Novel Hybrid Virtual Screening Protocol Based on Molecular Docking and Structure-Based Pharmacophore for Discovery of Methionyl-tRNA Synthetase Inhibitors as Antibacterial Agents

Methione tRNA synthetase (MetRS) is an essential enzyme involved in protein biosynthesis in all living organisms and is a potential antibacterial target. In the current study, the structure-based pharmacophore (SBP)-guided method has been suggested to generate a comprehensive pharmacophore of MetRS based on fourteen crystal structures of MetRS-inhibitor complexes. In this investigation, a hybrid protocol of a virtual screening method, comprised of pharmacophore model-based virtual screening (PBVS), rigid and flexible docking-based virtual screenings (DBVS), is used for retrieving new MetRS inhibitors from commercially available chemical databases. This hybrid virtual screening approach was then applied to screen the Specs (202,408 compounds) database, a structurally diverse chemical database. Fifteen hit compounds were selected from the final hits and shifted to experimental studies. These results may provide important information for further research of novel MetRS inhibitors as antibacterial agents.     

Hybrid Pharmacophore- and Structure-Based Virtual Screening Pipeline to Identify Novel EGFR Inhibitors That Suppress Non-Small Cell Lung Cancer Cell Growth

Dysregulated epidermal growth factor receptor (EGFR) expression is frequently observed in non-small cell lung cancer (NSCLC) growth and metastasis. Despite recent successes in the development of tyrosine kinase inhibitors (TKIs), inevitable resistance to TKIs has led to urgent calls for novel EGFR inhibitors. Herein, we report a rational workflow used to identify novel EGFR-TKIs by combining hybrid ligand- and structure-based pharmacophore models. Three types of models were developed in this workflow, including 3D QSAR-, common feature-, and structure-based EGFR-TK domain-containing pharmacophores. A National Cancer Institute (NCI) compound dataset was adopted for multiple-stage pharmacophore-based virtual screening (PBVS) of various pharmacophore models. The six top-scoring compounds were identified through the PBVS pipeline coupled with molecular docking. Among these compounds, NSC609077 exerted a significant inhibitory effect on EGFR activity in gefitinib-resistant H1975 cells, as determined by an enzyme-linked immunosorbent assay (ELISA). Further investigations showed that NSC609077 inhibited the anchorage-dependent growth and migration of lung cancer cells. Furthermore, NSC609077 exerted a suppressive effect on the EGFR/PI3K/AKT pathway in H1975 cells. In conclusion, these findings suggest that hybrid virtual screening may accelerate the development of targeted drugs for lung cancer treatment.     

A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors

B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q² = 0.621, r²_pred = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained.     

Combined Structure-Based Pharmacophore and 3D-QSAR Studies on Phenylalanine Series Compounds as TPH1 Inhibitors

Tryptophan hydroxylase-1 (TPH1) is a key enzyme in the synthesis of serotonin. As a neurotransmitter, serotonin plays important physiological roles both peripherally and centrally. In this study, a combination of ligand-based and structure-based methods is used to clarify the essential quantitative structure-activity relationship (QSAR) of known TPH1 inhibitors. A multicomplex-based pharmacophore (MCBP) guided method has been suggested to generate a comprehensive pharmacophore of TPH1 kinase based on three crystal structures of TPH1-inhibitor complex. This model has been successfully used to identify the bioactive conformation and align 32 structurally diverse substituted phenylalanine derivatives. The QSAR analyses have been performed on these TPH1 inhibitors based on the MCBP guided alignment. These results may provide important information for further design and virtual screening of novel TPH1 inhibitors.     

Identification of New KRAS G12D Inhibitors through Computer-Aided Drug Discovery Methods

Owing to several mutations, the oncogene Kirsten rat sarcoma 2 viral oncogene homolog (KRAS) is activated in the majority of cancers, and targeting it has been pharmacologically challenging. In this study, using an in silico approach comprised of pharmacophore modeling, molecular docking, and molecular dynamics simulations, potential KRAS G12D inhibitors were investigated. A ligand-based common feature pharmacophore model was generated to identify the framework necessary for effective KRAS inhibition. The chemical features in the selected pharmacophore model comprised two hydrogen bond donors, one hydrogen bond acceptor, two aromatic rings and one hydrophobic feature. This model was used for screening in excess of 214,000 compounds from InterBioScreen (IBS) and ZINC databases. Eighteen compounds from the IBS and ten from the ZINC database mapped onto the pharmacophore model and were subjected to molecular docking. Molecular docking results highlighted a higher affinity of four hit compounds towards KRAS G12D in comparison to the reference inhibitor, BI-2852. Sequential molecular dynamics (MD) simulation studies revealed all four hit compounds them possess higher KRAS G12D binding free energy and demonstrate stable polar interaction with key residues. Further, Principal Component Analysis (PCA) analysis of the hit compounds in complex with KRAS G12D also indicated stability. Overall, the research undertaken provides strong support for further in vitro testing of these newly identified KRAS G12D inhibitors, particularly Hit1 and Hit2.     

