Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches

DO-GMA: An End-to-End Drug-Target Interaction Identification Framework with a Depthwise Overparameterized Convolutional Network and the Gated Multihead Attention Mechanism

Identification of potential drug-target interactions (DTIs) is a crucial step in drug discovery and repurposing. Although deep learning effectively deciphers DTIs, most deep learning-based methods represent drug features from only a single perspective. Moreover, the fusion method of drug and protein features needs further refinement. To address the above two problems, in this study, we develop a novel end-to-end framework named DO-GMA for potential DTI identification by incorporating Depthwise Overparameterized convolutional neural network and the Gated Multihead Attention mechanism with shared-learned queries and bilinear model concatenation. DO-GMA first designs a depthwise overparameterized convolutional neural network to learn drug representations from their SMILES strings and protein representations from their amino acid sequences. Next, it extracts drug representations from their 2D molecular graphs through a graph convolutional network. Subsequently, it fuses drug and protein features by combining the gated attention mechanism and the multihead attention mechanism with shared-learned queries and bilinear model concatenation. Finally, it takes the fused drug-target features as inputs and builds a multilayer perceptron to classify unlabeled drug-target pairs (DTPs). DO-GMA was benchmarked against six newest DTI prediction methods (CPI-GNN, BACPI, CPGL, DrugBAN, BINDTI, and FOTF-CPI) under four different experimental settings on four DTI data sets (i.e., DrugBank, BioSNAP, C.elegans, and BindingDB). The results show that DO-GMA significantly outperformed the above six methods based on AUC, AUPR, accuracy, F1-score, and MCC. An ablation study, robust statistical analysis, sensitivity analysis of parameters, visualization of the fused features, computational cost analysis, and case analysis further validated the powerful DTI identification performance of DO-GMA. In addition, DO-GMA predicted that two drug-protein pairs (i.e., DB00568 and P06276, and DB09118 and Q9UQD0) could be interacting. DO-GMA is freely available at https://github.com/plhhnu/DO-GMA.

Insight to phytochemical investigation and anti-hyperlipidemic effects of Eucalyptus camaldulensis leaf extract using in vitro, in vivo and in silico approach

Hyperlipidemia is a key risk factor mainly for hypertension and cardiac abnormalities. Previously eucalyptus plant (river red gum) had been used for its medicinal value for the treatment of many ailments. This study focused on phytochemical examination, investigation of an in vitro potential and in vivo effects in mice fed with high cholesterol diet, GC-MS analysis of extracts of Eucalyptus camaldulensis leaves and further confirmation of anti-hyperlipidemic potential of different constituents of plant extracts by using in silico technique. For in vitro study screening of different extracts of Eucalyptus camaldulensis leaves was performed by using pancreatic lipase enzyme inhibition assay. Ethanolic extract presented the highest potential among all the extracts by inhibiting pancreatic lipase having IC50-11.88 µg/mL. For in vivo study mice were fed with high cholesterol diet for induction of Hyperlipidemia. Water extract showed great anti-hyperlipidemic potential by reducing the level of cholesterol, triglycerides, low density lipoproteins and increasing high density lipoproteins level significantly (p < 0.05). Moreover, molecular docking and prime MM-GBSA study were applied for screening of compounds having anti-hyperlipidemic potential which showed that Alpha-cadinol was the lead compound for inhibition of pancreatic lipase enzyme having docking score (-6.604). The ADMET properties and toxicity profile of the top docked compounds were also detailed for ensuring their safety aspects. In this way in silico analysis substantiate the experimental findings by showing anti-hyperlipidemic potential in constituents of eucalyptus plant. Thus, there is a need of advanced research for isolation of active constituents having said anti-hyperlipidemic potential in the Eucalyptus camaldulensis plant.Communicated by Ramaswamy H. Sarma.

Discovery of novel Akt1 inhibitors by an ensemble-based virtual screening method, molecular dynamics simulation, and in vitro biological activity testing

Akt1, as an important member of the Akt family, plays a controlled role in cancer cell growth and survival. Inhibition of Akt1 activity can promote cancer cell apoptosis and inhibit tumor growth. Therefore, in this investigation, a multilayer virtual screening approach, including receptor-ligand interaction-based pharmacophore, 3D-QSAR, molecular docking, and deep learning methods, was utilized to construct a virtual screening platform for Akt1 inhibitors. 17 representative compounds with different scaffolds were identified as potential Akt1 inhibitors from three databases. Among these 17 compounds, the Hit9 exhibited the best inhibitory activity against Akt1 with inhibition rate of 33.08% at concentration of 1 μM. The molecular dynamics simulations revealed that Hit9 and Akt1 could form a compact and stable complex. Moreover, Hit9 interacted with some key residues by hydrophobic, electrostatic, and hydrogen bonding interactions and induced substantial conformation changes in the hinge region of the Akt1 active site. The average binding free energies for the Akt1-CQU, Akt1-Ipatasertib, and Akt1-Hit9 systems were - 34.44, - 63.37, and - 39.14 kJ mol-1, respectively. In summary, the results obtained in this investigation suggested that Hit9 with novel scaffold may be a promising lead compound for developing new Akt1 inhibitor for treatment of various cancers with Akt1 overexpressed.

