SARS-CoV-2 Main Protease Inhibitors from Natural Product Repository as Therapeutic Candidates for the Treatment of Coronaviridae Infections.

BACKGROUND: The main protease (Mpro) is a crucial enzyme for the life cycle of SARS-CoV-2 and a validated target for the treatment of COVID-19 infection. Natural products have been a proper alternative for treating viral diseases by modulating different steps of the life cycle of many viruses. OBJECTIVE: This review article is designed to summarize the cumulative information of natural-derived Mpro inhibitors that are validated by experimental biological testing. METHODS: The natural-derived Mpro inhibitors of SARS-CoV-2 that have been discovered since the emergence of the COVID-19 pandemic are reviewed in this article. Only natural products with experimental validation are reported in this article. Collected compounds are classified according to their chemical identity into flavonoids, phenolic acids, quinones, alkaloids, chromones, stilbenes, tannins, lignans, terpenes, and other polyphenolic and miscellaneous natural-derived Mpro inhibitors. CONCLUSION: These compounds could serve as scaffolds for further lead-structure optimization for desirable potency, a larger margin of safety, and better oral activity.

Discovery of natural-derived Mpro inhibitors as therapeutic candidates for COVID-19: Structure-based pharmacophore screening combined with QSAR analysis.

The main protease (Mpro ) is an essential enzyme for the life cycle of SARS-CoV-2 and a validated target for treatment of COVID-19 infection. Structure-based pharmacophore modeling combined with QSAR calculations were employed to identify new chemical scaffolds of Mpro inhibitors from natural products repository. Hundreds of pharmacophore models were manually built from their corresponding X-ray crystallographic structures. A pharmacophore model that was validated by receiver operating characteristic (ROC) curve analysis and selected using the statistically optimum QSAR equation was implemented as a 3D-search tool to mine AnalytiCon Discovery database of natural products. Captured hits that showed the highest predicted inhibitory activities were bioassayed. Three active Mpro inhibitors (pseurotin A, lactupicrin, and alpinetin) were successfully identified with IC50 values in low micromolar range.

Synthesis and Biological Evaluation of Thiazole-Based Fibroblast Growth Factor Receptor-1 Inhibitors.

BACKGROUND: The Fibroblast Growth Factor Receptor-1 (FGFR-1) is a tyrosine kinase and a validated target for treatment of different cancer types. OBJECTIVE: Design and synthesis of novel thiazole-based analogues of anticancer agents. METHODS: Series of 2-aryl-5-methylthiazole analogues linked to structurally variable basic heads were synthesized as novel anticancer agents. Developed compounds were tested for their cytotoxic activities against several cancer cell lines. RESULTS: Many analogues exhibited strong antiproliferative activities against breast cancer cell lines, with higher potency towards the highly metastatic form (MDA-MB-231). Pharmacophoric profiling using in-house pharmacophore database identified FGFR-1 as a molecular target of active analogues. Synthesized compounds were bioassayed for their FGFR-1 inhibitory activities and many hits exhibited IC50 values in the low micromolar to nanomolar range. CONCLUSION: The 2-aryl-5-methylthiazole linked to a basic head is a novel chemical scaffold of ATP-competitive inhibitor of FGFR-1 with potential therapeutic activities against different types of cancer.

Oxadiazol-based mTOR inhibitors with potent antiproliferative activities: synthetic and computational modeling.

Series of N-aryl-1,3,4-oxadiazole-2-amines and 3-aryl-1,2,4-oxadiazole-5-carboxamides derivatives were synthesized as novel chemotherapeutic agents. Synthesized compounds were evaluated for their anticancer activities against several cancer cell lines. Many analogues of 1,3,4-oxadiazole scaffold showed potent antiproliferative activities against breast cancer cell lines, with higher activities toward the metastatic breast cancer cell line (MDA-MB-231). Active analogues were profiled using in-house pharmacophore database in search for molecular target. Active analogues (2j and 2k) were found to fit the pharmacophoric map of ATP-competitive inhibitors of mTOR. The mTOR inhibitory activities of the most active compounds were confirmed with IC50 values in nanomolar range. The N-aryl-1,3,4-oxadiazole-2-amines linked to a basic head is a novel ATP-competitive inhibitors of mTOR with potential activities for treatment of different types of cancer.

Structure-based Pharmacophore Screening Coupled with QSAR Analysis Identified Potent Natural-product-derived IRAK-4 Inhibitors.

