Abstract: Outcomes for most patients with Ewing sarcoma (ES) have remained unchanged for the last 30 years, emphasising the need for more effective and tolerable treatments. We have hypothesised that using small-molecule inhibitors to kill the self-renewing chemotherapy-resistant cells (Ewing sarcoma cancer stem-like cells; ES-CSCs) responsible for progression and relapse could improve outcomes and minimise treatment-induced morbidities. For the first time, we demonstrate that ABCG1, a potential oncogene in some cancers, is highly expressed in ES-CSCs independently of CD133. Using functional models, transcriptomics and a bespoke in silico drug-repurposing pipeline, we have prioritised a group of tractable small-molecule inhibitors for further preclinical studies. Consistent with the cellular origin of ES, 21 candidate molecular targets of pluripotency, stemness and chemoresistance were identified. Small-molecule inhibitors to 13 of the 21 molecular targets (62%) were identified. POU5F1/OCT4 was the most promising new therapeutic target in Ewing sarcoma, interacting with 10 of the 21 prioritised molecular targets and meriting further study. The majority of small-molecule inhibitors (72%) target one of two drug efflux proteins, p-glycoprotein (n = 168) or MRP1 (n = 13). In summary, we have identified a novel cell surface marker of ES-CSCs and cancer/non-cancer drugs to targets expressed by these cells that are worthy of further preclinical evaluation. If effective in preclinical models, these drugs and drug combinations might be repurposed for clinical evaluation in patients with ES.

Abstract: BACKGROUND: Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy. METHODS: We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning. RESULTS: To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF. CONCLUSIONS: All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

Abstract: Fragment-based drug design relies heavily on structural information for the elaboration and optimisation of hits. The ability to identify neighbouring binding hot spots, energetically favourable interactions and conserved binding motifs in protein structures through X-ray crystallography can inform the evolution of fragments into lead-like compounds through structure-based design. The composition of fragment libraries can be designed and curated to fit this purpose and herein, we describe and compare screening libraries containing compounds comprising between 2 and 18 heavy atoms. We evaluate the properties of the compounds in these libraries and assess their ability to probe protein surfaces for binding hot spots.

Abstract: Rheumatoid arthritis (RA) is a chronic, systemic, autoimmune disease that may lead to joint damage, deformity, and disability, if not treated effectively. Hedyotis diffusa Willd (HDW) and its main components have been widely used to treat a variety of tumors and inflammatory diseases. The present study utilized a network pharmacology approach, microarray data analysis and molecular docking to predict the key active ingredients and mechanisms of HDW against RA. Eleven active ingredients in HDW and 180 potential anti-RA targets were identified. The ingredients-targets-RA network showed that stigmasterol, beta-sitosterol, quercetin, kaempferol, and 2-methoxy-3-methyl-9,10-anthraquinone were key components for RA treatment. KEGG pathway results revealed that the 180 potential targets were inflammatory-related pathways with predominant enrichment of the AGE-RAGE, TNF, IL17, and PI3K-Akt signaling pathways. Screened through the PPI network and with Cytoscape software, RELA, TNF, IL6, TP53, MAPK1, AKT1, IL10, and ESR1 were identified as the hub targets in the HDW for RA treatment. Molecular docking was used to identify the binding of 5 key components and the 8 related-RA hub targets. Moreover, the results of network pharmacology were verified by vitro experiments. HDW inhibits cell proliferation in MH7A cells in a dose and time-dependent manner. RT-qPCR and WB results suggest that HDW may affect hub targets through PI3K/AKT signaling pathway, thereby exerting anti-RA effect. This study provides evidence for a clinical effect of HDW on RA and a research basis for further investigation into the active ingredients and mechanisms of HDW against RA.

Abstract: Medicinal fungi, including mushrooms, have well-documented therapeutic uses. In this study, we perform a cheminformatics-based investigation of the scaffold and structural diversity of the secondary metabolite space of medicinal fungi and, moreover, perform a detailed comparison with approved drugs, other natural product libraries, and semi-synthetic libraries. We find that the secondary metabolite space of medicinal fungi has similar or higher scaffold diversity in comparison to other natural product libraries analyzed here. Notably, 94% of the scaffolds in the secondary metabolite space of medicinal fungi are not present in the approved drugs. Further, we find that the secondary metabolites, on the one hand, are structurally far from the approved drugs, while, on the other hand, they are close in terms of molecular properties to the approved drugs. Lastly, chemical space visualization using dimensionality reduction methods showed that the secondary metabolite space has minimal overlap with the approved drug space. In a nutshell, our results underscore that the secondary metabolite space of medicinal fungi is a valuable resource for identifying potential lead molecules for natural product-based drug discovery.

