Coumarin-furo[2,3-d]pyrimidone hybrid molecules targeting human liver cancer cells: synthesis, anticancer effect, EGFR inhibition and molecular docking studies.

The design, synthesis and investigation of antitumor activities of some coumarin-furo[2,3-d]pyrimidone hybrid molecules are reported. In vitro, HepG2 cells were used to investigate the cytotoxicity of 6a-n and 10a-n. The results demonstrated that coupling a furopyrimidone scaffold with coumarin through a hydrazide linker can effectively improve their synergistic anticancer activity. The coumarin-furo[2,3-d]pyrimidone combination 10a exhibited significant inhibitory activity against HepG2 cells with IC50 = 7.72 ± 1.56 µM, which is better than those of gefitinib and sorafenib. It is worth mentioning that the coumarin-furo[2,3-d]pyrimidone combination 10a showed excellent inhibition of the EGFR enzymatic activity with IC50 = 1.53 µM and 90% inhibition at 10 µM concentration. In silico investigation predicts the possibility of direct binding between the new coumarin-furo[2,3-d]pyrimidone hybrid molecules and the EGFR. The results suggest that coumarin-furo[2,3-d]pyrimidone hybrid molecules are potential antitumor agents targeting human liver cancer cells.

Role of Computer-Aided Drug Design in Drug Development.

The introduction of computational techniques to pharmaceutical chemistry and molecular biology in the 20th century has changed the way people develop drugs [...].

Novel Coumarin-furo[2,3-d]pyrimidinone hybrid derivatives as anticancer agents: Synthesis, biological evaluation and molecular docking.

A series of coumarin-furo[2,3-d]pyrimidinone hybrid derivatives were synthesized, characterized by HR-MS, 1H NMR and 13C NMR. All synthesized compounds were evaluated for antiproliferative activities against hepatic carcinoma (HepG2) and cervical carcinoma (Hela) cell lines in vitro, and results shown that most of the compounds exhibited potent antitumor activity. Moreover, compound 3i, 8d and 8i were selected to induce apoptosis in HepG2 cells, and it displayed a significant concentration-dependent. Further, transwell migration assay was used to detect the most potent compound 8i, and the results revealed that 8i can significantly inhibit HepG2 cells migration and invasion. In addition, kinase activity assay showed compound 8i may be a multi-target inhibitor, which 8i has an inhibition rate of 40-20% on RON, ABL, GSK3α and so on ten different kinases at the concentration 1 µmol/L. At the same time, molecular docking studies revealed the possible binding modes of compounds 3i, 8d and 8i with kinase recepteur d'origine nantais (RON). A comparative molecular field analysis (CoMFA) model was established from 3D-QSAR study that guide us to a more bulkly and electro-positive Y group at the C-2 position of furo[2,3-d]pyrimidinone ring was preferable for the bioactivity improvement of our compounds. Our preliminary research indicated that the coumarin skeleton introducing to the furo[2,3-d]pyrimidine system had a significantly influence on the biological activities.

Comparison of the molecular mechanisms of Fuzi Lizhong Pill and Huangqin decoction in the treatment of the cold and heat syndromes of ulcerative colitis based on network pharmacology.

