3D-QSAR, molecular dynamics simulations, and molecular docking studies on pyridoaminotropanes and tetrahydroquinazoline as mTOR inhibitors

Amphiphilic amidines as potential plasmic membrane-targeting antifungal agents: synthesis, bio-activities and QSAR

BACKGROUND

Cationic antimicrobial peptides (AMPs) possess broad-spectrum biological activities with less inclination to inducing antibiotic resistance. Herein a battery of amphiphilic amidines were designed by mimicking the characteristics of AMPs. The antifungal activities and the effects to the hyphal morphology and membrane permeability were investigated.

RESULTS

The results indicated the inhibitory rates of ten compounds were over 80% to Botrytis cinerea and ten compounds over 90% to Valsa mali Miyabe et Yamada at 50 mg L-1. The half maximal effective concentration (EC50) values of compound 5g and 6g to V. mali were 1.21 and 1.90 mg L-1 respectively. The protective rate against apple canker of compound 5g reached 93.4% at 100 mg L-1 on twigs, superior to carbendazim (53.3%). When treated with 5g, the cell membrane permeability and leakage of content of V. mali increased, accompanied with the decrease of superoxide dismutase (SOD) and catalase (CAT) level. Concurrently, the mycelial hyphae contracted, wrinkled, and collapsed, providing evidence of membrane perturbation. A three-dimensional quantitative structure-activity relationship (3D-QSAR) between the topic compounds and the EC50 to V. mali was established showing good predictability (r2 = 0.971).

CONCLUSION

Amphiphilic amidines can acquire antifungal activities by acting on the plasmic membrane. Compound 5g could be a promising lead in discovering novel fungicidal candidates. © 2024 Society of Chemical Industry.

Molecular simulations required to target novel and potent inhibitors of cancer invasion

INTRODUCTION

Computer-aided drug design (CADD) is a computational approach used to discover, develop, and analyze drugs and active molecules with similar biochemical properties. Molecular simulation technology has significantly accelerated drug research and reduced manufacturing costs. It is an optimized drug discovery method that greatly improves the efficiency of novel drug development processes.

AREASCOVERED

This review discusses the development of molecular simulations of effective cancer inhibitors and traces the main outcomes of in silico studies by introducing representative categories of six important anticancer targets. The authors provide views on this topic from the perspective of both medicinal chemistry and artificial intelligence, indicating the major challenges and predicting trends.

EXPERT OPINION

The goal of introducing CADD into cancer treatment is to realize a highly efficient, accurate, and desired approach with a high success rate for identifying potent drug candidates. However, the major challenge is the lack of a sophisticated data-filtering mechanism to verify bottom data from mixed-quality references. Consequently, despite the continuous development of algorithms, computer power, and interface optimization, specific data filtering mechanisms will become an urgent and crucial issue in the future.

In silico studies of a novel scaffold of benzoxazole derivatives as anticancer agents by 3D-QSAR, molecular docking and molecular dynamics simulations

The vascular endothelial growth factor receptor-2 kinases (VEGFR-2) expressed on tumor cells and vessels are attractive targets for cancer treatment. Potent inhibitors for the VEGFR-2 receptor are novel strategies to develop anti-cancer drugs. In this work, template ligand-based 3D-QSAR studies were performed on a series of benzoxazole derivatives toward different cell lines (HepG2, HCT-116 and MCF-7). Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques were used to generate 3D-QSAR models. Good predictability was derived for the optimal CoMFA models (HepG2: R_cv² = 0.509, R_pred² = 0.5128; HCT-116: R_cv² = 0.574, R_pred² = 0.5597; MCF-7: R_cv² = 0.568, R_pred² = 0.5057) and CoMSIA models (HepG2: R_cv² = 0.711, R_pred² = 0.6198; HCT-116: R_cv² = 0.531, R_pred² = 0.5804; MCF-7: R_cv² = 0.669, R_pred² = 0.6577). In addition, the contour maps derived from CoMFA and CoMSIA models were also generated to illustrate the relationship between different fields and the inhibitory activities. Moreover, molecular docking and molecular dynamics (MD) simulations were also conducted to understand the binding modes and the potential interactions between the receptor and the inhibitors. Some key residues (Leu35, Val43, Lys63, Leu84, Gly117, Leu180 and Asp191) were pointed out for stabilizing the inhibitors in the binding pocket. The binding free energies for the inhibitors agreed well with the experimental inhibitory activity and indicated that steric, electrostatic and hydrogen bond interactions are the main driving force for inhibitor-receptor binding. Overall, a good consistency between theoretical 3D-SQAR and molecular docking and MD simulation studies would provide directions for the design of new candidates, avoiding time-consuming and costly synthesis and biological evaluations. On the whole, the results derived from this study could expand the understanding of benzoxazole derivatives as anticancer agents and would be of great help in lead optimization for early drug discovery of highly potent anticancer activity targeting VEGFR-2.

