Non-peptide angiotensin II receptor antagonists: chemical feature based pharmacophore identification

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.

Applications of the Novel Quantitative Pharmacophore Activity Relationship Method QPhAR in Virtual Screening and Lead-Optimisation

Pharmacophores are an established concept for the modelling of ligand–receptor interactions based on the abstract representations of stereoelectronic molecular features. They became widely popular as filters for the fast virtual screening of large compound libraries. A lot of effort has been put into the development of sophisticated algorithms and strategies to increase the computational efficiency of the screening process. However, hardly any focus has been put on the development of automated procedures that optimise pharmacophores towards higher discriminatory power, which still has to be done manually by a human expert. In the age of machine learning, the researcher has become the decision-maker at the top level, outsourcing analysis tasks and recurrent work to advanced algorithms and automation workflows. Here, we propose an algorithm for the automated selection of features driving pharmacophore model quality using SAR information extracted from validated QPhAR models. By integrating the developed method into an end-to-end workflow, we present a fully automated method that is able to derive best-quality pharmacophores from a given input dataset. Finally, we show how the QPhAR-generated models can be used to guide the researcher with insights regarding (un-)favourable interactions for compounds of interest.

Design, Synthesis, Molecular Docking Analysis and Biological Evaluations of 4-[(Quinolin-4-yl)amino]benzamide Derivatives as Novel Anti-Influenza Virus Agents

In this study, a series of 4-[(quinolin-4-yl)amino]benzamide derivatives as the novel anti-influenza agents were designed and synthesized. Cytotoxicity assay, cytopathic effect assay and plaque inhibition assay were performed to evaluate the anti-influenza virus A/WSN/33 (H1N1) activity of the target compounds. The target compound G07 demonstrated significant anti-influenza virus A/WSN/33 (H1N1) activity both in cytopathic effect assay (EC₅₀ = 11.38 ± 1.89 µM) and plaque inhibition assay (IC₅₀ = 0.23 ± 0.15 µM). G07 also exhibited significant anti-influenza virus activities against other three different influenza virus strains A/PR/8 (H1N1), A/HK/68 (H3N2) and influenza B virus. According to the result of ribonucleoprotein reconstitution assay, G07 could interact well with ribonucleoprotein with an inhibition rate of 80.65% at 100 µM. Furthermore, G07 exhibited significant activity target PA−PB1 subunit of RNA polymerase according to the PA−PB1 inhibitory activity prediction by the best pharmacophore Hypo1. In addition, G07 was well drug-likeness based on the results of Lipinski’s rule and ADMET prediction. All the results proved that 4-[(quinolin-4-yl)amino]benzamide derivatives could generate potential candidates in discovery of anti-influenza virus agents.

An Ugi Reaction/Intramolecular Cyclization/Oxidation Cascade towards Tetrazole-Linked Dibenzoxazepines

A series of tetrazole-linked dibenzo[b,f][1,4]oxazepines is synthesized through a short sequence involving an Ugi tetrazole reaction. The intermediate tetrazole undergoes a potassium carbonate mediated SNAr cyclization, followed by oxidation to afford the target tricyclic heterocyclic scaffold. The optimization, scope and limitations of this two-step and efficient methodology are investigated. A 1000-member library of tetrazole-linked dibenzo[b,f][1,4]oxazepines is generated and the physicochemical properties are analyzed. great

In-silico protein-ligand docking studies against the estrogen protein of breast cancer using pharmacophore based virtual screening approaches

Breast cancer in woman is the most common cancer and in 2018 there were around 2 million new cases recorded. The maximum rate of breast cancer is reported in Belgium followed by Luxembourg. It is the second most general cancer, Lung cancer being the first. If the cancer tumor is located only in the breast, the survival rate would be 99%. If the tumor has wide to lymph nodes around the survival rate would be 85% and if the tumor had extend to distant parts, the survival rate would come down to 27%. Mammary gland is an important organ in mammals which has potential function to secrete, synthesize and deliver milk to the infants for nourishment, improvement and protection. Generally, cancer is named after the body part in which it originated; thus, breast cancer refers to the erratic development and proliferation of cells that originate in the breast tissue (7). There are some kinds of tumors that may grow within various areas of the breast. Most tumors are the outcome of benign (non-cancerous) alters within the breast. The estrogen receptors (ER) in ordinary and diseased states are significant for the improvement of relevant therapeutic strategies. Two main forms of ER exist, ERα and ERβ, which are encoded by separate genes. Estrogens play a central role in breast cancer improvement with ERα status being the mainly significant predictor of breast cancer prognosis. The potent lead molecule binding mode, residue-interaction patterns and docking energy were examined by molecular docking and binding free energy studies. The lead compounds and 3ERT complex structural stability and dynamic behavior were monitored by molecular dynamics analysis. The drug-likeness properties of lead compounds were predicted ADME analysis.

