3D-QSAR using pharmacophore-based alignment and virtual screening for discovery of novel MCF-7 cell line inhibitors

A virtual insight into mushroom secondary metabolites: 3D-QSAR, docking, pharmacophore-based analysis and molecular modeling to analyze their anti-breast cancer potential

Breast cancer is a major issue of investigation in drug discovery due to its rising frequency and global dominance. Plants are significant natural sources for the development of novel medications and therapies. Medicinal mushrooms have many biological response modifiers and are used for the treatment of many physical illnesses. In this research, a database of 89 macro-molecules with anti-breast cancer activity, which were previously isolated from the mushrooms in literature, has been selected for the three-dimensional quantitative structure-activity relationships (3D-QSAR) studies. The 3D-QSAR model was necessarily used in Pharmacopoeia virtual evaluation of the database to develop novel MCF-7 inhibitors. With the known potential targets of breast cancer, the docking studies were achieved. Using molecular dynamics simulations, the targets' stability with the best-chosen natural product molecule was found. Furthermore, the absorption, distribution, metabolism, excretion, and toxicity of three compounds, resulting after the docking study, were predicted. The compound C1 (Pseudonocardian A) showed the features of effective compounds because it has bioavailability from different coral species and is toxicity-free for the prevention of many dermatological illnesses. C1 is chemically active and possesses charge transfer inside the monomer, as seen by the band gaps of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) electrons. The reactivity descriptors ionization potential, electron affinity, chemical potential (μ), hardness (η), softness (S), electronegativity (χ), and electrophilicity index (ω) have been estimated using the energies of frontier molecular orbitals (HOMO-LUMO). Additionally, molecular electrostatic potential maps were created to show that the C1 is reactive.Communicated by Ramaswamy H. Sarma.

Computational Approaches: A New Frontier in Cancer Research

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

S1PR1 modulators in multiple sclerosis: Efficacy, safety, comparison, and chemical structure insights

Multiple sclerosis (MS) is a neurological disease that leads to severe physical and cognitive disabilities. Drugs used in the treatment of MS vary from small synthetic molecules to large macromolecules such as antibodies. Sphingosine 1-phosphate receptor modulators are frequently used for the treatment of MS. These medicines prevent the egress of lymphocytes from secondary lymphoid organs leading to immune system suppression. Currently, four S1PR modulators are on the market and several potential drug candidates are in clinical trials for the treatment of MS. These compounds differ in chemical structure, adverse effects, and efficacy points of view. The current article reviews the latest studies on S1PR1 modulators and compares them with other MS drugs in terms of efficacy, tolerability, and safety. A special focus was dedicated to discussing the structure-activity relationships of these compounds and performing a three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis to gain better insight into the ligand-receptor interaction mode.

A comparison between 2D and 3D descriptors in QSAR modeling based on bio-active conformations

QSAR models are widely and successfully used in many research areas. The success of such models highly depends on molecular descriptors typically classified as 1D, 2D, 3D, or 4D. While 3D information is likely important, e. g., for modeling ligand-protein binding, previous comparisons between the performances of 2D and 3D descriptors were inconclusive. Yet in such comparisons the modeled ligands were not necessarily represented by their bioactive conformations. With this in mind, we mined the PDB for sets of protein-ligand complexes sharing the same protein for which uniform activity data were reported. The results, totaling 461 structures spread across six series were compiled into a carefully curated, first of its kind dataset in which each ligand is represented by its bioactive conformation. Next, each set was characterized by 2D, 3D and 2D + 3D descriptors and modeled using three machine learning algorithms, namely, k-Nearest Neighbors, Random Forest and Lasso Regression. Models' performances were evaluated on external test sets derived from the parent datasets either randomly or in a rational manner. We found that many more significant models were obtained when combining 2D and 3D descriptors. We attribute these improvements to the ability of 2D and 3D descriptors to code for different, yet complementary molecular properties.

3D QSAR based Virtual Screening of Flavonoids as Acetylcholinesterase Inhibitors

In an attempt to develop therapeutic agents to treat Alzheimer's disease, a series of flavonoid analogues were collected, which already had established acetylcholinesterase (AChE) enzyme inhibition activity. For each molecule we also collected biological activity data (Ki). Then, 3D-QSAR (quantitative structure-activity relationship model) was developed which showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 and q2. This SAR data can explain the key descriptors which can be related to AChE inhibitory activity. Using the QSAR model, pharmacophores were developed based on which, virtual screening was done and a dataset was obtained which loaded as a prediction set to fit the developed QSAR model. Top 10 compounds fitting the QSAR model were subjected to molecular docking. CHEMBL1718051 was found to be the lead compound. This study is offering an example of a computationally-driven tool for prioritisation and discovery of probable AChE inhibitors. Further, in vivo and in vitro testing will show its therapeutic potential.

