scholarly journals Identification of Novel HBV/HDV Entry Inhibitors by Pharmacophore- and QSAR-Guided Virtual Screening

Viruses ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1489
Author(s):  
Michael Kirstgen ◽  
Simon Franz Müller ◽  
Kira Alessandra Alicia Theresa Lowjaga ◽  
Nora Goldmann ◽  
Felix Lehmann ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Peptide Binding ◽  
Quantitative Structure Activity Relationship ◽  
Hek293 Cells ◽  
Virus Binding ◽  
Entry Inhibitors ◽  
Binding Inhibition ◽  
Qsar Models ◽  
Ic50 Values

The hepatic bile acid transporter Na+/taurocholate co-transporting polypeptide (NTCP) was identified in 2012 as the high-affinity hepatic receptor for the hepatitis B and D viruses (HBV/HDV). Since then, this carrier has emerged as promising drug target for HBV/HDV virus entry inhibitors, but the synthetic peptide Hepcludex® of high molecular weight is the only approved HDV entry inhibitor so far. The present study aimed to identify small molecules as novel NTCP inhibitors with anti-viral activity. A ligand-based bioinformatic approach was used to generate and validate appropriate pharmacophore and QSAR (quantitative structure–activity relationship) models. Half-maximal inhibitory concentrations (IC50) for binding inhibition of the HBV/HDV-derived preS1 peptide (as surrogate parameter for virus binding to NTCP) were determined in NTCP-expressing HEK293 cells for 150 compounds of different chemical classes. IC50 values ranged from 2 µM up to >1000 µM. The generated pharmacophore and QSAR models were used for virtual screening of drug-like chemicals from the ZINC15 database (~11 million compounds). The 20 best-performing compounds were then experimentally tested for preS1-peptide binding inhibition in NTCP-HEK293 cells. Among them, four compounds were active and revealed experimental IC50 values for preS1-peptide binding inhibition of 9, 19, 20, and 35 µM, which were comparable to the QSAR-based predictions. All these compounds also significantly inhibited in vitro HDV infection of NTCP-HepG2 cells, without showing any cytotoxicity. The best-performing compound in all assays was ZINC000253533654. In conclusion, the present study demonstrates that virtual compound screening based on NTCP-specific pharmacophore and QSAR models can predict novel active hit compounds for the development of HBV/HDV entry inhibitors.

scholarly journals Hepatitis D Virus Entry Inhibitors Based on Repurposing Intestinal Bile Acid Reabsorption Inhibitors

Viruses ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 666
Author(s):  
Michael Kirstgen ◽  
Kira Alessandra Alicia Theresa Lowjaga ◽  
Simon Franz Müller ◽  
Nora Goldmann ◽  
Felix Lehmann ◽  
...  
Keyword(s):  
Bile Acid ◽  
Taurocholic Acid ◽  
Hek293 Cells ◽  
Acid Uptake ◽  
Hepatitis D ◽  
Entry Inhibitors ◽  
Ic50 Values ◽  
Bile Acid Transporter ◽  
Acid Transporter

Identification of Na+/taurocholate co-transporting polypeptide (NTCP) as high-affinity hepatic entry receptor for the Hepatitis B and D viruses (HBV/HDV) opened the field for target-based development of cell-entry inhibitors. However, most of the HBV/HDV entry inhibitors identified so far also interfere with the physiological bile acid transporter function of NTCP. The present study aimed to identify more virus-selective inhibitors of NTCP by screening of 87 propanolamine derivatives from the former development of intestinal bile acid reabsorption inhibitors (BARIs), which interact with the NTCP-homologous intestinal apical sodium-dependent bile acid transporter (ASBT). In NTCP-HEK293 cells, the ability of these compounds to block the HBV/HDV-derived preS1-peptide binding to NTCP (virus receptor function) as well as the taurocholic acid transport via NTCP (bile acid transporter function) were analyzed in parallel. Hits were subsequently validated by performing in vitro HDV infection experiments in NTCP-HepG2 cells. The most potent compounds S985852, A000295231, and S973509 showed in vitro anti-HDV activities with IC50 values of 15, 40, and 70 µM, respectively, while the taurocholic acid uptake inhibition occurred at much higher IC50 values of 24, 780, and 490 µM, respectively. In conclusion, repurposing of compounds from the BARI class as novel HBV/HDV entry inhibitors seems possible and even enables certain virus selectivity based on structure-activity relationships.

