•Nanocomposites of multiwalled carbon nanotube (MWCNT)/Biosynthesised gold nanoparticles (AuNPs) have been employed in the design of a picomolar sensor for xylitol detection.•Chemically reactivity of ...the analyte confirmed by HOMO-LUMO plots obtained from density functional theory calculations.•Monte Carlo and molecular dynamics simulations of GCE/MWCNT/AuNPs-xylitol complex correlated with the experimental CV and EIS results.•ANN machine learning was used to prediction of the voltametric signal with good accuracy.•An excellent detection limit of 9.8 × 10−6 pM was obtained at the designed sensor and a good practicability with satisfactory recoveries of 97–100% with 2.83–3.33% RSDs in chewing gum.
A novel sensor was proposed for the detection of xylitol in sugar free chewing gum using Au nanoparticles (NPs) derived from Callistemon viminalis leaf extract coupled with multiwalled carbon nanotubes (MWCNTs) doped onto glassy carbon electrode (GCE). In comparison to the bare GCE, the modified GCE/MWCNT/AuNPs sensor showed about 45-fold better electrochemical response to xylitol. Under the optimal conditions, the designed sensor achieved a detection limit of 9.8 × 10−6 pM for concentrations ranging from 9.9 × 10−6 to 2.9 × 10−5 pM. The practicability was tested on sugar-free sample yielding recoveries of 97–100% with RSDs of 2.83–3.33%. Machine learning (ML) was used to predict changes in voltammetric signal with changing potential over time demonstrating the fundamental knowledge of the electrochemical reaction. The performance of the Artificial Neural Network (ANN) provides good accuracy and precision in predicting the intensity (I) along with repeated ANN runs, with a mean square error (MSE) of 0.007 (± 0.002) and a determination coefficient (R2) of 0.9992 ± 0.0006. Additionally, the interaction of xylitol on the electrode surfaces were investigated using Monte Carlo adsorption studies and 1000 ps Molecular Dynamics simulations under NVT conditions. According to the frontier molecular orbitals obtained through Density Functional Theory calculations, the reactive sites of xylitol occur at the hydroxyl group on the second carbon. Using complementary measurement techniques, this new strategy exhibits a great potential for rapid detection of xylitol in food and dental products.
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Many of the currently available drugs are chiral compounds that are marketed as racemates or, to a lesser extent, in the form of one of the enantiomers since a pair of enantiomers may have different ...toxicological and ecotoxicological properties compared to each other. The evaluation of enantioselectivity in biodegradation processes is essential for environmental risk assessment. The objective of this research is to study the enantioselectivity in the biodegradation of two common chiral drugs, citalopram and verapamil, using highly sulphated-γ-cyclodextrin (HS-γ-CD) as chiral selector in Capillary Electrophoresis. Biodegradation experiments were performed in batch mode using a minimal salt medium inoculated with an activated sludge and supplemented with the corresponding enantiomeric mixture. The cultures were incubated at 20 °C for 28 days. Abiotic degradation of verapamil and citalopram enantiomers was also assessed. The concentration of the enantiomers of verapamil and citalopram were monitored using 0.7% and 0.1% m/v HS-γ-CD solutions as chiral selector, respectively. Separations were carried out using the complete filling technique. The results of biodegradability tests indicate that citalopram could be considered potentially persistent while verapamil is presumed to be a non-persistent compound. No evidence of enantioselectivity was observed in any of the biodegradation processes.
This study addresses the need for accurate structural data regarding the toxicity of fragrances in sanitizers and disinfectants. We compare the predictive and descriptive (model stability) potential ...of multiple linear regression (MLR) and partial least squares (PLS) models optimized through variable selection (VS). A novel hybrid chaotic neural network algorithm with competitive learning (CCLNNA)–PLS modeling strategy can offer specific optimization with satisfactory results, even for a limited dataset. While also exploring the preliminary comparative analysis, the goal is to introduce an adapted novel CCLNNA optimization strategy for VS, inspired by neural networks, along with exploring the influence of the percentage of significant descriptors in the optimization function to enhance the final model's capabilities.