The Pharmacophore Concept and Its Applications in Computer-Aided Drug Design

Pharmacophore-based techniques currently are an integral part of many computer-aided drug design workflows and have been successfully and extensively applied for tasks such as virtual screening, de novo design, and lead optimization. Pharmacophore models can be derived both in a receptor-based and in a ligand-based manner, and provide an abstract description of essential non-bonded interactions that typically occur between small-molecule ligands and macromolecular targets. Due to their simplistic and abstract nature, pharmacophores are both perfectly suited for efficient computer processing and easy to comprehend by life and physical scientists. As a consequence, they have also proven to be a valuable tool for communicating between computational and medicinal chemists.This chapter aims to provide a short overview of the pharmacophore concept and its applications in modern computer-aided drug design. The chapter is divided into three distinct parts. The first section contains a brief introduction to the pharmacophore concept. The second section provides a description of the most common nonbonded interaction types and their representation as pharmacophoric features. Furthermore, it gives an overview of the various methods for pharmacophore generation and important pharmacophore-based techniques in drug design. This part concludes with examples for recent pharmacophore concept-related research and development. The last section is dedicated to a review of research in the field of natural product chemistry as carried out by employing pharmacophore-based drug design methods.

Computer-aided discovery of new FGFR-1 inhibitors followed by in vitro validation

AIM

FGFR-1 is an oncogenic kinase involved in several cancers. FGFR1-specific inhibitors have shown promising results against several human cancers prompting us to model this interesting target. Toward the end, we implemented elaborate ligand-based and structure-based computational workflows to explore the pharmacophoric requirements for potent FGFR-1 inhibitors. Results & methodology: Structure-based and ligand-based modeling applied on 59 diverse FGFR-1 inhibitors yielded novel pharmacophore and quantitative structure-activity relationship models that were used to scan the National Cancer Institute's structural database for novel leads. Four potent hits were captured, with the most active having IC₅₀ of 426 nM. Identities and purities of active hits were established using nuclear magnetic resonance and mass spectroscopy.

CONCLUSION

Elaborate ligand-based (pharmacophore/quantitaive structure-activity relationship) and structure-based (docking-based comparative intermolecular contacts analysis) modeling provided deep understanding of ligand binding within FGFR-1 as evidenced by the virtually captured new potent leads.

Ligand-based modeling of diverse aryalkylamines yields new potent P-glycoprotein inhibitors

The P-glycoprotein (P-gp) efflux pump has an important role as a natural detoxification system in many types of normal and cancer cells. P-gp is implicated in multiple drug resistance (MDR) exhibited by several types of cancer against a multitude of anticancer chemotherapeutic agents, and therefore, it is clinically validated target for cancer therapy. Accordingly, in this study we combined exhaustive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent P-gp inhibitors employing 130 known P-gp ligands. Genetic function algorithm (GFA) coupled with k nearest neighbor (kNN) or multiple linear regression (MLR) analyses were employed to build self-consistent and predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. Successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. Optimal QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of new promising P-gp inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. Several potent hits were captured. The most potent hit decreased the IC50 of doxorubicin from 0.906 to 0.190 μM on doxorubicin resistant MCF7 cell-line.

Protocol for fragment hopping

Fragment hopping is a fragment-based approach to designing biologically active small molecules. The key of this approach is the determination of the minimal pharmacophoric elements in the three-dimensional space. Based on the derived minimal pharmacophoric elements, new fragments with different chemotypes can be generated and positioned to the active site of the target protein. Herein, we detail a protocol for performing fragment hopping. This approach can not only explore a wide chemical space to produce new ligands with novel scaffolds but also characterize and utilize the delicate differences in the active sites between isofunctional proteins to produce new ligands with high target selectivity/specificity.

