Design, synthesis, insilco study and biological evaluation of new isatin-sulfonamide derivatives by using mono amide linker as possible as histone deacetylase inhibitors.

OBJECTIVE: Aim: To evaluate the cytotoxic activity of newly synthesized a series of novel HDAC inhibitors comprising sulfonamide as zinc binding group and Isatin derivatives as cap group joined by mono amide linker as required to act as HDAC inhibitors. PATIENTS AND METHODS: Materials and Methods: The utilization of sulfonamide as zinc binding group joined by N-alkylation reaction with ethyl-bromo hexanoate as linker group that joined by amide reaction with Isatin derivatives as cap groups which known to possess antitumor activity in the designed of new histone deacetylase inhibitors and using the docking and MTT assay to evaluate the compounds. RESULTS: Results: Four compounds have been synthesized and characterized successfully by ART-FTIR, NMR and ESI-Ms. the compounds were synthesized and characterized by successfully by ART-FTIR, NMR and ESI- Ms. Assessed for their cytotoxic activity against human colon adenocarcinoma MCF-7 (IC50, I=105.15, II=60.00, III=54.11, IV=56.57, vorinostat=28.41) and hepatoblastoma HepG2 (IC50, I=63.91, II=135.18, III=118.85, IV=51.46, vorinostat=37.50). Most of them exhibited potent HDAC inhibitory activity and significant cytotoxicity. CONCLUSION: Conclusions: The synthesized compounds (I, II, III and IV) showed cytotoxicity toward MCF-7 and HepG2 cancer cell lines and their docking analysis provided a preliminary indication that they are viable [HDAC6] candidates.

Synthesis, docking study, and antitumor evaluation of benzamides and oxadiazole derivatives of 3-phenoxybenzoic acid as VEGFR-2 inhibitors.

Current chemotherapeutic agents have several limitations, including lack of selectivity, the development of undesirable side effects, and chemoresistance. As a result, there is an unmet need for the development of novel small molecules with minimal side effects and the ability to specifically target tumor cells. A new series of 3-phenoxybenzoic acid derivatives, including 1,3,4-oxadiazole derivatives (4a-d) and benzamides derivatives (5a-e) were synthesized; their chemical structures were confirmed by Fourier-transform infrared spectroscopy, 1H nuclear magnetic resonance (NMR), 13C NMR, and mass spectra; and various physicochemical properties were determined. The antiproliferative activities of the new derivatives were evaluated by means of the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Three compounds (4b, 4c, and 4d) exhibited cytotoxicity against two of the three cell lines tested, five compounds (3, 4a, 5a, 5b, and 5e) were toxic to one cell line, while two compounds (5c and 5d) were not cytotoxic to any of the three cell lines tested in the current study. Based on docking scores, MTT assay findings, and vascular endothelial growth factor receptor 2 (VEGFR-2) kinase activity data, Compound 4d was selected for further biological investigation. Flow cytometry was used to determine the mode of cell death (apoptosis vs. necrosis) and the effect on cell cycle progression. Compound 4d arrested HepG2 hepatocellular carcinoma cells in the G2/M phase and activated both the intrinsic and extrinsic apoptosis pathways. In conclusion, Compound 4d has shown promising results for future research as a potent VEGFR-2 tyrosine kinase inhibitor.

A new paradigm for applying deep learning to protein-ligand interaction prediction.

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

Integrated Computational Approaches for Drug Design Targeting Cruzipain.

Cruzipain inhibitors are required after medications to treat Chagas disease because of the need for safer, more effective treatments. Trypanosoma cruzi is the source of cruzipain, a crucial cysteine protease that has driven interest in using computational methods to create more effective inhibitors. We employed a 3D-QSAR model, using a dataset of 36 known inhibitors, and a pharmacophore model to identify potential inhibitors for cruzipain. We also built a deep learning model using the Deep purpose library, trained on 204 active compounds, and validated it with a specific test set. During a comprehensive screening of the Drug Bank database of 8533 molecules, pharmacophore and deep learning models identified 1012 and 340 drug-like molecules, respectively. These molecules were further evaluated through molecular docking, followed by induced-fit docking. Ultimately, molecular dynamics simulation was performed for the final potent inhibitors that exhibited strong binding interactions. These results present four novel cruzipain inhibitors that can inhibit the cruzipain protein of T. cruzi.

