The Discovery of New Drug-Target Interactions for Breast Cancer Treatment.
Ontology highlight
ABSTRACT:
SUBMITTER: Song J
PROVIDER: S-EPMC8704452 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Breast cancer is a heterogeneous disease that develops through a multistep process via the accumulation of genetic/epigenetic alterations in various cancer-related genes. Current treatment options for breast cancer patients include surgery, radiotherapy, and chemotherapy including conventional cytotoxic and molecular-targeted anticancer drugs for each intrinsic subtype, such as endocrine therapy and antihuman epidermal growth factor receptor 2 (HER2) therapy. However, these therapies often fail to prevent recurrence and metastasis due to resistance. Overall, understanding the molecular mechanisms of breast carcinogenesis and progression will help to establish therapeutic modalities to improve treatment. The recent development of comprehensive omics technologies has led to the discovery of driver genes, including oncogenes and tumor-suppressor genes, contributing to the development of molecular-targeted anticancer drugs. Here, we review the development of anticancer drugs targeting cancer-specific functional therapeutic targets, namely, MELK (maternal embryonic leucine zipper kinase), TOPK (T-lymphokine-activated killer cell-originated protein kinase), and BIG3 (brefeldin A-inhibited guanine nucleotide-exchange protein 3), as identified through comprehensive breast cancer transcriptomics.
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Project description:Metabolites produced in the human gut are known modulators of host immunity. However, large-scale identification of metabolite-host receptor interactions remains a daunting challenge. Here, we employed computational approaches to identify 983 potential metabolite-target interactions using the Inflammatory Bowel Disease (IBD) cohort dataset of the Human Microbiome Project 2 (HMP2). Using a consensus of multiple machine learning methods, we ranked metabolites based on importance to IBD, followed by virtual ligand-based screening to identify possible human targets and adding evidence from compound assay, differential gene expression, pathway enrichment, and genome-wide association studies. We confirmed known metabolite-target pairs such as nicotinic acid-GPR109a or linoleoyl ethanolamide-GPR119 and inferred interactions of interest including oleanolic acid-GABRG2 and alpha-CEHC-THRB. Eleven metabolites were tested for bioactivity in vitro using human primary cell-types. By expanding the universe of possible microbial metabolite-host protein interactions, we provide multiple drug targets for potential immune-therapies.
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Project description:Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict PPI essentiality by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at https://github.com/cooperlab/MEDICI. Datasets are available at https://ctd2.nci.nih.gov/dataPortal.
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Project description:Protein-protein interaction (PPI) is a hot topic in clinical research as protein networking has a major impact in human disease. Such PPIs are potential drugs targets, leading to the need to inhibit/block specific PPIs. While small molecule inhibitors have had some success and reached clinical trials, they have generally failed to address the flat and large nature of PPI surfaces. As a result, larger biologics were developed for PPI surfaces and they have successfully targeted PPIs located outside the cell. However, biologics have low bioavailability and cannot reach intracellular targets. A novel class -hydrocarbon-stapled α-helical peptides that are synthetic mini-proteins locked into their bioactive structure through site-specific introduction of a chemical linker- has shown promise. Stapled peptides show an ability to inhibit intracellular PPIs that previously have been intractable with traditional small molecule or biologics, suggesting that they offer a novel therapeutic modality. In this review, we highlight what stapling adds to natural-mimicking peptides, describe the revolution of synthetic chemistry techniques and how current drug discovery approaches have been adapted to stabilize active peptide conformations, including ring-closing metathesis (RCM), lactamisation, cycloadditions and reversible reactions. We provide an overview on the available stapled peptide high-resolution structures in the protein data bank, with four selected structures discussed in details due to remarkable interactions of their staple with the target surface. We believe that stapled peptides are promising drug candidates and open the doors for peptide therapeutics to reach currently "undruggable" space.
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Project description:The remodelling of the extracellular matrix (ECM) has been shown to be highly upregulated in cancer and inflammation and is critically linked to the processes of invasion and metastasis. One of the key enzymes involved in specifically degrading the heparan sulphate (HS) component of the ECM is the endo-beta-glucuronidase enzyme heparanase. Processing of HS by heparanase releases both a host of bioactive growth factors anchored within the mesh of the ECM as well as defined fragments of HS capable of promoting cellular proliferation. The finding that heparanase is elevated in a wide variety of tumor types and is subsequently linked to the development of pathological processes has led to an explosion of therapeutic strategies to inhibit its enzyme activity. So far only one compound, the sulphated oligosaccharide PI88, which both inhibits heparanase activity and has effects on growth factor binding has reached clinical trials where it has shown to have promising efficacy. The scene has clearly been set however for a new generation of compounds, either specific to the enzyme or with dual roles, to emerge from the lab and enter the clinic. The aim of this review is to describe the current drug discovery status of small molecule, sugar and neutralising antibody inhibitors of heparanase enzyme activity. Potential strategies will also be discussed on the selection of suitable biomarker strategies for specific monitoring of in vivo heparanase inhibition which will be crucial for both animal model and clinical trial testing.
