Abstract: New protocol for screening efficient and environmentally friendly solvents was proposed and experimentally verified. The guidance for solvent selection comes from computed solubility via COSMO-RS approach. Furthermore, solute-solvent affinities computed using advanced quantum chemistry level were used as a rationale for observed solvents ranking. The screening protocol pointed out that 4-formylomorpholine (4FM) is an attractive solubilizer compared to commonly used aprotic solvents such as DMSO and DMF. This was tested experimentally by measuring the solubility of the title compounds in aqueous binary mixtures in the temperature range between 298.15 K and 313.15 K. Additional measurements were also performed for aqueous binary mixtures of DMSO and DMF. It has been found that the solubility of studied aromatic amides is very high and quite similar in all three aprotic solvents. For most aqueous binary mixtures, a significant decrease in solubility with a decrease in the organic fraction is observed, indicating that all systems can be regarded as efficient solvent-anti-solvent pairs. In the case of salicylamide dissolved in aqueous-4FM binary mixtures, a strong synergistic effect has been found leading to the highest solubility for 0.6 mole fraction of 4-FM.

Abstract: Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal and synthetic dosage lethal (SL/SDL) partners of such altered host genes. Pursuing this disparate antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL/SDL with altered host genes. The predicted SL/SDL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. We further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming noninfected healthy cells.

Abstract: Cyclin-dependent kinase 2 (CDK2) complex is significantly over-activated in many cancers. While it makes CDK2 an attractive target for cancer therapy, most inhibitors against CDK2 are ATP competitors that are either nonspecific or highly toxic, and typically fail clinical trials. One alternative approach is to develop non-ATP competitive inhibitors; they disrupt interactions between CDK2 and either its partners or substrates, resulting in specific inhibition of CDK2 activities. In this report, we identify two potential druggable pockets located in the protein-protein interaction interface (PPI) between CDK2 and Cyclin A. To target the potential druggable pockets, we perform a LIVS in silico screening of a library containing 1925 FDA approved drugs. Using this approach, homoharringtonine (HHT) shows high affinity to the PPI and strongly disrupts the interaction between CDK2 and cyclins. Further, we demonstrate that HHT induces autophagic degradation of the CDK2 protein via tripartite motif 21 (Trim21) in cancer cells, which is confirmed in a leukemia mouse model and in human primary leukemia cells. These results thus identify an autophagic degradation mechanism of CDK2 protein and provide a potential avenue towards treating CDK2-dependent cancers.

Abstract: The increased use of polyphenols nowadays poses the need for identification of their new pharmacological targets. Recently, structure similarity-based virtual screening of DrugBank outlined pseudopurpurin, a hydroxyanthraquinone from Rubia cordifolia spp., as similar to gatifloxacin, a synthetic antibacterial agent. This suggested the bacterial DNA gyrase and DNA topoisomerase IV as potential pharmacological targets of pseudopurpurin. In this study, estimation of structural similarity to referent antibacterial agents and molecular docking in the DNA gyrase and DNA topoisomerase IV complexes were performed for a homologous series of four hydroxyanthraquinones. Estimation of shape- and chemical feature-based similarity with (S)-gatifloxacin, a DNA gyrase inhibitor, and (S)-levofloxacin, a DNA topoisomerase IV inhibitor, outlined pseudopurpurin and munjistin as the most similar structures. The docking simulations supported the hypothesis for a plausible antibacterial activity of hydroxyanthraquinones. The predicted docking poses were grouped into 13 binding modes based on spatial similarities in the active site. The simultaneous presence of 1-OH and 3-COOH substituents in the anthraquinone scaffold were emphasized as relevant features for the binding modes' variability and ability of the compounds to strongly bind in the DNA-enzyme complexes. The results reveal new potential pharmacological targets of the studied polyphenols and help in their prioritization as drug candidates and dietary supplements.