Optimizing Neural Networks for Chemical Reaction Prediction: Insights from Methylene Blue Reduction Reactions.

This paper offers a thorough investigation of hyperparameter tuning for neural network architectures using datasets encompassing various combinations of Methylene Blue (MB) Reduction by Ascorbic Acid (AA) reactions with different solvents and concentrations. The aim is to predict coefficients of decay plots for MB absorbance, shedding light on the complex dynamics of chemical reactions. Our findings reveal that the optimal model, determined through our investigation, consists of five hidden layers, each with sixteen neurons and employing the Swish activation function. This model yields an NMSE of 0.05, 0.03, and 0.04 for predicting the coefficients A, B, and C, respectively, in the exponential decay equation A + B · e-x/C. These findings contribute to the realm of drug design based on machine learning, providing valuable insights into optimizing chemical reaction predictions.

Treatment of highly virulent mammarenavirus infections-status quo and future directions.

INTRODUCTION: Mammarenaviruses are negative-sense bisegmented enveloped RNA viruses that are endemic in Africa, the Americas, and Europe. Several are highly virulent, causing acute human diseases associated with high case fatality rates, and are considered to be significant with respect to public health impact or bioterrorism threat. AREAS COVERED: This review summarizes the status quo of treatment development, starting with drugs that are in advanced stages of evaluation in early clinical trials, followed by promising candidate medical countermeasures emerging from bench analyses and investigational animal research. EXPERT OPINION: Specific therapeutic treatments for diseases caused by mammarenaviruses remain limited to the off-label use of ribavirin and transfusion of convalescent sera. Progress in identifying novel candidate medical countermeasures against mammarenavirus infection has been slow in part because of the biosafety and biosecurity requirements. However, novel methodologies and tools have enabled increasingly efficient high-throughput molecular screens of regulatory-agency-approved small-molecule drugs and led to the identification of several compounds that could be repurposed for the treatment of infection with several mammarenaviruses. Unfortunately, most of them have not yet been evaluated in vivo. The most promising treatment under development is a monoclonal antibody cocktail that is protective against multiple lineages of the Lassa virus in nonhuman primate disease models.

Design, Synthesis, and Pharmacological Evaluation of Spiro[carbazole-3,3'-pyrrolidine] Derivatives as cGAS Inhibitors for Treatment of Acute Lung Injury.

Overactivation of cyclic GMP-AMP synthase (cGAS) is implicated in the occurrence of many inflammatory and autoimmune diseases, and inhibition of cGAS with a specific inhibitor has been proposed as a potential therapeutic strategy. However, only a few low-potency cGAS inhibitors have been reported, and few are suitable for clinical investigation. As a continuation of our structural optimization on the reported cGAS inhibitor 6 (G140), we developed a series of spiro[carbazole-3,3'-pyrrolidine] derivatives bearing a unique 2-azaspiro[4.5]decane structural motif, among which compound 30d-S was identified with high cellular effects against cGAS. This compound showed improved plasma exposure, lower clearance, and an oral bioavailability of 35% in rats. Moreover, in the LPS-induced acute lung injury (ALI) mice model, oral administration of compound 30d-S at 30 mg/kg markedly reduced lung inflammation and alleviated histopathological changes. These results confirm that 30d-S is a new efficacious cGAS inhibitor and is worthy of further investigation.

Rational Design of a Novel Class of Human ClpP Agonists through a Ring-Opening Strategy with Enhanced Antileukemia Activity.