Interleukin-1 Receptor-Associated Kinase 4 (IRAK-4) has crucial functions in inflammation, innate immunity, and malignancy. Structure-based pharmacophore modeling integrated with validated QSAR analysis was implemented to discover structurally novel IRAK-4 inhibitors from natural products database. The QSAR model combined molecular descriptors with structure-based pharmacophore capable of explaining bioactivity variation of structurally diverse IRAK-4 inhibitors. Manually built pharmacophore model, validated with receiver operating characteristic curve, and selected using the statistically optimum QSAR equation, was applied as a 3D-search query to mine AnalytiCon Discovery database of natural products. Experimental in vitro testing of highest-ranked hits identified uvaretin, saucerneol, and salvianolic acid B as active IRAK-4 inhibitors with IC50 values in low micromolar range.

Discovery of Potent Natural-Product-Derived SIRT2 Inhibitors Using Structure- Based Exploration of SIRT2 Pharmacophoric Space Coupled With QSAR Analyses.

BACKGROUND: SIRT2 belongs to a class III of Histone Deacetylase (HDAC) and has crucial roles in neurodegeneration and malignancy. OBJECTIVE: The objective of this study is to discover structurally novel natural-product-derived SIRT2 inhibitors. METHODS: Structure-based pharmacophore modeling integrated with validated QSAR analysis was implemented to discover structurally novel SIRT2 inhibitors from the natural products database. The targeted QSAR model combined molecular descriptors with structure-based pharmacophore capable of explaining bioactivity variation of structurally diverse SIRT2 inhibitors. Manually built pharmacophore model, validated with receiver operating characteristic curve, and selected using the statistically optimum QSAR equation, was applied as a 3Dsearch query to mine AnalytiCon Discovery database of natural products. RESULTS: Experimental in vitro testing of highest-ranked hits identified asperphenamate and salvianolic acid B as active SIRT2 inhibitors with IC50 values in low micromolar range. CONCLUSION: New chemical scaffolds of SIRT2 inhibitors have been identified that could serve as a starting point for lead-structure optimization.

Discovery of potent IRAK-4 inhibitors as potential anti-inflammatory and anticancer agents using structure-based exploration of IRAK-4 pharmacophoric space coupled with QSAR analyses.

Interleukin-1 Receptor-Associated Kinase 4 (IRAK-4) has an important role in immunity, inflammation, and malignancy. The significant role of IRAK-4 makes it an interesting target for the discovery and development of potent small molecule inhibitors. In the current study, multiple linear regression -based QSAR analyses coupled with structure-based pharmacophoric exploration was applied to reveal the structural and physiochemical properties required for IRAK-4 inhibition. Manually built pharmacophoric models were initially validated with receiver operating characteristic curve, and best-ranked models were subsequently integrated in QSAR analysis along with other physiochemical descriptors. The pharmacophore model, selected using the statistically optimum QSAR equation, was implied as a 3D-search filter to mine the National Cancer Institute database for novel IRAK-4 inhibitors. Whereas the associated QSAR model prioritized the bioactivities of captured hits for in vitro evaluation. Experimental validation identified several potent IRAK-4 inhibitors of novel structural scaffolds. The most potent captured hit exhibited an IC50 value of 157 nM.

New Pim-1 Kinase Inhibitor From the Co-culture of Two Sponge-Associated Actinomycetes.

Saccharomonospora sp. UR22 and Dietzia sp. UR66, two actinomycetes derived from the Red Sea sponge Callyspongia siphonella, were co-cultured and the induced metabolites were monitored by HPLC-DAD and TLC. Saccharomonosporine A (1), a novel brominated oxo-indole alkaloid, convolutamydine F (2) along with other three known induced metabolites (3-5) were isolated from the EtOAc extract of Saccharomonospora sp. UR22 and Dietzia sp. UR66 co-culture. Additionally, axenic culture of Saccharomonospora sp. UR22 led to isolation of six known microbial metabolites (6-11). A kinase inhibition assay results showed that compounds 1 and 3 were potent Pim-1 kinase inhibitors with an IC50 value of 0.3 ± 0.02 and 0.95 ± 0.01 µM, respectively. Docking studies revealed the binding mode of compounds 1 and 3 in the ATP pocket of Pim-1 kinase. Testing of compounds 1 and 3 displayed significant antiproliferative activity against the human colon adenocarcinoma HT-29, (IC50 3.6 and 3.7 µM, respectively) and the human promyelocytic leukemia HL-60, (IC50 2.8 and 4.2 µM, respectively). These results suggested that compounds 1 and 3 act as potential Pim-1 kinase inhibitors that mediate the tumor cell growth inhibitory effect. This study highlighted the co-cultivation approach as an effective strategy to increase the chemical diversity of the secondary metabolites hidden in the genomes of the marine actinomycetes.

