2D-QSAR and 3D-QSAR/CoMSIA Studies on a Series of (R)-2-((2-(1H-Indol-2-yl)ethyl)amino)-1-Phenylethan-1-ol with Human β₃-Adrenergic Activity

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Background

Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA).

Results

The 2D-QSAR modeling results showed GFA-MLR ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9192, Q2 = 0.8767, R2adj = 0.8991, RMSE = 0.0959, $$R_{{{\text{test}}}}^{2}$$ R test 2  = 0.8943, $$R_{{{\text{pred}}}}^{2}$$ R pred 2  = 0.7745) and GFA-ANN ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9227, Q2 = 0.9212, RMSE = 0.0940, $$R_{{{\text{test}}}}^{2}$$ R test 2  = 0.8831, $$R_{{{\text{pred}}}}^{2}$$ R pred 2  = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9620, Q2 = 0.643) and CoMSIA_SED ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.8770, Q2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> − 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents.

Conclusion

The drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski’s rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Influenza virus disease is one of the most infectious diseases responsible for many human deaths, and the high mutability of the virus causes drug resistance effects in recent times. As such, it became necessary to explore more inhibitors that could avert future influenza pandemics. The present research utilized some in-silico modelling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions on some 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase (NA) inhibitors. The 2D-QSAR modelling results revealed GFA-MLR (R train 2 =0.8414, Q2 = 0.7680) and GFA-ANN (R train ​ 2 =0.8754, Q2 = 0.8753) models with the most relevant descriptors (MATS3i, SpMax5_Bhe, minsOH and VE3_D) for predicting the inhibitory activities of the molecules which has passed the global criteria of accepting QSAR models. The results of the 3D-QSAR modelling results showed that CoMFA_ES (R train ​ 2 =0.9030, Q2 = 0.5390) and CoMSIA_EA (R train 2 =0.880, Q2 = 0.547) models are having good predicting ability among other developed models. The molecules were virtually screened via molecular docking simulation with the active site of NA protein receptor (pH1N1) which confirms their resilient potency when compared with zanamivir standard drug. Molecule 11 as the most potent molecule formed more H-bond interactions with the key residues such as TRP178, ARG152, ARG292, ARG371, and TYR406 that triggered the catalytic reactions for NA inhibition. Furthermore, six (6) molecules (9, 10, 11, 17, 22, and 31) with relatively high inhibitory activities and docking scores were identified as the possible leads for in-silico exploration of novel NA inhibitors. The drug-likeness and ADMET predictions of the lead molecules revealed non-violation of Lipinski's rule and good pharmacokinetic profiles respectively, which are important guidelines for rational drug design. Hence, the outcome of this study overlaid a solid foundation for the in-silico design and exploration of novel NA inhibitors with improved potency.

Rac1 as a Target to Treat Dysfunctions and Cancer of the Bladder

Bladder pathologies, very common in the aged population, have a considerable negative impact on quality of life. Novel targets are needed to design drugs and combinations to treat diseases such as overactive bladder and bladder cancers. A promising new target is the ubiquitous Rho GTPase Rac1, frequently dysregulated and overexpressed in bladder pathologies. We have analyzed the roles of Rac1 in different bladder pathologies, including bacterial infections, diabetes-induced bladder dysfunctions and bladder cancers. The contribution of the Rac1 protein to tumorigenesis, tumor progression, epithelial-mesenchymal transition of bladder cancer cells and their metastasis has been analyzed. Small molecules selectively targeting Rac1 have been discovered or designed, and two of them—NSC23766 and EHT 1864—have revealed activities against bladder cancer. Their mode of interaction with Rac1, at the GTP binding site or the guanine nucleotide exchange factors (GEF) interaction site, is discussed. Our analysis underlines the possibility of targeting Rac1 with small molecules with the objective to combat bladder dysfunctions and to reduce lower urinary tract symptoms. Finally, the interest of a Rac1 inhibitor to treat advanced chemoresistance prostate cancer, while reducing the risk of associated bladder dysfunction, is discussed. There is hope for a better management of bladder pathologies via Rac1-targeted approaches.

Design, Synthesis, and Molecular Modeling Studies of a Novel Benzimidazole as an Aromatase Inhibitor

In this study, a series of novel 1,3,4-oxadiazole-benzimidazole derivatives were designed and synthesized. Their cytotoxic activities against five cancer cell lines, including A549, MCF-7, C6, HepG2, and HeLa, were evaluated by the MTT assay. The compounds 5b,c showed satisfactory potencies with much higher anticancer activity in comparison to the reference drug doxorubicin against the studied cancer cell lines. In vitro, enzymatic inhibition assays of aromatase (ARO) enzymes were performed. Molecular docking, molecular dynamics simulations, and binding free energy analyses were used to better understand the structure-activity connections and mechanism of action of the aromatase inhibitors. Two types of satisfactory 3D-QSAR (CoMFA and CoMSIA) models were generated, to predict the inhibitory activities of the novel inhibitors. Molecular docking studies were also carried out to find their binding sites and types of their interactions with the aromatase enzyme. Additionally, molecular dynamics simulations were performed to explore the most likely binding modes of compounds 5b,c with CYP19A1.

QSAR-driven synthesis of antiproliferative chalcones against SH-SY5Y cancer cells: Design, biological evaluation, and redesign

Neuroblastoma is one of the most frequent types of cancer found in infants, and traditional chemotherapy has limited efficacy against this pathology. Thus, the development of new compounds with higher activity and selectivity than traditional drugs is a current challenge in medicinal chemistry research. In this study, we report the synthesis of 21 chalcones with antiproliferative activity and selectivity against the neuroblastoma cell line SH-SY5Y. Then, we developed three-dimensional quantitative structure-activity relationship models (comparative molecular field analysis and comparative molecular similarity index analysis) with high-quality statistical values (q²  > 0.7; r²  > 0.8; r² _pred  > 0.7), using IC₅₀ and selectivity index (SI) data as dependent variables. With the information derived from these theoretical models, we designed and synthesized 16 new molecules to prove their consistency, finding good antiproliferative activity against SH-SY5Y cells on these derivatives, with three of them showing higher SI than the referential drugs 5-fluorouracil and cisplatin, displaying also a proapoptotic effect comparable to these drugs, as proven by measuring their effects on executor caspases 3/7 activity induction, Bcl-2/Bax messenger RNA levels alteration, and DNA fragmentation promotion.

Combined 3D-QSAR and docking analysis for the design and synthesis of chalcones as potent and selective monoamine oxidase B inhibitors

Monoamine oxidases (MAOs) are important targets in medicinal chemistry, as their inhibition may change the levels of different neurotransmitters in the brain, and also the production of oxidative stress species. New chemical entities able to interact selectively with one of the MAO isoforms are being extensively studied, and chalcones proved to be promising molecules. In the current work, we focused our attention on the understanding of theoretical models that may predict the MAO-B activity and selectivity of new chalcones. 3D-QSAR models, in particular CoMFA and CoMSIA, and docking simulations analysis have been carried out, and their successful implementation was corroborated by studying twenty-three synthetized chalcones (151-173) based on the generated information. All the synthetized molecules proved to inhibit MAO-B, being ten out of them MAO-B potent and selective inhibitors, with IC₅₀ against this isoform in the nanomolar range, being (E)-3-(4-hydroxyphenyl)-1-(2,2-dimethylchroman-6-yl)prop-2-en-1-one (152) the best MAO-B inhibitor (IC₅₀ of 170 nM). Docking simulations on both MAO-A and MAO-B binding pockets, using compound 152, were carried out. Calculated affinity energy for the MAO-A was +2.3 Kcal/mol, and for the MAO-B was -10.3 Kcal/mol, justifying the MAO-B high selectivity of these compounds. Both theoretical and experimental structure-activity relationship studies were performed, and substitution patterns were established to increase MAO-B selectivity and inhibitory efficacy. Therefore, we proved that both 3D-QSAR models and molecular docking approaches enhance the probability of finding new potent and selective MAO-B inhibitors, avoiding time-consuming and costly synthesis and biological evaluations.