Elaboration of Novel TTK1 Inhibitory Leads via QSAR-Guided Selection of Crystallographic Pharmacophores Followed By In vitro Assay

2020 ◽  
Vol 16 ◽  
Author(s):  
Mahmoud A. Al-Sha'er ◽  
Mutasem O. Taha
Keyword(s):  
Standard Error ◽  
Qsar Model ◽  
Qsar Modeling ◽  
Vitro Assay ◽  
Physicochemical Descriptors ◽  
Qsar Models ◽  
Ic50 Values ◽  
Pharmacophore Hypotheses ◽  
Selection Of

Introduction: Tyrosine threonine kinase (TTK1) is a key regulator of chromosome segregation. TTK targeting received recent concern for the enhancement of possible anticancer therapies. Objective: In this regard we employed our well-known method of QSAR-guided selection of best crystallographic pharmacophore(s) to discover considerable binding interactions that anchore inhibitors into TTK1 binding site. Method:Sixtyone TTK1 crystallographic complexes were used to extract 315 pharmacophore hypotheses. QSAR modeling was subsequently used to choose a single crystallographic pharmacophore that when combined with other physicochemical descriptors elucidates bioactivity discrepancy within a list of 55 miscellaneous inhibitors. Results: The best QSAR model was robust and predictive (r2(55) = 0.75, r2LOO = 0.72 , r2press against external testing list of 12 compounds = 0.67), Standard error of estimate (training set) (S)= 0.63 , Standard error of estimate (testing set)(Stest) = 0.62. The resulting pharmacophore and QSAR models were used to scan the National Cancer Institute (NCI) database for new TTK1 inhibitors. Conclusion: Five hits confirmed significant TTK1 inhibitory profiles with IC50 values ranging between 11.7 and 76.6 micM.

scholarly journals Virtual Screening Guided Design, Synthesis and Bioactivity Study of Benzisoselenazolones (BISAs) on Inhibition of c-Met and Its Downstream Signalling Pathways

2019 ◽  
Vol 20 (10) ◽  
pp. 2489 ◽  
Author(s):  
Siqi Zhang ◽  
Qiaoling Song ◽  
Xueting Wang ◽  
Zhiqiang Wei ◽  
Rilei Yu ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Cancer Cell ◽  
Kinase Inhibitors ◽  
Antitumour Activity ◽  
Cancer Cell Line ◽  
Lung Cancer Cell Line ◽  
Signalling Pathways ◽  
Design Synthesis ◽  
Ic50 Values

c-Met is a transmembrane receptor tyrosine kinase and an important therapeutic target for anticancer drugs. In this study, we designed a small library containing 300 BISAs molecules that consisted of carbohydrates, amino acids, isothiourea, tetramethylthiourea, guanidine and heterocyclic groups and screened c-Met targeting compounds using docking and MM/GBSA. Guided by virtual screening, we synthesised a series of novel compounds and their activity on inhibition of the autophosphorylation of c-Met and its downstream signalling pathway proteins were evaluated. We found a panel of benzisoselenazolones (BISAs) obtained by introducing isothiourea, tetramethylthiourea and heterocyclic groups into the C-ring of Ebselen, including 7a, 7b, 8a, 8b and 12c (with IC50 values of less than 20 μM in MET gene amplified lung cancer cell line EBC-1), exhibited more potent antitumour activity than Ebselen by cell growth assay combined with in vitro biochemical assays. In addition, we also tested the antitumour activity of three cancer cell lines without MET gene amplification/activation, including DLD1, MDA-MB-231 and A549. The neuroblastoma SK-N-SH cells with HGF overexpression which activates MET signalling are sensitive to MET inhibitors. The results reveal that our compounds may be nonspecific multitarget kinase inhibitors, just like type-II small molecule inhibitors. Western blot analysis showed that these inhibitors inhibited autophosphorylation of c-MET, and its downstream signalling pathways, such as PI3K/AKT and MARK/ERK. Results suggest that bensoisoselenones can be used as a scaffold for the design of c-Met inhibiting drug leads, and this study opens up new possibilities for future antitumour drug design.

