CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations.

Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of the available data are produced using different technologies and scattered across individual computational resources without any explicit connections to each other, which hinders extensive and integrative multi-omics-based analysis. We aimed to address this issue by developing a new data integration/representation methodology and its application by constructing a biological data resource. CROssBAR is a comprehensive system that integrates large-scale biological/biomedical data from various resources and stores them in a NoSQL database. CROssBAR is enriched with the deep-learning-based prediction of relationships between numerous data entries, which is followed by the rigorous analysis of the enriched data to obtain biologically meaningful modules. These complex sets of entities and relationships are displayed to users via easy-to-interpret, interactive knowledge graphs within an open-access service. CROssBAR knowledge graphs incorporate relevant genes-proteins, molecular interactions, pathways, phenotypes, diseases, as well as known/predicted drugs and bioactive compounds, and they are constructed on-the-fly based on simple non-programmatic user queries. These intensely processed heterogeneous networks are expected to aid systems-level research, especially to infer biological mechanisms in relation to genes, proteins, their ligands, and diseases.

Treatment of breast cancer with autophagy inhibitory microRNAs carried by AGO2-conjugated nanoparticles.

Nanoparticle based gene delivery systems holds great promise. Superparamagnetic iron oxide nanoparticles (SPIONs) are being heavily investigated due to good biocompatibility and added diagnostic potential, rendering such nanoparticles theranostic. Yet, commonly used cationic coatings for efficient delivery of such anionic cargos, results in significant toxicity limiting translation of the technology to the clinic. Here, we describe a highly biocompatible, small and non-cationic SPION-based theranostic nanoparticles as novel gene therapy agents. We propose for the first-time, the usage of the microRNA machinery RISC complex component Argonaute 2 (AGO2) protein as a microRNA stabilizing agent and a delivery vehicle. In this study, AGO2 protein-conjugated, anti-HER2 antibody-linked and fluorophore-tagged SPION nanoparticles were developed (SP-AH nanoparticles) and used as a carrier for an autophagy inhibitory microRNA, MIR376B. These functionalized nanoparticles selectively delivered an effective amount of the microRNA into HER2-positive breast cancer cell lines in vitro and in a xenograft nude mice model of breast cancer in vivo, and successfully blocked autophagy. Furthermore, combination of the chemotherapy agent cisplatin with MIR376B-loaded SP-AH nanoparticles increased the efficacy of the anti-cancer treatment both in vitro in cells and in vivo in the nude mice. Therefore, we propose that AGO2 protein conjugated SPIONs are a new class of theranostic nanoparticles and can be efficiently used as innovative, non-cationic, non-toxic gene therapy tools for targeted therapy of cancer.

Vicinal Diaryl-Substituted Isoxazole and Pyrazole Derivatives with In Vitro Growth Inhibitory and In Vivo Antitumor Activity.

The vicinal diaryl heterocyclic framework has been widely used for the development of compounds with significant bioactivities. In this study, a series of diaryl heterocycles were designed and synthesized based on an in-house diaryl isoxazole derivative (9), and most of the newly synthesized derivatives demonstrated moderate to good antiproliferative activities against a panel of hepatocellular carcinoma and breast cancer cells, exemplified with the diaryl isoxazole 11 and the diaryl pyrazole 85 with IC50 values in the range of 0.7-9.5 µM. Treatments with both 11 and 85 induced apoptosis in these tumor cells, and they displayed antitumor activity in vivo in the Mahlavu hepatocellular carcinoma and the MDA-MB-231 breast cancer xenograft models, indicating that these compounds could be considered as leads for further development of antitumor agents. Important structural features of this compound class for the antitumor activity have also been proposed, which warrant further exploration to guide the design of new and more potent diaryl heterocycles.

Synthesis and biological evaluation of novel isoxazole-piperazine hybrids as potential anti-cancer agents with inhibitory effect on liver cancer stem cells.