Identification and Ranking of Binding Sites from Structural Ensembles: Application to SARS-CoV-2

Target identification and evaluation is a critical step in the drug discovery process. Although time-intensive and complex, the challenge becomes even more acute in the realm of infectious disease, where the rapid emergence of new viruses, the swift mutation of existing targets, and partial effectiveness of approved antivirals can lead to outbreaks of significant public health concern. The COVID-19 pandemic, caused by the SARS-CoV-2 virus, serves as a prime example of this, where despite the allocation of substantial resources, Paxlovid is currently the only effective treatment. In that case, significant effort pre-pandemic had been expended to evaluate the biological target for the closely related SARS-CoV. In this work, we utilize the computational hot spot mapping method, FTMove, to rapidly identify and rank binding sites for a set of nine SARS-CoV-2 drug/potential drug targets. FTMove takes into account protein flexibility by mapping binding site hot spots across an ensemble of structures for a given target. To assess the applicability of the FTMove approach to a wide range of drug targets for viral pathogens, we also carry out a comprehensive review of the known SARS-CoV-2 ligandable sites. The approach is able to identify the vast majority of all known sites and a few additional sites, which may in fact be yet to be discovered as ligandable. Furthermore, a UMAP analysis of the FTMove features for each identified binding site is largely able to separate predicted sites with experimentally known binders from those without known binders. These results demonstrate the utility of FTMove to rapidly identify actionable sites across a range of targets for a given indication. As such, the approach is expected to be particularly useful for assessing target binding sites for any emerging pathogen, as well as for indications in other disease areas, and providing actionable starting points for structure-based drug design efforts.

Marine Bioactive Molecules as Inhibitors of the Janus Kinases: A Comparative Molecular Docking and Molecular Dynamics Simulation Approach

A treasure trove of naturally occurring biomolecules can be obtained from sea living organisms to be used as potential antioxidant and anti-inflammatory agents. These bioactive molecules can target signaling molecules involved in the severity of chronic autoimmune diseases such as rheumatoid arthritis (RA). The intracellular tyrosine kinases family, Janus kinases (JAKs, includes JAK1, JAK2, and JAK3), is implicated in the pathogenesis of RA through regulating several cytokines and inflammatory processes. In the present study, we conducted molecular docking and structural analysis investigations to explore the role of a set of bioactive molecules from marine sources that can be used as JAKs' specific inhibitors. Around 200 antioxidants and anti-inflammatory molecules out of thousands of marine molecules found at the Comprehensive Marine Natural Products Database (CMNPD) website, were used in that analysis. The details of the interacting residues were compared to the recent FDA approved inhibitors tofacitinib and baricitinib for data validation. The shortlisted critical amino acids residues of our pharmacophore-based virtual screening were LYS905, GLU957, LEU959, and ASP1003 at JAK1, GLU930 and LEU932 at JAK2, and GLU905 and CYS909 of JAK3. Interestingly, marine biomolecules such as Sargachromanol G, Isopseudopterosin E, Seco-Pseudopterosin, and CID 10071610 showed specific binding and significantly higher binding energy to JAK1 active/potential sites when being compared with the approved inhibitors. In addition, Zoanthoxanthin and Fuscoside E bind to JAK2's critical residues, GLU930 and LEU932. Moreover, Phorbaketal and Fuscoside E appear to be potential candidates that can inhibit JAK3 activity. These results were validated using molecular dynamics simulation for the docked complexes, JAK1(6sm8)/SG, JAK2 (3jy9)/ZAX, and JAK3 (6pjc)/Fuscoside E, where stable and lower binding energy were found based on analyzing set of parameters, discussed below (videos are attached). A promising role of these marine bioactive molecules can be confirmed in prospective preclinical/clinical investigations using rheumatoid arthritis models.