Abstract: The progression of drugs into clinical phases requires proper toxicity assessment in animals and the correct identification of possible metabolites. Accordingly, different animal models are used to preliminarily evaluate toxicity and biotransformations. Rodents are the most common models used to preliminarily evaluate the safety of drugs; however, their use is subject to ethical consideration and elevated costs, and strictly regulated by national legislations. Herein, we developed a novel, cheap and convenient toxicity model using Tenebrio molitor coleoptera (TMC). A panel of 15 drugs-including antivirals and antibacterials-with different therapeutic applications was administered to TMC and the LD50 was determined. The values are comparable with those already determined in mice and rats. In addition, a TMC model was used to determine the presence of the main metabolites and in vivo pharmacokinetics (PK), and results were compared with those available from in vitro assays and the literature. Taken together, our results demonstrate that TMC can be used as a novel and convenient preliminary toxicity model to preliminarily evaluate the safety of experimental compounds and the formation of main metabolites, and to reduce the costs and number of rodents, according to 3R principles.

Abstract: Drug repositioning aims to discover novel clinical benefits of existing drugs, is an effective way to develop drugs for complex diseases such as cancer and may facilitate the process of traditional drug development. Meanwhile, network-based computational biology approaches, which allow the integration of information from different aspects to understand the relationships between biomolecules, has been successfully applied to drug repurposing. In this work, we developed a new strategy for network-based drug repositioning against cancer. Combining the mechanism of action and clinical efficacy of the drugs, a cancer-related drug similarity network was constructed, and the correlation score of each drug with a specific cancer was quantified. The top 5% of scoring drugs were reviewed for stability and druggable potential to identify potential repositionable drugs. Of the 11 potentially repurposable drugs for non-small cell lung cancer (NSCLC), 10 were confirmed by clinical trial articles and databases. The targets of these drugs were significantly enriched in cancer-related pathways and significantly associated with the prognosis of NSCLC. In light of the successful application of our approach to colorectal cancer as well, it provides an effective clue and valuable perspective for drug repurposing in cancer.

Abstract: Permeability across cellular membranes is a key factor that influences absorption and distribution. Before absorption, many drugs must pass through the mucus barrier that covers all the wet surfaces of the human body. Cell-free in vitro tools currently used to evaluate permeability fail to effectively model the complexity of mucosal barriers. Here, we present an in vitro mucosal platform as a possible strategy for assessing permeability in a high-throughput setup. The PermeaPad 96-well plate was used as a permeability system and further coupled to a pathological, tridimensional mucus model. The physicochemical determinants predicting passive diffusion were determined by combining experimental and computational approaches. Drug solubility, size, and shape were found to be the critical properties governing permeability, while the charge of the drug was found to be influential on the interaction with mucus. Overall, the proposed mucosal platform could be a promising in vitro tool to model the complexity of mucosal tissues and could therefore be adopted for drug-permeability profiling.

Abstract: With incidence of antimicrobial resistance rising globally, there is a continuous need for development of new antimicrobial molecules. Phenolic compounds having a versatile scaffold that allows for a broad range of chemical additions; they also exhibit potent antimicrobial activities which can be enhanced significantly through functionalization. Synthetic routes such as esterification, phosphorylation, hydroxylation or enzymatic conjugation may increase the antimicrobial activity of compounds and reduce minimal concentrations needed. With potent action mechanisms interfering with bacterial cell wall synthesis, DNA replication or enzyme production, phenolics can target multiple sites in bacteria, leading to a much higher sensitivity of cells towards these natural compounds. The current review summarizes some of the most important knowledge on functionalization of natural phenolic compounds and the effects on their antimicrobial activity.

Abstract: Developing drugs for treating Alzheimer's disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations. This pipeline showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases.