QSAR of the free radical scavenging potency of selected hydroxyanthraquinones

Anthraquinones from Rhamnus alaternus L.: A Phytocosmetic Ingredient with Photoprotective and Antimelanogenesis Properties

The purpose of the present work was to develop a phytocosmetic sunscreen emulsion with antioxidant activity and an anti-melanogenic effect, containing an anthraquinone-enriched extract of Rhamnus alaternus (A.E.). Our findings demonstrated that A.E. decreased the levels of reactive oxygen species, DNA damage, and malondialdehyde induced by UVA in human keratinocytes and melanocytes. Furthermore, the calculated SPF value in vitro of the cream containing A.E. was 14.26±0.152. Later, it was shown that A.E. extract had an inhibitory effect on the amount of melanin. This extract could also reduce B16F10 intracellular tyrosinase activity. Besides, docking studies were carried out to provide a logical justification for the anti-tyrosinase potential. The findings showed that, A.E. may provide protection against UVA-induced oxidative stress and could be thought of as a viable treatment for hyperpigmentation disorders.

Application of Machine Learning Methods to Predict the Air Half-Lives of Persistent Organic Pollutants

Persistent organic pollutants (POPs) are ubiquitous and bioaccumulative, posing potential and long-term threats to human health and the ecological environment. Quantitative structure-activity relationship (QSAR) studies play a guiding role in analyzing the toxicity and environmental fate of different organic pollutants. In the current work, five molecular descriptors are utilized to construct QSAR models for predicting the mean and maximum air half-lives of POPs, including specifically the energy of the highest occupied molecular orbital (HOMO_Energy_DMol3), a component of the dipole moment along the z-axis (Dipole_Z), fragment contribution to SAscore (SAscore_Fragments), subgraph counts (SC_3_P), and structural information content (SIC). The QSAR models were achieved through the application of three machine learning methods: partial least squares (PLS), multiple linear regression (MLR), and genetic function approximation (GFA). The determination coefficients (R2) and relative errors (RE) for the mean air half-life of each model are 0.916 and 3.489% (PLS), 0.939 and 5.048% (MLR), 0.938 and 5.131% (GFA), respectively. Similarly, the determination coefficients (R2) and RE for the maximum air half-life of each model are 0.915 and 5.629% (PLS), 0.940 and 10.090% (MLR), 0.939 and 11.172% (GFA), respectively. Furthermore, the mechanisms that elucidate the significant factors impacting the air half-lives of POPs have been explored. The three regression models show good predictive and extrapolation abilities for POPs within the application domain.

Investigation of the radical scavenging potential of vanillin-based pyrido-dipyrimidines: experimental and in silico approach

Antioxidants have a significant contribution in the cell protection against free radicals which may induce oxidative stress, and permanently damage the cells causing different disorders such as tumors, degenerative diseases, and accelerated aging. Nowadays, a multi-functionalized heterocyclic framework plays an important role in drug development, and it is of great importance in organic synthesis and medicinal chemistry. Encouraged by the bioactivity of the pyrido-dipyrimidine scaffold and vanillin core, herein, we made an effort to thoroughly investigate the antioxidant potential of the vanillin-based pyrido-dipyrimidines A-E to reveal novel promising free radical inhibitors. The structural analysis and the antioxidant action of the investigated molecules were performed in silico by DFT calculations. Studied compounds were screened for their antioxidant capacity using in vitro ABTS and DPPH assays. All the investigated compounds showed remarkable antioxidant activity, especially derivative A exhibiting inhibition of free radicals at the IC₅₀ value (ABTS and DPPH assay 0.1 mg ml^-1 and 0.081 mg ml^-1, respectively). Compound A has higher TEAC values implying its stronger antioxidant activity compared to a trolox standard. The applied calculation method and in vitro tests confirmed that compound A has a strong potential against free radicals and may be a novel candidate for application in antioxidant therapy.

QSPR Modeling and Experimental Determination of the Antioxidant Activity of Some Polycyclic Compounds in the Radical-Chain Oxidation Reaction of Organic Substrates

The present work addresses the quantitative structure−antioxidant activity relationship in a series of 148 sulfur-containing alkylphenols, natural phenols, chromane, betulonic and betulinic acids, and 20-hydroxyecdysone using GUSAR2019 software. Statistically significant valid models were constructed to predict the parameter logk7, where k7 is the rate constant for the oxidation chain termination by the antioxidant molecule. These results can be used to search for new potentially effective antioxidants in virtual libraries and databases and adequately predict logk7 for test samples. A combination of MNA- and QNA-descriptors with three whole molecule descriptors (topological length, topological volume, and lipophilicity) was used to develop six statistically significant valid consensus QSPR models, which have a satisfactory accuracy in predicting logk7 for training and test set structures: R2TR > 0.6; Q2TR > 0.5; R2TS > 0.5. Our theoretical prediction of logk7 for antioxidants AO1 and AO2, based on consensus models agrees well with the experimental value of the measure in this paper. Thus, the descriptor calculation algorithms implemented in the GUSAR2019 software allowed us to model the kinetic parameters of the reactions underlying the liquid-phase oxidation of organic hydrocarbons.

