Non-Linear Quantitative Structure⁻Activity Relationships Modelling, Mechanistic Study and In-Silico Design of Flavonoids as Potent Antioxidants

Flavonoid as a Potent Antioxidant: Quantitative Structure-Activity Relationship Analysis, Mechanism Study, and Molecular Design by Synergizing Molecular Simulation and Machine Learning

In this work, a quantitative structure-antioxidant activity relationship of flavonoids was performed using a machine learning (ML) method. To achieve lipid-soluble, highly antioxidant flavonoids, 398 molecular structures with various substitute groups were designed based on the flavonoid skeleton. The hydrogen dissociation energies (ΔG1, ΔG2, and ΔG3) related to multiple hydrogen atom transfer processes and the solubility parameter (δ) of flavonoids were calculated using molecular simulation. The group decomposition results and the calculated antioxidant parameters constituted the ML data set. The artificial neural network and random forest models were constructed to predict and analyze the contribution of the substitute groups and positions to the antioxidant activity. The results showed the hydroxyl group at positions B4', B5', and B6' and the branched alkyl group at position C3 in the flavonoid skeleton were the optimal choice for improving antioxidant activity and compatibility with apolar organic materials. Compared to the pyrogallol group-grafted flavonoid, the designed potent flavonoid decreased ΔG1 and δ by 2.2 and 15.1%, respectively, while ΔG2 and ΔG3 kept the favorable lower values. These findings suggest that an efficient flavonoid prefers multiple ortho-phenolic hydroxyl groups and suitable sites with hydrophobic groups. The combination of molecular simulation and the ML method may offer a new research approach for the molecular design of novel antioxidants.

Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling

Fungicide mixtures are an effective strategy in delaying the development of fungicide resistance. In this research, a fixed ratio ray design method was used to generate fifty binary mixtures of five fungicides with diverse modes of action. The interaction of these mixtures was then analyzed using CA and IA models. QSAR modeling was conducted to assess their fungicidal activity through multiple linear regression (MLR), support vector machine (SVM), and artificial neural network (ANN). Most mixtures exhibited additive interaction, with the CA model proving more accurate than the IA model in predicting fungicidal activity. The MLR model showed a good linear correlation between selected theoretical descriptors by the genetic algorithm and fungicidal activity. However, both ML-based models demonstrated better predictive performance than the MLR model. The ANN model showed slightly better predictability than the SVM model, with R2 and R2cv at 0.91 and 0.81, respectively. For external validation, the R2test value was 0.845. In contrast, the SVM model had values of 0.91, 0.78, and 0.77 for the same metrics. In conclusion, the proposed ML-based model can be a valuable tool for developing potent fungicidal mixtures to delay fungicidal resistance emergence.

Comparative Study of Machine Learning-Based QSAR Modeling of Anti-inflammatory Compounds from Durian Extraction

Quantitative structure-activity relationship (QSAR) analysis, an in silico methodology, offers enhanced efficiency and cost effectiveness in investigating anti-inflammatory activity. In this study, a comprehensive comparative analysis employing four machine learning algorithms (random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and artificial neural networks (ANNs)) was conducted to elucidate the activities of naturally derived compounds from durian extraction. The analysis was grounded in the exploration of structural attributes encompassing steric and electrostatic properties. Notably, the nonlinear SVR model, utilizing five key features, exhibited superior performance compared to the other models. It demonstrated exceptional predictive accuracy for both the training and external test datasets, yielding R2 values of 0.907 and 0.812, respectively; in addition, their RMSE resulted in 0.123 and 0.097, respectively. The study outcomes underscore the significance of specific structural factors (denoted as shadow ratio, dipole z, methyl, ellipsoidal volume, and methoxy) in determining anti-inflammatory efficacy. Thus, the findings highlight the potential of molecular simulations and machine learning as alternative avenues for the rational design of novel anti-inflammatory agents.

The Management of Diabetes with Hyperuricemia: Can We Hit Two Birds with One Stone?

