Chemometric QSAR modeling and in silico design of antioxidant NO donor phenols

In silico study of natural antioxidants

Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants. Over the past two decades, numerous studies have demonstrated the importance of in silico research in the development of novel natural and synthesized antioxidants. In silico methods such as quantitative structure-activity relationships (QSAR), pharmacophore, docking, and virtual screenings are play critical roles in designing effective antioxidants that may be synthesized and tested later. This chapter introduces the available in silico approaches for different classes of antioxidants. Many successful applications of in silico methods in the development and design of novel antioxidants are thoroughly discussed. The QSAR, pharmacophore, molecular docking techniques, and virtual screenings process summarized here would help readers to find out the proper mechanism for the interaction between the free radicals and antioxidant compounds. Furthermore, this chapter focuses on introducing new QSAR models in combination with other in silico methods to predict antioxidants activity and design more active antioxidants. In silico studies are essential to explore largely unknown plant tissue, food sources for antioxidant synthesis, as well as saving time and money in such studies.

SMILES-based 2D-QSAR and similarity search for identification of potential new scaffolds for development of SARS-CoV-2 MPRO inhibitors

COVID-19, whose etiological agent is the SARS-CoV-2 virus, has caused over 537.5 million cases and killed over 6.3 million people since its discovery in 2019. Despite the recent development of the first drugs indicated for treating people already infected, the great need to develop new anti-SARS-CoV-2 drugs still exists, mainly due to the possible emergence of new variants of this virus and resistant strains of the current variants. Thus, this work presents the results of QSAR and similarity search studies based only on 2D structures from a set of 32 bicycloproline derivatives, aiming to quickly, reproducibly, and reliably identify potentially useful compounds as scaffolds of new major protease inhibitors (M^pro) of the virus. The obtained QSAR model is based only on topological molecular descriptors. The model has good internal and external statistics, is robust, and does not present a chance correlation. This model was used as one of the tools to support the virtual screening stage carried out in the SwissADME web tool. Five molecules, from an initial set of 2695 molecules, proved to be the most promising, as they were within the model’s applicability domain and linearity range, with low potential to cause carcinogenic, teratogenic, and reproductive toxicity effects and promising pharmacokinetic properties. These five compounds were then selected as the most competent to generate, in future studies, new anti-SARS-CoV-2 agents with drug-likeness properties suitable for use in therapy.

Ligand-based design of chalcone analogues and thermodynamic analysis of their mechanism of free radical scavenge

Overproduction of free radicals in the body may result in oxidative stress, which plays an active role in the development of various health disorders. Consequently, the development of efficient free radical scavengers and evaluation of their antioxidant properties is a research area of interest. In the present research, computational quantum chemical approach based on the density functional theory (DFT) method was employed to elucidate the free radical scavenge of chalcone derivatives via thermodynamic studies. New set of chalcone antioxidants were designed. Their reactivity towards hydroperoxyl (HOO·) and methyl peroxyl (CH₃OO·) radicals were investigated through systematic study of their mechanism of free radical scavenge. Various reaction enthalpies and Gibbs free energy that characterize the various steps in these mechanisms were computed in the gas phase and aqueous solution, in order to identify the main channels of reaction. Results in the gas phase indicate that hydrogen atom transfer (HAT) and sequential proton loss electron transfer (SPLET) mechanisms represent the most plausible reaction pathways, while single electron transfer followed by proton transfer (SET-PT) mechanism was thermodynamically unfeasible. However, these mechanisms were thermodynamically favoured in aqueous solution. Also, these chalcone derivatives were observed to be more effective in scavenging HOO· than CH₃OO· radicals in both phases. Based on the exergonicity of the obtained results, the molecule MCHM 17 ((E)-1-(3-bromo-5-hydroxyphenyl)-3-(2,5-dihydroxyphenyl)prop-2-en-1-one) at the 5-OH site was found to exhibit the greatest potential to scavenge HOO· and CH₃OO· radicals in both phases. This research is a gateway to the efficient exploitation of these compounds in pharmacy and food chemistry.

Free radical scavenging mechanism of 1,3,4-oxadiazole derivatives: thermodynamics of O-H and N-H bond cleavage

The thermodynamics of free radical scavenge of 1,3,4-oxadiazole derivatives towards oxygen-centred free radicals were investigated by the density functional theory (DFT) method in the gas phase and aqueous solution. Three mechanisms of free radical scavenge namely, hydrogen atom transfer (HAT), single electron transfer followed by proton transfer (SET-PT) and sequential proton loss electron transfer (SPLET) were considered. The antioxidant descriptors that characterize these mechanisms such as, bond dissociation enthalpy (BDE), adiabatic ionization potential (AIP), proton dissociation enthalpy (PDE), proton affinity (PA) and electron transfer enthalpy (ETE) were evaluated. The sequence of electron donation as predicted by the HOMO results were in good agreement with the sequence of ETE for the considered molecules at their favoured sites of free radical scavenge. The reaction Gibbs free energy for inactivation of the selected peroxyl radicals, show that 1,3,4-oxadiazole antioxidants are more efficient radical scavengers by HAT and SPLET mechanisms than SET-PT mechanism in vacuum. In aqueous solution, the SET-PT mechanism was observed to be the dominant reaction pathway.