Exploration of potential EGFR inhibitors: a combination of pharmacophore-based virtual screening, atom-based 3D-QSAR and molecular docking analysis

Epidermal growth factor receptor (EGFR) protein tyrosine kinases are over expressed in several human cancers and considered as a promising target for developing novel anticancer drugs. In this study, the ligand-based pharmacophore mapping and atom-based 3D-QSAR approach was carried out on a series of 40 novel pyrrolo[3, 2-d]pyrimidine derivatives acting as EGFR inhibitors. The best pharmacophore hypothesis AAADRR.295 was selected and an atom-based 3D-QSAR model was generated by applying partial least-squares algorithm. The developed model was validated and used as a 3D query in sequential virtual screening study to filter five chemical databases. The obtained compounds were further filtered according to Lipinski rule of five and fitness score. Subsequently, a multistep molecular docking study was employed on the retrieved hits and finally, 12 compounds were prioritized as potential leads against EGFR, which exhibited high docking scores, correlated binding mode to experimentally proven compounds and constructive drug-like properties. The results of this study provide detailed structural insights and emphasize the important binding features of these compounds, which may assists in the design and development of novel EGFR inhibitors.

Elaborate ligand-based modeling coupled with QSAR analysis and in silico screening reveal new potent acetylcholinesterase inhibitors

Inhibition of the enzyme acetylcholinesterase (AChE) has been shown to alleviate neurodegenerative diseases prompting several attempts to discover and optimize new AChE inhibitors. In this direction, we explored the pharmacophoric space of 85 AChE inhibitors to identify high quality pharmacophores. Subsequently, we implemented genetic algorithm-based quantitative structure-activity relationship (QSAR) modeling to select optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of explaining bioactivity variation among training compounds (r2(68)=0.94, F-statistic=125.8, r2 LOO=0.92, r2 PRESS against 17 external test inhibitors = 0.84). Two orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least two binding modes accessible to ligands within AChE binding pocket. The successful pharmacophores were comparable with crystallographically resolved AChE binding pocket. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute list of compounds. Twenty-four low micromolar AChE inhibitors were identified. The most potent gave IC50 value of 1.0 μM.

Discovery of novel acyl coenzyme a: cholesterol acyltransferase inhibitors: pharmacophore-based virtual screening, synthesis and pharmacology

The present study describes ligand-based pharmacophore modeling of a series of structurally diverse acyl coenzyme A cholesterol acyltransferase inhibitors. Quantitative pharmacophore models were generated using HypoGen module of Discovery Studio 2.1, whereby the best pharmacophore model possessing two hydrophobic, one ring aromatic, and one hydrogen bond acceptor feature for inhibition of acyl coenzyme A cholesterol acyltransferase showed a very good correlation coefficient (r = 0.942) along with satisfactory cost analysis. Hypo1 was also validated by test set and cross-validation methods. Developed models were found to be predictive as indicated by low error values for test set molecules. Virtual screening against Maybridge database using Hypo1 was performed. The two most potent compounds (47 and 48; predicted IC₅₀ = 1 nM) of the retrieved hits were synthesized and biologically evaluated. These compounds showed 86% and 88% inhibition of acyl coenzyme A cholesterol acyltransferase (at 10 μg/mL) with IC₅₀ value of 3.6 and 2.5 nM, respectively. As evident from the close proximity of biological data to the predicted values, it can be concluded that the generated model (Hypo1) is a reliable and useful tool for lead optimization of novel acyl coenzyme A cholesterol acyltransferase inhibitors.

Discovery of potential integrin VLA-4 antagonists using pharmacophore modeling, virtual screening and molecular docking studies

Very late antigen-4 (VLA-4) is an integrin protein, and its antagonists are useful as anti-inflammatory drugs. The aim of this study is to discover novel virtual lead compounds to use them in designing potent VLA-4 antagonists. A best pharmacophore model was generated with correlation coefficient of 0.935, large cost difference of 114.078, comprising two hydrogen bond acceptors and three hydrophobic features. It was further validated and used in database screening for potential VLA-4 antagonists. A homology model of VLA-4 was built and employed in molecular docking of screened hit compounds. Finally, two compounds were identified as potential virtual leads to be deployed in the designing of novel potent VLA-4 antagonists.