Serum urate (SU) is an independent predictor for the incidence of diabetes. In current diabetes treatment regimens, there is insufficient appreciation of the importance of hyperuricemia (HU) in disease control and prevention. To summarize the updated knowledge on the effects of SU on β-cell function, insulin resistance and chronic diabetic complications, as well as to evaluate the management of patients with both HU and diabetes, we searched the MEDLINE PubMed database, and included 285 journal articles. An inverted U-shaped relationship between fasting plasma glucose and SU levels was established in this review. Elevated SU levels may enhance the development of chronic diabetic complications, including macrovascular and microvascular dysfunction. Diet and exercise are essential parts of the lifestyle changes necessary for HU and diabetes management. Glucose- and urate-lowering drug selection and combination should be made with the principle of ameliorating, and at least not deteriorating, diabetes and HU. Medical artificial intelligence technology and monitoring systems can help to improve the effectiveness of long-term management of HU and diabetes through digital healthcare. This study comprehensively reviews and provides a scientific and reliable basis for and viewpoints on the clinical management of diabetes and HU.

Enantioselective Synthesis of Aza-Flavanones with an All-Carbon Quaternary Stereocenter via NHC-Catalyzed Intramolecular Annulation

An enantioselective synthesis of functionalized aza-flavanone derivatives using the N-heterocyclic carbene-catalyzed intramolecular Stetter reaction of sulphoamido benzaldehydes has been reported. This procedure presents the first original approach for synthesizing chiral functionalized flavonoids at the 3-position, containing an all-carbon quaternary stereogenic center. This advancement significantly enriches the chemical toolbox for the preparation of complex nitrogen-containing compounds and opens up new avenues for further research and development in synthetic organic chemistry.

Rational Design of a Low-Data Regime of Pyrrole Antioxidants for Radical Scavenging Activities Using Quantum Chemical Descriptors and QSAR with the GA-MLR and ANN Concepts

A series of pyrrole derivatives and their antioxidant scavenging activities toward the superoxide anion (O₂^•-), hydroxyl radical (^•OH), and 1,1-diphenyl-2-picryl-hydrazyl (DPPH^•) served as the training data sets of a quantitative structure-activity relationship (QSAR) study. The steric and electronic descriptors obtained from quantum chemical calculations were related to the three O₂^•-, ^•OH, and DPPH^• scavenging activities using the genetic algorithm combined with multiple linear regression (GA-MLR) and artificial neural networks (ANNs). The GA-MLR models resulted in good statistical values; the coefficient of determination (R²) of the training set was greater than 0.8, and the root mean square error (RMSE) of the test set was in the range of 0.3 to 0.6. The main molecular descriptors that play an important role in the three types of antioxidant activities are the bond length, HOMO energy, polarizability, and AlogP. In the QSAR-ANN models, a good R² value above 0.9 was obtained, and the RMSE of the test set falls in a similar range to that of the GA-MLR models. Therefore, both the QSAR GA-MLR and QSAR-ANN models were used to predict the newly designed pyrrole derivatives, which were developed based on their starting reagents in the synthetic process.

Quantum-mechanical characteristics of apigenin: Antiradical, metal chelation and inhibitory properties in physiologically relevant media

Density functional theory was used to examine the antioxidant activity of apigenin. All protonated species that are present in a non-negligible population at physiological pH were considered in the study. The ability to scavenge the hydroperoxide radical was evaluated in lipid and aqueous environments. The capacity to halt the Fenton reaction by chelating Fe(III) and Cu(II) ions was also investigated, as was the ability to inhibit xanthine oxidase. The results indicate that these activities may be particularly important in describing the beneficial effects of apigenin, especially because of its lower anti-•OOH potential than Trolox or vitamin C. The findings underscore the significant role of dianion in the antiradical and chelating properties, despite its presence in much lower molar fractions than other ions.