Structure-Activity Relationships Based on 3D-QSAR CoMFA/CoMSIA and Design of Aryloxypropanol-Amine Agonists with Selectivity for the Human β3-Adrenergic Receptor and Anti-Obesity and Anti-Diabetic Profiles

The wide tissue distribution of the adrenergic β3 receptor makes it a potential target for the treatment of multiple pathologies such as diabetes, obesity, depression, overactive bladder (OAB), and cancer. Currently, there is only one drug on the market, mirabegron, approved for the treatment of OAB. In the present study, we have carried out an extensive structure-activity relationship analysis of a series of 41 aryloxypropanolamine compounds based on three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques. This is the first combined comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) study in a series of selective aryloxypropanolamines displaying anti-diabetes and anti-obesity pharmacological profiles. The best CoMFA and CoMSIA models presented values of r²_ncv = 0.993 and 0.984 and values of r²_test = 0.865 and 0.918, respectively. The results obtained were subjected to extensive external validation (q², r², r²_m, etc.) and a final series of compounds was designed and their biological activity was predicted (best pEC₅₀ = 8.561).

QSAR study on the removal efficiency of organic pollutants in supercritical water based on degradation temperature

This paper aims to study temperature-dependent quantitative structure activity relationship (QSAR) models of supercritical water oxidation (SCWO) process which were developed based on Arrhenius equation between oxidation reaction rate and temperature. Through exploring SCWO process, each kinetic rate constant was studied for 21 organic substances, including azo dyes, heterocyclic compounds and ionic compounds. We propose the concept of T_R95, which is defined as the temperature at removal ratio of 95%, it is a key indicator to evaluate compounds’ complete oxidation. By using Gaussian 09 and Material Studio 7.0, quantum chemical parameters were conducted for each organic compound. The optimum model is T_R95 = 654.775 + 1761.910f(+)_n − 177.211qH with squared regression coefficient R² = 0.620 and standard error SE = 35.1. Nearly all the compounds could obtain accurate predictions of their degradation rate. Effective QSAR model exactly reveals three determinant factors, which are directly related to degradation rules. Specifically, the lowest f(+) value of main-chain atoms (f(+)_n) indicates the degree of affinity for nucleophilic attack. qH shows the ease or complexity of valence-bond breakage of organic molecules. BO_x refers to the stability of a bond. Coincidentally, the degradation mechanism could reasonably be illustrated from each perspective, providing a deeper insight of universal and propagable oxidation rules. Besides, the satisfactory results of internal and external validations suggest the stability, reliability and predictive ability of optimum model.

Hyphenated 3D-QSAR statistical model-scaffold hopping analysis for the identification of potentially potent and selective sigma-2 receptor ligands

A 3D quantitative structure-activity relationship (3D-QSAR) model for predicting the σ₂ receptor affinity has been constructed with the aim of providing a useful tool for the identification, design, and optimization of novel σ₂ receptor ligands. The model has been built using a set of 500 selective σ₂ receptor ligands recovered from the sigma-2 receptor selective ligand database (S2RSLDB) and developed with the software Forge. The present model showed high statistical quality as confirmed by its robust predictive potential and satisfactory descriptive capability. The drawn up 3D map allows for a prompt visual comprehension of the electrostatic, hydrophobic, and shaping features underlying σ₂ receptor ligands interaction. A theoretic approach for the generation of new lead compounds with optimized σ₂ receptor affinity has been performed by means of scaffold hopping analysis. Obtained results further confirmed the validity of our model being some of the identified moieties have already been successfully employed in the development of potent σ₂ receptor ligands. For the first time is herein reported a 3D-QSAR model which includes a number of chemically diverse σ₂ receptor ligands and well accounts for the individual ligands affinities. These features will ensure prospectively advantageous applications to speed up the identification of new potent and selective σ₂ receptor ligands.