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drug target disease predict

Matthew T Patrick, Kalpana Raja, Keylonnie Miller, Jason Sotzen, Johann E Gudjonsson, James T Elder, Lam C Tsoi
Immune-mediated diseases affect >20% of the population and many autoimmune diseases affect the skin. Drug repurposing (aka repositioning) is a cost-effective approach for revealing drugs that can be used to treat diseases for which they are currently not prescribed. We implemented an efficient bioinformatics approach using "word embedding" to summarize drug information from >20 million articles, and applied machine learning to model the drug-disease relationship. We trained our drug repurposing approach separately on 9 cutaneous diseases (including psoriasis, atopic dermatitis and alopecia areata), as well as 8 other immune-mediated diseases, and obtained a mean AUROC of 0...
October 17, 2018: Journal of Investigative Dermatology
Soren Hayrabedyan, Krassimira Todorova, Marialuigia Spinelli, Eytan R Barnea, Martin Mueller
The central pathological feature of Alzheimer's disease (AD) is the sequential proteolytic processing of amyloid precursor protein (APP) to amyloid-β peptides (Aβ) agglomeration. The clearance of Aβ may be induced by the large zinc-binding protease insulin degrading enzyme (IDE). IDE is the common link between AD and Type II diabetes as insulin is an IDE target as well. Not surprisingly, the search for safe and effective drugs modulating IDE is ongoing. A new pregnancy derived peptide, PreImplantation Factor (PIF), inhibits neuro-inflammation and crosses the blood-brain-barrier...
September 21, 2018: Oncotarget
Aman Sharma, Rinkle Rani
BACKGROUND AND OBJECTIVE: Drug-target interaction prediction plays an intrinsic role in the drug discovery process. Prediction of novel drugs and targets helps in identifying optimal drug therapies for various stringent diseases. Computational prediction of drug-target interactions can help to identify potential drug-target pairs and speed-up the process of drug repositioning. In our present, work we have focused on machine learning algorithms for predicting drug-target interactions from the pool of existing drug-target data...
October 2018: Computer Methods and Programs in Biomedicine
Fuqiang Yin, Shang Yi, Luwei Wei, Bingbing Zhao, Jinqian Li, Xiangxue Cai, Caihua Dong, Xia Liu
The outcome for patients with ovarian cancer (OC) is poor because of drug resistance. Therefore, identification of factors that affect drug resistance and prognosis in OC is needed. In the present study, we identified 131 genes significantly dysregulated in 90 platinum-resistant OC tissues compared with 197 sensitive tissues, of which 30 were significantly associated with disease-free survival (DFS; n = 16), overall survival (OS; n = 6), or both (n = 8) in 489 OC patients of the The Cancer Genome Atlas cohort...
October 18, 2018: Journal of Cellular Biochemistry
Arjanneke F van de Merbel, Geertje van der Horst, Maaike H van der Mark, Janneke I M van Uhm, Erik J van Gennep, Peter Kloen, Lijkele Beimers, Rob C M Pelger, Gabri van der Pluijm
Urological malignancies, including prostate and bladder carcinoma, represent a major clinical problem due to the frequent occurrence of therapy resistance and the formation of incurable distant metastases. As a result, there is an urgent need for versatile and predictive disease models for the assessment of the individualized drug response in urological malignancies. Compound testing on ex vivo cultured patient-derived tumor tissues could represent a promising approach. In this study, we have optimized an ex vivo culture system of explanted human prostate and bladder tumors derived from clinical specimens and human cancer cell lines xenografted in mice...
2018: Frontiers in Oncology
Dorothée Diogo, Chao Tian, Christopher S Franklin, Mervi Alanne-Kinnunen, Michael March, Chris C A Spencer, Ciara Vangjeli, Michael E Weale, Hannele Mattsson, Elina Kilpeläinen, Patrick M A Sleiman, Dermot F Reilly, Joshua McElwee, Joseph C Maranville, Arnaub K Chatterjee, Aman Bhandari, Khanh-Dung H Nguyen, Karol Estrada, Mary-Pat Reeve, Janna Hutz, Nan Bing, Sally John, Daniel G MacArthur, Veikko Salomaa, Samuli Ripatti, Hakon Hakonarson, Mark J Daly, Aarno Palotie, David A Hinds, Peter Donnelly, Caroline S Fox, Aaron G Day-Williams, Robert M Plenge, Heiko Runz
Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints...
October 16, 2018: Nature Communications
Xing Chen, Na-Na Guan, Ya-Zhou Sun, Jian-Qiang Li, Jia Qu
Small molecule is a kind of low molecular weight organic compound with variety of biological functions. Studies have indicated that small molecules can inhibit a specific function of a multifunctional protein or disrupt protein-protein interactions and may have beneficial or detrimental effect against diseases. MicroRNAs (miRNAs) play crucial roles in cellular biology, which makes it possible to develop miRNA as diagnostics and therapeutic targets. Several drug-like compound libraries were screened successfully against different miRNAs in cellular assays further demonstrating the possibility of targeting miRNAs with small molecules...