Modulation Effect on Tubulin Polymerization, Cytotoxicity and Antioxidant Activity of 1H-Benzimidazole-2-Yl Hydrazones

1H-benzimidazol-2-yl hydrazones with varying hydroxy and methoxy phenyl moieties were designed. Their effect on tubulin polymerization was evaluated in vitro on porcine tubulin. The compounds elongated the nucleation phase and slowed down the tubulin polymerization comparably to nocodazole. The possible binding modes of the hydrazones with tubulin were explored by molecular docking at the colchicine binding site. The anticancer activity was evaluated against human malignant cell lines MCF-7 and AR-230, as well as against normal fibroblast cells 3T3 and CCL-1. The compounds demonstrated a marked antineoplastic activity in low micromolar concentrations in both screened in vitro tumor models. The most active were the trimethoxy substituted derivative 1i and the positional isomers 1j and 1k, containing hydroxy and methoxy substituents: they showed IC₅₀ similar to the reference podophyllotoxin in both tumor cell lines, accompanied with high selectivity towards the malignantly transformed cells. The compounds exerted moderate to high ability to scavenge peroxyl radicals and certain derivatives-1l containing metha-hydroxy and para-methoxy group, and 1b-e with di/trihydroxy phenyl moiety, revealed HORAC values high or comparable to those of well-known phenolic antioxidants. Thus the 1H-benisimidazol-2-yl hydrazones with hydroxy/methoxy phenyl fragments were recognized as new agents exhibiting promising combined antioxidant and antineoplastic action.

A DFT study on OH radical scavenging activities of eriodictyol, Isosakuranetin and pinocembrin

Flavonoids are natural compounds with antioxidant properties that have positive effects on human health, which reduce toxic effects of reactive oxygen species (ROS) and partially oxidative damage. In the work, the density functional theory (DFT/BMK) calculations were performed for antioxidant activity evaluation of pinocembrin (P), isosakuranetin (I) and eriodictyol (E). Four main mechanisms were examined: hydrogen atom transfer (HAT), radical adduct formation (RAF), single electron transfer-proton transfer (SET-PT) and Sequential proton loss electron transfer (SPLET). HAT and SPLET are thermodynamically the most probable process in gas phase and water. The three flavonoids examined + •OH HAT and RAF mechanisms for each possible location were investigated theoretically for the first time. The results were discussed by considering thermodynamic, kinetic and structural data of various reaction paths using IRC approach.Communicated by Ramaswamy H. Sarma.

Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants

In this study, a total of 302 molecular structures of phenylnaphthylamine antioxidants based on N-phenyl-1-naphthylamine and N-phenyl-2-naphthylamine skeletons with various substituents were modeled by exhaustive methods. Antioxidant parameters, including the hydrogen dissociation energy, solubility parameter, and binding energy, were calculated through molecular simulations. Then, a group decomposition scheme was determined to decompose 302 antioxidants. The antioxidant parameters and decomposition results constituted machine-learning data sets. Using an artificial neural network model, a correlation coefficient between the predicted and true values above 0.88 and an average relative error within 6% were achieved. Random forest models were used to analyze the factors affecting antioxidant activity from chemical and physical perspectives; the results showed that amino and alkyl groups were conducive to improving antioxidant performance. Moreover, substituent positions 1, 7, and 10 of N-phenyl-1-naphthylamine and 3, 7, and 10 of N-phenyl-2-naphthylamine were found to be the optimal positions for modifications to improve antioxidant activity. Two potentially efficient phenylnaphthylamine antioxidant structures were proposed and their antioxidant parameters were also calculated; the hydrogen dissociation energy and solubility parameter decreased by more than 9% and 7%, respectively, whereas the binding energy increased by more than 16% compared with the benchmark of N-phenyl-1-naphthylamine. These results indicate that molecular simulation and machine learning could provide alternative tools for the molecular design of new antioxidants.