Abstract: BACKGROUND: Diabetic retinopathy (DR), one of the most common and severe microvascular complication of diabetes mellitus (DM), is mainly caused by diabetic metabolic disorder. So far, there is no effective treatment for DR. Eriocauli Flos, a traditional Chinese herb, has been used in treating the ophthalmic diseases including DR. However, the active ingredients and molecular mechanisms of Eriocauli Flos to treat diabetic retinopathy remain elusive. METHODS: Here, the systems pharmacology model was developed via constructing network approach. 8 active components which were screened by oral bioavailability (OB >/= 30%) and drug-likeness (DL >/= 0.18) and 154 targets were selected from Eriocauli Flos through TCMSP database. Another 3593 targets related to DR were obtained from Genecards, OMIM, TTD, and Drugbank databases. The 103 intersecting targets of DR and Eriocauli Flos were obtained by Draw Venn Diagram. In addition, protein-protein interaction network was established from STRING database and the compound-target network was constructed by Cytoscape which screened top 12 core targets with cytoNCA module. Then the overlapping targets were analyzed by GO and KEGG enrichment. Moreover, two core targets were selected to perform molecular docking simulation. Subsequently, CCK8 assay, RT-PCR and Western blotting were applied to further reveal the mechanism of new candidate active component from Eriocauli Flos in high glucose-induced HRECs. RESULTS: The results showed that the overlapping targets by GO analysis were enriched in cellular response to chemical stress, response to oxidative stress, response to reactive oxygen species, reactive oxygen species metabolic process and so on. Besides, the overlapping targets principally regulated pathways such as AGE-RAGE signaling pathway in diabetic complications, lipid atherosclerosis, fluid shear stress and atherosclerosis, and PI3K-Akt signaling pathway. Molecular docking exhibited that VEGFA and TNF-alpha, had good bindings to the great majority of compounds, especially the compound hispidulin. In vitro, hispidulin ameliorated high-glucose induced proliferation by down-regulating the expression of p-ERK, p-Akt, and VEGFA; meanwhile inhibited the mRNA levels of TNF-alpha. CONCLUSIONS: In this study, through network pharmacology analysis and experimental validation, we found that hispidulin maybe has a potential targeted therapy effect for DR by decreasing the expression of p-Akt, p-ERK, and VEGFA, which resulted in ameliorating the proliferation in HRECs.

Abstract: OBJECTIVE: To investigate how the National Health Commission of China (NHCC)-recommended Chinese medicines (CMs) modulate the major maladjustments of coronavirus disease 2019 (COVID-19), particularly the clinically observed complications and comorbidities. METHODS: By focusing on the potent targets in common with the conventional medicines, we investigated the mechanisms of 11 NHCC-recommended CMs in the modulation of the major COVID-19 pathophysiology (hyperinflammations, viral replication), complications (pain, headache) and comorbidities (hypertension, obesity, diabetes). The constituent herbs of these CMs and their chemical ingredients were from the Traditional Chinese Medicine Information Database. The experimentally-determined targets and the activity values of the chemical ingredients of these CMs were from the Natural Product Activity and Species Source Database. The approved and clinical trial drugs against these targets were searched from the Therapeutic Target Database and DrugBank Database. Pathways of the targets was obtained from Kyoto Encyclopedia of Genes and Genomes and additional literature search. RESULTS: Overall, 9 CMs modulated 6 targets discovered by the COVID-19 target discovery studies, 8 and 11 CMs modulated 8 and 6 targets of the approved or clinical trial drugs for the treatment of the major COVID-19 complications and comorbidities, respectively. CONCLUSION: The coordinated actions of each NHCC-recommended CM against a few targets of the major COVID-19 pathophysiology, complications and comorbidities, partly have common mechanisms with the conventional medicines.

