Browse
Submit Data
Databases
API
Help

Dataset Information

11 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Phenotyping drug polypharmacology via eicosanoid profiling of blood.

ABSTRACT: It is widely accepted that small-molecule drugs, despite their selectivity at primary targets, exert pharmacological effects (and safety liabilities) through a multiplicity of pathways. As such, it has proved extremely difficult to experimentally assess polypharmacology in an agnostic fashion. Profiling of metabolites produced as part of physiological responses to pharmacological stimuli provides a unique opportunity to explore drug pharmacology. A total of 122 eicosanoid lipids in human whole blood were monitored from 10 different donors upon stimulation with several inducers of immunological responses and treatment with modulators of prostaglandin (PG) and leukotriene biosynthesis, including clinical and investigational molecules. Such analysis revealed differentiation between drugs nominally targeting different eicosanoid biosynthetic enzymes, or even those designed to target the same enzyme. Profiled agents, some of them marketed products, affect eicosanoid biosynthesis in ways that cannot be predicted from information on their intended targets. As an example, we used this platform to discriminate drugs based on their ability to silence PG biosynthesis in response to bacterial lipopolysaccharide, resulting in differential pharmacological activity in an in vivo model of endotoxemia. Some of the observed effects are subject to variability among individuals, indicating a potential application of this methodology to the patient stratification, based on their responses to benchmark drugs and experimental compounds read on the eicosanome via a simple blood test.

SUBMITTER: Song J

PROVIDER: S-EPMC4513990 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Exploiting polypharmacology for drug target deconvolution.

Project description:Polypharmacology (action of drugs against multiple targets) represents a tempting avenue for new drug development; unfortunately, methods capable of exploiting the known polypharmacology of drugs for target deconvolution are lacking. Here, we present an ensemble approach using elastic net regularization combined with mRNA expression profiling and previously characterized data on a large set of kinase inhibitors to identify kinases that are important for epithelial and mesenchymal cell migration. By profiling a selected optimal set of 32 kinase inhibitors in a panel against six cell lines, we identified cell type-specific kinases that regulate cell migration. Our discovery of several informative kinases with a previously uncharacterized role in cell migration (such as Mst and Taok family of MAPK kinases in mesenchymal cells) may represent novel targets that warrant further investigation. Target deconvolution using our ensemble approach has the potential to aid in the rational design of more potent but less toxic drug combinations.

| S-EPMC3977247 | biostudies-literature

Systematic polypharmacology and drug repurposing via an integrated L1000-based Connectivity Map database mining.

Project description:Drug repurposing aims to find novel indications of clinically used or experimental drugs. Because drug data already exist, drug repurposing may save time and cost, and bypass safety concerns. Polypharmacology, one drug with multiple targets, serves as a basis for drug repurposing. Large-scale databases have been accumulated in recent years, and utilization and integration of these databases would be highly helpful for polypharmacology and drug repurposing. The Connectivity Map (CMap) is a database collecting gene-expression profiles of drug-treated human cancer cells, which has been widely used for investigation of polypharmacology and drug repurposing. In this study, we integrated the next-generation L1000-based CMap and an analytic Web tool, the L1000FWD, for systematic analyses of polypharmacology and drug repurposing. Two different types of anti-cancer drugs were used as proof-of-concept examples, including histone deacetylase (HDAC) inhibitors and topoisomerase inhibitors. We identified KM-00927 and BRD-K75081836 as novel HDAC inhibitors and mitomycin C as a topoisomerase IIB inhibitor. Our study provides a prime example of utilization and integration of the freely available public resources for systematic polypharmacology analysis and drug repurposing.

| S-EPMC6281908 | biostudies-literature

KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling.

Project description:Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.

| S-EPMC10729787 | biostudies-literature

A multiparametric activity profiling platform for neuron disease phenotyping and drug screening.

Project description:Patient stem cell-derived models enable imaging of complex disease phenotypes and the development of scalable drug discovery platforms. Current preclinical methods for assessing cellular activity do not, however, capture the full intricacies of disease-induced disturbances and instead typically focus on a single parameter, which impairs both the understanding of disease and the discovery of effective therapeutics. Here, we describe a cloud-based image processing and analysis platform that captures the intricate activity profile revealed by GCaMP fluorescence recordings of intracellular calcium changes and enables the discovery of molecules that correct 153 parameters that define the amyotrophic lateral sclerosis motor neuron disease phenotype. In a high-throughput screen, we identified compounds that revert the multiparametric disease profile to that found in healthy cells, a novel and robust measure of therapeutic potential quite distinct from unidimensional screening. This platform can guide the development of therapeutics that counteract the multifaceted pathological features of diseased cellular activity.

| S-EPMC9265164 | biostudies-literature

Polypharmacology guided drug repositioning approach for SARS-CoV2.

Project description:Drug repurposing has emerged as an important strategy and it has a great potential in identifying therapeutic applications for COVID-19. An extensive virtual screening of 4193 FDA approved drugs has been carried out against 24 proteins of SARS-CoV2 (NSP1-10 and NSP12-16, envelope, membrane, nucleoprotein, spike, ORF3a, ORF6, ORF7a, ORF8, and ORF9b). The drugs were classified into top 10 and bottom 10 drugs based on the docking scores followed by the distribution of their therapeutic indications. As a result, the top 10 drugs were found to have therapeutic indications for cancer, pain, neurological disorders, and viral and bacterial diseases. As drug resistance is one of the major challenges in antiviral drug discovery, polypharmacology and network pharmacology approaches were employed in the study to identify drugs interacting with multiple targets and drugs such as dihydroergotamine, ergotamine, bisdequalinium chloride, midostaurin, temoporfin, tirilazad, and venetoclax were identified among the multi-targeting drugs. Further, a pathway analysis of the genes related to the multi-targeting drugs was carried which provides insight into the mechanism of drugs and identifying targetable genes and biological pathways involved in SARS-CoV2.

| S-EPMC10411734 | biostudies-literature

Evolution of kinase polypharmacology across HSP90 drug discovery.