October 16, 2018: Briefings in Bioinformatics
Jan Rožanc, Theodore Sakellaropoulos, Asier Antoranz, Cristiano Guttà, Biswajit Podder, Vesna Vetma, Nicole Rufo, Patrizia Agostinis, Vaia Pliaka, Thomas Sauter, Dagmar Kulms, Markus Rehm, Leonidas G Alexopoulos
Malignant melanoma is a highly aggressive form of skin cancer responsible for the majority of skin cancer-related deaths. Recent insight into the heterogeneous nature of melanoma suggests more personalised treatments may be necessary to overcome drug resistance and improve patient care. To this end, reliable molecular signatures that can accurately predict treatment responsiveness need to be identified. In this study, we applied multiplex phosphoproteomic profiling across a panel of 24 melanoma cell lines with different disease-relevant mutations, to predict responsiveness to MEK inhibitor trametinib...
October 15, 2018: Cell Death and Differentiation
Zhongyan Li, Qingqing Miao, Fugang Yan, Yang Meng, Peng Zhou
BACKGROUND: Protein-peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention. METHODS: We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein-peptide binding affinity, particularly focusing on its implications for therapeutic peptide design...
October 12, 2018: Current Drug Metabolism
Shiyu Song, Haiyan Min, Mengyuan Niu, Lei Wang, Yongzheng Wu, Bin Zhang, Xiufang Chen, Qiao Liang, Yanting Wen, Yong Wang, Long Yi, Hongwei Wang, Qian Gao
BACKGROUND: S1PR1-STAT3 inter-regulatory loop was initially suggested to be oncogenic in several cancer cells. However, the clinical relevance of this mechanism in tumor progression, disease prognosis and drug response was not established. METHODS: The correlations between S1PR1 transcription, overall survival and chemotherapy response of GC patients were tested using a large clinical database. The relevance of S1PR1 expression and STAT3 activation in both tumor tissues and cancer cell lines was also tested...
October 10, 2018: EBioMedicine
Kun Tian, Emyr Bakker, Michelle Hussain, Alice Guazzelli, Hasen Alhebshi, Parisa Meysami, Constantinos Demonacos, Jean-Marc Schwartz, Luciano Mutti, Marija Krstic-Demonacos
BACKGROUND: Malignant pleural mesothelioma (MPM) is an orphan disease that is difficult to treat using traditional chemotherapy, an approach which has been effective in other types of cancer. Most chemotherapeutics cause DNA damage leading to cell death. Recent discoveries have highlighted a potential role for the p53 tumor suppressor in this disease. Given the pivotal role of p53 in the DNA damage response, here we investigated the predictive power of the p53 interactome model for MPM patients' stratification...
October 13, 2018: Journal of Translational Medicine
Ming Hao, Stephen H Bryant, Yanli Wang
BACKGROUND: Fast and accurate identification of potential drug candidates against therapeutic targets (i.e., drug-target interactions, DTIs) is a fundamental step in the early drug discovery process. However, experimental determination of DTIs is time-consuming and costly, especially for testing the associations between the entire chemical and genomic spaces. Therefore, computationally efficient algorithms with accurate predictions are required to achieve such a challenging task. In this work, we design a new chemoinformatics approach derived from neighbor-based collaborative filtering (NBCF) to infer potential drug candidates for targets of interest...
October 11, 2018: Journal of Cheminformatics
Sophie Paczesny, Jochen Metzger
Allogeneic hematopoietic stem cell transplantation (HSCT) is the most effective form of tumor immunotherapy available to date. However, while HSCT can induce beneficial graft-versus-leukemia (GVL) effect, the adverse effect of graft-versus-host disease (GVHD), which is closely linked to GVL, is the major source of morbidity and mortality following HSCT. Until recently, available diagnostic and staging tools frequently fail to identify those at higher risk of disease progression or death. Furthermore, there are shortcomings in the prediction of the need for therapeutic interventions or the response rates to different forms of therapy...
October 11, 2018: Proteomics. Clinical Applications
Saddia Bano, Muhammad Asif Rasheed, Farrukh Jamil, Muhammad Ibrahim, Sumaira Kanwal
BACKGROUND: Apolipoprotein E4 (ApoE) is a major genetic risk factor due to its increase incidence of developing Alzheimer's disease (AD). ApoE plays a major role in the brain to maintain a constant supply of neuronal lipids for rapid and dynamic membrane synthesis. Aggregation of beta amyloid plaques (Aβ) and neurofibrillary tangles in the brain has responsible for onset of AD. Clearance of Aβ aggregation is required and any defect in this clearance may cause AD. APOE with ε4 allele is the major genetic risk factor for Alzheimer's disease (AD)...