Human Estrogen Receptor Alpha Antagonists, Part 3: 3-D Pharmacophore and 3-D QSAR Guided Brefeldin A Hit-to-Lead Optimization toward New Breast Cancer Suppressants

The estrogen receptor α (ERα) is an important biological target mediating 17β-estradiol driven breast cancer (BC) development. Aiming to develop innovative drugs against BC, either wild-type or mutated ligand-ERα complexes were used as source data to build structure-based 3-D pharmacophore and 3-D QSAR models, afterward used as tools for the virtual screening of National Cancer Institute datasets and hit-to-lead optimization. The procedure identified Brefeldin A (BFA) as hit, then structurally optimized toward twelve new derivatives whose anticancer activity was confirmed both in vitro and in vivo. Compounds as SERMs showed picomolar to low nanomolar potencies against ERα and were then investigated as antiproliferative agents against BC cell lines, as stimulators of p53 expression, as well as BC cell cycle arrest agents. Most active leads were finally profiled upon administration to female Wistar rats with pre-induced BC, after which 3DPQ-12, 3DPQ-3, 3DPQ-9, 3DPQ-4, 3DPQ-2, and 3DPQ-1 represent potential candidates for BC therapy.

Identification of novel leads as potent inhibitors of HDAC3 using ligand-based pharmacophore modeling and MD simulation

In the landscape of epigenetic regulation, histone deacetylase 3 (HDAC3) has emerged as a prominent therapeutic target for the design and development of candidate drugs against various types of cancers and other human disorders. Herein, we have performed ligand-based pharmacophore modeling, virtual screening, molecular docking, and MD simulations to design potent and selective inhibitors against HDAC3. The predicted best pharmacophore model ‘Hypo 1’ showed excellent correlation (R² = 0.994), lowest RMSD (0.373), lowest total cost value (102.519), and highest cost difference (124.08). Hypo 1 consists of four salient pharmacophore features viz. one hydrogen bond acceptor (HBA), one ring aromatic (RA), and two hydrophobic (HYP). Hypo 1 was validated by Fischer's randomization with a 95% of confidence level and the external test set of 60 compounds with a good correlation coefficient (R² = 0.970). The virtual screening of chemical databases, drug-like properties calculations followed by molecular docking resulted in identifying 22 representative hit compounds. Performed 50 ns of MD simulations on top three hits were retained the salient π-stacking, Zn²⁺ coordination, hydrogen bonding, and hydrophobic interactions with catalytic residues from the active site pocket of HDAC3. Total binding energy calculated by MM-PBSA showed that the Hit 1 and Hit 2 formed stable complexes with HDAC3 as compared to reference TSA. Further, the PLIP analysis showed a close resemblance between the salient pharmacophore features of Hypo 1 and the presence of molecular interactions in co-crystallized FDA-approved drugs. We conclude that the screened hit compounds may act as potent inhibitors of HDAC3 and further preclinical and clinical studies may pave the way for developing them as effective therapeutic agents for the treatment of different cancers and neurodegenerative disorders.

Discovery of novel hit compounds as potential HDAC1 inhibitors: The case of ligand- and structure-based virtual screening