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Project description:BACKGROUND:Identification of novel drug-target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. RESULTS:Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. CONCLUSIONS:DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind can be accessed online at: http://www.cbrc.kaust.edu.sa/daspfind.Graphical abstractThe conceptual workflow for predicting drug-target interactions using DASPfind.
| S-EPMC4793623 | biostudies-literature
Project description:Computational methods for predicting drug-target interactions have become important in drug research because they can help to reduce the time, cost, and failure rates for developing new drugs. Recently, with the accumulation of drug-related data sets related to drug side effects and pharmacological data, it has became possible to predict potential drug-target interactions. In this study, we focus on drug-drug interactions (DDI), their adverse effects ([Formula: see text]) and pharmacological information ([Formula: see text]), and investigate the relationship among chemical structures, side effects, and DDIs from several data sources. In this study, [Formula: see text] data from the STITCH database, [Formula: see text] from drugs.com, and drug-target pairs from ChEMBL and SIDER were first collected. Then, by applying two machine learning approaches, a support vector machine (SVM) and a kernel-based L1-norm regularized logistic regression (KL1LR), we showed that DDI is a promising feature in predicting drug-target interactions. Next, the accuracies of predicting drug-target interactions using DDI were compared to those obtained using the chemical structure and side effects based on the SVM and KL1LR approaches, showing that DDI was the data source contributing the most for predicting drug-target interactions.
| S-EPMC3836969 | biostudies-literature
Project description:BACKGROUND:Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interactions (i.e., positive samples) for the predictions. Their performance is severely impeded by the lack of reliable negative samples. RESULTS:We propose a method to construct highly-reliable negative samples for drug target prediction by a pairwise drug-target similarity measurement and OCSVM with a high-recall constraint. On one hand, we measure the pairwise similarity between every two drug-target interactions by combining the chemical similarity between their drugs and the Gene Ontology-based similarity between their targets. Then we calculate the accumulative similarity with all known drug-target interactions for each unobserved drug-target interaction. On the other hand, we obtain the signed distance from OCSVM learned from the known interactions with high recall (?0.95) for each unobserved drug-target interaction. After normalizing all accumulative similarities and signed distances to the range [0,1], we compute the score for each unobserved drug-target interaction via averaging its accumulative similarity and signed distance. Unobserved interactions with lower scores are preferentially served as reliable negative samples for the classification algorithms. The performance of the proposed method is evaluated on the interaction data between 1094 drugs and 1556 target proteins. Extensive comparison experiments using four classical classifiers and one domain predictive method demonstrate the superior performance of the proposed method. A better decision boundary has been learned from the constructed reliable negative samples. CONCLUSIONS:Proper construction of highly-reliable negative samples can help the classification models learn a clear decision boundary which contributes to the performance improvement.
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Project description:Breast cancer is the second largest cause of cancer death among U.S. women and the leading cause of cancer death among women worldwide. Genome-wide association studies (GWAS) have identified several genetic variants associated with susceptibility to breast cancer, but these still explain less than half of the estimated genetic contribution to the disease. Combinations of variants (i.e. genetic interactions) may play an important role in breast cancer susceptibility. However, due to a lack of statistical power, the current tests for genetic interactions from GWAS data mainly leverage prior knowledge to focus on small sets of genes or SNPs that are known to have an association with breast cancer. Thus, many genetic interactions, particularly among novel variants, remain understudied. Reverse-genetic interaction screens in model organisms have shown that genetic interactions frequently cluster into highly structured motifs, where members of the same pathway share similar patterns of genetic interactions. Based on this key observation, we recently developed a method called BridGE to search for such structured motifs in genetic networks derived from GWAS studies and identify pathway-level genetic interactions in human populations. We applied BridGE to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts. Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts. The discovered interactions implicated the glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk. Notably, while many of the pathways identified by BridGE show clear relevance to breast cancer, variants in these pathways had not been previously discovered by traditional single variant association tests, or single pathway enrichment analysis that does not consider SNP-SNP interactions.
| S-EPMC5619706 | biostudies-literature
Project description:Several machine learning approaches have been proposed for predicting new benefits of the existing drugs. Although these methods have introduced new usage(s) of some medications, efficient methods can lead to more accurate predictions. To this end, we proposed a novel machine learning method which is based on a new optimization algorithm, named Trader. To show the capabilities of the proposed algorithm which can be applied to the different scope of science, it was compared with ten other state-of-the-art optimization algorithms based on the standard and advanced benchmark functions. Next, a multi-layer artificial neural network was designed and trained by Trader to predict drug-target interactions (DTIs). Finally, the functionality of the proposed method was investigated on some DTIs datasets and compared with other methods. The data obtained by Trader showed that it eliminates the disadvantages of different optimization algorithms, resulting in a better outcome. Further, the proposed machine learning method was found to achieve a significant level of performance compared to the other popular and efficient approaches in predicting unknown DTIs. All the implemented source codes are freely available at https://github.com/LBBSoft/Trader .
| S-EPMC6597553 | biostudies-literature