We analyzed an available dataset of 24 molecules, incorporating ADMET and PaDEL descriptors as predictor variables, to explore the relationship between the response/target variable (pLC50) and the meticulously optimized set of descriptors. The suitability of the selected PLS models (cross- and external-validated accuracy combined with percentage of significant descriptors at a level equal to or >80 %) underscores the importance of expanding the dataset to amplify the validation protocols, thus enhancing future model reliability and environmental impact.
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•New Chaotic Neural Network Algorithm with Competitive Learning (CCLNNA)-PLS modeling proposed•Optimized PLS models for fragrance toxicity prediction from structural data•Comparative stability of PLS models influenced by significant descriptors in optimization•Enhanced toxicity prediction through dataset expansion to address environmental impact demands
The interaction of ten natural polyphenolic compounds (chlorogenic acid, apigenin, catechin, epicatechin, flavanone, flavone, quercetin, rutin, vicenin-2 and vitexin) with human serum albumin and ...mixtures of human serum albumin and α
1
-acid glycoprotein under near physiological conditions is studied by capillary electrophoresis–frontal analysis. Furthermore, the binding of these polyphenolic compounds to total plasmatic proteins is evaluated using ultrafiltration and capillary electrophoresis. In spite of the relatively small differences in the chemical structures of the compounds studied, large differences were observed in their binding behaviours to plasmatic proteins. The hydrophobicity, the presence/absence of some functional groups, steric hindrance and spatial arrangement seem to be key factors in the affinity of natural polyphenols towards plasmatic proteins.
The stereoselective binding of the frequently ingested nutraceutical (±)-catechin, with demonstrated differential biological activity between enantiomers, to human serum albumin (HSA), with the ...largest complexation and enantioselectivity potential among the plasmatic proteins, is studied by combining simulations to optimize the experimental design, robust in vitro electrokinetic chromatographic data, and molecular docking–chiral recognition estimates. Methodological and mathematical drawbacks in previous reports on (±)-catechin–HSA are detected and eliminated. Recent and novel direct equations extracted from the classical interaction model allows advantageous univariate mathematical data treatment, providing the first evidence of quantitative (±)-catechin–HSA enantioselectivity. Also, the binding site in HSA of the enantiomers is approached, and both the experimental enantioselectivity and the main binding site information are contrasted with a molecular docking approach.
The selection of suitable combinations of chiral stationary phases (CSPs) and mobile phases (MPs) for the enantioresolution of chiral compounds is a complex issue that often requires considerable ...experimental effort and can lead to significant waste. Linking the structure of a chiral compound to a CSP/MP system suitable for its enantioseparation can be an effective solution to this problem. In this study, we evaluate algorithmic tools for this purpose. Our proposed consensus model, which uses multiple optimized artificial neural networks (ANNs), shows potential as an intelligent recommendation system (IRS) for ranking chromatographic systems suitable for the enantioresolution of chiral compounds with different molecular structures. To evaluate the IRS potential in a proof-of-concept stage, 56 structural descriptors for 56 structurally unrelated chiral compounds across 14 different families are considered. Chromatographic systems under study comprise 7 cellulose and amylose derivative CSPs and acetonitrile or methanol aqueous MPs (14 chromatographic systems in all). The ANNs are optimized using a fit-for-purpose version of the chaotic neural network algorithm with competitive learning (CCLNNA), a novel approach not previously applied in the chemical domain. CCLNNA is adapted to define the inner ANN complexity and perform feature selection of the structural descriptors. A customized target function evaluates the correctness of recommending the appropriate CSP/MP system. The ANN-consensus model exhibits no advisory failures and requires only an experimental attempt to verify the IRS recommendation for complete enantioresolution. This outstanding performance highlights its potential to effectively resolve this problem.The selection of suitable combinations of chiral stationary phases (CSPs) and mobile phases (MPs) for the enantioresolution of chiral compounds is a complex issue that often requires considerable experimental effort and can lead to significant waste. Linking the structure of a chiral compound to a CSP/MP system suitable for its enantioseparation can be an effective solution to this problem. In this study, we evaluate algorithmic tools for this purpose. Our proposed consensus model, which uses multiple optimized artificial neural networks (ANNs), shows potential as an intelligent recommendation system (IRS) for ranking chromatographic systems suitable for the enantioresolution of chiral compounds with different molecular structures. To evaluate the IRS potential in a proof-of-concept stage, 56 structural descriptors for 56 structurally unrelated chiral compounds across 14 different families are considered. Chromatographic systems under study comprise 7 cellulose and amylose derivative CSPs and acetonitrile or methanol aqueous MPs (14 chromatographic systems in all). The ANNs are optimized using a fit-for-purpose version of the chaotic neural network algorithm with competitive learning (CCLNNA), a novel approach not previously applied in the chemical domain. CCLNNA is adapted to define the inner ANN complexity and perform feature selection of the structural descriptors. A customized target function evaluates the correctness of recommending the appropriate CSP/MP system. The ANN-consensus model exhibits no advisory failures and requires only an experimental attempt to verify the IRS recommendation for complete enantioresolution. This outstanding performance highlights its potential to effectively resolve this problem.