scholarly journals Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents

Molecules ◽  
2019 ◽  
Vol 24 (21) ◽  
pp. 3909 ◽  
Author(s):  
Amit Kumar Halder ◽  
Amal Kanta Giri ◽  
Maria Natália Dias Soeiro Cordeiro
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Linear Model ◽  
Anticancer Agents ◽  
Quantitative Structure Activity Relationship ◽  
Experimental Conditions ◽  
Fragment Analysis ◽  
Linear Discriminant ◽  
Qsar Models ◽  
Dynamics Simulations

Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure–activity relationship (mt-QSAR) models based on the Box–Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.

scholarly journals Network-based piecewise linear regression for QSAR modelling

2019 ◽  
Vol 33 (9) ◽  
pp. 831-844
Author(s):  
Jonathan Cardoso-Silva ◽  
Lazaros G. Papageorgiou ◽  
Sophia Tsoka
Keyword(s):  
Virtual Screening ◽  
Linear Regression ◽  
Predictive Accuracy ◽  
Piecewise Linear ◽  
Chemical Properties ◽  
Structural Similarity ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Piecewise Linear Regression ◽  
Qsar Models

Abstract Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads.

scholarly journals Quantitative Structure–Activity Relationship Evaluation of MDA-MB-231 Cell Anti-Proliferative Leads

Molecules ◽  
2021 ◽  
Vol 26 (16) ◽  
pp. 4795
Author(s):  
Ajaykumar Gandhi ◽  
Vijay Masand ◽  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Anis Ben Ghorbal ◽  
...  
Keyword(s):  
De Novo ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Structure Activity ◽  
Tnbc Cell ◽  
Qsar Models ◽  
Anti Tumor Activity

In the present endeavor, for the dataset of 219 in vitro MDA-MB-231 TNBC cell antagonists, a (QSAR) quantitative structure–activity relationships model has been carried out. The quantitative and explicative assessments were performed to identify inconspicuous yet pre-eminent structural features that govern the anti-tumor activity of these compounds. GA-MLR (genetic algorithm multi-linear regression) methodology was employed to build statistically robust and highly predictive multiple QSAR models, abiding by the OECD guidelines. Thoroughly validated QSAR models attained values for various statistical parameters well above the threshold values (i.e., R2 = 0.79, Q2LOO = 0.77, Q2LMO = 0.76–0.77, Q2-Fn = 0.72–0.76). Both de novo QSAR models have a sound balance of descriptive and statistical approaches. Decidedly, these QSAR models are serviceable in the development of MDA-MB-231 TNBC cell antagonists.

Hydrolytic activity of mitochondrial F1Fo ATP synthase as a target for myocardial ischemia: discovery and in vitro evaluation of novel inhibitors

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
P.E Nikolaou ◽  
P Bessis-Lazarou ◽  
P Efentakis ◽  
D Karagiannis ◽  
N Lougiakis ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Myocardial Ischemia ◽  
Atp Synthase ◽  
Inhibitory Activity ◽  
Hydrolytic Activity ◽  
Cardiac Response ◽  
H9c2 Cells ◽  
F1fo Atp Synthase ◽  
Ic50 Values