The Development of FAK Inhibitors: A Five-Year Update

Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase over-expressed in different solid cancers. In recent years, FAK has been recognized as a new target for the development of antitumor agents, useful to contrast tumor development and metastasis formation. To date, studies on the role of FAK and FAK inhibitors are of great interest for both pharmaceutical companies and academia. This review is focused on compounds able to block FAK with different potencies and with different mechanisms of action, that have appeared in the literature since 2017. Furthermore, new emerging PROTAC molecules have appeared in the literature. This summary could improve knowledge of new FAK inhibitors and provide information for future investigations, in particular, from a medicinal chemistry point of view.     

Novel Natural Inhibitors of CYP1A2 Identified by in Silico and in Vitro Screening

Inhibition of cytochrome P450 (CYP) is a major cause of herb-drug interactions. The CYP1A2 enzyme plays a major role in the metabolism of drugs in humans. Its broad substrate specificity, as well as its inhibition by a vast array of structurally diverse herbal active ingredients, has indicated the possibility of metabolic herb-drug interactions. Therefore nowadays searching inhibitors for CYP1A2 from herbal medicines are drawing much more attention by biological, chemical and pharmological scientists. In our work, a pharmacophore model as well as the docking technology is proposed to screen inhibitors from herbal ingredients data. Firstly different pharmaphore models were constructed and then validated and modified by 202 herbal ingredients. Secondly the best pharmaphore model was chosen to virtually screen the herbal data (a curated database of 989 herbal compounds). Then the hits (147 herbal compounds) were continued to be filtered by a docking process, and were tested in vitro successively. Finally, five of eighteen candidate compounds (272, 284, 300, 616 and 817) were found to have inhibition of CYP1A2 activity. The model developed in our study is efficient for in silico screening of large herbal databases in the identification of CYP1A2 inhibitors. It will play an important role to prevent the risk of herb-drug interactions at an early stage of the drug development process.     

Discovery of Non‐ATP‐Competitive Inhibitors of Polo‐like Kinase 1

Polo‐like kinase 1 (Plk1) is an evolutionarily conserved serine/threonine kinase, and its N‐terminal kinase domain (KD) controls cell signaling through phosphorylation. Inhibitors of Plk1 are potential anticancer drugs. Most known Plk1 KD inhibitors are ATP‐competitive compounds, which may suffer from low selectivity. In this study we discovered novel non‐ATP‐competitive Plk1 KD inhibitors by virtual screening and experimental studies. Potential binding sites in Plk1 KD were identified by using the protein binding site detection program Cavity. The identified site was subjected to molecular‐docking‐based virtual screening. The activities of top‐ranking compounds were evaluated by in vitro enzyme assay with full‐length Plk1 and direct binding assay with Plk1 KD. Several compounds showed inhibitory activity, and the most potent was found to be 3‐((2‐oxo‐2‐(thiophen‐2‐yl)ethyl)thio)‐6‐(pyridin‐3‐ylmethyl)‐1,2,4‐triazin‐5(4H)‐one (compound 4 ) with an IC₅₀ value of 13.1±1.7 μm . Our work provides new insight into the design of kinase inhibitors that target non‐ATP binding sites.     

Discovery of Akt Kinase Inhibitors through Structure-Based Virtual Screening and Their Evaluation as Potential Anticancer Agents

Akt acts as a pivotal regulator in the PI3K/Akt signaling pathway and represents a potential drug target for cancer therapy. To search for new inhibitors of Akt kinase, we performed a structure-based virtual screening using the DOCK 4.0 program and the X-ray crystal structure of human Akt kinase. From the virtual screening, 48 compounds were selected and subjected to the Akt kinase inhibition assay. Twenty-six of the test compounds showed more potent inhibitory effects on Akt kinase than the reference compound, H-89. These 26 compounds were further evaluated for their cytotoxicity against HCT-116 human colon cancer cells and HEK-293 normal human embryonic kidney cells. Twelve compounds were found to display more potent or comparable cytotoxic activity compared to compound H-89 against HCT-116 colon cancer cells. The best results were obtained with Compounds a46 and a48 having IC₅₀ values (for HCT-116) of 11.1 and 9.5 µM, respectively, and selectivity indices (IC₅₀ for HEK-293/IC₅₀ for HCT-116) of 12.5 and 16.1, respectively. Through structure-based virtual screening and biological evaluations, we have successfully identified several new Akt inhibitors that displayed cytotoxic activity against HCT-116 human colon cancer cells. Especially, Compounds a46 and a48 may serve as useful lead compounds for further development of new anticancer agents.     