•A complex enantioresolution-structure-neural networks study is designed.•Adjusted IA strategy models the enantioresolution of structurally unrelated compounds.•Important structure data for ...separating chiral molecules on a cellulose column.•Surface tension and NHR groups connect with enantioresolution.
Artificial neural networks (ANN; feed-forward mode) are used to quantitatively estimate the enantioresolution (Rs) in cellulose tris(3,5-dimethylphenylcarbamate) of chiral molecules from their structural information. To the best of our knowledge, for the first time, a dataset of structurally unrelated compounds is modelled using ANN, attempting to approach a model of general applicability. After setting a strategy compatible with the data complexity and their relatively limited size (56 molecules), by prefixing initial ANN inner weights and the validation and cross-validation subsets, the ANN optimisation based on a novel quality indicator calculated from 9 ANN outputs allows selecting a proper (predictive) ANN architecture (a single hidden layer of 7 neurons) and performing a forward-stepwise feature selection process (8 variables are selected). Such relatively simple ANN offers reasonable good general performance in predicting Rs (e.g. validation plot statistics: mean squared error = 0.047 and R = 0.98 and 0.92, for all or just the validation molecules, respectively). Finally, a study of the relative importance of the selected variables, combining the estimation from two approaches, suggests that the surface tension (positive overall contribution to Rs) and the –NHR groups (negative overall contribution to Rs) are found to be the main variables explaining the enantioresolution in the current conditions.
•5 cellulose chiral stationary phases and ACN or MeOH hydro-organic mobile phases•56 structurally unrelated basic and neutral chiral compounds•Retention behaviour comparative study•Dual RPLC/HILIC ...retention behaviour even for non-chlorinated chiral stationary phases•Enantioresolution and enantioselectivity comparative study
A comparative study on the retention behaviour and enantioresolution of 54 structurally unrelated neutral and basic compounds using five commercial cellulose-based chiral stationary phases (CSPs) and hydro-organic mobile phases compatible with MS detection is performed. Four phenylcarbamate-type cellulose CSPs (cellulose tris(3,5-dimethylphenylcarbamate), Cell1; cellulose tris(3-chloro-4-methylphenylcarbamate), Cell2; cellulose tris(4-chloro-3-methylphenylcarbamate), Cell4 and cellulose tris(3,5- dichlorophenylcarbamate), Cell5) and one benzoate-type cellulose CSP (cellulose tris(4-methylbenzoate), Cell3) are assayed. Mobile phases consist of binary mixtures of methanol (30–90% MeOH) or acetonitrile (10-98% ACN) with 5 mM ammonium bicarbonate (pH = 8.0).
The existence of reversed phase (RPLC) and hydrophilic interaction liquid chromatography (HILIC) retention behaviour domains is explored. In MeOH/H2O mobile phases, for all compounds and CSPs, the typical RPLC retention behaviour is observed. When using ACN/H2O mobile phases, for all compounds in all CSPs (even in the non-chlorinated CSPs) a U-shaped retention behaviour depending on the ACN/H2O content is observed which indicates the coexistence of the RPLC- (< 80% ACN) and HILIC- (∼80–98% ACN) domains. The magnitude of retention changes in both domains is related to the hydrophobicity of the compound as well as to the nature of the CSP.