The activation of Homo sapiens Casein lysing protease P (HsClpP) by a chemical or genetic strategy has been proved to be a new potential therapy in acute myeloid leukemia (AML). However, limited efficacy has been achieved with classic agonist imipridone ONC201. Here, a novel class of HsClpP agonists is designed and synthesized using a ring-opening strategy based on the lead compound 1 reported in our previous study. Among these novel scaffold agonists, compound 7k exhibited remarkably enhanced proteolytic activity of HsClpP (EC50 = 0.79 ± 0.03 µM) and antitumor activity in vitro (IC50 = 0.038 ± 0.003 µM). Moreover, the intraperitoneal administration of compound 7k markedly suppressed tumor growth in Mv4-11 xenograft models, achieving a tumor growth inhibition rate of 88%. Concurrently, 7k displayed advantageous pharmacokinetic properties in vivo. This study underscores the promise of compound 7k as a significant HsClpP agonist and an antileukemia drug candidate, warranting further exploration for AML treatment.

Novel CDK19-Targeted Radiotracers: A Potential Design Strategy to Improve the Pharmacokinetics and Tumor Uptake.

Cyclin-dependent kinase 19 (CDK19) is overexpressed in prostate cancer, making it an attractive target for both imaging and therapy. Since little is known about the optimized approach for radioligands of nuclear proteins, linker optimization strategies were used to improve pharmacokinetics and tumor absorption, including the adjustment of the length, flexibility/rigidity, and hydrophilicity/lipophilicity of linkers. Molecular docking was conducted for virtual screening and followed by IC50 determination. Both BALB/c mice and P-16 xenografts were used for tissue distribution and PET/CT imaging. The ligand 68Ga-10c demonstrated high absorption in tumor 5 min after injection and sustains long-term imaging within 3 h. Furthermore, 68Ga-10c exhibited slow clearance within the tumor and was predominantly metabolized in both the liver and kidneys, showing the potential to alleviate metabolic pressure and enhance tissue safety. Therefore, the linker optimization strategy is well suited for CDK19 and provides a reference for the radioactive ligands of other nuclear targets.

Design, Synthesis, and In Vivo Evaluation of Isosteviol Derivatives as New SIRT3 Activators with Highly Potent Cardioprotective Effects.

Cardiovascular diseases (CVDs) persist as the predominant cause of mortality, urging the exploration of innovative pharmaceuticals. Mitochondrial dysfunction stands as a pivotal contributor to CVDs development. Sirtuin 3 (SIRT3), a prominent mitochondrial deacetylase known for its crucial role in protecting mitochondria against damage and dysfunction, has emerged as a promising therapeutic target for CVDs treatment. Utilizing isosteviol, a natural ent-beyerene diterpenoid, 24 derivatives were synthesized and evaluated in vivo using a zebrafish model, establishing a deduced structure-activity relationship. Among these, derivative 5v exhibited significant efficacy in doxorubicin-induced cardiomyopathy in zebrafish and murine models. Subsequent investigations revealed that 5v selectively elevated SIRT3 expression, leading to the upregulation of SOD2 and OPA1 expression, effectively preventing mitochondrial dysfunction, mitigating oxidative stress, and preserving cardiomyocyte viability. As a novel structural class of SIRT3 activators with robust therapeutic effects, 5v emerges as a promising candidate for further drug development.

Discovery of Highly Potent Solute Carrier 13 Member 5 (SLC13A5) Inhibitors for the Treatment of Hyperlipidemia.