Design, synthesis, and biological evaluation of novel oxadiazole- and thiazole-based histamine H3R ligands.

Histamine H3 receptor (H3R) is largely expressed in the CNS and modulation of the H3R function can affect histamine synthesis and liberation, and modulate the release of many other neurotransmitters. Targeting H3R with antagonists/inverse agonists may have therapeutic applications in neurodegenerative disorders, gastrointestinal and inflammatory diseases. This prompted us to design and synthesize azole-based H3R ligands, i.e. having oxadiazole- or thiazole-based core structures. While ligands of oxadiazole scaffold were almost inactive, thiazole-based ligands were very potent and several exhibited binding affinities in a nanomolar concentration range. Ligands combining 4-cyanophenyl moiety as arbitrary region, para-xylene or piperidine carbamoyl linkers, and/or pyrrolidine or piperidine basic heads were found to be the most active within this series of thiazole-based H3R ligands. The most active ligands were in silico screened for ADMET properties and drug-likeness. They fulfilled Lipinski's and Veber's rules and exhibited potential activities for oral administration, blood-brain barrier penetration, low hepatotoxicity, combined with an overall good toxicity profile.

Discovery of potent polyphosphate kinase 1 (PPK1) inhibitors using structure-based exploration of PPK1Pharmacophoric space coupled with docking analyses.

Inorganic polyphosphate (polyP) is present in all living forms of life. Studied mainly in prokaryotes, polyP and its associated enzymes are vital in diverse metabolic activities, in some structural functions, and most importantly in stress responses. Bacterial species, including many pathogens, encode a homolog of a major polyP synthesis enzyme, Poly Phosphate Kinase (PPK) with 2 different genes coding for PPK1 and PPK2. Genetic deletion of the ppk1 gene leads to reduced polyP levels and the consequent loss of virulence and stress adaptation responses. This far, no PPK1 homolog has been identified in higher-order eukaryotes, and, therefore, PPK1 represents a novel target for chemotherapy. The aim of the current study is to investigate PPK1 from Escherichia coli with comprehensive understanding of the enzyme's structure and binding sites, which were used to design pharmacophores and screen a library of compounds for potential discovery of selective PPK1 inhibitors. Verification of the resultant inhibitors activities was conducted using a combination of mutagenic and chemical biological approaches. The metabolic phenotypic maps of the wild type E. coli (WT) and ppk1 knockout mutant were generated and compared with the metabolic map of the chemically inhibited WT. In addition, biofilm formation ability was measured in WT, ppk1 knockout mutant, and the chemically inhibited WT. The results demonstrated that chemical inhibition of PPK1, with the designed inhibitors, was equivalent to gene deletion in altering specific metabolic pathways, changing the metabolic fingerprint, and suppressing the ability of E. coli to form a biofilm.

Unsupervised pharmacophore modeling combined with QSAR analyses revealed novel low micromolar SIRT2 inhibitors.

Situin 2 (SIRT2) enzyme is a histone deacetylase that has important role in neuronal development. SIRT2 is clinically validated target for neurodegenerative diseases and some cancers. In this study, exhaustive unsupervised pharmacophore modeling was combined with quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent SIRT2 inhibitors using 146 known SIRT2 ligands. A computational workflow that combines genetic function algorithm with k-nearest neighbor or multiple linear regression was implemented to build self-consistent and predictive QSAR models based on combinations of pharmacophores and physicochemical descriptors. Successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve profiles. Optimal QSAR models and their associated pharmacophore hypotheses were experimentally validated by identification and in vitro evaluation of several new promising SIRT2 inhibitory leads retrieved from the National Cancer Institute structural database. The most potent hit illustrated IC50 value of 5.4µM. The chemical structures of active hits were validated by proton nuclear magnetic resonance and mass spectroscopy.

Discovery of Potent Bruton's Tyrosine Kinase Inhibitors Using Ligand Based Modeling.