Elaborate ligand-based modeling reveals new nanomolar heat shock protein 90α inhibitors

Heat shock protein (Hsp90α) has been recently implicated in cancer prompting several attempts to discover and optimize new Hsp90α inhibitors. Toward this end, we explored the pharmacophoric space of 83 Hsp90α inhibitors using six diverse sets of inhibitors to identify high-quality pharmacophores. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of accessing a self-consistent quantitative structure activity relationship (QSAR) of optimal predictive potential (r(67)(2)=0.811, F 42.8, r(LOO)(2)=0.748, r(PRESS)(2) (against 16 external test inhibitors) = 0.619). Three orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least three binding modes accessible to ligands within the Hsp90α binding pocket. Receiver operating characteristic (ROC) curves analysis established the validity of QSAR-selected pharmacophores. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute (NCI) list of compounds and our in-house-built drugs and agrochemicals database (DAC). Twenty-five nanomolar and low micromolar Hsp90α inhibitors were identified. The most potent were formoterol, amodaquine, primaquine, and midodrine with IC(50) values of 3, 5, 6, and 20 nM, respectively.

Deciphering ligand dependent degree of binding site closure and its implication in inhibitor design: A modeling study on human adenosine kinase

Protein flexibility plays a significant role in drug research due to its effect on accurate prediction of ligand binding mode and activity. Adenosine kinase (AK) represents a highly flexible binding site and is known to exhibit large conformational changes as a result of substrate or inhibitor binding. Here we propose a semi-open conformation for ligand binding in human AK, in addition to the known closed and open forms. The modeling study illustrates the necessity of thorough understanding of the conformational states of protein for docking and binding mode prediction. It has been shown that predicting activity in the context of correct binding mode can improve the insight into conserved interactions and mechanism of action for inhibition of AK. Integrating the knowledge about the binding modes of ligands in different conformational states of the protein, separate pharmacophore models were generated and used for virtual screening to explore potential novel hits. In addition, 2D descriptor based clustering was done to differentiate the ligands, binding to closed, semi-open and open conformations of human AK. The results indicated that binding of all AK inhibitors cannot be described by same rules, instead, they represent a rule based preference for inhibition. This inference about tubercidins binding to semi-open conformation of human AK may facilitate in finding much extensive space for AK inhibitors.

Pharmacophore and QSAR modeling of estrogen receptor beta ligands and subsequent validation and in silico search for new hits

The pharmacophoric space of estrogen receptor beta (ERbeta) was explored using a set of 119 known ligands. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select optimal combinations of pharmacophoric models and physicochemical descriptors in self-consistent and predictive quantitative structure-activity relationships (QSARs) (r(96)(2)=0.79-0.83, F-statistic=40.96-36.20, r(LOO)(2)=0.74-0.76 and r(PRESS)(2) against 23 external compounds=0.54-0.56, respectively). Four binding hypotheses emerged in two optimal QSAR equations suggesting the existence of distinct binding modes accessible to ligands within ERbeta binding pocket. The close similarity among the resulting pharmacophores prompted us to merge them in two hybrid models. The hybrid pharmacophores illustrated superior receiver operator characteristic curves (ROCs) and closely resembled binding interactions suggested by docking experiments. The resulting models and associated QSAR equations were employed to screen the national cancer institute (NCI) list of compounds and an in house built database of known drugs and agrochemicals to search for new ERbeta ligands.

Discovery of DPP IV inhibitors by pharmacophore modeling and QSAR analysis followed by in silico screening

Dipeptidyl peptidase IV (DPP IV) deactivates the natural hypoglycemic incretin hormones. Inhibition of this enzyme should restore glucose homeostasis in diabetic patients making it an attractive target for the development of new antidiabetic drugs. With this in mind, the pharmacophoric space of DPP IV was explored using a set of 358 known inhibitors. Thereafter, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors that yield selfconsistent and predictive quantitative structure-activity relationships (QSAR) (r(2) (287)=0.74, F-statistic=44.5, r(2) (BS)=0.74, r(2) (LOO)=0.69, r(2) (PRESS) against 71 external testing inhibitors=0.51). Two orthogonal pharmacophores (of cross-correlation r(2)=0.23) emerged in the QSAR equation suggesting the existence of at least two distinct binding modes accessible to ligands within the DPP IV binding pocket. Docking experiments supported the binding modes suggested by QSAR/pharmacophore analyses. The validity of the QSAR equation and the associated pharmacophore models were established by the identification of new low-micromolar anti-DPP IV leads retrieved by in silico screening. One of our interesting potent anti-DPP IV hits is the fluoroquinolone gemifloxacin (IC(50)=1.12 muM). The fact that gemifloxacin was recently reported to potently inhibit the prodiabetic target glycogen synthase kinase 3beta (GSK-3beta) suggests that gemifloxacin is an excellent lead for the development of novel dual antidiabetic inhibitors against DPP IV and GSK-3beta.