Identification of the structural features of quinazoline derivatives as EGFR inhibitors using 3D-QSAR modeling, molecular docking, molecular dynamics simulations and free energy calculations

Epidermal growth factor receptor (EGFR) is a promising target for the treatment of different types of malignant tumors. Therefore, a combined molecular modeling study was performed on a series of quinazoline derivatives as EGFR inhibitors. The optimum ligand-based CoMFA and CoMSIA models showed reliable and satisfactory predictability (with R²_cv=0.681, R²_ncv=0.844, R²_pred=0.8702 and R²_cv=0.643, R²_ncv=0.874, R²_pred=0.6423). The derived contour maps provide structural features to improve inhibitory activity. Furthermore, the contour maps, molecular docking, and molecular dynamics (MD) simulations have good consistency, illustrating that the derived models are reliable. In addition, MD simulations and binding free energy calculations were also carried out to understand the conformational fluctuations at the binding pocket of the receptor. The results indicate that hydrogen bond, hydrophobic and electrostatic interactions play significant roles on activity and selectivity. Furthermore, amino acids Val31, Lys50, Thr95, Leu149 and Asp160 are considered as essential residues to participate in the ligand-receptor interactions. Overall, this work would offer reliable theoretical basis for future structural modification, design and synthesis of novel EGFR inhibitors with good potency.Communicated by Ramaswamy H. Sarma.

Design, synthesis, biological evaluation and molecular dynamics of LAR inhibitors

In this study, firstly, the pharmacophore model was established based on LAR inhibitors. ZINC database and drug-like database were screened by Hypo-1-LAR model, and the embryonic compound ZINC71414996 was obtained. Based on this compound, we designed 9 compounds. Secondly, the synthetic route of the compound was determined by consulting Reaxys and Scifinder databases, and 9 compounds (1a-1i) were synthesized by nucleophilic substitution, Suzuki reaction and so on. Meanwhile, their structures were confirmed by ¹H NMR and ¹³C NMR. Thirdly, the Enzymatic assays was carried out, the biological evaluation of compounds 1a-1i led to the identification of a novel PTP-LAR inhibitor 1c, which displayed an IC₅₀ value of 4.8 μM. At last, molecular dynamics simulation showed that compounds could interact strongly with the key amino acids LYS1350, LYS1352, ARG1354, TYR1355, LYS1433, ASP1435, TRP1488, ASP1490, VAL1493, SER1523, ARG1528, ARG1561, GLN1570, LYS1681, thereby inhibiting the protein activity. This study constructed the pharmacophore model of LAR protein, designed small-molecule inhibitors, conducted compound synthesis and enzyme activity screening, so as to provide a basis for searching for drug-capable lead compounds.

Improved 3D-QSAR Prediction by Multiple Conformational Alignments and Molecular Docking Studies to Design and Discover HIV-I Protease Inhibitors

BACKGROUND

Inhibition of HIV-I protease enzyme is a strategic step for providing better treatment in retrovirus infections, which avoids resistance and possesses less toxicity.

OBJECTIVES

In the course of our research to discover new and potent protease inhibitors, 3D-QSAR (CoMFA and CoMSIA) models were generated using 3 different alignment techniques, including multifit alignment, docking based and Distill based alignment for 63 compounds. Novel molecules were designed from the output of this study.

METHODS

A total of 3 alignment methods were used to generate CoMFA and CoMSIA models. A Distill based alignment method was considered a better method according to different validation parameters. A 3D-QSAR model was generated and contour maps were discussed. The biological activity of designed molecules was predicted using the generated QSAR model to validate QSAR. The newly designed molecules were docked to predict binding affinity.

RESULTS

In CoMFA, leave one out cross-validated coefficient (q²), conventional coefficient (r²) and predicted correlation coefficient (r²_Predicted) values were found to be 0.721, 0.991 and 0.780, respectively. The best obtained CoMSIA model also showed significant cross-validated coefficient (q²), conventional coefficient (r²) and predicted correlation coefficient (r²_Predicted) values of 0.714, 0.987 and 0.721, respectively. Steric and electrostatic contour maps generated from CoMFA and hydrophobic and hydrogen bond donor and hydrogen bond acceptor contour maps from CoMSIA models were used to design new and bioactive protease inhibitors by incorporating bioisosterism and knowledge-based structure-activity relationship.

CONCLUSION

The results from both these approaches, ligand-based drug design and structure-based drug design, are adequate and promising to discover protease inhibitors.