Identification of Highly Potent Protein Kinase C‐Related Kinase 1 Inhibitors by Virtual Screening,Binding Free Energy Rescoring,and in vitro Testing

Despite the considerable interest in protein kinase C‐related kinase 1 (PRK1) as a target in cancer research, there is still a lack of PRK1 inhibitors with suitable selectivity profiles and physicochemical properties. To identify new PRK1 inhibitors we applied a virtual screening approach, which combines ensemble docking, minimization of the protein–ligand complex, binding free energy calculations, and application of quantitative structure–activity relationship (QSAR) models for predicting in vitro activity. The developed approach was then applied in a prospective manner to screen available libraries of kinase inhibitors from Selleck and GlaxoSmithKline (GSK). Compounds that showed favorable prediction were then tested in vitro for PRK1 inhibition. Some of the hits were found to inhibit PRK1 in the low‐nanomolar range. Three in vitro hits were additionally tested in a mass‐spectrometry‐based cellular kinase profiling assay to examine selectivity. Our findings show that nanomolar and drug‐like inhibitors can be identified by the virtual screening approach presented herein. The identified inhibitors are valuable tools for gaining a better understanding of PRK1 inhibition, and the identified hits can serve as starting points for further chemical optimization.     

Three-dimensional pharmacophore design and biochemical screening identifies substituted 1,2,4-triazoles as inhibitors of the annexin A2-S100A10 protein interaction

Protein interactions are increasingly appreciated as targets in small‐molecule drug discovery. The interaction between the adapter protein S100A10 and its binding partner annexin A2 is a potentially important drug target. To obtain small‐molecule starting points for inhibitors of this interaction, a three‐dimensional pharmacophore model was constructed from the X‐ray crystal structure of the complex between S100A10 and annexin A2. The pharmacophore model represents the favourable hydrophobic and hydrogen bond interactions between the two partners, as well as spatial and receptor site constraints (excluded volume spheres). Using this pharmacophore model, UNITY flex searches were carried out on a 3D library of 0.7 million commercially available compounds. This resulted in 568 hit compounds. Subsequently, GOLD docking studies were performed on these hits, and a set of 190 compounds were purchased and tested biochemically for inhibition of the protein interaction. Three compounds of similar chemical structure were identified as genuine inhibitors of the binding of annexin A2 to S100A10. The binding modes predicted by GOLD were in good agreement with their UNITY‐generated conformations. We synthesised a series of analogues revealing areas critical for binding. Thus computational predictions and biochemical screening can be used successfully to derive novel chemical classes of protein–protein interaction blockers.     

Discovery of Tyrosine Kinase Inhibitors by Docking into an Inactive Kinase Conformation Generated by Molecular Dynamics

Several small molecules that bind to the inactive DFG‐out conformation of tyrosine kinases (called type II inhibitors) have shown a good selectivity profile over other kinase targets. To obtain a set of DFG‐out structures, we performed an explicit solvent molecular dynamics (MD) simulation of the complex of the catalytic domain of a tyrosine kinase receptor, ephrin type‐A receptor 3 (EphA3), and a manually docked type II inhibitor. Automatic docking of four previously reported type II inhibitors was used to select a single snapshot from the MD trajectory for virtual screening. High‐throughput docking of a pharmacophore‐tailored library of 175 000 molecules resulted in about 4 million poses, which were further filtered by van der Waals efficiency and ranked according to a force‐field‐based energy function. Notably, around 20 % of the compounds with predicted binding energy smaller than ?10 kcal mol^?1 are known type II inhibitors. Moreover, a series of 5‐(piperazine‐1‐yl)isoquinoline derivatives was identified as a novel class of low‐micromolar inhibitors of EphA3 and unphosphorylated Abelson tyrosine kinase (Abl1). The in silico predicted binding mode of the new inhibitors suggested a similar affinity to the gatekeeper mutant T315I of Abl1, which was verified in vitro by using a competition binding assay. Additional evidence for the type II binding mode was obtained by two 300 ns MD simulations of the complex between N‐(3‐chloro‐4‐(difluoromethoxy)phenyl)‐2‐(4‐(8‐nitroisoquinolin‐5‐yl)piperazin‐1‐yl)acetamide and EphA3.     

Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics

Heparanase (Hpse) is an endo-β-D-glucuronidase capable of cleaving heparan sulfate side chains. Its upregulated expression is implicated in tumor growth, metastasis and angiogenesis, thus making it an attractive target in cancer therapeutics. Currently, a few small molecule inhibitors have been reported to inhibit Hpse, with promising oral administration and pharmacokinetic (PK) properties. In the present study, a ligand-based pharmacophore model was generated from a dataset of well-known active small molecule Hpse inhibitors which were observed to display favorable PK properties. The compounds from the InterBioScreen database of natural (69,034) and synthetic (195,469) molecules were first filtered for their drug-likeness and the pharmacophore model was used to screen the drug-like database. The compounds acquired from screening were subjected to molecular docking with Heparanase, where two molecules used in pharmacophore generation were used as reference. From the docking analysis, 33 compounds displayed higher docking scores than the reference and favorable interactions with the catalytic residues. Complex interactions were further evaluated by molecular dynamics simulations to assess their stability over a period of 50 ns. Furthermore, the binding free energies of the 33 compounds revealed 2 natural and 2 synthetic compounds, with better binding affinities than reference molecules, and were, therefore, deemed as hits. The hit compounds presented from this in silico investigation could act as potent Heparanase inhibitors and further serve as lead scaffolds to develop compounds targeting Heparanase upregulation in cancer.     

Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors

The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC₅₀ units from measured inhibition affinities and a Pearson’s correlation coefficient R² of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors.     

Front Cover: Development and Characterization of Selective FAK Inhibitors and PROTACs with In Vivo Activity (ChemBioChem 19/2023)

Focal adhesion kinase (FAK) is an attractive drug target due to its overexpression in cancer. FAK functions as a non-receptor tyrosine kinase and scaffolding protein, coordinating several downstream signaling effectors and cellular processes. While drug discovery efforts have largely focused on targeting FAK kinase activity, FAK inhibitors have failed to show efficacy as single agents in clinical trials. Here, using structure-guided design, we report the development of a selective FAK inhibitor (BSJ-04-175) and degrader (BSJ-04-146) to evaluate the consequences and advantages of abolishing all FAK activity in cancer models. BSJ-04-146 achieves rapid and potent FAK degradation with high proteome-wide specificity in cancer cells and induces durable degradation in mice. Compared to kinase inhibition, targeted degradation of FAK exhibits pronounced improved activity on downstream signaling and cancer cell viability and migration. Together, BSJ-04-175 and BSJ-04-146 are valuable chemical tools to dissect the specific consequences of targeting FAK through small-molecule inhibition or degradation.     

Identification and Structure–Activity Relationship Studies of Small‐Molecule Inhibitors of the Methyllysine Reader Protein Spindlin1

The methyllysine reader protein Spindlin1 has been implicated in the tumorigenesis of several types of cancer and may be an attractive novel therapeutic target. Small‐molecule inhibitors of Spindlin1 should be valuable as chemical probes as well as potential new therapeutics. We applied an iterative virtual screening campaign, encompassing structure‐ and ligand‐based approaches, to identify potential Spindlin1 inhibitors from databases of commercially available compounds. Our in silico studies coupled with in vitro testing were successful in identifying novel Spindlin1 inhibitors. Several 4‐aminoquinazoline and quinazolinethione derivatives were among the active hit compounds, which indicated that these scaffolds represent promising lead structures for the development of Spindlin1 inhibitors. Subsequent lead optimization studies were hence carried out, and numerous derivatives of both lead scaffolds were synthesized. This resulted in the discovery of novel inhibitors of Spindlin1 and helped explore the structure–activity relationships of these inhibitor series.     

Computer‐Guided Design,Synthesis, and Biological Evaluation of Quinoxalinebisarylureas as FLT3 Inhibitors

Activating mutations of FMS‐like tyrosine kinase 3 (FLT3) are present in ～30 % of patients with acute myeloid leukemia (AML) and are associated with poor prognosis. Point mutations in the tyrosine kinase domain (TKD) are observed as primary mutations or are acquired as secondary mutations in FLT3 with internal tandem duplications (ITDs) after treatment with tyrosine kinase inhibitors (TKIs). Although dozens of potent inhibitors against FLT3 ITD have been reported, activating TKD point mutations, especially at residues F691 and D835, remain the leading cause for therapy resistance, highlighting the consistent need for new potent inhibitors. Herein we report the identification and characterization of novel quinoxaline‐based FLT3 inhibitors. We used the pharmacophore features of diverse known inhibitors as a starting point for a new optimization algorithm for type II TKIs, starting from an in silico library pharmacophore search and induced‐fit docking in the known FLT3 structure. This led to the design of a set of diverse quinoxalinebisarylureas, which were profiled in an FLT3 kinase activity assay. The most promising compounds were further evaluated in a zebrafish embryo phenotype assay.     

Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design

Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.