BACKGROUND: Bruton's Tyrosine Kinase (BTK) is a one of the Tec tyrosine kinase family. It has an essential role in B-cell development and function. Activation of BTK has been associated with the pathogenesis of many types of lymphomas and leukemia, and involved in non-life threatening autoimmune diseases. OBJECTIVE: In this study, exhaustive pharmacophore modeling was combined with QSAR analyses to examine the structural requirements for anti-BTK activities. METHOD: Genetic function algorithm (GFA) was coupled with multiple linear regression (MLR) analysis to select the best combinations of physicochemical descriptors and pharmacophoric hypothesis capable of generating predictive and self-consistent QSAR models. The optimum pharmacophores were decorated with exclusion volumes to improve their receiver operating characteristic (ROC) curve properties. The best predictive QSAR model and its corresponding pharmacophore models were validated by discovering of novel promising BTK inhibitors retrieved from the National Cancer Institute (NCI) database. RESULTS: Several potent hits exhibited anti-proliferative activities on U-937 cell-line in low micromolar IC50, and one active compound showed nontoxic activities on normal fibroblast cell line. CONCLUSION: Our efforts culminated in the identification of potent BTK ligands having desired inhibitory activities and structurally distinct from known active reference compounds (i.e., training compounds) and represent new chemotypes.

Discovery of potent NEK2 inhibitors as potential anticancer agents using structure-based exploration of NEK2 pharmacophoric space coupled with QSAR analyses.

High expression of Nek2 has been detected in several types of cancer and it represents a novel target for human cancer. In the current study, structure-based pharmacophore modeling combined with multiple linear regression (MLR)-based QSAR analyses was applied to disclose the structural requirements for NEK2 inhibition. Generated pharmacophoric models were initially validated with receiver operating characteristic (ROC) curve, and optimum models were subsequently implemented in QSAR modeling with other physiochemical descriptors. QSAR-selected models were implied as 3D search filters to mine the National Cancer Institute (NCI) database for novel NEK2 inhibitors, whereas the associated QSAR model prioritized the bioactivities of captured hits for in vitro evaluation. Experimental validation identified several potent NEK2 inhibitors of novel structural scaffolds. The most potent captured hit exhibited an [Formula: see text] value of 237 nM.

Multiple Targeting Approaches on Histamine H3 Receptor Antagonists.

With the very recent market approval of pitolisant (Wakix®), the interest in clinical applications of novel multifunctional histamine H3 receptor antagonists has clearly increased. Since histamine H3 receptor antagonists in clinical development have been tested for a variety of different indications, the combination of pharmacological properties in one molecule for improved pharmacological effects and reduced unwanted side-effects is rationally based on the increasing knowledge on the complex neurotransmitter regulations. The polypharmacological approaches on histamine H3 receptor antagonists on different G-protein coupled receptors, transporters, enzymes as well as on NO-signaling mechanism are described, supported with some lead structures.

Discovery of potent adenosine A2a antagonists as potential anti-Parkinson disease agents. Non-linear QSAR analyses integrated with pharmacophore modeling.

Adenosine A2A receptor antagonists are of great interest in the treatment for Parkinson's disease. In this study, we combined extensive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent Adenosine A2A antagonists. Genetic function algorithm (GFA) joined with k nearest neighbor (kNN) analyses were applied to build predictive QSAR models. Successful pharmacophores were complemented with exclusion spheres to improve their receiver operating characteristic curve (ROC) profiles. Best QSAR models and their associated pharmacophore hypotheses were validated by identification of several novel Adenosine A2A antagonist leads retrieved from the National Cancer Institute (NCI) structural database. The most potent hit illustrated IC50 value of 545.7 nM.

Olive Oil-derived Oleocanthal as Potent Inhibitor of Mammalian Target of Rapamycin: Biological Evaluation and Molecular Modeling Studies.

The established anticancer and neuroprotective properties of oleocanthal combined with the reported role of mammalian target of rapamycin (mTOR) in cancer and Alzheimer's disease development encouraged us to examine the possibility that oleocanthal inhibits mTOR. To validate this hypothesis, we docked oleocanthal into the adenosine triphosphate binding pocket of a close mTOR protein homologue, namely, PI3K-γ. Apparently, oleocanthal shared nine out of ten critical binding interactions with a potent dual PIK3-γ/mTOR natural inhibitor. Subsequent experimental validation indicated that oleocanthal indeed inhibited the enzymatic activity of mTOR with an IC50 value of 708 nM. Oleocanthal inhibits the growth of several breast cancer cell lines at low micromolar concentration in a dose-dependent manner. Oleocanthal treatment caused a marked downregulation of phosphorylated mTOR in metastatic breast cancer cell line (MDA-MB-231). These results strongly indicate that mTOR inhibition is at least one of the factors of the reported anticancer and neuroprotective properties of oleocanthal.