Repurposing FDA-approved compounds to target JAK2 for colon cancer treatment

Colorectal cancer is one of the common cancers worldwide and the second leading cause of cancer-related death. The current treatment has the inherent drawbacks and there is a need of developing a new treatment. Interleukin-6 a pleiotropic cytokine involved in immune regulation and activation of JAK2/STAT3 pathway in colorectal cancer. JAK2/STAT3 signaling pathway functions as a critical regulator of cell growth, differentiation, and immune expression. The abnormality in the JAK2/STAT3 pathway is involved in the tumorigenesis of colon cancer including apoptosis. In this study, we identified novel inhibitors for JAK2 protein by performing virtual screening against FDA-approved compounds. To address the selectivity issue, we implemented cross-docking method followed by DFT calculations to understand the chemical reactivity of the identified compounds. Additionally, molecular dynamics (MD) simulations were performed for the top FDA compounds against JAK2 to understand the molecular interactions and structural stability of the complex over a period of 200 ns. Our results indicated that ergotamine, entrectinib, exatecan, dihydroergotamine, and paritaprevir can be used as alternative drugs for colon cancer. In addition, ergotamine was found to efficiently lower the cell viability with IC50 values of 100 µM on colon cancer cell lines. The long-term inhibitory effect of the ergotamine led to a decrease in colony size, and the toxicity properties were studied using hemolysis assay. Our study shows the potential of targeting JAK2 as a novel approach to colon cancer treatment, and demonstrate that ergotamine as a promising effects as an anti-cancer drug.

Identification of new potent NLRP3 inhibitors by multi-level in-silico approaches

Nod-like receptor protein 3 (NLRP-3), is an intracellular sensor that is involved in inflammasome activation, and the aberrant expression of NLRP3 is responsible for diabetes mellitus, its complications, and many other inflammatory diseases. NLRP3 is considered a promising drug target for novel drug design. Here, a pharmacophore model was generated from the most potent inhibitor, and its validation was performed by the Gunner-Henry scoring method. The validated pharmacophore was used to screen selected compounds databases. As a result, 646 compounds were mapped on the pharmacophore model. After applying Lipinski's rule of five, 391 hits were obtained. All the hits were docked into the binding pocket of target protein. Based on docking scores and interactions with binding site residues, six compounds were selected potential hits. To check the stability of these compounds, 100 ns molecular dynamic (MD) simulations were performed. The RMSD, RMSF, DCCM and hydrogen bond analysis showed that all the six compounds formed stable complex with NLRP3. The binding free energy with the MM-PBSA approach suggested that electrostatic force, and van der Waals interactions, played a significant role in the binding pattern of these compounds. Thus, the outcomes of the current study could provide insights into the identification of new potential NLRP3 inflammasome inhibitors against diabetes and its related disorders.

Discovery of novel JAK1 inhibitors through combining machine learning, structure-based pharmacophore modeling and bio-evaluation

BACKGROUND

Janus kinase 1 (JAK1) plays a critical role in most cytokine-mediated inflammatory, autoimmune responses and various cancers via the JAK/STAT signaling pathway. Inhibition of JAK1 is therefore an attractive therapeutic strategy for several diseases. Recently, high-performance machine learning techniques have been increasingly applied in virtual screening to develop new kinase inhibitors. Our study aimed to develop a novel layered virtual screening method based on machine learning (ML) and pharmacophore models to identify the potential JAK1 inhibitors.

METHODS

Firstly, we constructed a high-quality dataset comprising 3834 JAK1 inhibitors and 12,230 decoys, followed by establishing a series of classification models based on a combination of three molecular descriptors and six ML algorithms. To further screen potential compounds, we constructed several pharmacophore models based on Hiphop and receptor-ligand algorithms. We then used molecular docking to filter the recognized compounds. Finally, the binding stability and enzyme inhibition activity of the identified compounds were assessed by molecular dynamics (MD) simulations and in vitro enzyme activity tests.

RESULTS

The best performance ML model DNN-ECFP4 and two pharmacophore models Hiphop3 and 6TPF 08 were utilized to screen the ZINC database. A total of 13 potentially active compounds were screened and the MD results demonstrated that all of the above molecules could bind with JAK1 stably in dynamic conditions. Among the shortlisted compounds, the four purchasable compounds demonstrated significant kinase inhibition activity, with Z-10 being the most active (IC50 = 194.9 nM).

CONCLUSION

The current study provides an efficient and accurate integrated model. The hit compounds were promising candidates for the further development of novel JAK1 inhibitors.

Kunxian capsule alleviates renal damage by inhibiting the JAK1/STAT1 pathway in lupus nephritis

ETHNOPHARMACOLOGICAL RELEVANCE

Kunxian capsule (KXC) is a new traditional Chinese medicine drug included in "The key science and technology achievements" in the Ninth Five Year Plan of China. KXC has been clinically used for more than 10 years in the treatment of lupus nephritis (LN). However, the underlying role and molecular mechanism of KXC in LN remain unclear.

