scholarly journals A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

2017 ◽  
Author(s):  
Michael P Menden ◽  
Dennis Wang ◽  
Yuanfang Guan ◽  
Mike J Mason ◽  
Bence Szalai ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Targeted Therapies ◽  
Drug Target ◽  
Pi3k Pathway ◽  
Drug Combinations ◽  
Data Driven ◽  
Methodological Development ◽  
Sanger Institute ◽  
Response Profiles

AbstractThe effectiveness of most cancer targeted therapies is short lived since tumors evolve and develop resistance. Combinations of drugs offer the potential to overcome resistance, however the number of possible combinations is vast necessitating data-driven approaches to find optimal treatments tailored to a patient’s tumor. AstraZeneca carried out 11,576 experiments on 910 drug combinations across 85 cancer cell lines, recapitulating in vivo response profiles. These data, the largest openly available screen, were hosted by DREAM alongside deep molecular characterization from the Sanger Institute for a Challenge to computationally predict synergistic drug pairs and associated biomarkers. 160 teams participated to provide the most comprehensive methodological development and subsequent benchmarking to date. Winning methods incorporated prior knowledge of putative drug target interactions. For >60% of drug combinations synergy was reproducibly predicted with an accuracy matching biological replicate experiments, however 20% of drug combinations were poorly predicted by all methods. Genomic rationale for synergy predictions were identified, including antagonism unique to combined PIK3CB/D inhibition with the ADAM17 inhibitor where synergy is seen with other PI3K pathway inhibitors. All data, methods and code are freely available as a resource to the community.

scholarly journals Sulpiride Serves, a Substrate for the Gut Microbiome

Dose-Response ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 155932582098794
Author(s):  
Imran Mukhtar ◽  
Haseeb Anwar ◽  
Osman Asghar Mirza ◽  
Qasim Ali ◽  
Muhammad Umar Ijaz ◽  
...  
Keyword(s):  
Oral Administration ◽  
Gut Microbiome ◽  
Drug Target ◽  
Membrane Transporters ◽  
Intestinal Microbiome ◽  
Albino Rats ◽  
Body Organ ◽  
Total Absorbance ◽  
Research World

In the contemporary research world, the intestinal microbiome is now envisioned as a new body organ. Recently, the gut microbiome represents a new drug target in the gut, since various orthologues of intestinal drug transporters are also found present in the microbiome that lines the small intestine of the host. Owing to this, absorbance of sulpiride by the gut microbiome in an in vivo albino rats model was assessed after the oral administration with a single dose of 20mg/kg b.w. The rats were subsequently sacrificed at 2, 3, 4, 5 and 6 hours post oral administration to collect the gut microbial mass pellet. The drug absorbance by the gut microbiome was determined by pursuing the microbial lysate through RP-HPLC-UV. Total absorbance of sulpiride by the whole gut microbiome and drug absorbance per milligram of microbial pellet were found significantly higher at 4 hours post-administration as compared to all other groups. These results affirm the hypothesis that the structural homology between membrane transporters of the gut microbiome and intestinal epithelium of the host might play an important role in drug absorbance by gut microbes in an in vivo condition.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

scholarly journals Anti-CD63 antibodies suppress IgE-dependent allergic reactions in vitro and in vivo

2005 ◽  
Vol 201 (3) ◽  
pp. 385-396 ◽  
Author(s):  
Stefan Kraft ◽  
Tony Fleming ◽  
James M. Billingsley ◽  
Shih-Yao Lin ◽  
Marie-Hélène Jouvin ◽  
...  
Keyword(s):  
Pi3k Pathway ◽  
Therapeutic Agents ◽  
Allergic Reactions ◽  
Ecm Proteins ◽  
Broad Array ◽  
High Affinity Ige Receptor ◽  
Nonadherent Cells ◽  
Tetraspanin Cd63

High-affinity IgE receptor (FcεRI) cross-linking on mast cells (MCs) induces secretion of preformed allergy mediators (degranulation) and synthesis of lipid mediators and cytokines. Degranulation produces many symptoms of immediate-type allergic reactions and is modulated by adhesion to surfaces coated with specific extracellular matrix (ECM) proteins. The signals involved in this modulation are mostly unknown and their contribution to allergic reactions in vivo is unclear. Here we report the generation of monoclonal antibodies that potently suppress FcεRI-induced degranulation, but not leukotriene synthesis. We identified the antibody target as the tetraspanin CD63. Tetraspanins are membrane molecules that form multimolecular complexes with a broad array of molecules including ECM protein-binding β integrins. We found that anti-CD63 inhibits MC adhesion to fibronectin and vitronectin. Furthermore, anti-CD63 inhibits FcεRI-mediated degranulation in cells adherent to those ECM proteins but not in nonadherent cells. Thus the inhibition of degranulation by anti-CD63 correlates with its effect on adhesion. In support of a mechanistic linkage between the two types of inhibition, anti-CD63 had no effect on FcεRI-induced global tyrosine phosphorylation and calcium mobilization but impaired the Gab2–PI3K pathway that is known to be essential for both degranulation and adhesion. Finally, we showed that these antibodies inhibited FcεRI-mediated allergic reactions in vivo. These properties raise the possibility that anti-CD63 could be used as therapeutic agents in MC-dependent diseases.