Machine Learning for Evaluating the Cytotoxicity of Mixtures of Nano-TiO2 and Heavy Metals: QSAR Model Apply Random Forest Algorithm after Clustering Analysis

With the development and application of nanomaterials, their impact on the environment and organisms has attracted attention. As a common nanomaterial, nano-titanium dioxide (nano-TiO₂) has adsorption properties to heavy metals in the environment. Quantitative structure-activity relationship (QSAR) is often used to predict the cytotoxicity of a single substance. However, there is little research on the toxicity of interaction between nanomaterials and other substances. In this study, we exposed human renal cortex proximal tubule epithelial (HK-2) cells to mixtures of eight heavy metals with nano-TiO₂, measured absorbance values by CCK-8, and calculated cell viability. PLS and two ensemble learning algorithms are used to build multiple QSAR models for data sets, and the test set R² is increased from 0.38 to 0.78 and 0.85, and RMSE is decreased from 0.18 to 0.12 and 0.10. After selecting the better random forest algorithm, the K-means clustering algorithm is used to continue to optimize the model, increasing the test set R² to 0.95 and decreasing the RMSE to 0.08 and 0.06. As a reliable machine algorithm, random forest can be used to predict the toxicity of the mixture of nano-metal oxides and heavy metals. The cluster analysis can effectively improve the stability and predictability of the model, and provide a new idea for the prediction of cytotoxicity model in the future.

Hybrid Classification/Regression Approach to QSAR Modeling of Stoichiometric Antiradical Capacity Assays' Endpoints

Quantitative structure-activity relationships (QSAR) are a widely used methodology allowing not only a better understanding of the mechanisms of chemical reactions, including radical scavenging, but also to predict the relevant properties of chemical compounds without their synthesis, isolation and experimental testing. Unlike the QSAR modeling of the kinetic antioxidant assays, modeling of the assays with stoichiometric endpoints depends strongly on the number of hydroxyl groups in the antioxidant molecule, as well as on some integral molecular descriptors characterizing the proportion of OH-groups able to enter and complete the radical scavenging reaction. In this work, we tested the feasibility of a "hybrid" classification/regression approach, consisting of explicit classification of individual OH-groups as involved in radical scavenging reactions, and using further the number of these OH-groups as a descriptor in simple-regression QSAR models of antiradical capacity assays with stoichiometric endpoints. A simple threshold classification based on the sum of trolox-equivalent antiradical capacity values was used, selecting OH-groups with specific radical stability- and reactivity-related electronic parameters or their combination as "active" or "inactive". We showed that this classification/regression modeling approach provides a substantial improvement of the simple-regression QSAR models over those built on the number of total phenolic OH-groups only, and yields a statistical performance similar to that of the best reported multiple-regression QSARs for antiradical capacity assays with stoichiometric endpoints.

In Silico Studies for Bacterystic Evaluation against Staphylococcus aureus of 2-Naphthoic Acid Analogues

Background:

Staphylococcus aureus is a gram-positive spherical bacterium commonly present in nasal fossae and in the skin of healthy people; however, in high quantities, it can lead to complications that compromise health. The pathologies involved include simple infections, such as folliculitis, acne, and delay in the process of wound healing, as well as serious infections in the CNS, meninges, lung, heart, and other areas.

Aim:

This research aims to propose a series of molecules derived from 2-naphthoic acid as a bioactive in the fight against S. aureus bacteria through in silico studies using molecular modeling tools.

Methods:

A virtual screening of analogues was done in consideration of the results that showed activity according to the prediction model performed in the KNIME Analytics Platform 3.6, violations of the Lipinski rule, absorption rate, cytotoxicity risks, energy of binder-receptor interaction through molecular docking, and the stability of the best profile ligands in the active site of the proteins used (PDB ID 4DXD and 4WVG).

Results:

Seven of the 48 analogues analyzed showed promising results for bactericidal action against S. aureus.

Conclusion:

It is possible to conclude that ten of the 48 compounds derived from 2-naphthoic acid presented activity based on the prediction model generated, of which seven presented no toxicity and up to one violation to the Lipinski rule.