AIM OF THE STUDY

This study aimed to explore the efficacy and potential mechanisms of KXC through pharmacological network, in vitro and in vivo studies.

MATERIALS AND METHODS

Pharmacological network analysis of KXC treatment in LN was performed using data acquired from the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP, https://old.tcmsp-e.com/tcmsp.php) and NCBI Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/database). HK-2 cells were chosen as an in vitro model of the tubular immune response by simulation with interferon γ (IFN-γ). MRL/lpr mice were used to explore the mechanism of KXC in vivo. Finally, the specific active molecules of KXC were further analyzed by molecular docking.

RESULTS

The pharmacological network analysis showed that STAT1 is a key factor in the effects of KXC. In vitro and in vivo experiments confirmed the therapeutic effect of KXC on LN renal function and tubular inflammation. The protective effect of KXC is mediated by STAT1 blockade, which further reduces T-cell infiltration and improves the renal microenvironment in LN. Two main components of KXC, Tripterygium hypoglaucum (H.Lév.) Hutch (Shanhaitang) and Epimedium brevicornu Maxim (Yinyanghuo) could block JAK1-STAT1 activation. Furthermore, we found 8 molecules that could bind to the ATP pocket of JAK1 with high affinities by performing docking analysis.

CONCLUSIONS

KXC inhibits renal damage and T-cell infiltration in LN by blocking the JAK1-STAT1 pathway.

Identification of Activated Cdc42-Associated Kinase Inhibitors as Potential Anticancer Agents Using Pharmacoinformatic Approaches

BACKGROUND

Activated Cdc42-associated kinase (ACK1) is essential for numerous cellular functions, such as growth, proliferation, and migration. ACK1 signaling occurs through multiple receptor tyrosine kinases; therefore, its inhibition can provide effective antiproliferative effects against multiple human cancers. A number of ACK1-specific inhibitors were designed and discovered in the previous decade, but none have reached the clinic. Potent and selective ACK1 inhibitors are urgently needed.

METHODS

In the present investigation, the pharmacophore model (PM) was rationally built utilizing two distinct inhibitors coupled with ACK1 crystal structures. The generated PM was utilized to screen the drug-like database generated from the four chemical databases. The binding mode of pharmacophore-mapped compounds was predicted using a molecular docking (MD) study. The selected hit-protein complexes from MD were studied under all-atom molecular dynamics simulations (MDS) for 500 ns. The obtained trajectories were ranked using binding free energy calculations (ΔG kJ/mol) and Gibb's free energy landscape.

RESULTS

Our results indicate that the three hit compounds displayed higher binding affinity toward ACK1 when compared with the known multi-kinase inhibitor dasatinib. The inter-molecular interactions of Hit1 and Hit3 reveal that compounds form desirable hydrogen bond interactions with gatekeeper T205, hinge region A208, and DFG motif D270. As a result, we anticipate that the proposed scaffolds might help in the design of promising selective ACK1 inhibitors.

Modern drug discovery for inflammatory bowel disease: The role of computational methods

Inflammatory bowel diseases (IBDs) comprising ulcerative colitis, Crohn’s disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract. IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized. Cell therapy, intestinal microecology, apheresis therapy, exosome therapy and small molecules are emerging therapeutic options for IBD. Currently, it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD. Several small molecule inhibitors are being developed as a promising alternative for IBD therapy. The use of highly efficient and time-saving techniques, such as computational methods, is still a viable option for the development of these small molecule drugs. The computer-aided (in silico) discovery approach is one drug development technique that has mostly proven efficacy. Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner. This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods. Some computational approaches to IBD genomic studies, target identification, and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.

An Industrial Approach to Production of Tofacitinib Citrate (TFC) as an Anti-COVID-19 Agent: A Joint Experimental and Theoretical Study

In this report, we have presented our experience about a facile method for synthesis of tofacitinib citrate (TFC). The developed analytical methods for identification and qualifications are also included. As TFC seems to be effective in treatment of the symptoms of COVID-19 (SARS family), manufacturing of this active pharmaceutical ingredient (API) could be helpful. The API of TFC was prepared from the diamine intermediate in an ambient and solvent-free condition. Elimination of the reaction solvent resulted in decreasing the cost and preventing the rejection of the organic volatile impurity (OVI) test. The final citrate addition step was carried out using water as a solvent (the citrate content was 37.5% by potentiometry). Moreover, the results of the Karl-Fischer (KF) titration analysis was about 0.24%, which showed that the use of water does not increase the water content of the crystal structure.