Structure-based design and discovery of potent and selective lysine-specific demethylase 1 (LSD1) inhibitors

A Quinquennial Review of Potent LSD1 Inhibitors Explored for the Treatment of Different Cancers, with Special Focus on SAR Studies

Cancer bears a significant share of global mortality. The enzyme Lysine Specific Demethylase 1 (LSD1, also known as KDM1A), since its discovery in 2004, has captured the attention of cancer researchers due to its overexpression in several cancers like acute myeloid leukaemia (AML), solid tumours, etc. The Lysine Specific Demethylase (LSD1) downregulation is reported to have an effect on cancer proliferation, migration, and invasion. Therefore, research to discover safer and more potent LSD1 inhibitors can pave the way for the development of better cancer therapeutics. These efforts have resulted in the synthesis of many types of derivatives containing diverse structural nuclei. The present manuscript describes the role of Lysine Specific Demethylase 1 (LSD1) in carcinogenesis, reviews the LSD1 inhibitors explored in the past five years and discusses their comprehensive structural activity characteristics apart from the thorough description of LSD1. Besides, the potential challenges, opportunities, and future perspectives in the development of LSD1 inhibitors are also discussed. The review suggests that tranylcypromine derivatives are the most promising potent LSD1 inhibitors, followed by triazole and pyrimidine derivatives with IC₅₀ values in the nanomolar and sub-micromolar range. A number of potent LSD1 inhibitors derived from natural sources like resveratrol, protoberberine alkaloids, curcumin, etc. are also discussed. The structural-activity relationships discussed in the manuscript can be exploited to design potent and relatively safer LSD1 inhibitors as anticancer agents.

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning

Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning. Pharmacophores generated by structure-based (SB) (either crystallographic-based or docking-based) and ligand-based (LB) (either supervised or unsupervised) modelling methods were allowed to compete within the context of genetic algorithm/machine learning and were assessed by Shapley additive explanation values (SHAP) to end up with three successful pharmacophores that were used to screen the National Cancer Institute (NCI) database. Seventy-five NCI hits were tested for their LSD-1 inhibitory properties against neuroblastoma SH-SY5Y cells, pancreatic carcinoma Panc-1 cells, glioblastoma U-87 MG cells and in vitro enzymatic assay, culminating in 3 nanomolar LSD-1 inhibitors of novel chemotypes.

Design and Synthesis of Benzene Homologues Tethered with 1,2,4-Triazole and 1,3,4-Thiadiazole Motifs Revealing Dual MCF-7/HepG2 Cytotoxic Activity with Prominent Selectivity via Histone Demethylase LSD1 Inhibitory Effect

In this study, an efficient multistep synthesis of novel aromatic tricyclic hybrids incorporating different biological active moieties, such as 1,3,4-thiadiazole and 1,2,4-triazole, was reported. These target scaffolds are characterized by having terminal lipophilic or hydrophilic parts, and their structures are confirmed by different spectroscopic methods. Further, the cytotoxic activities of the newly synthesized compounds were evaluated using in vitro MTT cytotoxicity screening assay against three different cell lines, including HepG-2, MCF-7, and HCT-116, compared with the reference drug Taxol. The results showed variable performance against cancer cell lines, exhibiting MCF-7 and HepG-2 selectivities by active analogs. Among these derivatives, 1,2,4-triazoles 11 and 13 and 1,3,4-thiadiazole 18 were found to be the most potent compounds against MCF-7 and HepG-2 cancer cells. Moreover, structure–activity relationship (SAR) studies led to the identification of some potent LSD1 inhibitors. The tested compounds showed good LSD1 inhibitory activities, with an IC₅₀ range of 0.04–1.5 μM. Compounds 27, 23, and 22 were found to be the most active analogs with IC₅₀ values of 0.046, 0.065, and 0.074 μM, respectively. In addition, they exhibited prominent selectivity against a MAO target with apparent cancer cell apoptosis, resulting in DNA fragmentation. This research provides some new aromatic-centered 1,2,4-triazole-3-thione and 1,3,4-thiadiazole analogs as highly effective anticancer agents with good LSD1 target selectivity.

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.

QSAR Modeling, Molecular Docking and Molecular Dynamics Simulations Studies of Lysine-Specific Demethylase 1 (LSD1) Inhibitors as Anticancer Agents

BACKGROUND

Histone Lysine Demetylases1 (LSD1) is a promising medication to treat cancer, which plays a crucial role in epigenetic modulation of gene expression. Inhibition of LSD1with small molecules has emerged as a vital mechanism to treat cancer.

OBJECTIVE

In the present research, molecular modeling investigations, such as CoMFA, CoMFA-RF, CoMSIA and HQSAR, molecular docking and Molecular Dynamics (MD) simulations were carried out on some tranylcypromine derivatives as LSD1 inhibitors.

METHODS

The QSAR models were carried out on a series of Tranylcypromine derivatives as data set via the SYBYL-X2.1.1 program. Molecular docking and MD simulations were carried out by the MOE software and the SYBYL program, respectively. The internal and external predictability performances related to the generated models for these LSD1 inhibitors were justified by evaluating cross-validated correlation coefficient (q²), noncross- validated correlation coefficient (r²_ncv) and predicted correlation coefficient (r²_pred) of the training and test set molecules, respectively.

RESULTS

The CoMFA (q², 0.670; r²_ncv, 0.930; r²_pred, 0.968), CoMFA-RF (q², 0.694; r²_ncr, 0.926; r²_pred, 0.927), CoMSIA (q², 0.834; r²_ncv, 0.956; r²_pred, 0.958) and HQSAR models (q², 0.854; r²_ncv, 0.900; r²_pred, 0.728) for training as well as the test set of LSD1 inhibition resulted in significant findings.

CONCLUSION

These QSAR models were found to be perfect and strong with better predictability. Contour maps of all models were generated and it was proven by molecular docking studies and molecular dynamics simulation that the hydrophobic, electrostatic and hydrogen bonding fields are crucial in these models for improving the binding affinity and determining the structure-activity relationship. These theoretical results are possibly beneficial to design new strong LSD1 inhibitors with enhanced activity to treat cancer.

Histone lysine specific demethylase 1 inhibitors

LSD1 plays a pivotal role in numerous biological functions. The overexpression of LSD1 is reported to be associated with different malignancies. Over the last decade, LSD1 has emerged as an interesting target for the treatment of acute myeloid leukaemia (AML). Numerous researchers have designed, synthesized, and evaluated various LSD1 inhibitors with diverse chemical architectures. Some of these inhibitors have entered clinical trials and are currently at different phases of clinical evaluation. This comprehensive review enlists recent research developments in LSD1 targeting pharmacophores reported over the last few years.

Discovery of Reversible Inhibitors of KDM1A Efficacious in Acute Myeloid Leukemia Models

Lysine-specific demethylase 1 (LSD1 or KDM1A) is a FAD-dependent enzyme that acts as a transcription corepressor or coactivator by regulating the methylation status of histone H3 lysines K4 and K9, respectively. KDM1A represents an attractive target for cancer therapy. While, in the past, the main medicinal chemistry strategy toward KDM1A inhibition was based on the optimization of ligands that irreversibly bind the FAD cofactor within the enzyme catalytic site, we and others have also identified reversible inhibitors. Herein we reported the discovery of 5-imidazolylthieno[3,2-b]pyrroles, a new series of KDM1A inhibitors endowed with picomolar inhibitory potency, active in cells and efficacious after oral administration in murine leukemia models.