Design and development of Tetrahydro-Quinoline derivatives as dual mTOR-C1/C2 inhibitors for the treatment of lung cancer

Lung cancer is one of the most prevailed cancer worldwide. Many genes get mutated in lung cancer but the involvement of EGFR, KRAS, PTEN and PIK3CA are more common. Unavailability of potent drugs and resistance to the available drugs are major concern in the treatment of lung cancer. In the present research, mTOR was selected as an important alternative target for the treatment of lung cancer which involves the PI3K/AKT/mTOR pathway. We studied binding interactions of AZD-2014 with the mTOR protein to identify important interactions required to design potent mTOR inhibitors which was supported by QSAR studies. Pharmacophore based virtual screening studies provided core scaffold, THQ. Based on molecular docking interactions, 31 THQ derivatives were synthesized and characterized. All compounds were screened for cellular mTOR enzyme assay along with antiproliferative activity against the panel of cancerous cell lines, from which 6 compounds were further screened for colony forming assay. Two most potent compounds, HB-UC-1 and HB-UC-5, were further screened for flow cytometry analysis, gene expression study and western blot analysis. Gene expression study revealed the efficiency of compound HB-UC-1 against both mTORC1 and mTORC2 by affecting downstream regulators of mTORC1 (E₄BP₄, eIF₄EBP₁) and mTORC2 (PCK1), respectively. In western blot analysis, both compounds, inhibited phosphorylation of AKT S473 which proved the efficiency these compounds against the mTORC2. These two compounds were further screened for in-vivo biological evaluation. Both compounds increased lifespan of cancer-bearing animals with improvement in mean survival time. Further, in bezopyrene induced lung cancer animal model, both compounds showed effectiveness through the biochemical parameters and histopathological evaluation of the lung tissue. In future, potent hit compound from this series could be modified to develop lead mTOR inhibitors for the treatment of lung cancer.

Design, synthesis and pharmacological evaluation of a novel mTOR-targeted anti-EV71 agent

Due to the limitations of existing anti-EV71 targets, we have been eager to discover a new anti-EV71 agent based on mTOR (the mammalian target of rapamycin), which is an important target for finding antiviral agents based on host cells. Torin2 is a second-generation ATP competitive mTOR kinase inhibitor (IC₅₀ = 0.25 nM). Our research team tested the anti-EV71 activity of Torin2 in vitro for the first time. The result showed that Torin2 had significant anti-EV71 activity (IC₅₀ = 0.01 μM). In this study, thirty novel Torin2 derivatives were synthesized and evaluated for anti-EV71 activity. Among them, 11a, 11b, 11d, 11e and 11m displayed similar activity to Torin2. 11e displayed the most potent activity, with an IC₅₀ value of 0.027 μM, which was closest to Torin2, and displayed potent mTOR kinase inhibitory activity. A molecular modeling study showed that 11e interacted with Val2240 and Lys2187 via hydrogen bonds and had a good match with the receptor. Additionally, a mechanism study showed that most of the compounds had significant inhibition for the mTOR pathway substrates p70S6K and Akt. The water solubility test of compounds with potent activity revealed that 11a and 11m were improved by approximately 5-15-fold compared to Torin2. These data suggest that 11a and 11m may be potential candidates for anti-EV71 treatment.

Molecular Modeling and Design Studies of Purine Derivatives as Novel CDK2 Inhibitors

Cyclin-dependent kinase 2 (CDK2) is a potential target for treating cancer. Purine heterocycles have attracted particular attention as the scaffolds for the development of CDK2 inhibitors. To explore the interaction mechanism and the structure–activity relationship (SAR) and to design novel candidate compounds as potential CDK2 inhibitors, a systematic molecular modeling study was conducted on 35 purine derivatives as CDK2 inhibitors by combining three-dimensional quantitative SAR (3D-QSAR), virtual screening, molecular docking, and molecular dynamics (MD) simulations. The predictive CoMFA model (q² = 0.743, rpred2 = 0.991), the CoMSIA model (q² = 0.808, rpred2 = 0.990), and the Topomer CoMFA model (q² = 0.779, rpred2 = 0.962) were obtained. Contour maps revealed that the electrostatic, hydrophobic, hydrogen bond donor and steric fields played key roles in the QSAR models. Thirty-one novel candidate compounds with suitable predicted activity (predicted pIC₅₀ > 8) were designed by using the results of virtual screening. Molecular docking indicated that residues Asp86, Glu81, Leu83, Lys89, Lys33, and Gln131 formed hydrogen bonds with the ligand, which affected activity of the ligand. Based on the QSAR model prediction and molecular docking, two candidate compounds, I13 and I60 (predicted pIC₅₀ > 8, docking score > 10), with the most potential research value were further screened out. MD simulations of the corresponding complexes of these two candidate compounds further verified their stability. This study provided valuable information for the development of new potential CDK2 inhibitors.