Abstract: During the development of drug and clinical applications, due to the co-administration of different drugs that have a high risk of interfering with each other's mechanisms of action, correctly identifying potential drug-drug interactions (DDIs) is important to avoid a reduction in drug therapeutic activities and serious injuries to the organism. Therefore, to explore potential DDIs, we develop a computational method of integrating multi-level information. Firstly, the information of chemical sequence is fully captured by the Natural Language Processing (NLP) algorithm, and multiple biological function similarity information is fused by Similarity Network Fusion (SNF). Secondly, we extract deep network structure information through Hierarchical Representation Learning for Networks (HARP). Then, a highly representative comprehensive feature descriptor is constructed through the self-attention module that efficiently integrates biochemical and network features. Finally, a deep neural network (DNN) is employed to generate the prediction results. Contrasted with the previous supervision model, BioChemDDI innovatively introduced graph collapse for extracting a network structure and utilized the biochemical information during the pre-training process. The prediction results of the benchmark dataset indicate that BioChemDDI outperforms other existing models. Moreover, the case studies related to three cancer diseases, including breast cancer, hepatocellular carcinoma and malignancies, were analyzed using BioChemDDI. As a result, 24, 18 and 20 out of the top 30 predicted cancer-related drugs were confirmed by the databases. These experimental results demonstrate that BioChemDDI is a useful model to predict DDIs and can provide reliable candidates for biological experiments. The web server of BioChemDDI predictor is freely available to conduct further studies.

Abstract: The use of metal-binding pharmacophores (MBPs) in fragment-based drug discovery has proven effective for targeted metalloenzyme drug development. However, MBPs can still suffer from pharmacokinetic liabilities. Bioisostere replacement is an effective strategy utilized by medicinal chemists to navigate these issues during the drug development process. The quinoline pharmacophore and its bioisosteres, such as quinazoline, are important building blocks in the design of new therapeutics. More relevant to metalloenzyme inhibition, 8-hydroxyquinoline (8-HQ) and its derivatives can serve as MBPs for metalloenzyme inhibition. In this report, 8-HQ isosteres are designed and the coordination chemistry of the resulting metal-binding isosteres (MBIs) is explored using a bioinorganic model complex. In addition, the physicochemical properties and metalloenzyme inhibition activity of these MBIs were investigated to establish drug-like profiles. This report provides a new group of 8-HQ-derived MBIs that can serve as novel scaffolds for metalloenzyme inhibitor development with tunable, and potentially improved, physicochemical properties.

Abstract: MOTIVATION: Evaluating the blood-brain barrier (BBB) permeability of drug molecules is a critical step in brain drug development. Traditional methods for the evaluation require complicated in vitro or in vivo testing. Alternatively, in silico predictions based on machine learning have proved to be a cost-efficient way to complement the in vitro and in vivo methods. However, the performance of the established models has been limited by their incapability of dealing with the interactions between drugs and proteins, which play an important role in the mechanism behind the BBB penetrating behaviors. To address this limitation, we employed the relational graph convolutional network (RGCN) to handle the drug-protein interactions as well as the properties of each individual drug. RESULTS: The RGCN model achieved an overall accuracy of 0.872, an area under the receiver operating characteristic (AUROC) of 0.919 and an area under the precision-recall curve (AUPRC) of 0.838 for the testing dataset with the drug-protein interactions and the Mordred descriptors as the input. Introducing drug-drug similarity to connect structurally similar drugs in the data graph further improved the testing results, giving an overall accuracy of 0.876, an AUROC of 0.926 and an AUPRC of 0.865. In particular, the RGCN model was found to greatly outperform the LightGBM base model when evaluated with the drugs whose BBB penetration was dependent on drug-protein interactions. Our model is expected to provide high-confidence predictions of BBB permeability for drug prioritization in the experimental screening of BBB-penetrating drugs. AVAILABILITY AND IMPLEMENTATION: The data and the codes are freely available at https://github.com/dingyan20/BBB-Penetration-Prediction. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: The deployment of various networks (e.g., Internet of Things [IoT] and mobile networks), databases (e.g., nutrition tables and food compositional databases), and social media (e.g., Instagram and Twitter) generates huge amounts of food data, which present researchers with an unprecedented opportunity to study various problems and applications in food science and industry via data-driven computational methods. However, these multi-source heterogeneous food data appear as information silos, leading to difficulty in fully exploiting these food data. The knowledge graph provides a unified and standardized conceptual terminology in a structured form, and thus can effectively organize these food data to benefit various applications. In this review, we provide a brief introduction to knowledge graphs and the evolution of food knowledge organization mainly from food ontology to food knowledge graphs. We then summarize seven representative applications of food knowledge graphs, such as new recipe development, diet-disease correlation discovery, and personalized dietary recommendation. We also discuss future directions in this field, such as multimodal food knowledge graph construction and food knowledge graphs for human health.