Rational design of novel coumarins: A potential trend for antioxidants in cosmetics

Coumarins are well-known for their antioxidant effect and aromatic property, thus, they are one of ingredients commonly added in cosmetics and personal care products. Quantitative structure-activity relationships (QSAR) modeling is an in silico method widely used to facilitate rational design and structural optimization of novel drugs. Herein, QSAR modeling was used to elucidate key properties governing antioxidant activity of a series of the reported coumarin-based antioxidant agents (1-28). Several types of descriptors (calculated from 4 softwares i.e., Gaussian 09, Dragon, PaDEL and Mold2 softwares) were used to generate three multiple linear regression (MLR) models with preferable predictive performance (Q 2 LOO-CV = 0.813-0.908; RMSE LOO-CV = 0.150-0.210; Q 2 Ext = 0.875-0.952; RMSE Ext = 0.104-0.166). QSAR analysis indicated that number of secondary amines (nArNHR), polarizability (G2p), electronegativity (D467, D580, SpMin2_Bhe, and MATS8e), van der Waals volume (D491 and D461), and H-bond potential (SHBint4) are important properties governing antioxidant activity. The constructed models were also applied to guide in silico rational design of an additional set of 69 structurally modified coumarins with improved antioxidant activity. Finally, a set of 9 promising newly design compounds were highlighted for further development. Structure-activity analysis also revealed key features required for potent activity which would be useful for guiding the future rational design. In overview, our findings demonstrated that QSAR modeling could possibly be a facilitating tool to enhance successful development of bioactive compounds for health and cosmetic applications.

In silico design of hydrazone antioxidants and analysis of their free radical-scavenging mechanism by thermodynamic studies

Background

Antioxidants are very crucial in maintaining the normal function of body cells, as they scavenge excess free radical in the body. A set of hydrazone antioxidants was designed by in silico screening. The density functional theory (DFT) method was employed to explore the reaction energetics of their free radical-scavenging mechanism. With the aid of the developed quantitative structure-activity relationship (QSAR) model for hydrazone antioxidants, the structure and antioxidant activity of these compounds were predicted. Three potential reaction mechanisms were investigated, namely, hydrogen atom transfer (HAT), single-electron transfer followed by proton transfer (SET-PT) and sequential proton loss electron transfer (SPLET). Bond dissociation enthalpy (BDE), adiabatic ionization potential (AIP), proton dissociation enthalpy (PDE), proton affinity (PA), electron transfer enthalpy (ETE) and Gibbs free energy that characterize the various steps in these mechanisms were calculated in the gas phase.

Results

A total of 25 hydrazone antioxidants were designed, in which the molecule MHD 017 gave the best antioxidant activity. Among the tested molecules, MHD 017 at the 10-OH site gave the best results for the various thermodynamic parameters calculated. The reaction Gibbs free energy results also indicate that this is the most favoured site for free radical scavenge.

Conclusion

The obtained results show that HAT and SPLET mechanisms are the thermodynamically plausible reaction pathways of free radical scavenge by hydrazone antioxidants. The reactivity of these compounds towards the hydroperoxyl radical (HOO·) was greater than that towards the methyl peroxyl radical (CH3OO·) based on the exergonicity of the calculated reaction Gibbs free energy.

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Novel coumarin-6-sulfonamides as apoptotic anti-proliferative agents: synthesis, in vitro biological evaluation, and QSAR studies

Herein, we report the synthesis of different novel sets of coumarin-6-sulfonamide derivatives bearing different functionalities (4a, b, 8a-d, 11a-d, 13a, b, and 15a-c), and in vitro evaluation of their growth inhibitory activity towards the proliferation of three cancer cell lines; HepG2 (hepatocellular carcinoma), MCF-7 (breast cancer), and Caco-2 (colon cancer). HepG2 cells were the most sensitive cells to the influence of the target coumarins. Compounds 13a and 15a emerged as the most active members against HepG2 cells (IC50 = 3.48 ± 0.28 and 5.03 ± 0.39 µM, respectively). Compounds 13a and 15a were able to induce apoptosis in HepG2 cells, as assured by the upregulation of the Bax and downregulation of the Bcl-2, besides boosting caspase-3 levels. Besides, compound 13a induced a significant increase in the percentage of cells at Pre-G1 by 6.4-folds, with concurrent significant arrest in the G2-M phase by 5.4-folds compared to control. Also, 13a displayed significant increase in the percentage of annexin V-FITC positive apoptotic cells from 1.75-13.76%. Moreover, QSAR models were established to explore the structural requirements controlling the anti-proliferative activities.