Histone deacetylases (HDACs) as an important family of epigenetic regulatory enzymes are implicated in the onset and progression of carcinomas. As a result, HDAC inhibition has been proven as a compelling strategy for reversing the aberrant epigenetic changes associated with cancer. However, non-selective profile of most developed HDAC inhibitors (HDACIs) leads to the occurrence of various side effects, limiting their clinical utility. This evidence provides a solid ground for ongoing research aimed at identifying isoform-selective inhibitors. Among the isoforms, HDAC1 have particularly gained increased attention as a preferred target for the design of selective HDACIs. Accordingly, in this paper, we have developed a reliable virtual screening process, combining different ligand- and structure-based methods, to identify novel benzamide-based analogs with potential HDAC1 inhibitory activity. For this purpose, a focused library of 736,160 compounds from PubChem database was first compiled based on 80% structural similarity with four known benzamide-based HDAC1 inhibitors, Mocetinostat, Entinostat, Tacedinaline, and Chidamide. Our inclusive in-house 3D-QSAR model, derived from pharmacophore-based alignment, was then employed as a 3D-query to discriminate hits with the highest predicted HDAC1 inhibitory activity. The selected hits were subjected to subsequent structure-based approaches (induced-fit docking (IFD), MM-GBSA calculations and molecular dynamics (MD) simulation) to retrieve potential compounds with the highest binding affinity for HDAC1 active site. Additionally, in silico ADMET properties and PAINS filtration were also considered for selecting an enriched set of the best drug-like molecules. Finally, six top-ranked hit molecules, CID_38265326, CID_56064109, CID_8136932, CID_55802151, CID_133901641 and CID_18150975 were identified to expose the best stability profiles and binding mode in the HDAC1 active site. The IFD and MD results cooperatively confirmed the interactions of the promising selected hits with critical residues within HDAC1 active site. In summary, the presented computational approach can provide a set of guidelines for the further development of improved benzamide-based derivatives targeting HDAC1 isoform.

Development of In Vitro Corneal Models: Opportunity for Pharmacological Testing

The human eye is a specialized organ with a complex anatomy and physiology, because it is characterized by different cell types with specific physiological functions. Given the complexity of the eye, ocular tissues are finely organized and orchestrated. In the last few years, many in vitro models have been developed in order to meet the 3Rs principle (Replacement, Reduction and Refinement) for eye toxicity testing. This procedure is highly necessary to ensure that the risks associated with ophthalmic products meet appropriate safety criteria. In vitro preclinical testing is now a well-established practice of significant importance for evaluating the efficacy and safety of cosmetic, pharmaceutical, and nutraceutical products. Along with in vitro testing, also computational procedures, herein described, for evaluating the pharmacological profile of potential ocular drug candidates including their toxicity, are in rapid expansion. In this review, the ocular cell types and functionality are described, providing an overview about the scientific challenge for the development of three-dimensional (3D) in vitro models.

Computer-Driven Development of an in Silico Tool for Finding Selective Histone Deacetylase 1 Inhibitors

Histone deacetylases (HDACs) are a class of epigenetic modulators overexpressed in numerous types of cancers. Consequently, HDAC inhibitors (HDACIs) have emerged as promising antineoplastic agents. Unfortunately, the most developed HDACIs suffer from poor selectivity towards a specific isoform, limiting their clinical applicability. Among the isoforms, HDAC1 represents a crucial target for designing selective HDACIs, being aberrantly expressed in several malignancies. Accordingly, the development of a predictive in silico tool employing a large set of HDACIs (aminophenylbenzamide derivatives) is herein presented for the first time. Software Phase was used to derive a 3D-QSAR model, employing as alignment rule a common-features pharmacophore built on 20 highly active/selective HDAC1 inhibitors. The 3D-QSAR model was generated using 370 benzamide-based HDACIs, which yielded an excellent correlation coefficient value (R² = 0.958) and a satisfactory predictive power (Q² = 0.822; Q²_F3 = 0.894). The model was validated (r²_{ext_ts} = 0.794) using an external test set (113 compounds not used for generating the model), and by employing a decoys set and the receiver-operating characteristic (ROC) curve analysis, evaluating the Güner-Henry score (GH) and the enrichment factor (EF). The results confirmed a satisfactory predictive power of the 3D-QSAR model. This latter represents a useful filtering tool for screening large chemical databases, finding novel derivatives with improved HDAC1 inhibitory activity.

In Silico Target Prediction for Small Molecules

Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.

Insight Analysis of Promiscuous Estrogen Receptor α-Ligand Binding by a Novel Machine Learning Scheme

Estrogen receptor α (ERα) plays a significant role in occurrence of breast cancer and may cause various adverse side-effects when ERα is an off-target protein. A theoretical model was derived to predict the binding affinity of ERα using the pharmacophore ensemble/support vector machine (PhE/SVM) scheme to consider the promiscuous characteristic of ERα. The estimations by PhE/SVM were discovered to be in good agreement with the observed values for those training molecules ( n = 31, r² = 0.80, q_CV² = 0.77, RMSE = 0.57, s = 0.58), test molecules ( n = 179, q² = 0.91-0.96, RMSE = 0.33, s = 0.26) and outliers ( n = 15, q² = 0.80-0.86, RMSE = 0.56, s = 0.49). When subjected to various statistical validations, the PhE/SVM model consistently fulfilled the strictest criteria. A mock test also asserted its predictivity. When compared with crystal structures, the calculated results are consistent with the reported ERα-ligand co-complex structure, and the plasticity nature of ERα is also disclosed. Consequently, this precise, fast, and robust model can be adopted to predict ERα-ligand binding affinities and to design safer non-ERα-targeted pharmaceuticals in the process of drug discovery and development.

Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

Medicinal chemistry projects involve some steps aiming to develop a new drug, such as the analysis of biological targets related to a given disease, the discovery and the development of drug candidates for these targets, performing parallel biological tests to validate the drug effectiveness and side effects. Approaches as quantitative study of activity-structure relationships (QSAR) involve the construction of predictive models that relate a set of descriptors of a chemical compound series and its biological activities with respect to one or more targets in the human body. Datasets used to perform QSAR analyses are generally characterized by a small number of samples and this makes them more complex to build accurate predictive models. In this context, transfer and multi-task learning techniques are very suitable since they take information from other QSAR models to the same biological target, reducing efforts and costs for generating new chemical compounds. Therefore, this review will present the main features of transfer and multi-task learning studies, as well as some applications and its potentiality in drug design projects.

Computational Tool for Fast in silico Evaluation of hERG K+ Channel Affinity

The development of a novel comprehensive approach for the prediction of hERG activity is herein presented. Software Phase has been used to derive a 3D-QSAR model, employing as alignment rule a common pharmacophore built on a subset of 22 highly active compounds (threshold Ki: 50 nM) against hERG K⁺ channel. Five features comprised the pharmacophore: two aromatic rings (R₁ and R₂), one hydrogen-bond acceptor (A), one hydrophobic site (H), and one positive ionizable function (P). The sequential 3D-QSAR model developed with a set of 421 compounds (randomly divided in training and test set) yielded a test set (Q²) = 0.802 and proved to be predictive with respect to an external test set of 309 compounds that were not used to generate the model (rext_ts2 = 0.860). Furthermore, the model was submitted to an in silico validation for assessing the reliability of the approach, by applying a decoys set, evaluating the Güner and Henry score (GH) and the Enrichment Factor (EF), and by using the ROC curve analysis. The outcome demonstrated the high predictive power of the inclusive 3D-QSAR model developed for the hERG K⁺ channel blockers, confirming the fundamental validity of the chosen approach for obtaining a fast proprietary cardiotoxicity predictive tool to be employed for rationally designing compounds with reduced hERG K⁺ channel activity at the early steps of the drug discovery trajectory.

In silico study of subtilisin-like protease 1 (SUB1) from different Plasmodium species in complex with peptidyl-difluorostatones and characterization of potent pan-SUB1 inhibitors

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Homology models of four SUB1 orthologues from P. falciparum species were produced.

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We analyzed the binding mode of our previous difluorostatone inhibitors to six SUB1.

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In vitro activity of our difluorostatone-based inhibitors was correctly predicted.

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We derived a structure-based pan-SUB1 pharmacophore, and validated it in silico.

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We confirmed that development of pan-SUB1 inhibitors is a feasible task.

Plasmodium falciparum subtilisin-like protease 1 (SUB1) is a novel target for the development of innovative antimalarials. We recently described the first potent difluorostatone-based inhibitors of the enzyme ((4S)-(N-((N-acetyl-l-lysyl)-l-isoleucyl-l-threonyl-l-alanyl)-2,2-difluoro-3-oxo-4-aminopentanoyl)glycine (1) and (4S)-(N-((N-acetyl-l-isoleucyl)-l-threonyl-l-alanylamino)-2,2-difluoro-3-oxo-4-aminopentanoyl)glycine (2)). As a continuation of our efforts towards the definition of the molecular determinants of enzyme-inhibitor interaction, we herein propose the first comprehensive computational investigation of the SUB1 catalytic core from six different Plasmodium species, using homology modeling and molecular docking approaches. Investigation of the differences in the binding sites as well as the interactions of our inhibitors 1,2 with all SUB1 orthologues, allowed us to highlight the structurally relevant regions of the enzyme that could be targeted for developing pan-SUB1 inhibitors. According to our in silico predictions, compounds 1,2 have been demonstrated to be potent inhibitors of SUB1 from all three major clinically relevant Plasmodium species (P. falciparum, P. vivax, and P. knowlesi). We next derived multiple structure-based pharmacophore models that were combined in an inclusive pan-SUB1 pharmacophore (SUB1-PHA). This latter was validated by applying in silico methods, showing that it may be useful for the future development of potent antimalarial agents.

