Investigating the Binding Mode of Reversible LSD1 Inhibitors Derived from Stilbene Derivatives by 3D-QSAR, Molecular Docking, and Molecular Dynamics Simulation

Unveiling Arformoterol as a potent LSD1 inhibitor for breast cancer treatment: A comprehensive study integrating 3D-QSAR pharmacophore modeling, molecular docking, molecular dynamics simulations and in vitro assays

Lysine Specific Demethylase 1 (LSD1) has been identified as a chromatin-modifying enzyme implicated in various cancer pathogeneses, highlighting the potential for novel epigenetic cancer treatments through the development of effective inhibitors. We employed 3D-QSAR pharmacophore modeling, molecular docking, and molecular dynamics simulations to identify a promising drug candidate for LSD1 inhibition. RMSD, RMSF, H-bond, and DSSP analysis demonstrated that ZINC02599970 (Arformoterol) and ZINC13453966 exhibited the highest LSD1 inhibitory potential. Experimental validation using MCF-7 and MDA-MB-231 cell lines revealed that Arformoterol displayed potent antiproliferative activity with IC50 values of 12.30 ± 1.48 μM and 19.69 ± 1.15 μM respectively. In contrast, the IC50 values obtained for the control (tranylcypromine) in exposure to MCF-7 and MDA-MB-231 cells were 104.6 ± 1.69 μM and 77 ± 0.67 μM, respectively. Arformoterol demonstrated greater LSD1 inhibitory potency in MCF-7 cells compared to MDA-MB-231 cells. Also, the expression of genes involved in chromatin rearrangement (LSD1), angiogenesis (VEGF1), cell migration (RORα), signal transduction (S100A8), apoptosis, and cell cycle (p53) were investigated. Arformoterol enhanced apoptosis and induced cell cycle arrest at the G2/M phase, both in MCF-7 and MDA-MB-231 cancer cells. Based on our findings, we propose that Arformoterol represents a promising candidate for breast cancer treatment, owing to its potent LSD1 inhibitory activity.

Lysine-Specific Demethylase 1 Inhibitors: A Comprehensive Review Utilizing Computer-Aided Drug Design Technologies

Lysine-specific demethylase 1 (LSD1/KDM1A) has emerged as a promising therapeutic target for treating various cancers (such as breast cancer, liver cancer, etc.) and other diseases (blood diseases, cardiovascular diseases, etc.), owing to its observed overexpression, thereby presenting significant opportunities in drug development. Since its discovery in 2004, extensive research has been conducted on LSD1 inhibitors, with notable contributions from computational approaches. This review systematically summarizes LSD1 inhibitors investigated through computer-aided drug design (CADD) technologies since 2010, showcasing a diverse range of chemical scaffolds, including phenelzine derivatives, tranylcypromine (abbreviated as TCP or 2-PCPA) derivatives, nitrogen-containing heterocyclic (pyridine, pyrimidine, azole, thieno[3,2-b]pyrrole, indole, quinoline and benzoxazole) derivatives, natural products (including sanguinarine, phenolic compounds and resveratrol derivatives, flavonoids and other natural products) and others (including thiourea compounds, Fenoldopam and Raloxifene, (4-cyanophenyl)glycine derivatives, propargylamine and benzohydrazide derivatives and inhibitors discovered through AI techniques). Computational techniques, such as virtual screening, molecular docking and 3D-QSAR models, have played a pivotal role in elucidating the interactions between these inhibitors and LSD1. Moreover, the integration of cutting-edge technologies such as artificial intelligence holds promise in facilitating the discovery of novel LSD1 inhibitors. The comprehensive insights presented in this review aim to provide valuable information for advancing further research on LSD1 inhibitors.

Design Two Novel Tetrahydroquinoline Derivatives against Anticancer Target LSD1 with 3D-QSAR Model and Molecular Simulation

Lysine-specific demethylase 1 (LSD1) is a histone-modifying enzyme, which is a significant target for anticancer drug research. In this work, 40 reported tetrahydroquinoline-derivative inhibitors targeting LSD1 were studied to establish the three-dimensional quantitative structure-activity relationship (3D-QSAR). The established models CoMFA (Comparative Molecular Field Analysis (q² = 0.778, Rpred2 = 0.709)) and CoMSIA (Comparative Molecular Similarity Index Analysis (q² = 0.764, Rpred2 = 0.713)) yielded good statistical and predictive properties. Based on the corresponding contour maps, seven novel tetrahydroquinoline derivatives were designed. For more information, three of the compounds (D1, D4, and Z17) and the template molecule 18x were explored with molecular dynamics simulations, binding free energy calculations by MM/PBSA method as well as the ADME (absorption, distribution, metabolism, and excretion) prediction. The results suggested that D1, D4, and Z17 performed better than template molecule 18x due to the introduction of the amino and hydrophobic groups, especially for the D1 and D4, which will provide guidance for the design of LSD1 inhibitors.