Influence of Molecular Structures on Fluorescence of Flavonoids and Their Detection in Mammalian Cells

Flavonoids are being increasingly applied for the treatment of various diseases due to their anti-cancer, anti-oxidant, anti-inflammatory, and anti-viral properties. However, it is often challenging to detect their presence in cells and tissues through bioimaging, as most of them are not fluorescent or are too weak to visualize. Here, fluorescence possibilities of nine naturally occurring analogous flavonoids have been investigated through UV/visible spectroscopy, molecular structure examination, fluorescent images in mammalian cells and their statistical analysis employing aluminum chloride and diphenylboric acid 2-aminoethyl ester as fluorescence enhancers. It is found that, in order to form a stable fluorescent complex with an enhancer, flavonoids should have a keto group at C4 position and at least one -OH group at C3 or C5 position. Additionally, the presence of a double bond at C2–C3 can stabilize extended quinonoid structure at the cinnamoyl moiety, which thereby enhances the complex stability. A possible restriction to the free rotation of ring B around C1′–C2 single bond can contribute to the further enhancement of fluorescence. Thus, these findings can act as a guide for distinguishing flavonoids capable of exhibiting fluorescence from thousands of their analogues. Finally, using this technique, flavonoids are detected in neuroblastoma cells and their time course assay is conducted via fluorescence imaging. Their cellular uptake efficiency is found to be high and differential in nature and their distribution throughout the cytoplasm is clearly detected.

Current Trends in Computational Quantum Chemistry Studies on Antioxidant Radical Scavenging Activity

The antioxidative nature of chemicals is now routinely studied using computational quantum chemistry. Scientists are constantly proposing new approaches to investigate those methods, and the subject is evolving at a rapid pace. The goal of this review is to collect, consolidate, and present current trends in a clear, methodical, and reference-rich manner. This paper is divided into several sections, each of which corresponds to a different stage of elaborations: preliminary concerns, electronic structure analysis, and general reactivity (thermochemistry and kinetics). The sections are further subdivided based on methodologies used. Concluding remarks and future perspectives are presented based on the remaining elements.

Radical Scavenging Mechanisms of Phenolic Compounds: A Quantitative Structure-Property Relationship (QSPR) Study

Due to their antioxidant properties, secondary plant metabolites can scavenge free radicals such as reactive oxygen species and protect foods from oxidation processes. Our aim was to study structural influences, like basic structure, number of hydroxyl groups and number of Bors criteria on the outcome of the oxygen radical absorbance capacity (ORAC) assay. Furthermore, similarities and differences to other in vitro antioxidant assays were analyzed by principal component analysis. Our studies confirmed that the antioxidant behavior in the ORAC assay is dominated by the number and types of substituents and not by the Bors criteria, as long as no steric hindrance occurs. For example, morin (MOR) with five hydroxyl groups and two Bors criteria reached an area under the curve of (3.64 ± 0.08) × 105, which was significantly higher than quercetin-7-D-glucoside (QGU7) (P < 0.001), and thus the highest result. Principal component analysis showed different dependencies regarding structural properties of Folin-Ciocalteu (FC)- and 2,2-diphenyl-1-picrylhydrazyl (DPPH)-assays or 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS)- and ORAC-assays, respectively. Therefore, we conclude that they are based on different reaction mechanisms. The number of hydroxyl groups showed a stronger influence on the antioxidant activity than the Bors criteria. Due to these differences, the correlation of these rapid tests to specific applications should be validated.

A Newly Synthesized Flavone from Luteolin Escapes from COMT-Catalyzed Methylation and Inhibits Lipopolysaccharide-Induced Inflammation in RAW264.7 Macrophages via JNK, p38 and NF-κB Signaling Pathways

Luteolin is a common dietary flavone possessing potent anti-inflammatory activities. However, when administrated in vivo, luteolin becomes methylated by catechol-O-methyltransferases (COMT) owing to the catechol ring in the chemical structure, which largely diminishes its anti-inflammatory effect. In this study, we made a modification on luteolin, named LUA, which was generated by the chemical reaction between luteolin and 2,2'-azobis(2-amidinopropane) dihydrochloride (AAPH). Without a catechol ring in the chemical structure, this new flavone could escape from the COMT-catalyzed methylation, thus affording the potential to exert its functions in the original form when administrated in the organism. Moreover, an LPS-stimulated RAW cell model was applied to detect the anti-inflammatory properties. LUA showed much more superior inhibitory effect on LPS-induced production of NO than diosmetin (a major methylated form of luteolin) and significantly suppressed upregulation of iNOS and COX-2 in macrophages. LUA treatment dramatically reduced LPS-stimulated reactive oxygen species (ROS) and mRNA levels of pro-inflammatory mediators such as IL-1β, IL-6, IL-8 and IFN-β. Furthermore, LUA significantly reduced the phosphorylation of JNK and p38 without affecting that of ERK. LUA also inhibited the activation of NF-κB through suppression of p65 phosphorylation and nuclear translocation.