Comparative Analysis of Virtual Screening Approaches in the Search for Novel EphA2 Receptor Antagonists

The EphA2 receptor and its ephrin-A1 ligand form a key cell communication system, which has been found overexpressed in many cancer types and involved in tumor growth. Recent medicinal chemistry efforts have identified bile acid derivatives as low micromolar binders of the EphA2 receptor. However, these compounds suffer from poor physicochemical properties, hampering their use in vivo. The identification of compounds able to disrupt the EphA2-ephrin-A1 complex lacking the bile acid scaffold may lead to new pharmacological tools suitable for in vivo studies. To identify the most promising virtual screening (VS) protocol aimed at finding novel EphA2 antagonists, we investigated the ability of both ligand-based and structure-based approaches to retrieve known EphA2 antagonists from libraries of decoys with similar molecular properties. While ligand-based VSs were conducted using UniPR129 and ephrin-A1 ligand as reference structures, structure-based VSs were performed with Glide, using the X-ray structure of the EphA2 receptor/ephrin-A1 complex. A comparison of enrichment factors showed that ligand-based approaches outperformed the structure-based ones, suggesting ligand-based methods using the G-H loop of ephrin-A1 ligand as template as the most promising protocols to search for novel EphA2 antagonists.

Discovery of GPCR ligands for probing signal transduction pathways

G protein-coupled receptors (GPCRs) are seven integral transmembrane proteins that are the primary targets of almost 30% of approved drugs and continue to represent a major focus of pharmaceutical research. All of GPCR targeted medicines were discovered by classical medicinal chemistry approaches. After the first GPCR crystal structures were determined, the docking screens using these structures lead to discovery of more novel and potent ligands. There are over 360 pharmaceutically relevant GPCRs in the human genome and to date about only 30 of structures have been determined. For these reasons, computational techniques such as homology modeling and molecular dynamics simulations have proven their usefulness to explore the structure and function of GPCRs. Furthermore, structure-based drug design and in silico screening (High Throughput Docking) are still the most common computational procedures in GPCRs drug discovery. Moreover, ligand-based methods such as three-dimensional quantitative structure–selectivity relationships, are the ideal molecular modeling approaches to rationalize the activity of tested GPCR ligands and identify novel GPCR ligands. In this review, we discuss the most recent advances for the computational approaches to effectively guide selectivity and affinity of ligands. We also describe novel approaches in medicinal chemistry, such as the development of biased agonists, allosteric modulators, and bivalent ligands for class A GPCRs. Furthermore, we highlight some knockout mice models in discovering biased signaling selectivity.

Optimization, pharmacophore modeling and 3D-QSAR studies of sipholanes as breast cancer migration and proliferation inhibitors

Sipholenol A, a triterpene isolated from the Red Sea sponge Callyspongia siphonella, was previously shown to reverse multidrug resistance in P-glycoprotein-overexpressing cancer cells. Moreover, sipholanes showed promising in vitro inhibitory effects against the invasion and migration of the metastatic human breast cancer cell line MDA-MB-231. The breast tumor kinase (Brk), a mediator of cancer cell phenotypes important for proliferation, survival, and migration, was proposed as a potential target. This study reports additional semisynthetic optimization of sipholenol A esters to improve the breast cancer antimigratory and antiproliferative activities as well as Brk phosphorylation inhibition. Fifteen new sipholenol A analogs (25-39) were semisynthesized. Sipholenol A 4β-4',5'-dichlorobenzoate ester (29) was the most potent, with an IC50 value of 1.3 μM in the migration assay. The level of Brk phosphorylation inhibition of 29 was assessed using the Z'-LYTE™ kinase assay and Western blot analysis. Active analogs showed no toxicity on the non-tumorigenic epithelial breast cell line MCF10A at doses equal to their IC50 values or higher in migration and proliferation assays, suggesting their selectivity towards malignant cells. Pharmacophore modeling and 3D-QSAR studies were conducted to identify important pharmacophoric features and correlate 3D-chemical structure with activity. These studies provided the evidence for future design of novel antimigratory compounds based on a simplified sipholane structure possessing rings A and B (perhydrobenzoxepine) connected to substituted aromatic esters, with the elimination of rings C and D ([5,3,0]bicyclodecane system). This will enable the future synthesis of the new active entities feasibly and cost-effectively. These results demonstrate the potential of marine natural products for the discovery of novel scaffolds for the control and management of metastatic breast cancer.