Abstract Background/Introduction F1Fo ATP synthase is the mitochondrial complex responsible for ATP production. During myocardial ischemia, ATP synthase reverses its activity to hydrolyse ATP leading to cellular energetic deficit, contracture and lethal cardiomyocyte injury. Therefore, inhibition of ATP hydrolase is of major significance and the discovery of selective hydrolase inhibitors could be of particular interest in terms of cardiac response to ischemia. Purpose We performed an in vitro screening in order to identify and evaluate novel specific inhibitors of the hydrolytic activity of ATP synthase. Methods Initially, we identified inhibitors of the hydrolytic activity of ATP synthase using virtual screening methods. Docking-scoring calculations were performed targeting the three major inhibition sites of a holistic model of ATP synthase of mus musculus that was modelled for the first time. In silico ligand-based virtual screening was carried out using known binders such as BMS199264. The workflow was implemented on our in-house library of 2000 compounds, Pharmalab, plus 266,151 compounds of the National Cancer Institute (NCI) database. The best candidates were evaluated in vitro on isolated murine heart mitochondria to verify their inhibitory effect on ATP synthase hydrolytic activity. Moreover, we confirmed their action at a cellular level. H9C2 cells were treated with the three best candidates in the presence of rotenone and their ability to maintain membrane potential was monitored. The experiment was repeated in absence of rotenone in order to detect toxic or non-specific effects (n=5 independent experiments). Finally, the compounds were tested using an assay in which primary adult rat ventricular cardiomyocytes (ARVCs) were challenged with the mitochondrial uncoupler CCCP to induce reverse ATPase activity, and time until contracture was determined. For the in vitro experiments, oligomycin, a non-selective ATPase inhibitor and BTB06584 a selective hydrolase inhibitor were used as positive controls. Results Different steps of filtering through the virtual screening provided 38 molecules from Pharmalab and 15 candidates form NCI database. From the 53 candidates in total, five compounds displayed in vitro inhibitory activity at 200μM on isolated mitochondria and their IC50 values were determined. Among them, three synthetic derivatives possessing a central pyrazolopyridine core displayed the best IC50 values (81.7±1.3 μM, 99.8±1.2 μM, 144.8±1.3μM) and were evaluated on the H9C2 cells. They exhibited significant inhibitory activity at 50μM (p<0.01 compared to vehicle) while the BTB06584 inhibitor was inactive at the same concentration and two of them were selective. Finally, the final novel inhibitors significantly extended (p<0.001) the time until shortening of the ARVCs. Conclusion We have discovered two novel agents that act as selective inhibitors of ATP hydrolase and can be tested against myocardial ischemia. Funding Acknowledgement Type of funding source: None

scholarly journals Ligand-Based and Docking-Based Virtual Screening of MDM2 Inhibitors as Potent Anticancer Agents

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bing-Hui Li ◽  
Jun-Qi Ge ◽  
Ya-Li Wang ◽  
Li-Jun Wang ◽  
Qi Zhang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Cell Lines ◽  
Anticancer Agents ◽  
Antiproliferative Activity ◽  
Pharmacophore Model ◽  
Inhibition Assay ◽  
Ic50 Values

A ligand-based and docking-based virtual screening was carried out to identify novel MDM2 inhibitors. A pharmacophore model with four features was used for virtual screening, followed by molecular docking. Seventeen compounds were selected for an in vitro MDM2 inhibition assay, and compounds AO-476/43250177, AG-690/37072075, AK-968/15254441, AO-022/43452814, and AF-399/25108021 showed promising MDM2 inhibition activities with K i values of 9.5, 8.5, 23.4, 3.2, and 23.1 μM, respectively. Four compounds also showed antiproliferative activity, and compound AO-022/43452814 was the most potent hit with IC50 values of 19.35, 26.73, 12.63, and 24.14 μM against MCF7 (p53 +/+), MCF7 (p53 -/-), HCT116 (p53 +/+), and HCT116 (p53 -/-) cell lines, respectively. Compound AO-022/43452814 could be used as a scaffold for the development of anticancer agents targeting MDM2.

scholarly journals Evaluation of QSAR Equations for Virtual Screening

2020 ◽  
Vol 21 (21) ◽  
pp. 7828
Author(s):  
Jacob Spiegel ◽  
Hanoch Senderowitz
Keyword(s):  
Virtual Screening ◽  
Quantitative Structure Activity Relationship ◽  
Screening Tests ◽  
Support Vector ◽  
Small Scale ◽  
Active Compounds ◽  
Qsar Models ◽  
Continuous Responses ◽  
Test Sets ◽  
Binary Classifiers

Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure-based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties and for designing new compounds with favorable properties. Due to the large number of calculate-able descriptors and consequently, the much larger number of descriptors combinations, the derivation of QSAR models could be treated as an optimization problem. For continuous responses, metrics which are typically being optimized in this process are related to model performances on the training set, for example, R2 and QCV2. Similar metrics, calculated on an external set of data (e.g., QF1/F2/F32), are used to evaluate the performances of the final models. A common theme of these metrics is that they are context -” ignorant”. In this work we propose that QSAR models should be evaluated based on their intended usage. More specifically, we argue that QSAR models developed for Virtual Screening (VS) should be derived and evaluated using a virtual screening-aware metric, e.g., an enrichment-based metric. To demonstrate this point, we have developed 21 Multiple Linear Regression (MLR) models for seven targets (three models per target), evaluated them first on validation sets and subsequently tested their performances on two additional test sets constructed to mimic small-scale virtual screening campaigns. As expected, we found no correlation between model performances evaluated by “classical” metrics, e.g., R2 and QF1/F2/F32 and the number of active compounds picked by the models from within a pool of random compounds. In particular, in some cases models with favorable R2 and/or QF1/F2/F32 values were unable to pick a single active compound from within the pool whereas in other cases, models with poor R2 and/or QF1/F2/F32 values performed well in the context of virtual screening. We also found no significant correlation between the number of active compounds correctly identified by the models in the training, validation and test sets. Next, we have developed a new algorithm for the derivation of MLR models by optimizing an enrichment-based metric and tested its performances on the same datasets. We found that the best models derived in this manner showed, in most cases, much more consistent results across the training, validation and test sets and outperformed the corresponding MLR models in most virtual screening tests. Finally, we demonstrated that when tested as binary classifiers, models derived for the same targets by the new algorithm outperformed Random Forest (RF) and Support Vector Machine (SVM)-based models across training/validation/test sets, in most cases. We attribute the better performances of the Enrichment Optimizer Algorithm (EOA) models in VS to better handling of inactive random compounds. Optimizing an enrichment-based metric is therefore a promising strategy for the derivation of QSAR models for classification and virtual screening.

scholarly journals QSAR Models for Human Carcinogenicity: An Assessment Based on Oral and Inhalation Slope Factors

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 127
Author(s):  
Cosimo Toma ◽  
Alberto Manganaro ◽  
Giuseppa Raitano ◽  
Marco Marzo ◽  
Domenico Gadaleta ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Regression Models ◽  
External Validation ◽  
Inhalation Exposure ◽  
Quantitative Structure Activity Relationship ◽  
Learning Approaches ◽  
Chemical Safety ◽  
Qsar Models ◽  
Using Data

Carcinogenicity is a crucial endpoint for the safety assessment of chemicals and products. During the last few decades, the development of quantitative structure–activity relationship ((Q)SAR) models has gained importance for regulatory use, in combination with in vitro testing or expert-based reasoning. Several classification models can now predict both human and rat carcinogenicity, but there are few models to quantitatively assess carcinogenicity in humans. To our knowledge, slope factor (SF), a parameter describing carcinogenicity potential used especially for human risk assessment of contaminated sites, has never been modeled for both inhalation and oral exposures. In this study, we developed classification and regression models for inhalation and oral SFs using data from the Risk Assessment Information System (RAIS) and different machine learning approaches. The models performed well in classification, with accuracies for the external set of 0.76 and 0.74 for oral and inhalation exposure, respectively, and r2 values of 0.57 and 0.65 in the regression models for oral and inhalation SFs in external validation. These models might therefore support regulators in (de)prioritizing substances for regulatory action and in weighing evidence in the context of chemical safety assessments. Moreover, these models are implemented on the VEGA platform and are now freely downloadable online.

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