OBJECTIVE: The aim of this study was to illuminate the similarities and differences of two prescriptions as "cold" and "heat" drugs for treating ulcerative colitis (UC) with the simultaneous occurrence of heat and cold syndrome via network pharmacology. METHODS: (1) Active compounds of Fuzi-Lizhong Pill (FLP) and Huangqin Decoction (HQT) were retrieved from the TCMSP database, and their common active compounds were compared using the Venn diagram. (2) Potential proteins targeted to three sets of compounds either (i) shared by FLP and HQT, (ii) unique to FLP or (iii) unique to HQT were screened from the STP, STITCH and TCMSP databases, and three corresponding core compound sets were identified in Herb-Compound-Target (H-C-T) networks. (3) Targets related to UC were identified from the DisGeNET and GeneCards databases and compared with the FLP-HQT common targets to identify potential targets of FLP-HQT compounds related to UC. (4) Three potential target sets were imported into the STRING database for protein‒protein interaction (PPI) analysis, and three core target sets were defined. (5) The binding capabilities and interacting modes between core compounds and key targets were verified by molecular docking via Discovery Studio 2019 and molecular dynamics (MD) simulations via Amber 2018. (6) The target sets were enriched for KEGG pathways using the DAVID database. RESULTS: (1) FLP and HQT included 95 and 113 active compounds, respectively, with 46 common compounds, 49 FLP-specific compounds and 67 HQT-specific compounds. (2) 174 targets of FLP-HQT common compounds, 168 targets of FLP-specific compounds, and 369 targets of HQT-specific compounds were predicted from the STP, STITCH and TCMSP databases; six core compounds specific to FLP and HQT were screened in the FLP-specific and HQT-specific H-C-T networks, respectively. (3) 103 targets overlapped from the 174 predicted targets and the 4749 UC-related targets; two core compounds for FLP-HQT were identified from the FLP-HQT H-C-T network. (4) 103 FLP-HQT-UC common targets, 168 of FLP-specific targets and 369 of HQT-specific targets had shared core targets (AKT1, MAPK3, TNF, JUN and CASP3) based on the PPI network analysis. (5) Molecular docking demonstrated that naringenin, formononetin, luteolin, glycitein, quercetin, kaempferol and baicalein of FLP and HQT play a critical role in treating UC; meanwhile, MD simulations revealed the stability of protein‒ligand interactions. (6) The enriched pathways indicated that most targets were related to anti-inflammatory, immunomodulatory and other pathways. Compared with the pathways identified using traditional methods, FLP-specific pathways included the PPAR signaling pathway and the bile secretion pathway, and HQT-specific pathways included the vascular smooth muscle contraction pathway and the natural killer cell-mediated cytotoxicity pathway etc. CONCLUSION: In this study, we clarified the common mechanisms of FLP and HQT in treating UC and their specific mechanisms in treating cold and heat syndrome in UC through compound, target and pathway distinction and a literature comparison based on network pharmacology; these results provide a new perspective on the detailed mechanism of "multidrugs and single-disease" thought in traditional Chinese medicine.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets.

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Green synthesis, structure optimization and biological evalution of Rhopaladins' analog 2-styryl-5-oxopyrrolidine-2- carboxamide RPDPRH on CaSki cells.

We have synthesized Rhopaladins' analog (2E,4E)-4-chlorobenzylidene-2-(4-chlorostyryl)-N-cyclohexyl-1-(4-fluorophenyl)-5-oxopyrrolidine-2-carboxamide (RPDPRH) via a highly facile, inexpensive and green approach and verified the structural superiority of compound RPDPRH through molecular docking. Moreover, we further detected the anti-proliferation, apoptosis and HPV E6/E7 effects of RPDPRH on CaSki cells. Finally, we confirmed that compared with the previous compound (E)-N-(tert-butyl)-2-(4-chlorobenzoyl)-4-(4-fluorobenzylidene)-1-isopropyl-5-oxopyrrolidine-2-carboxamide (RPDPB), RPDPRH could better inhibit proliferation, induce apoptosis, and down-regulate HPV E6/E7 mRNA expression on Caski cells. And preliminary RT-PCR experiments have demonstrated that RPDPRH also could affect the expression of Bcl-2, Bax and Caspase-3 mRNA in Caski cells. In summary, RPDPRH has potential as an effective agent against cervical cancer and will play an important role in our subsequent research.

Discovery of 3-pyrazolyl-substituted pyrazolo[1,5-a]pyrimidine derivatives as potent TRK inhibitors to overcome clinically acquired resistance.

Several secondary tropomyosin receptor kinase (TRK) mutations located in the solvent front, xDFG, and gatekeeper regions, are a common cause of clinical resistance. Mutations in the xDFG motif in particular limit sensitivity to second-generation TRK inhibitors, which represent an unmet clinical need. We designed a series of 3-pyrazolyl-substituted pyrazolo[1,5-a]pyrimidine derivatives toward these secondary mutations using ring-opening and scaffold-hopping strategies. Compound 5n was the most potent, with IC50 values of 2.3 nM, 0.4 nM, and 0.5 nM against TRKAG667C, TRKAF589L, and TRKAG595R, compared to selitrectinib with IC50 values of 12.6 nM, 5.8 nM, and 7.6 nM, respectively (approximately 5.4, 14.5, and 15.2-fold increases). Furthermore, 5n displayed favorable pharmacokinetic properties and satisfactory antitumor efficacy (tumor growth inhibition of 97% at 30 mg/kg and 73% at 100 mg/kg) in TRKAWT and TRKAG667C xenograft mouse models. Collectively, 5n is a promising TRK inhibitor lead compound for overcoming clinically acquired resistance to second-generation inhibitors, particularly for resistant tumors harboring the TRKAG667C mutation in the xDFG motif.

