RS). CO2 solubility was experimentally determined in two different DES, namely, L-menthol/thymol in 1:2 molar ratio and thymol/2,6-xylenol in 1:1 molar ratio, at various temperatures and pressures. CO2 solubility in the studied systems was higher than that reported in the literature for ionic DES and ionic liquids. This study demonstrates that nonionic DES containing phenolic alcohols can be excellent, inexpensive, and simple solvents for CO2 capture.

| S-EPMC8980276 | biostudies-literature

Solubility Characteristics of Acetaminophen and Phenacetin in Binary Mixtures of Aqueous Organic Solvents: Experimental and Deep Machine Learning Screening of Green Dissolution Media.

Project description:The solubility of active pharmaceutical ingredients is a mandatory physicochemical characteristic in pharmaceutical practice. However, the number of potential solvents and their mixtures prevents direct measurements of all possible combinations for finding environmentally friendly, operational and cost-effective solubilizers. That is why support from theoretical screening seems to be valuable. Here, a collection of acetaminophen and phenacetin solubility data in neat and binary solvent mixtures was used for the development of a nonlinear deep machine learning model using new intuitive molecular descriptors derived from COSMO-RS computations. The literature dataset was augmented with results of new measurements in aqueous binary mixtures of 4-formylmorpholine, DMSO and DMF. The solubility values back-computed with the developed ensemble of neural networks are in perfect agreement with the experimental data, which enables the extensive screening of many combinations of solvents not studied experimentally within the applicability domain of the trained model. The final predictions were presented not only in the form of the set of optimal hyperparameters but also in a more intuitive way by the set of parameters of the Jouyban-Acree equation often used in the co-solvency domain. This new and effective approach is easily extendible to other systems, enabling the fast and reliable selection of candidates for new solvents and directing the experimental solubility screening of active pharmaceutical ingredients.

| S-EPMC9781932 | biostudies-literature

Aqueous solutions of surface-active ionic liquids: remarkable alternative solvents to improve the solubility of triterpenic acids and their extraction from biomass.

Project description:Triterpenic acids (TTAs) are well known for their relevant biological properties and have been facing an increasing interest for nutraceutical and pharmaceutical applications. To overcome the concerns associated to the commonly used volatile organic solvents for their extraction from biomass, here we investigate the potential of aqueous solutions of ionic liquids (ILs) as alternative solvents. The solubility of ursolic acid (UA) was firstly determined in several aqueous solutions of ILs (hydrotropes or surface-active) at 30°C to appraise the dissolution phenomenon. Conventional surfactants were also investigated for comparison purposes. The collected data reveal a remarkable enhancement in the solubility of UA (8 orders of magnitude) in surface-active ILs aqueous solutions when compared to pure water. Afterwards, the potential of these ILs aqueous solutions was confirmed by their use in the extraction of TTAs from apple peels. Total extractions yield of TTAs of 2.62 wt.% were obtained using aqueous solutions of surface-active ILs at moderate conditions, overwhelming the extraction yields of 2.48 wt.% obtained with chloroform and 1.37 wt.% with acetone using similar conditions.

| S-EPMC6157723 | biostudies-literature

Conductometric Studies of Formation the Inclusion Complexes of Phenolic Acids with β-Cyclodextrin and 2-HP-β-Cyclodextrin in Aqueous Solutions.

Project description:An attempt was made to evaluate the possibility of creating and assessing the stability of inclusion complexes of selected phenolic acids [trans-4-hydroxycinnamic acid (trans-p-coumaric acid), trans-3,4-dihydroxycinnamic acid (trans-caffeic acid), trans-4-hydroxy-3-methoxycinnamic acid, (trans-ferulic acid) and trans-3-phenylacrylic acid (trans-cinnamic acid)] with β-cyclodextrin and 2-HP-β-cyclodextrin in aqueous solutions in a wide temperature range 283.15 K-313.15 K. On the basis of the values of the limiting molar conductivity (ΛCDNaDod), calculated from the experimental data, the values of the formation constants and the thermodynamic functions of formation (standard enthalpy, entropy, and Gibs standard enthalpy) of the studied complexes were determined. It has been found that the stability of the studied complexes increases with lowering of the molar mass of cyclodextrin and lowering of the temperature.

| S-EPMC9822027 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data