QSAR of Antioxidants

Antioxidants are substances that protect cells from the damaging effects of oxygen radicals, which are chemicals that play a part in some diseases such as cancer and others. Antioxidants are expected to be promising drugs in the management of these diseases by removing oxidative stress. Most of the modeling approaches involved in designing new antioxidants is based on Quantitative Structure-Activity Relationship (QSAR). A number of QSAR studies have been conducted to elucidate the structural requirements of antioxidants for their activities in order to predict the potency of these compounds with regard to the targeted activity and to direct the synthesis of more potent analogues. The main focus of this chapter is on the QSAR modeling of antioxidant compounds. The authors provide different QSAR studies of antioxidant compounds and try to compare between them in terms of the best models obtained and their use in designing potential new drugs.

Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach

The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates.

The effect of different log P algorithms on the modeling of the soil sorption coefficient of nonionic pesticides

Collecting data on the effects of pesticides on the environment is a slow and costly process. Therefore, significant efforts have been focused on the development of models that predict physical, chemical or biological properties of environmental interest. The soil sorption coefficient normalized to the organic carbon content (Koc) is a key parameter that is used in environmental risk assessments. Thus, several log Koc prediction models that use the hydrophobic parameter log P as a descriptor have been reported in the literature. Often, algorithms are used to calculate the value of log P due to the lack of experimental values for this property. Despite the availability of various algorithms, previous studies fail to describe the procedure used to select the appropriate algorithm. In this study, models that correlate log Koc with log P were developed for a heterogeneous group of nonionic pesticides using different freeware algorithms. The statistical qualities and predictive power of all of the models were evaluated. Thus, this study was conducted to assess the effect of the log P algorithm choice on log Koc modeling. The results clearly demonstrate that the lack of a selection criterion may result in inappropriate prediction models. Seven algorithms were tested, of which only two (ALOGPS and KOWWIN) produced good results. A sensible choice may result in simple models with statistical qualities and predictive power values that are comparable to those of more complex models. Therefore, the selection of the appropriate log P algorithm for modeling log Koc cannot be arbitrary but must be based on the chemical structure of compounds and the characteristics of the available algorithms.

Quantification of contributions of different molecular fragments for antioxidant activity of coumarin derivatives based on QSAR analyses

Attempts have been made in the present work using in silico techniques for identification of essential structural features imparting antioxidant potential to naturally available coumarin molecules and their synthetic derivatives. Four different types of modeling tools have been employed for the qualitative and quantitative assessment of the molecular fragments constituting the biological pharmacophore. The descriptor-based quantitative structure–activity relationship (QSAR) and group-based QSAR (G-QSAR) models provide a quantitative estimation of the substituent requirements and the chemical nature of the parent moiety. Subsequently, 3D pharmacophore and hologram QSAR (HQSAR) models enable identification of the key molecular components necessary for the antioxidant potency to the molecules. All of the different models infer the importance of the hydrogen bond acceptor ketonic fragment for interaction of the antioxidant molecules with the neighbouring toxic radicals. Additionally, the phenyl substituent attached to the side chain and the benzene nucleus of the benzopyran moiety also constitute the response pharmacophore for the molecules under study. The models thus developed may serve as an essential query tool for screening of databases for selection of molecules bearing the essential fragments and subsequent prediction of their free radical scavenging potency.

A new quantitative structure-property relationship model to predict bioconcentration factors of polychlorinated biphenyls (PCBs) in fishes using E-state index and topological descriptors

A quantitative structure-property relationship (QSPR) study for predicting the logarithm of bioconcentration factors (LogBCF) of polychlorinated biphenyls (PCBs) is presented in this work. For this, the descriptors were obtained using only the Simplified Molecular Input Line Entry System (SMILES) strings in the free web server Parameter Client. The model was built using the Partial Least Squares (PLS) regression method. The best model presented five descriptors (one E-state index and four topological descriptors) and a high quality for fit, internal, and external predictions. The leave-N-out (LNO) cross validation and the y-randomization test showed the model is robust and has no shown chance correlation. With a second test set, the model was compared to other models and presented a root mean square error (RMSE) very close to the best model. The mechanistic interpretation was corroborated by other works in the literature and by the descriptors' theory. Thus, the results meet the five Organization for Economic Co-operation and Development (OECD) principles for validation of QSA(P)R models, and it is expected the model can effectively predict the BCF values in fishes of the PCB congeners without highly reliable experimental BCF.