In silico Methods for Identification of Potential Therapeutic Targets.

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods-comparative genomics and network-based methods-for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Discovery of Next-Generation Tropomyosin Receptor Kinase Inhibitors for Combating Multiple Resistance Associated with Protein Mutation.

Tropomyosin receptor kinase (TRK) inhibition is an effective therapeutic approach for treatment of a variety of cancers. Despite the use of first-generation TRK inhibitor (TRKI) larotrectinib (1) resulting in significant therapeutic response in patients, acquired resistance develops invariably. The emergence of secondary mutations occurring at the solvent-front, xDFG, and gatekeeper regions of TRK represents a common mechanism for acquired resistance. However, xDFG mutations remain insensitive to second-generation macrocyclic TRKIs selitrectinib (3) and repotrectinib (4) designed to overcome the resistance mediated by solvent-front and gatekeeper mutations. Here, we report the structure-based drug design and discovery of a next-generation TRKI. The structure-activity relationship studies culminated in the identification of a promising drug candidate 8 that showed excellent in vitro potency on a panel of TRK mutants, especially TRKAG667C in the xDFG motif, and improved in vivo efficacy than 1 and 3 in TRK wild-type and mutant fusion-driven tumor xenograft models, respectively.

Protocol for hit-to-lead optimization of compounds by auto in silico ligand directing evolution (AILDE) approach.

Hit-to-lead (H2L) optimization is crucial for drug design, which has become an increasing concern in medicinal chemistry. A virtual screening strategy of auto in silico ligand directing evolution (AILDE) has been developed to yield promising lead compounds rapidly and efficiently. The protocol includes instructions for fragment compound library construction, conformational sampling by molecular dynamics simulation, ligand modification by fragment growing, as well as the binding free energy prediction. For complete details on the use and execution of this protocol, please refer to Wu et al. (2020).

PIIMS Server: A Web Server for Mutation Hotspot Scanning at the Protein-Protein Interface.

Protein-protein interactions (PPIs) play vital roles in regulating biological processes, such as cellular and signaling pathways. Hotspots are certain residues located at protein-protein interfaces that contribute more in protein-protein binding than other residues. Research on the mutational effects of hotspots is important for understanding basic aspects of protein association. Hence, various computational tools have been developed to explore the impact of mutation hotspots, which will allow a better understanding of the forces that drive PPIs. However, tools that may provide comprehensive substitutions at hotspots are still rare. Hence, there is a strong need for a new free web server to explore mutational effects of hotspots. Herein we introduce a web server named PIIMS that integrates molecular dynamics simulation and one-step free energy perturbation. It contains two main computational functions: (1) computational alanine scanning analysis to identify hotspots and (2) full mutation scanning analysis to evaluate the effects of hotspot mutations. We rigidly validated its ability to predict binding free energy changes by using large and diverse datasets including 1,341 mutations from 50 PPIs with the correlation coefficient R = 0.75. The difference from the existing tools is that PIIMS can perform further evaluation of hotspot residues with regard to their different mutations. The PIIMS web server (accessible at http://chemyang.ccnu.edu.cn/ccb/server/PIIMS/index.php) is free and open to all users without login requirements.

HISNAPI: a bioinformatic tool for dynamic hot spot analysis in nucleic acid-protein interface with a case study.

Protein-nucleic acid interactions play essential roles in many biological processes, such as transcription, replication and translation. In protein-nucleic acid interfaces, hotspot residues contribute the majority of binding affinity toward molecular recognition. Hotspot residues are commonly regarded as potential binding sites for compound molecules in drug design projects. The dynamic property is a considerable factor that affects the binding of ligands. Computational approaches have been developed to expedite the prediction of hotspot residues on protein-nucleic acid interfaces. However, existing approaches overlook hotspot dynamics, despite their essential role in protein function. Here, we report a web server named Hotspots In silico Scanning on Nucleic Acid and Protein Interface (HISNAPI) to analyze hotspot residue dynamics by integrating molecular dynamics simulation and one-step free energy perturbation. HISNAPI is capable of not only predicting the hotspot residues in protein-nucleic acid interfaces but also providing insights into their intensity and correlation of dynamic motion. Protein dynamics have been recognized as a vital factor that has an effect on the interaction specificity and affinity of the binding partners. We applied HISNAPI to the case of SARS-CoV-2 RNA-dependent RNA polymerase, a vital target of the antiviral drug for the treatment of coronavirus disease 2019. We identified the hotspot residues and characterized their dynamic behaviors, which might provide insight into the target site for antiviral drug design. The web server is freely available via a user-friendly web interface at http://chemyang.ccnu.edu.cn/ccb/server/HISNAPI/ and http://agroda.gzu.edu.cn:9999/ccb/server/HISNAPI/.

