wSDTNBI: a novel network-based inference method for virtual screening

NADPHnet: a novel strategy to predict compounds for regulation of NADPH metabolism via network-based methods

Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 μM, with Oxyberberine showing promising effects even at 10 μM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.

Network pharmacology, molecular docking and experimental study of CEP in nasopharyngeal carcinoma

ETHNOPHARMACOLOGICAL RELEVANCE

The Stephania cephalantha Hayata is an important traditional medicinal plant widely used in traditional medicine to treat cancer. Cepharanthine (CEP) was extracted from the roots of Stephania cephalantha Hayata. It has been found to exhibit anticancer activity in different types of cancer cells. Nevertheless, the activity of CEP against nasopharyngeal carcinoma (NPC) and its underlying mechanism warrant further investigation.

AIMS OF THE STUDY

NPC is an invasive and highly metastatic malignancy that affects the head and neck region. This research aimed to investigate the pharmacological properties and underlying mechanism of CEP against NPC, aiming to offer novel perspectives on treating NPC using CEP.

MATERIALS AND METHODS

In vitro, the pharmacological activity of CEP against NPC was evaluated using the CCK-8 assay. To predict and elucidate the anticancer mechanism of CEP against NPC, we employed network pharmacology, conducted molecular docking analysis, and performed Western blot experiments. In vivo validation was performed through a nude mice xenograft model of human NPC, Western blot and immunohistochemical (IHC) assays to confirm pharmacological activity and the mechanism.

RESULTS

In a dose-dependent manner, the proliferation and clonogenic capacity of NPC cells were significantly inhibited by CEP. Additionally, NPC cell migration was suppressed by CEP. The results obtained from network pharmacology experiments revealed that anti-NPC effect of CEP was associated with 8 core targets, including EGFR, AKT1, PIK3CA, and mTOR. By performing molecular docking, the binding capacity of CEP to the candidate core proteins (EGFR, AKT1, PIK3CA, and mTOR) was predicted, resulting in docking energies of -10.0 kcal/mol for EGFR, -12.4 kcal/mol for PIK3CA, -10.8 kcal/mol for AKT1, and -8.6 kcal/mol for mTOR. The Western blot analysis showed that CEP effectively suppressed the expression of EGFR and the phosphorylation levels of downstream signaling proteins, including PI3K, AKT, mTOR, and ERK. After CEP intervention, a noteworthy decrease in tumor size, without inducing any toxicity, was observed in NPC xenograft nude mice undergoing in vivo treatment. Additionally, IHC analysis demonstrated a significant reduction in the expression levels of EGFR and Ki-67 following CEP treatment.

CONCLUSION

CEP exhibits significant pharmacological effects on NPC, and its mechanistic action involves restraining the activation of the EGFR/PI3K/AKT pathway. CEP represents a promising pharmaceutical agent for addressing and mitigating NPC.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

mtADENet: A novel interpretable method integrating multiple types of network-based inference approaches for prediction of adverse drug events

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and accelerate drug safety assessment. ADEs are mainly caused by unintended interactions with primary or additional targets (off-targets). In this study, we proposed a novel interpretable method named mtADENet, which integrates multiple types of network-based inference approaches for ADE prediction. Different from phenotype-based methods, mtADENet introduced computational target profiles predicted by network-based methods to bridge the gap between chemical structures and ADEs, and hence can not only predict ADEs for drugs and novel compounds within or outside the drug-ADE association network, but also provide insights for the elucidation of molecular mechanisms of the ADEs caused by drugs. We constructed a series of network-based prediction models for 23 ADE categories. These models achieved high AUC values ranging from 0.865 to 0.942 in 10-fold cross validation. The best model further showed high performance on four external validation sets, which outperformed two previous network-based methods. To show the practical value of mtADENet, we performed case studies on developmental neurotoxicity and cardio-oncology, and over 50 % of predicted ADEs and targets for drugs and novel compounds were validated by literature. Moreover, mtADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer/). In summary, mtADENet would be a powerful tool for ADE prediction and drug safety assessment in drug discovery and development.

Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.

EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (http://lmmd.ecust.edu.cn/edcpred/). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.

Uncovering the mechanism of resveratrol in the treatment of diabetic kidney disease based on network pharmacology, molecular docking, and experimental validation

BACKGROUND

Diabetic kidney disease (DKD) has been the leading cause of chronic kidney disease in developed countries. Evidence of the benefits of resveratrol (RES) for the treatment of DKD is accumulating. However, comprehensive therapeutic targets and underlying mechanisms through which RES exerts its effects against DKD are limited.

METHODS

Drug targets of RES were obtained from Drugbank and SwissTargetPrediction Databases. Disease targets of DKD were obtained from DisGeNET, Genecards, and Therapeutic Target Database. Therapeutic targets for RES against DKD were identified by intersecting the drug targets and disease targets. GO functional enrichment analysis, KEGG pathway analysis, and disease association analysis were performed using the DAVID database and visualized by Cytoscape software. Molecular docking validation of the binding capacity between RES and targets was performed by UCSF Chimera software and SwissDock webserver. The high glucose (HG)-induced podocyte injury model, RT-qPCR, and western blot were used to verify the reliability of the effects of RES on target proteins.