October 8, 2018: Current Computer-aided Drug Design
Aman Sharma, Rinkle Rani
Combination drug therapy is considered a better treatment option for various diseases, such as cancer, HIV, hypertension, and infections as compared to targeted drug therapies. Combination or synergism helps to overcome drug resistance, reduction in drug toxicity and dosage. Considering the complexity and heterogeneity among cancer types, drug combination provides promising treatment strategy. Increase in drug combination data raises a challenge for developing a computational approach that can effectively predict drugs synergism...
June 28, 2018: Journal of Bioinformatics and Computational Biology
Anupriya Sadhasivam, Umashankar Vetrivel
Chlamydia trachomatis (C.t) is a gram-negative obligate intracellular bacteria, which is a major causative of infectious blindness and sexually transmitted diseases. A surge in multidrug resistance among chlamydial species has posed a challenge to adopt alternative drug targeting strategies. Recently, in C.t, L,L-diaminopimelate aminotransferase (CtDAP-AT) is proven to be a potential drug target due its essential role in cell survival and host nonspecificity. Hence, in this study, a multilevel precision-based virtual screening of CtDAP-AT was performed to identify potential inhibitors, wherein, an integrative stringent scoring and filtration were performed by coupling, glide docking score, binding free energy, ADMET (absorption, distribution, metabolism, and excretion, toxicity) prediction, density functional theory (quantum mechanics), and molecular dynamics simulation (molecular mechanics)...
October 10, 2018: Journal of Cellular Biochemistry
Minzhe Zhang, Sangin Lee, Bo Yao, Guanghua Xiao, Lin Xu, Yang Xie
Motivation: Synergistic drug combinations are a promising approach to achieve a desirable therapeutic effect in complex diseases through the multi-target mechanism. However, in vivo screening of all possible multi-drug combinations remains cost-prohibitive. An effective and robust computational model to predict drug synergy in silico will greatly facilitate this process. Results: We developed DIGREM (Drug-Induced Genomic Response models for identification of Effective Multi-drug combinations), an online tool kit that can effectively predict drug synergy...
October 8, 2018: Bioinformatics
Peter G M Mol, Aliza Thompson, Hiddo J L Heerspink, Hubert G M Leufkens
Over the past 15 years, three new classes of drugs, glucagon-like peptide-1 (GLP-1) receptor agonists, dipeptidyl peptidase 4 (DPP-4) inhibitors and sodium glucose cotransporter-2 (SGLT-2) inhibitors have been approved to treat type 2 diabetes based on effects on glycemic control. Although large randomized controlled trials have played an important role in characterizing the efficacy and safety of these agents on a population level, questions remain about how best to individualize therapy and target the "right" medicine to the "right" patient...
October 2018: Diabetes, Obesity & Metabolism
Erica Dugnani, Valeria Sordi, Silvia Pellegrini, Raniero Chimienti, Ilaria Marzinotto, Valentina Pasquale, Daniela Liberati, Gianpaolo Balzano, Claudio Doglioni, Michele Reni, Alessandra Gandolfi, Massimo Falconi, Vito Lampasona, Lorenzo Piemonti
BACKGROUND: Despite the recent introduction of new drugs and the development of innovative multi-target treatments, the prognosis of pancreatic ductal adenocarcinoma (PDAC) remains very poor. Even when PDAC is resectable, the rate of local or widespread disease recurrence remains particularly high. Currently, reliable prognostic biomarkers of recurrence are lacking. We decided to explore the potential usefulness of pancreatic developmental regulators as biomarkers of PDAC relapse. METHODS: We analyzed by quantitative real-time PCR the mRNA of selected factors involved either in pancreatic organogenesis (ISL1, NEUROD1, NGN3, NKX2...
September 25, 2018: Pancreatology: Official Journal of the International Association of Pancreatology (IAP) ... [et Al.]
Ibon Eguiluz-Gracia, Tunn Ren Tay, Mark Hew, Maria M Escribese, Domingo Barber, Robyn E O'Hehir, Maria J Torres
The potential of precision medicine in allergy and asthma has only started to be explored. A significant clarification in the pathophysiology of rhinitis, chronic rhinosinusitis, asthma, food allergy and drug hypersensitivity was made in the last decade. This improved understanding led to a better classification of the distinct phenotypes, and to the discovery of new drugs such as biologicals, targeting phenotype-specific mechanisms. Nevertheless, many conditions remain poorly understood such as non-eosinophilic airway diseases or non-IgE mediated food allergy...
October 5, 2018: Allergy
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