Bioinformatics toolbox for exploring protein phosphorylation network.

A clear systematic delineation of the interactions between phosphorylation sites on substrates and their effector kinases plays a fundamental role in revealing cellular activities, understanding signaling modulation mechanisms and proposing novel hypotheses. The emergence of bioinformatics tools contributes to studying phosphorylation network. Some of them feature the visualization of network, enabling more effective trace of the underlying biological problems in a clear and succinct way. In this review, we aimed to provide a toolbox for exploring phosphorylation network. We first systematically surveyed 19 tools that are available for exploring phosphorylation networks, and subsequently comparatively analyzed and summarized these tools to guide tool selection in terms of functionality, data sources, performance, network visualization and implementation, and finally briefly discussed the application cases of these tools. In different scenarios, the conclusion on the suitability of a tool for a specific user may vary. Nevertheless, easily accessible bioinformatics tools are proved to facilitate biological findings. Hopefully, this work might also assist non-specialists, students, as well as computational scientists who aim at developing novel tools in the field of phosphorylation modification.

Cloud 3D-QSAR: a web tool for the development of quantitative structure-activity relationship models in drug discovery.

Effective drug discovery contributes to the treatment of numerous diseases but is limited by high costs and long cycles. The Quantitative Structure-Activity Relationship (QSAR) method was introduced to evaluate the activity of a large number of compounds virtually, reducing the time and labor costs required for chemical synthesis and experimental determination. Hence, this method increases the efficiency of drug discovery. To meet the needs of researchers to utilize this technology, numerous QSAR-related web servers, such as Web-4D-QSAR and DPubChem, have been developed in recent years. However, none of the servers mentioned above can perform a complete QSAR modeling and supply activity prediction functions. We introduce Cloud 3D-QSAR by integrating the functions of molecular structure generation, alignment, molecular interaction field (MIF) computing and results analysis to provide a one-stop solution. We rigidly validated this server, and the activity prediction correlation was R2 = 0.934 in 834 test molecules. The sensitivity, specificity and accuracy were 86.9%, 94.5% and 91.5%, respectively, with AUC = 0.981, AUCPR = 0.971. The Cloud 3D-QSAR server may facilitate the development of good QSAR models in drug discovery. Our server is free and now available at http://chemyang.ccnu.edu.cn/ccb/server/cloud3dQSAR/ and http://agroda.gzu.edu.cn:9999/ccb/server/cloud3dQSAR/.

Computational Approaches in Preclinical Studies on Drug Discovery and Development.

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Structure-activity relationship study of novel quinazoline-based 1,6-naphthyridinones as MET inhibitors with potent antitumor efficacy.

As a privileged scaffold, the quinazoline ring is widely used in the development of EGFR inhibitors, while few quinazoline-based MET inhibitors are reported. In our ongoing efforts to develop new MET-targeted anticancer drug candidates, a series of quinazoline-based 1,6-naphthyridinone derivatives were designed, synthesized, and evaluated for their biological activities. The preliminary SARs studies indicate that the quinazoline scaffold was also acceptable for the block A of class II MET inhibitors. The further pharmacokinetic studies led to the identification of the most promising compound 22a with favorable in vitro potency (MET, IC50 = 9.0 nM), human microsomal metabolic stability (t1/2 = 621.2 min) and oral bioavailability (F = 42%). Moreover, 22a displayed good in vivo antitumor efficacy (IR of 81% in 75 mg/kg) in MET-positive human glioblastoma U-87 MG xenograft model. These positive results indicated that 22a is a potential new MET-targeted antitumor drug lead, which is worthy of further development.

Auto In Silico Ligand Directing Evolution to Facilitate the Rapid and Efficient Discovery of Drug Lead.