Molecular docking-based virtual screening, molecular dynamic simulation, and 3-D QSAR modeling of some pyrazolopyrimidine analogs as potent anti-filarial agents

Lymphatic filariasis and onchocerciasis are common filarial diseases caused by filarial worms, which co-habit symbiotically with the Wolbachia organism. One good treatment method seeks Wolbachia as a drug target. Here, a computer-aided molecular docking screening and 3-D QSAR modeling were conducted on a series of Fifty-two (52) pyrazolopyrimidine derivatives against four Wolbachia receptors, including a pharmacokinetics study and Molecular Dynamic (MD) investigation, to find a more potent anti-filarial drug. The DFT approach (B3LYP with 6-31G** option) was used for the structural optimization. Five ligand-protein interaction pairs with the highest binding affinities were identified in the order; 23_7ESX (-10.2 kcal/mol) > 14_6EEZ (- 9.0) > 29_3F4R (- 8.0) > 26_6W9O (- 7.7) ≈ doxycycline_7ESX (- 7.7), with good pharmacological interaction profiles. The built 3-D QSAR model satisfied the requirement of a good model with R2 = 0.9425, Q2 LOO = 0.5019, SDEC = 0.1446, and F test = 98.282. The selected molecules (14, 23, 26, and 29) perfectly obeyed Lipinski's RO5 for oral bio-availability, and showed excellent ADMET properties, except 14 with positive AMES toxicity. The result of the MD simulation showed the great stability associated with the binding of 23 onto 7ESX's binding pocket with an estimated binding free energy (MM/GBSA) of - 60.6552 kcal/mol. Therefore, 23 could be recommended as a potential anti-filarial drug molecule, and/or template for the design of more prominent inhibitors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-022-00136-y.

Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1

A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular fingerprints. The dataset, consisting of 931 molecules with LSD1 inhibition activity, was obtained from the ChEMBL database. An evaluation of several candidate algorithms on the main dataset revealed that the support vector regressor gave the best model, with a coefficient of determination (R2) of 0.703. Virtual screening, using this model, identified five predicted potent inhibitors from the ZINC database comprising more than 300,000 molecules. The virtual screening recovered a known inhibitor, RN1, as well as four compounds where activity against LSD1 had not previously been suggested. Thus, we performed a machine-learning-enabled virtual screening of LSD1 inhibitors using only the structural information of the molecules.

Design and identification of two novel resveratrol derivatives as potential LSD1 inhibitors

Background: Overexpression of LSD1 is associated with the occurrence of many diseases, including cancers, which makes LSD1 a significant target for anticancer drug research. Methodology & Results: With the aid of 3D quantitative structure-activity relationship models established with 34 reported resveratrol derivative LSD1 inhibitors, derivatives 35-40 were designed. Absorption, distribution, metabolism and excretion calculations showed that they may have good bioavailability and drug likeness. Additionally, 35 and 37 presented good antitumor effects in an in vitro antiproliferative assay. Molecular docking and molecular dynamics simulation results indicated that 35 and 37 can establish extensive interactions with LSD1. Conclusion: The results of computational prediction and experimental validation suggest that 35 and 37 are effective antitumor inhibitors, which provides some ideas and directions for the development of new anticancer LSD1 inhibitors.

Anti-chronic myeloid leukemia activity and quantitative structure-activity relationship of novel thiazole aminobenzamide derivatives

The anti-chronic myeloid leukemia activity of thiazole aminobenzamide derivatives in vitro was tested by a methanethiosulfonate (MTS)-based viability assay method, and the result showed that some compounds exhibited good inhibitory activities against human chronic myeloid leukemia cell line K562, imatinib-resistant strain K562/R and T135I mutant cell line BaF₃-ABL-BCR-T315I. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods were used to analyze the relationship between the structure of thiazole aminobenzamide derivatives and the inhibition of K562/R cell activity. In CoMFA, Q² was 0.899 and R² was 0.963; in CoMSIA, Q² and R² were 0.840 and 0.903, respectively. These data indicated that the selected test set showed suitable external predictive ability. Combined with the contour map results, we further analyzed the three-dimensional quantitative structure (3D-QSAR) model. The results demonstrated that in the backbone of the thiazole aminobenzamide derivative, the substitution of a small group at R¹ position, or the introduction of a hydrophilic group at R² position, or the introduction of a large-volume amino acid at R³ position may be beneficial to improve the anti-CML activity of the compound.