RESULTS

After the intersection of the 86 drug targets and 566 disease targets, 25 therapeutic targets for RES against DKD were obtained. And the target proteins were classified into 6 functional categories. A total of 11 cellular components terms and 27 diseases, and the top 20 enriched biological processes, molecular functions, and KEGG pathways potentially involved in the RES action against DKD were recorded. Molecular docking studies showed that RES had a strong binding affinity toward PPARA, ESR1, SLC2A1, SHBG, AR, AKR1B1, PPARG, IGF1R, RELA, PIK3CA, MMP9, AKT1, INSR, MMP2, TTR, and CYP2C9 domains. The HG-induced podocyte injury model was successfully constructed and validated by RT-qPCR and western blot. RES treatment was able to reverse the abnormal gene expression of PPARA, SHBG, AKR1B1, PPARG, IGF1R, MMP9, AKT1, and INSR.

CONCLUSIONS

RES may target PPARA, SHBG, AKR1B1, PPARG, IGF1R, MMP9, AKT1, and INSR domains to act as a therapeutic agent for DKD. These findings comprehensively reveal the potential therapeutic targets for RES against DKD and provide theoretical bases for the clinical application of RES in the treatment of DKD.

Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target's binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low μM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%.

Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs

The exploration of biologically relevant chemical space for the discovery of small bioactive molecules present in marine organisms has led not only to important advances in certain therapeutic areas, but also to a better understanding of many life processes. The still largely untapped reservoir of countless metabolites that play biological roles in marine invertebrates and microorganisms opens new avenues and poses new challenges for research. Computational technologies provide the means to (i) organize chemical and biological information in easily searchable and hyperlinked databases and knowledgebases; (ii) carry out cheminformatic analyses on natural products; (iii) mine microbial genomes for known and cryptic biosynthetic pathways; (iv) explore global networks that connect active compounds to their targets (often including enzymes); (v) solve structures of ligands, targets, and their respective complexes using X-ray crystallography and NMR techniques, thus enabling virtual screening and structure-based drug design; and (vi) build molecular models to simulate ligand binding and understand mechanisms of action in atomic detail. Marine natural products are viewed today not only as potential drugs, but also as an invaluable source of chemical inspiration for the development of novel chemotypes to be used in chemical biology and medicinal chemistry research.

Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening

Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among NRs was not considered yet. Herein we proposed a novel computational framework named NR-Profiler for prediction of potential NR modulators with high affinity and specificity. First, we built a comprehensive NR data set including 42 684 interactions to connect 42 NRs and 31 033 compounds. Then, we used multi-task deep neural network and multi-task graph convolutional neural network architectures to construct multi-task multi-classification models. To improve the predictive capability and robustness, we built a consensus model with an area under the receiver operating characteristic curve (AUC) = 0.883. Compared with conventional machine learning and structure-based approaches, the consensus model showed better performance in external validation. Using this consensus model, we demonstrated the practical value of NR-Profiler in virtual screening for NRs. In addition, we designed a selectivity score to quantitatively measure the specificity of NR modulators. Finally, we developed a freely available standalone software for users to make profiling predictions for their compounds of interest. In summary, our NR-Profiler provides a useful tool for NR-profiling prediction and is expected to facilitate NR-based drug discovery.

Th17 cells and inflammation in neurological disorders: Possible mechanisms of action

Neurological disorders (NDs) are one of the leading causes of global death. A sustained neuroinflammatory response has been reported to be associated with the pathogenesis of multiple NDs, including Parkinson’s disease (PD), multiple sclerosis (MS), Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), and major depressive disorder (MDD). Accumulating evidence shows that the recruitment of abundant lymphocytes in the central nervous system may contribute to promoting the development and progress of inflammation in neurological disorders. As one subset of T lymphocytes, CD4⁺ T cells have a critical impact on the inflammation of neurological disorders. T helper (Th) 17 is one of the most studied CD4⁺ Th subpopulations that produces cytokines (e.g., IL-17A, IL-23, IL-21, IL-6, and IFN-γ), leading to the abnormal neuroinflammatory response including the excessive activation of microglia and the recruitment of other immune cell types. All these factors are involved in several neurological disorders. However, the possible mechanisms of Th17 cells and their associated cytokines in the immunopathology of the abovementioned neurological disorders have not been clarified completely. This review will summarize the mechanisms by which encephalitogenic inflammatory Th17 cells and their related cytokines strongly contribute to chronic neuroinflammation, thus perpetuating neurodegenerative processes in NDs. Finally, the potential therapeutic prospects of Th17 cells and their cytokines in NDs will also be discussed.