Motivated by the growing demand for reducing the chemical optimization burden of H2L, we developed auto in silico ligand directing evolution (AILDE, http://chemyang.ccnu.edu.cn/ccb/server/AILDE), an efficient and general approach for the rapid identification of drug leads in accessible chemical space. This computational strategy relies on minor chemical modifications on the scaffold of a hit compound, and it is primarily intended for identifying new lead compounds with minimal losses or, in some cases, even increases in ligand efficiency. We also described how AILDE greatly reduces the chemical optimization burden in the design of mesenchymal-epithelial transition factor (c-Met) kinase inhibitors. We only synthesized eight compounds and found highly efficient compound 5g, which showed an ∼1,000-fold improvement in in vitro activity compared with the hit compound. 5g also displayed excellent in vivo antitumor efficacy as a drug lead. We believe that AILDE may be applied to a large number of studies for rapid design and identification of drug leads.

Discovery of N-substituted-3-phenyl-1,6-naphthyridinone derivatives bearing quinoline moiety as selective type II c-Met kinase inhibitors against VEGFR-2.

New N-substituted-3-phenyl-1,6-naphthyridinone derivatives are designed and synthesized, based on structural modification of our previously reported compound 3. Extensive enzyme-based SAR studies and PK evaluation led to the discovery of compound 4r, with comparable c-Met potency to that of Cabozantinib and high VEGFR-2 selectivity, while Cabozantinib displayed no VEGFR-2 selectivity. More importantly, at oral doses of 45 mg/kg (Q.D.), compound 4r exhibits significant tumor growth inhibition (93%) in a U-87MG human gliobastoma xenograft model. The promising selectivity against VEGFR-2 and excellent tumor growth inhibition of compound 4r suggest that it could be used as a new lead molecule for further discovery of selective type II c-Met inhibitors.

Discovery of Novel Pyrazole-Quinazoline-2,4-dione Hybrids as 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors.

4-Hydroxyphenylpyruvate dioxygenase (HPPD, EC 1.13.11.27) has been identified as one of the most significant targets in herbicide discovery for resistant weed control. In a continuing effort to discover potent novel HPPD inhibitors, we adopted a ring-expansion strategy to design a series of novel pyrazole-quinazoline-2,4-dione hybrids based on the previously discovered pyrazole-isoindoline-1,3-dione scaffold. One compound, 3-(2-chlorophenyl)-6-(5-hydroxy-1,3-dimethyl-1H-pyrazole-4-carbonyl)-1,5-dimethylquinazoline-2,4(1H,3H)-dione (9bj), displayed excellent potency against AtHPPD, with an IC50 value of 84 nM, which is approximately 16-fold more potent than pyrasulfotole (IC50 = 1359 nM) and 2.7-fold more potent than mesotrione (IC50 = 226 nM). Furthermore, the co-crystal structure of the AtHPPD-9bj complex (PDB ID 6LGT) was determined at a resolution of 1.75 Å. Similar to the existing HPPD inhibitors, compound 9bj formed a bidentate chelating interaction with the metal ion and a π-π stacking interaction with Phe381 and Phe424. In contrast, o-chlorophenyl at the N3 position of quinazoline-2,4-dione with a double conformation was surrounded by hydrophobic residues (Met335, Leu368, Leu427, Phe424, Phe392, and Phe381). Remarkably, the greenhouse assay indicated that most compounds displayed excellent herbicidal activity (complete inhibition) against at least one of the tested weeds at the application rate of 150 g of active ingredient (ai)/ha. Most promisingly, compounds 9aj and 9bi not only exhibited prominent weed control effects with a broad spectrum but also showed very good crop safety to cotton, peanuts, and corn at the dose of 150 g of ai/ha.

AIMMS suite: a web server dedicated for prediction of drug resistance on protein mutation.

Drug resistance is one of the most intractable issues for successful treatment in current clinical practice. Although many mutations contributing to drug resistance have been identified, the relationship between the mutations and the related pharmacological profile of drug candidates has yet to be fully elucidated, which is valuable both for the molecular dissection of drug resistance mechanisms and for suggestion of promising treatment strategies to counter resistant. Hence, effective prediction approach for estimating the sensitivity of mutations to agents is a new opportunity that counters drug resistance and creates a high interest in pharmaceutical research. However, this task is always hampered by limited known resistance training samples and accurately estimation of binding affinity. Upon this challenge, we successfully developed Auto In Silico Macromolecular Mutation Scanning (AIMMS), a web server for computer-aided de novo drug resistance prediction for any ligand-protein systems. AIMMS can qualitatively estimate the free energy consequences of any mutations through a fast mutagenesis scanning calculation based on a single molecular dynamics trajectory, which is differentiated with other web services by a statistical learning system. AIMMS suite is available at http://chemyang.ccnu.edu.cn/ccb/server/AIMMS/.