In our effort for the development of novel anticancer therapeutics, a series of isoxazole-piperazine analogues were prepared, and primarily screened for their antiproliferative potential against hepatocellular carcinoma (HCC; Huh7/Mahlavu) and breast (MCF-7) cancer cells. All compounds demonstrated potent to moderate cytotoxicity on all cell lines with IC50 values in the range of 0.09-11.7 µM. Further biological studies with 6a and 13d in HCC cells have shown that both compounds induced G1 or G2/M arrests resulting in apoptotic cell death. Subsequent analysis of proteins involved in cell cycle progression as well as proliferation of HCC cells revealed that 6a and 13d may affect cellular survival pathways differently depending on the mutation profiles of cells (p53 and PTEN), epidermal/mesenchymal characteristics, and activation of cell mechanisms through p53 dependent/independent pathways. Lastly, we have demonstrated the potential anti-stemness properties of these compounds in which the proportion of liver CSCs in Huh7 cells (CD133+/EpCAM+) were significantly reduced by 6a and 13d. Furthermore, both compounds caused a significant reduction in expression of stemness markers, NANOG or OCT4 proteins, in Mahlavu and Huh7 cells, as well as resulted in a decreased sphere formation capacity in Huh7 cells. Together, these novel isoxazole-piperazine derivatives may possess potential as leads for development of effective anti-cancer drugs against HCC cells with stem cell-like properties.

Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools.

PURPOSE: Computational approaches have been used at different stages of drug development with the purpose of decreasing the time and cost of conventional experimental procedures. Lately, techniques mainly developed and applied in the field of artificial intelligence (AI), have been transferred to different application domains such as biomedicine. METHODS: In this study, we conducted an investigative analysis via data-driven evaluation of potential hepatocellular carcinoma (HCC) therapeutics in the context of AI-assisted drug discovery/repurposing. First, we discussed basic concepts, computational approaches, databases, modeling approaches, and featurization techniques in drug discovery/repurposing. In the analysis part, we automatically integrated HCC-related biological entities such as genes/proteins, pathways, phenotypes, drugs/compounds, and other diseases with similar implications, and represented these heterogeneous relationships via a knowledge graph using the CROssBAR system. RESULTS: Following the system-level evaluation and selection of critical genes/proteins and pathways to target, our deep learning-based drug/compound-target protein interaction predictors DEEPScreen and MDeePred have been employed for predicting new bioactive drugs and compounds for these critical targets. Finally, we embedded ligands of selected HCC-associated proteins which had a significant enrichment with the CROssBAR system into a 2-D space to identify and repurpose small molecule inhibitors as potential drug candidates based on their molecular similarities to known HCC drugs. CONCLUSIONS: We expect that these series of data-driven analyses can be used as a roadmap to propose early-stage potential inhibitors (from database-scale sets of compounds) to both HCC and other complex diseases, which may subsequently be analyzed with more targeted in silico and experimental approaches.

DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations.

The identification of physical interactions between drug candidate compounds and target biomolecules is an important process in drug discovery. Since conventional screening procedures are expensive and time consuming, computational approaches are employed to provide aid by automatically predicting novel drug-target interactions (DTIs). In this study, we propose a large-scale DTI prediction system, DEEPScreen, for early stage drug discovery, using deep convolutional neural networks. One of the main advantages of DEEPScreen is employing readily available 2-D structural representations of compounds at the input level instead of conventional descriptors that display limited performance. DEEPScreen learns complex features inherently from the 2-D representations, thus producing highly accurate predictions. The DEEPScreen system was trained for 704 target proteins (using curated bioactivity data) and finalized with rigorous hyper-parameter optimization tests. We compared the performance of DEEPScreen against the state-of-the-art on multiple benchmark datasets to indicate the effectiveness of the proposed approach and verified selected novel predictions through molecular docking analysis and literature-based validation. Finally, JAK proteins that were predicted by DEEPScreen as new targets of a well-known drug cladribine were experimentally demonstrated in vitro on cancer cells through STAT3 phosphorylation, which is the downstream effector protein. The DEEPScreen system can be exploited in the fields of drug discovery and repurposing for in silico screening of the chemogenomic space, to provide novel DTIs which can be experimentally pursued. The source code, trained "ready-to-use" prediction models, all datasets and the results of this study are available at ; https://github.com/cansyl/DEEPscreen.