Quo vadis Cardiac Glycoside Research?

Cardiac glycosides (CGs), toxins well-known for numerous human and cattle poisoning, are natural compounds, the biosynthesis of which occurs in various plants and animals as a self-protective mechanism to prevent grazing and predation. Interestingly, some insect species can take advantage of the CG’s toxicity and by absorbing them, they are also protected from predation. The mechanism of action of CG’s toxicity is inhibition of Na⁺/K⁺-ATPase (the sodium-potassium pump, NKA), which disrupts the ionic homeostasis leading to elevated Ca²⁺ concentration resulting in cell death. Thus, NKA serves as a molecular target for CGs (although it is not the only one) and even though CGs are toxic for humans and some animals, they can also be used as remedies for various diseases, such as cardiovascular ones, and possibly cancer. Although the anticancer mechanism of CGs has not been fully elucidated, yet, it is thought to be connected with the second role of NKA being a receptor that can induce several cell signaling cascades and even serve as a growth factor and, thus, inhibit cancer cell proliferation at low nontoxic concentrations. These growth inhibitory effects are often observed only in cancer cells, thereby, offering a possibility for CGs to be repositioned for cancer treatment serving not only as chemotherapeutic agents but also as immunogenic cell death triggers. Therefore, here, we report on CG’s chemical structures, production optimization, and biological activity with possible use in cancer therapy, as well as, discuss their antiviral potential which was discovered quite recently. Special attention has been devoted to digitoxin, digoxin, and ouabain.

Growth, survival, and metabolic activities of probiotics Lactobacillus rhamnosus GG and Saccharomyces cerevisiae var. boulardii CNCM-I745 in fermented coffee brews

Amidst rising demand for non-dairy probiotic foods, and growing interest in coffees with added functionalities, it would be opportune to ferment coffee brews with probiotics. However, challenges exist in maintaining probiotic viability in high-moisture food products. Here, we aimed to enhance the viability of the probiotic bacteria, Lactobacillus rhamnosus GG, in coffee brews by co-culturing with the probiotic yeast, Saccharomyces cerevisiae var. boulardii CNCM-I745. The yeast significantly enhanced the viability of L. rhamnosus GG, as bacterial populations beyond 7 Log CFU/mL were maintained throughout 14 weeks of storage at 4 and 25 °C. In contrast, the single culture of L. rhamnosus GG suffered viability losses below 6 Log CFU/mL within 10 weeks at 4 °C, and 3 weeks at 25 °C. Growth and survival of S. boulardii CNCM-I745 remained unaffected by the presence of L. rhamnosus GG. Volatile profiles of coffee brews were altered by probiotic metabolic activities, but co-culturing led to suppressed generation of diacetyl and ethanol compared to single cultures. Probiotic fermentation did not alter principal coffee bioactive compounds and antioxidant capacities; however, declines in peroxyl radical scavenging capacities were observed after ambient storage. Overall, we illustrate that yeasts are effective in enhancing probiotic bacterial viability in coffee brews, which may be useful in developing shelf stable probiotic food products.