In the face of escalating metabolic disease prevalence, largely driven by modern lifestyle factors, this study addresses the critical need for novel therapeutic approaches. We have identified the sodium-coupled citrate transporter (NaCT or SLC13A5) as a target for intervention. Utilizing rational drug design, we developed a new class of SLC13A5 inhibitors, anchored by the hydroxysuccinic acid scaffold, refining the structure of PF-06649298. Among these, LBA-3 emerged as a standout compound, exhibiting remarkable potency with an IC50 value of 67 nM, significantly improving upon PF-06649298. In vitro assays demonstrated LBA-3's efficacy in reducing triglyceride levels in OPA-induced HepG2 cells. Moreover, LBA-3 displayed superior pharmacokinetic properties and effectively lowered triglyceride and total cholesterol levels in diverse mouse models (PCN-stimulated and starvation-induced), without detectable toxicity. These findings not only spotlight LBA-3 as a promising candidate for hyperlipidemia treatment but also exemplify the potential of targeted molecular design in advancing metabolic disorder therapeutics.

Design and Synthesis of Novel Macrocyclic Derivatives as Potent and Selective Cyclin-Dependent Kinase 7 Inhibitors.

The duality of function (cell cycle regulation and gene transcription) of cyclin-dependent kinase 7 (CDK7) makes it an attractive oncology target and the discovery of CDK7 inhibitors has been a long-term pursuit by academia and pharmaceutical companies. However, achieving selective leading compounds is still difficult owing to the similarities among the ATP binding pocket. Herein, we detail the design and synthesis of a series of macrocyclic derivatives with pyrazolo[1,5-a]-1,3,5-triazine core structure as potent and selective CDK7 inhibitors. The diverse manners of macrocyclization led to distinguished selectivity profiles of the CDK family. Molecular dynamics (MD) simulation explained the binding difference between 15- and 16-membered macrocyclic compounds. Further optimization generated compound 37 exhibiting good CDK7 inhibitory activity and high selectivity over other CDKs. This work clearly demonstrated macrocyclization is a versatile method to finely tune the selectivity profile of small molecules and MD simulation can be a valuable tool in prioritizing designs of the macrocycle.

Design and Synthesis of Novel Ultralong-Acting Peptides as EDP-EBP Interaction Inhibitors for Pulmonary Fibrosis Treatment.

The increased remodeling of the extracellular matrix (ECM) in pulmonary fibrosis (PF) generates bioactive ECM fragments called matricryptins, which include elastin-derived peptides (EDPs). The interaction between EDPs and their receptors, including elastin-binding protein (EBP), plays a crucial role in exacerbating fibrosis. Here, we present LXJ-02 for the first time, a novel ultralong-acting inhibitor that disrupts the EDPs/EBP peptide-protein interaction, promoting macrophages to secrete matrix metalloproteinase-12 (MMP-12), and showing great promise as a stable peptide. MMP-12 has traditionally been implicated in promoting inflammation and fibrosis in various acute and chronic diseases. However, we reveal a novel role of LXJ-02 that activates the macrophage-MMP-12 axis to increase MMP-12 expression and degrade ECM components like elastin. This leads to the preventing of PF while also improving EDP-EBP interaction. LXJ-02 effectively reverses PF in mouse models with minimal side effects, holding great promise as an excellent therapeutic agent for lung fibrosis.

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery.

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

Discovery of YFJ-36: Design, Synthesis, and Antibacterial Activities of Catechol-Conjugated ß-Lactams against Gram-Negative Bacteria.

Cefiderocol is the first approved catechol-conjugated cephalosporin against multidrug-resistant Gram-negative bacteria, while its application was limited by poor chemical stability associated with the pyrrolidinium linker, moderate potency against Klebsiella pneumoniae and Acinetobacter baumannii, intricate procedures for salt preparation, and potential hypersensitivity. To address these issues, a series of novel catechol-conjugated derivatives were designed, synthesized, and evaluated. Extensive structure-activity relationships and structure-metabolism relationships (SMR) were conducted, leading to the discovery of a promising compound 86b (Code no. YFJ-36) with a new thioether linker. 86b exhibited superior and broad-spectrum in vitro antibacterial activity, especially against A. baumannii and K. pneumoniae, compared with cefiderocol. Potent in vivo efficacy was observed in a murine systemic infection model. Furthermore, the physicochemical stability of 86b in fluid medium at pH 6-8 was enhanced. 86b also reduced potential the risk of allergy owing to the quaternary ammonium linker. The improved properties of 86b supported its further research and development.