Design and Evaluation of 3-(Benzylthio)benzamide Derivatives as Potent and Selective SIRT2 Inhibitors.

Inhibitors of sirtuin-2 (SIRT2) deacetylase have been shown to be protective in various models of Huntington's disease (HD) by decreasing polyglutamine aggregation, a hallmark of HD pathology. The present study was directed at optimizing the potency of SIRT2 inhibitors containing the 3-(benzylsulfonamido)benzamide scaffold and improving their metabolic stability. Molecular modeling and docking studies revealed an unfavorable role of the sulfonamide moiety for SIRT2 binding. This prompted us to replace the sulfonamide with thioether, sulfoxide, or sulfone groups. The thioether analogues were the most potent SIRT2 inhibitors with a two- to three-fold increase in potency relative to their corresponding sulfonamide analogues. The newly synthesized compounds also demonstrated higher SIRT2 selectivity over SIRT1 and SIRT3. Two thioether-derived compounds (17 and 18) increased α-tubulin acetylation in a dose-dependent manner in at least one neuronal cell line, and 18 was found to inhibit polyglutamine aggregation in PC12 cells.

Combining docking-based comparative intermolecular contacts analysis and k-nearest neighbor correlation for the discovery of new check point kinase 1 inhibitors.

Check point kinase 1 (Chk1) is an important protein in G2 phase checkpoint arrest required by cancer cells to maintain cell cycle and to prevent cell death. Therefore, Chk1 inhibitors should have potential as anti-cancer therapeutics. Docking-based comparative intermolecular contacts analysis (dbCICA) is a new three-dimensional quantitative structure activity relationship method that depends on the quality and number of contact points between docked ligands and binding pocket amino acid residues. In this presented work we implemented a novel combination of k-nearest neighbor/genetic function algorithm modeling coupled with dbCICA to select critical ligand-Chk1 contacts capable of explaining anti-Chk1 bioactivity among a long list of inhibitors. The finest set of contacts were translated into two valid pharmacophore hypotheses that were used as 3D search queries to screen the National Cancer Institute's structural database for new Chk1 inhibitors. Three potent Chk1 inhibitors were discovered with IC50 values ranging from 2.4 to 69.7 µM.

Discovery of nanomolar phosphoinositide 3-kinase gamma (PI3Kγ) inhibitors using ligand-based modeling and virtual screening followed by in vitro analysis.

Phosphoinositide 3-kinase gamma (PI3Kγ) is member of a family of enzymes involved in cancer pathogenesis. Accordingly, considerable efforts have been carried out to develop new PI3Kγ inhibitors. Towards this end we explored the pharmacophoric space of PI3Kγ using three diverse sets of inhibitors. Subsequently, we employed genetic algorithm-based QSAR analysis to select optimal combination of pharmacophoric models and physicochemical descriptors that can explain bioactivity variation within training inhibitors. Interestingly, two successful pharmacophores were selected within two statistically consistent QSAR models. The close similarity among the two binding models prompted us to merge them in a hybrid pharmacophore. The resulting model showed superior receiver operator characteristic curve (ROC) and closely resembled binding interactions seen in crystallographic ligand-PI3Kγ complexes. The resulting model was employed to screen the national cancer institute (NCI) list of compounds to search for new PI3Kγ ligands. After testing captured hits in vitro, 19 compounds showed nanomolar IC50 values against PI3Kγ. The chemical structures and purities of most potent hits were validated using NMR and MS experiments.

The use of docking-based comparative intermolecular contacts analysis to identify optimal docking conditions within glucokinase and to discover of new GK activators.

Glucokinase (GK) is involved in normal glucose homeostasis and therefore it is a valid target for drug design and discovery efforts. GK activators (GKAs) have excellent potential as treatments of hyperglycemia and diabetes. The combined recent interest in GKAs, together with docking limitations and shortages of docking validation methods prompted us to use our new 3D-QSAR analysis, namely, docking-based comparative intermolecular contacts analysis (dbCICA), to validate docking configurations performed on a group of GKAs within GK binding site. dbCICA assesses the consistency of docking by assessing the correlation between ligands' affinities and their contacts with binding site spots. Optimal dbCICA models were validated by receiver operating characteristic curve analysis and comparative molecular field analysis. dbCICA models were also converted into valid pharmacophores that were used as search queries to mine 3D structural databases for new GKAs. The search yielded several potent bioactivators that experimentally increased GK bioactivity up to 7.5-folds at 10 µM.