Comparative chemometric and quantitative structure-retention relationship analysis of anisotropic lipophilicity of 1-arylsuccinimide derivatives determined in high-performance thin-layer chromatography system with aprotic solvents

Numerous structurally different amides and imides including succinimide derivatives exhibit diverse bioactive potential. The development of new compounds requires rationalization in the design in order to provide structural changes that guarantee favorable physico-chemical properties, pharmacological activity and safety. In the present research, a comprehensive study with comparison of the chromatographic lipophilicity and other physico-chemical properties of five groups of 1-arylsuccinimide derivatives was conducted. The chemometric analysis of their physico-chemical properties was carried out by using unsupervised (hierarchical cluster analysis and principal component analysis) and supervised pattern recognition methods (linear discriminant analysis), while the correlations between the in silico molecular features and chromatographic lipophilicity were examined applying linear and non-linear Quantitative Structure-Retention Relationship (QSRR) approaches. The main aim of the conducted research was to determine similarities and dissimilarities among the studied 1-arylsuccinimides, to point out the molecular features which have significant influence on their lipophilicity, as well as to establish high-quality QSRR models which can be used in prediction of chromatographic lipophilicity of structurally similar 1-arylsuccinimides. This study is a continuation of analysis and determination of the physico-chemical properties of 1-arylsuccinimides which could be important guidelines in further in vitro and eventually in vivo studies of their biological potential.

Digital Pharmaceutical Sciences

Artificial intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, and the continuous developments in machine learning algorithms have resulted in a rapid increase in new machine learning applications in different areas of pharmaceutical sciences. This review summarizes the past, present, and potential future impacts of machine learning technologies on different areas of pharmaceutical sciences, including drug design and discovery, preformulation, and formulation. The machine learning methods commonly used in pharmaceutical sciences are discussed, with a specific emphasis on artificial neural networks due to their capability to model the nonlinear relationships that are commonly encountered in pharmaceutical research. AI and machine learning technologies in common day-to-day pharma needs as well as industrial and regulatory insights are reviewed. Beyond traditional potentials of implementing digital technologies using machine learning in the development of more efficient, fast, and economical solutions in pharmaceutical sciences are also discussed.

Mechanistic Chromatographic Column Characterization for the Analysis of Flavonoids Using Quantitative Structure-Retention Relationships Based on Density Functional Theory

This work aimed to unravel the retention mechanisms of 30 structurally different flavonoids separated on three chromatographic columns: conventional Kinetex C18 (K-C18), Kinetex F5 (K-F5), and IAM.PC.DD2. Interactions between analytes and chromatographic phases governing the retention were analyzed and mechanistically interpreted via quantum chemical descriptors as compared to the typical 'black box' approach. Statistically significant consensus genetic algorithm-partial least squares (GA-PLS) quantitative structure retention relationship (QSRR) models were built and comprehensively validated. Results showed that for the K-C18 column, hydrophobicity and solvent effects were dominating, whereas electrostatic interactions were less pronounced. Similarly, for the K-F5 column, hydrophobicity, dispersion effects, and electrostatic interactions were found to be governing the retention of flavonoids. Conversely, besides hydrophobic forces and dispersion effects, electrostatic interactions were found to be dominating the IAM.PC.DD2 retention mechanism. As such, the developed approach has a great potential for gaining insights into biological activity upon analysis of interactions between analytes and stationary phases imitating molecular targets, giving rise to an exceptional alternative to existing methods lacking exhaustive interpretations.

Quantitative Structure-Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order

In this work, we employed a non-linear programming (NLP) approach via quantitative structure-retention relationships (QSRRs) modelling for prediction of elution order in reversed phase-liquid chromatography. With our rapid and efficient approach, error in prediction of retention time is sacrificed in favor of decreasing the error in elution order. Two case studies were evaluated: (i) analysis of 62 organic molecules on the Supelcosil LC-18 column; and (ii) analysis of 98 synthetic peptides on seven reversed phase-liquid chromatography (RP-LC) columns with varied gradients and column temperatures. On average across all the columns, all the chromatographic conditions and all the case studies, percentage root mean square error (%RMSE) of retention time exhibited a relative increase of 29.13%, while the %RMSE of elution order a relative decrease of 37.29%. Therefore, sacrificing %RMSE(tR) led to a considerable increase in the elution order predictive ability of the QSRR models across all the case studies. Results of our preliminary study show that the real value of the developed NLP-based method lies in its ability to easily obtain better-performing QSRR models that can accurately predict both retention time and elution order, even for complex mixtures, such as proteomics and metabolomics mixtures.