Protein click chemistry and its potential for medical applications.

A revolution in chemical biology occurred with the introduction of click chemistry. Click chemistry plays an important role in protein chemistry modifications, providing specific, sensitive, rapid, and easy-to-handle methods. Under physiological conditions, click chemistry often overlaps with bioorthogonal chemistry, defined as reactions that occur rapidly and selectively without interfering with biological processes. Click chemistry is used for the posttranslational modification of proteins based on covalent bond formations. With the contribution of click reactions, selective modification of proteins would be developed, representing an alternative to other technologies in preparing new proteins or enzymes for studying specific protein functions in different biological processes. Click-modified proteins have potential in diverse applications such as imaging, labeling, sensing, drug design, and enzyme technology. Due to the promising role of proteins in disease diagnosis and therapy, this review aims to highlight the growing applications of click strategies in protein chemistry over the last two decades, with a special emphasis on medicinal applications.

De novo design of drug-binding proteins with predictable binding energy and specificity.

The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule-binding proteins with tuned interaction energies is feasible entirely from computation.

Design, synthesis, and evaluation of novel arecoline-linked amino acid derivatives for insecticidal and antifungal activities.

A series of arecoline derivatives with amino acid moieties were designed and synthesised using an acylamide condensation strategy, taking arecoline as the foundational structure. The insecticidal efficacy of these compounds against Aphis craccivora and Tetranychus cinnabarinus was evaluated. Notably, derivatives 3h and 3i demonstrated superior insecticidal activity compared with arecoline. Additionally, 3h and 3i showed good fungicidal effectiveness against two types of plant fungi. Moreover, molecular docking analyses suggested that 3h and 3i could affect the nervous systems of A. craccivora and T. cinnabarinus by binding to neuronal nicotinic acetylcholine receptors. These findings suggest that compounds 3h and 3i represent promising leads for further development in insecticide and fungicide research.

Targeting bacterial growth in biofilm conditions: rational design of novel inhibitors to mitigate clinical and food contamination using QSAR.

Antimicrobial resistance (AMR) is a pressing global issue exacerbated by the abuse of antibiotics and the formation of bacterial biofilms, which cause up to 80% of human bacterial infections. This study presents a computational strategy to address AMR by developing three novel quantitative structure-activity relationship (QSAR) models based on molecular topology to identify potential anti-biofilm and antibacterial agents. The models aim to determine the chemo-topological pattern of Gram (+) antibacterial, Gram (-) antibacterial, and biofilm formation inhibition activity. The models were applied to the virtual screening of a commercial chemical database, resulting in the selection of 58 compounds. Subsequent in vitro assays showed that three of these compounds exhibited the most promising antibacterial activity, with potential applications in enhancing food and medical device safety.

HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES.

Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug design (LBDD) method for predicting the inhibitory activity of small molecules against HBV and HCV. HBCVTr employs a hybrid model consisting of double encoders of transformers and a deep neural network to learn the relationship between small molecules' simplified molecular-input line-entry system (SMILES) and their antiviral activity against HBV or HCV. The prediction accuracy of HBCVTr has surpassed baseline machine learning models and existing methods, with R-squared values of 0.641 and 0.721 for the HBV and HCV test sets, respectively. The trained models were successfully applied to virtual screening against 10 million compounds within 240 h, leading to the discovery of the top novel inhibitor candidates, including IJN04 for HBV and IJN12 and IJN19 for HCV. Molecular docking and dynamics simulations identified IJN04, IJN12, and IJN19 target proteins as the HBV core antigen, HCV NS5B RNA-dependent RNA polymerase, and HCV NS3/4A serine protease, respectively. Overall, HBCVTr offers a new and rapid drug discovery and development screening method targeting HBV and HCV.