New targeted therapies for treatment of thrombosis in antiphospholipid syndrome

2007 ◽  
Vol 9 (30) ◽  
pp. 1-15 ◽  
Author(s):  
Silvia S. Pierangeli ◽  
Mariano E. Vega-Ostertag ◽  
Emilio B. González
Keyword(s):  
Antiphospholipid Syndrome ◽  
Targeted Therapies ◽  
Lupus Erythematosus ◽  
Pregnancy Loss ◽  
Cell Activation ◽  
Clinical Manifestations ◽  
Target Cells ◽  
Endothelial Cell Activation ◽  
In Vivo Models

Antiphospholipid (aPL) antibodies (Abs) are associated with thrombosis and pregnancy loss in antiphospholipid syndrome (APS), a disorder initially characterised in patients with systemic lupus erythematosus (SLE) but now known to occur in the absence of other autoimmune disease. There is strong evidence that aPL Abs are pathogenic in vivo, from studies of animal models of thrombosis, endothelial cell activation and pregnancy loss. In recent years, progress has been made in characterising the molecular basis of this pathogenicity, which includes direct effects on platelets, endothelial cells and monocytes as well as activation of complement. This review summarises the clinical manifestations of APS and current modalities of treatment, and explains recent advances in understanding the molecular events triggered by aPL Abs on target cells in coagulation pathways as well as effects of aPL Abs on complement activation. Based on this information and on additional scientific evidence using in vitro and in vivo models, new potential targeted therapies for treatment and/or prevention of thrombosis in APS are proposed and discussed.

scholarly journals Open-source chemogenomic data-driven algorithms for predicting drug–target interactions

2018 ◽  
Vol 20 (4) ◽  
pp. 1465-1474 ◽  
Author(s):  
Ming Hao ◽  
Stephen H Bryant ◽  
Yanli Wang
Keyword(s):  
Drug Discovery ◽  
Human Genome ◽  
High Throughput Screening ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Research Field ◽  
Data Driven ◽  
Source Codes ◽  
Novel Technologies

AbstractWhile novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug–target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

PET Screening of Anticancer Drugs: A Faster Route to Drug/Target Evaluations In Vivo

2003 ◽  
pp. 279-294
Author(s):  
Anna Fredriksson ◽  
Sharon Stone-Elander
Keyword(s):  
Anticancer Drugs ◽  
Drug Target

COMPUTATIONAL APPROACHES FOR DRUG DISCOVERY FROM MEDICINAL PLANTS IN THE ERA OF DATA DRIVEN RESEARCH

INDIAN DRUGS ◽  
2021 ◽  
Vol 58 (08) ◽  
pp. 7-23
Author(s):  
Pratibha Pansari ◽  
Keyword(s):  
Drug Discovery ◽  
Medicinal Plants ◽  
In Silico ◽  
Drug Target ◽  
Target Gene ◽  
Scientific Work ◽  
Data Driven ◽  
Network Pharmacology ◽  
Gene Pathway ◽  
Topological Network

The significant scientific work on the development of bio-active compound databases, computational technologies, and the integration of Information Technology with Biotechnology has brought a revolution in the domain of drug discovery. These tools facilitate the medicinal plant-based in silico drug discovery, which has become the frontier of pharmacological science. In this review article, we elucidate the methodology of in silico drug discovery for the medicinal plants and present an outlook on recent tools and technologies. Further, we explore the multi-component, multi-target, and multi-pathway mechanism of the bio-active compounds with the help of Network Pharmacology, which enables us to create a topological network between drug, target, gene, pathway, and disease.

scholarly journals A causal role for TRESK loss of function in migraine mechanisms

Brain ◽  
2019 ◽  
Vol 142 (12) ◽  
pp. 3852-3867 ◽  
Author(s):  
Philippa Pettingill ◽  
Greg A Weir ◽  
Tina Wei ◽  
Yukyee Wu ◽  
Grace Flower ◽  
...  
Keyword(s):  
Potassium Channel ◽  
Drug Target ◽  
Causal Role ◽  
Potential Drug Target ◽  
Potential Drug ◽  
Loss Of Function

The two-pore potassium channel TRESK is a potential drug target in pain and migraine. Pettingill et al. show that the F139WfsX2 mutation causes TRESK loss of function and hyperexcitability in nociceptors derived from iPSCs of patients with migraine. Cloxyquin, a TRESK activator, reverses migraine-relevant phenotypes in vitro and in vivo.

Sign in / Sign up

Export Citation Format

Share Document