Project description:Most small molecules interact with several target proteins but this polypharmacology is seldom comprehensively investigated or explicitly exploited during drug discovery. Here, we use computational and experimental methods to identify and systematically characterize the kinase cross-pharmacology of representative HSP90 inhibitors. We demonstrate that the resorcinol clinical candidates ganetespib and, to a lesser extent, luminespib, display unique off-target kinase pharmacology as compared with other HSP90 inhibitors. We also demonstrate that polypharmacology evolved during the optimization to discover luminespib and that the hit, leads, and clinical candidate all have different polypharmacological profiles. We therefore recommend the computational and experimental characterization of polypharmacology earlier in drug discovery projects to unlock new multi-target drug design opportunities.

| S-EPMC8550792 | biostudies-literature

Epigenetic polypharmacology: A new frontier for epi-drug discovery.

Project description:Recently, despite the great success achieved by the so-called "magic bullets" in the treatment of different diseases through a marked and specific interaction with the target of interest, the pharmacological research is moving toward the development of "molecular network active compounds," embracing the related polypharmacology approach. This strategy was born to overcome the main limitations of the single target therapy leading to a superior therapeutic effect, a decrease of adverse reactions, and a reduction of potential mechanism(s) of drug resistance caused by robustness and redundancy of biological pathways. It has become clear that multifactorial diseases such as cancer, neurological, and inflammatory disorders, may require more complex therapeutic approaches hitting a certain biological system as a whole. Concerning epigenetics, the goal of the multi-epi-target approach consists in the development of small molecules able to simultaneously and (often) reversibly bind different specific epi-targets. To date, two dual histone deacetylase/kinase inhibitors (CUDC-101 and CUDC-907) are in an advanced stage of clinical trials. In the last years, the growing interest in polypharmacology encouraged the publication of high-quality reviews on combination therapy and hybrid molecules. Hence, to update the state-of-the-art of these therapeutic approaches avoiding redundancy, herein we focused only on multiple medication therapies and multitargeting compounds exploiting epigenetic plus nonepigenetic drugs reported in the literature in 2018. In addition, all the multi-epi-target inhibitors known in literature so far, hitting two or more epigenetic targets, have been included.

| S-EPMC6917854 | biostudies-literature

Applying polypharmacology approach for drug repurposing for SARS-CoV2.

Project description:Exploring the new therapeutic indications of known drugs for treating COVID-19, popularly known as drug repurposing, is emerging as a pragmatic approach especially owing to the mounting pressure to control the pandemic. Targeting multiple targets with a single drug by employing drug repurposing known as the polypharmacology approach may be an optimised strategy for the development of effective therapeutics. In this study, virtual screening has been carried out on seven popular SARS-CoV-2 targets (3CLpro, PLpro, RdRp (NSP12), NSP13, NSP14, NSP15, and NSP16). A total of 4015 approved drugs were screened against these targets. Four drugs namely venetoclax, tirilazad, acetyldigitoxin, and ledipasvir have been selected based on the docking score, ability to interact with four or more targets and having a reasonably good number of interactions with key residues in the targets. The MD simulations and MM-PBSA studies showed reasonable stability of protein-drug complexes and sustainability of key interactions between the drugs with their respective targets throughout the course of MD simulations. The identified four drug molecules were also compared with the known drugs namely elbasvir and nafamostat. While the study has provided a detailed account of the chosen protein-drug complexes, it has explored the nature of seven important targets of SARS-CoV-2 by evaluating the protein-drug complexation process in great detail.Graphical abstractDrug repurposing strategy against SARS-CoV2 drug targets. Computational analysis was performed to identify repurposable approved drug candidates against SARS-CoV2 using approaches such as virtual screening, molecular dynamics simulation and MM-PBSA calculations. Four drugs namely venetoclax, tirilazad, acetyldigitoxin, and ledipasvir have been selected as potential candidates.Supplementary informationThe online version contains supplementary material available at 10.1007/s12039-022-02046-0.

| S-EPMC9028909 | biostudies-literature

Mining significant substructure pairs for interpreting polypharmacology in drug-target network.

Project description:A current key feature in drug-target network is that drugs often bind to multiple targets, known as polypharmacology or drug promiscuity. Recent literature has indicated that relatively small fragments in both drugs and targets are crucial in forming polypharmacology. We hypothesize that principles behind polypharmacology are embedded in paired fragments in molecular graphs and amino acid sequences of drug-target interactions. We developed a fast, scalable algorithm for mining significantly co-occurring subgraph-subsequence pairs from drug-target interactions. A noteworthy feature of our approach is to capture significant paired patterns of subgraph-subsequence, while patterns of either drugs or targets only have been considered in the literature so far. Significant substructure pairs allow the grouping of drug-target interactions into clusters, covering approximately 75% of interactions containing approved drugs. These clusters were highly exclusive to each other, being statistically significant and logically implying that each cluster corresponds to a distinguished type of polypharmacology. These exclusive clusters cannot be easily obtained by using either drug or target information only but are naturally found by highlighting significant substructure pairs in drug-target interactions. These results confirm the effectiveness of our method for interpreting polypharmacology in drug-target network.

| S-EPMC3044142 | biostudies-literature

An up-to-date overview of computational polypharmacology in modern drug discovery.

Project description:INTRODUCTION:In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success. AREAS COVERED:In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies. EXPERT OPINION:Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

| S-EPMC7415563 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data