Common pharmacophore identification using frequent clique detection algorithm

The knowledge of a pharmacophore, or the 3D arrangement of features in the biologically active molecule that is responsible for its pharmacological activity, can help in the search and design of a new or better drug acting upon the same or related target. In this paper, we describe two new algorithms based on the frequent clique detection in the molecular graphs. The first algorithm mines all frequent cliques that are present in at least one of the conformers of each (or a portion of all) molecules. The second algorithm exploits the similarities among the different conformers of the same molecule and achieves an order of magnitude performance speedup compared to the first algorithm. Both algorithms are guaranteed to find all common pharmacophores in the data set, which is confirmed by the validation on the set of molecules for which pharmacophores have been determined experimentally. In addition, these algorithms are able to scale to data sets with arbitrarily large number of conformers per molecule and identify multiple ligand binding modes or multiple binding sites of the target.

Chemical function-based pharmacophore generation of selective kappa-opioid receptor agonists by catalyst and phase

Two chemical function-based pharmacophore models of selective kappa-opioid receptor agonists were generated by using two different programs: Catalyst/HypoGen and Phase. The best output hypothesis (Hypo1) of HypoGen consisted of five features: one hydrogen-bond acceptor (HA), three hydrophobic points (HY), and one positive ionizable function (PI). The highest scoring model (Hypo2) produced by Phase comprised four features: one acceptor (A), one positive ionizable function (P), and two aromatic ring features (R). These two models (Hypo1 and Hypo2) were then validated by test set prediction and enrichment factors. They were shown to be able to identify highly potent kappa-agonists within a certain range, and satisfactory enrichments were achieved. The features of these two pharmacophore models were similar and consistent with experiment data. The models produced here were also generally in accord with other reported models. Therefore, our pharmacophore models were considered as valuable tools for 3D virtual screening, and could be useful for designing novel kappa-agonists.

Progress in the understanding of drug-receptor interactions, part 2: experimental and theoretical electrostatic moments and interaction energies of an angiotensin II receptor antagonist (C30H30N6(O)3S)

A combined experimental and theoretical charge density study of an angiotensin II receptor antagonist (1) is presented focusing on electrostatic properties such as atomic charges, molecular electric moments up to the fourth rank and energies of the intermolecular interactions, to gain an insight into the physical nature of the drug-receptor interaction. Electrostatic properties were derived from both the experimental electron density (multipole refinement of X-ray data collected at T=17 K) and the ab initio wavefunction (single molecule and fully periodic calculations at the DFT level). The relevance of SO and SN intramolecular interactions on the activity of 1 is highlighted by using both the crystal and gas-phase geometries and their electrostatic nature is documented by means of QTAIM atomic charges. The derived electrostatic properties are consistent with a nearly spherical electron density distribution, characterised by an intermingling of electropositive and -negative zones rather than by a unique electrophilic region opposed to a nucleophilic area. This makes the first molecular moment scarcely significant and ill-determined, whereas the second moment is large, significant and highly reliable. A comparison between experimental and theoretical components of the third electric moment shows a few discrepancies, whereas the agreement for the fourth electric moment is excellent. The most favourable intermolecular bond is show to be an NHN hydrogen bond with an energy of about 50 kJ mol(-1). Key pharmacophoric features responsible for attractive electrostatic interactions include CHX hydrogen bonds. It is shown that methyl and methylene groups, known to be essential for the biological activity of the drug, provide a significant energetic contribution to the total binding energy. Dispersive interactions are important at the thiophene and at both the phenyl fragments. The experimental estimates of the electrostatic contribution to the intermolecular interaction energies of six molecular pairs, obtained by a new model proposed by Spackman, predict the correct relative electrostatic energies with no exceptions.