The study of the effect of the nature and concentration of the organic solvent, as well as the nature of the CSP on the enantioresolution reveals that: (i) the use of MeOH/H2O or ACN/H2O greatly affects the enantioselectivity and enantioresolution degree of the chromatographic systems, being, in general, better the results obtained with ACN/H2O mobile phases. (ii) The ACN-RPLC-domain provides much better enantioresolution than HILIC-domain. (iii) Cell2, especially with ACN/H2O mobile phases, is the CSP that allows baseline enantioresolution for a higher number of compounds. (iv) Phenylcarbamate-type CSPs do not offer clear complementary enantioselectivity to that of Cell2. (v) Cell3 is the only CSP that provides marked complementary enantioselectivity to that of Cell2, almost orthogonal in MeOH/H2O mobile phases.
Several pharmacokinetic processes are affected by enantioselectivity (ES). At the level of distribution, protein binding (PB) is one of the most important. The enantioselective binding of fluoxetine ...(FLX) to HSA has been evaluated in this work by ultrafiltration of FLX–HSA mixtures and chiral analysis of unbound fractions by EKC‐CD. PB, affinity constants (K) and ES were obtained for both enantiomers of FLX. In order to improve the consistency of the estimations, the evaluation of affinity constants of each enantiomer was performed using two designs, one keeping constant the total concentration of protein and varying the total concentration of the enantiomers, and the other in the opposite way, in both cases via an unusual short‐concentration interval strategy to assure model validity. Different mathematical approaches were compared and characterised and some of them, judged as the most consistent under the experimental conditions used, were selected to provide final estimates. Quality considerations include criteria for three critical aspects: (i) detecting/eliminating outliers, (ii) checking the number of binding sites in the protein and (iii) evaluating the robustness of each approach. The differences on estimates from the selected approaches were used as an uncertainty source to delimit the reported values. The ES of HSA for FLX enantiomers was approximate. Estimates include the assumptions of independent and competitive models. In the last case, a SIMPLEX function was designed capable of simultaneously optimizing the non‐linear binding models for both enantiomers, thus improving the consistence of results.
•Polysaccharide-based columns in chiral RPLC for ibuprofen and ketoprofen enantiomers.•Calculations and models use directly chromatographic peak areas as input variables.•(R)-ibuprofen is ...preferentially biodegraded by an activated sludge.•Biodegradation-time model to anticipate half-life and complete biodegradation times.•Enantiomeric fraction-time model to anticipate the maximum enantioselectivity.
The quantification of the enantiomeric fraction (EF) during the biodegradation process is essential for environmental risk assessment. In this paper the enantioselective biodegradation of ibuprofen, IBU, and ketoprofen, KET, two of the drugs most consumed, was evaluated. Biodegradation experiments were performed in batch mode using a minimal salts medium inoculated with an activated sludge (collected from a Valencian Waste Water Treatment Plant) and supplemented with the racemate of each compound. The inoculum activity was verified using fluoxetine as reference compound. The experimental conditions used (analyte concentration and volume of inoculum) were chosen according to OECD guidelines. In parallel, the optical density at 600 nm was measured to control the biomass growth and to connect it with enantioselectivity. Two RPLC methods for chiral separations of IBU and KET using polysaccharides-based stationary phases were developed. Novel calculations and adapted models, using directly the chromatographic peak areas as dependent variable, were proposed to estimate significant parameters related to the biodegradation process: biodegradation (BD) and EF values at given time, half-life times of (R)- and (S)-enantiomers, number of days to reach a complete BD and the minimum EF expected. The modelled BD and EF curves fitted adequately the data (R2 > 0.94). The use of these new equations provided similar results to those obtained using concentration data. However, the use of chromatographic peak areas data, eliminates the uncertainty associated to the use of the calibration curves. The results obtained in this paper indicate that an enantiorecognition towards IBU enantiomers by the microorganisms present in the activated sludge used in this study occurred, being the biodegradation of (R)-IBU higher than that of (S)-IBU. For KET, non-enantioselective biodegradation was observed.
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