1,306 research outputs found
Polygenic overlap between schizophrenia risk and antipsychotic response: a genomic medicine approach
Therapeutic treatments for schizophrenia do not alleviate symptoms for all patients and efficacy is limited by common, often severe, side-effects. Genetic studies of disease can identify novel drug targets, and drugs for which the mechanism has direct genetic support have increased likelihood of clinical success. Large-scale genetic studies of schizophrenia have increased the number of genes and gene sets associated with risk. We aimed to examine the overlap between schizophrenia risk loci and gene targets of a comprehensive set of medications to potentially inform and improve treatment of schizophrenia
Leveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associations
Additional file 5: Figure S4. Number of side effects and targets for each drug in the target-phenotype model
Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.
Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference
Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 ”M. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning
Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning
Simple Summary Drug repurposing is an accelerated route for drug development and a promising approach for finding medications for orphan and common diseases. Here, we compiled databases that comprise both computationally- or experimentally-derived data, and categorized them based on quiddity and origin of data, further focusing on those that present high throughput omic data or drug screens. These databases were then contextualized with genome-wide screening methods such as CRISPR/Cas9 and RNA interference, as well as state of art systems biology approaches that enable systematic characterizations of multi-omic data to find new indications for approved drugs or those that reached the latest phases of clinical trials. Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches
Systems biology approaches to a rational drug discovery paradigm
The published manuscript is available at EurekaSelect via http://www.eurekaselect.com/openurl/content.php?genre=article&doi=10.2174/1568026615666150826114524.Prathipati P., Mizuguchi K.. Systems biology approaches to a rational drug discovery paradigm. Current Topics in Medicinal Chemistry, 16, 9, 1009. https://doi.org/10.2174/1568026615666150826114524
Analyse transcriptomique et applications en développement préclinique des médicaments
LâĂ©mergence des MĂ©gadonnĂ©es (« Big Data ») en biologie molĂ©culaire, surtout Ă travers la transcriptomique, a rĂ©volutionnĂ© la façon dont nous Ă©tudions diverses disciplines telles que le processus de dĂ©veloppement du mĂ©dicament ou la recherche sur le cancer. Ceci fut associĂ© Ă un nouveau concept, la mĂ©decine de prĂ©cision, dont le principal but est de comprendre les mĂ©canismes molĂ©culaires entraĂźnant une meilleure rĂ©ponse thĂ©rapeutique chez le patient.
Cette thĂšse est Ă mi-chemin entre les Ă©tudes pharmaco â et toxicogĂ©nomiques expĂ©rimentales, et les Ă©tudes cliniques et translationnelles. Le but de cette thĂšse est surtout de montrer le potentiel et les limites de ces jeux de donnĂ©es et leur pertinence pour la dĂ©couverte de biomarqueurs de rĂ©ponse ainsi que la comprĂ©hension des mĂ©canismes dâaction/toxicitĂ© de mĂ©dicaments, en vue dâutiliser ces informations Ă des fins thĂ©rapeutiques. LâoriginalitĂ© de cette thĂšse rĂ©side dans son approche globale pour analyser les plus larges jeux de donnĂ©es pharmaco/toxicogĂ©nomiques publiĂ©s Ă ce jour et ceci pour : 1) Aborder la notion de biomarqueurs de rĂ©ponse aux mĂ©dicaments en pharmacogĂ©nomique du cancer, en Ă©tudiant les facteurs discordants entre deux grandes Ă©tudes publiĂ©es en 2012; 2) Comprendre le mĂ©canisme dâaction des mĂ©dicaments et construire une taxonomie performante en utilisant une approche intĂ©grative; et 3) CrĂ©er un rĂ©pertoire toxicogĂ©nomique Ă partir des hĂ©patocytes humains, exposĂ©s Ă diffĂ©rentes classes de mĂ©dicaments et composĂ©s chimiques.
Mes contributions principales sont les suivantes :
âą Jâai dĂ©veloppĂ© une approche bioinformatique pour Ă©tudier les facteurs discordants entre deux grandes Ă©tudes pharmacogĂ©nomiques et suggĂ©rĂ©es que les diffĂ©rences observĂ©es Ă©mergeaient plutĂŽt de lâabsence de standardisation des mesures pharmacologiques qui pourrait limiter la validation de biomarqueurs de rĂ©ponse aux mĂ©dicaments.
âą Jâai implĂ©mentĂ© une approche bioinformatique qui montre la supĂ©rioritĂ© de lâintĂ©gration tenant en compte des diffĂ©rents paramĂštres pour les mĂ©dicaments (structure, cytotoxicitĂ©, perturbation du transcriptome) afin dâĂ©lucider leur mĂ©canisme dâaction (MoA).
âą Jâai dĂ©veloppĂ© un pipeline bioinformatique pour Ă©tudier le niveau de conservation des mĂ©canismes molĂ©culaires entre les Ă©tudes toxicogĂ©nomiques in vivo et in vitro dĂ©montrant que les hĂ©patocytes humains sont un modĂšle fiable pour dĂ©tecter les produits toxiques hĂ©patocarcinogĂšnes.
Au total, nos Ă©tudes ont permis de fournir un cadre de travail original pour lâexploitation de diffĂ©rents types de donnĂ©es transcriptomiques pour comprendre lâimpact des produits chimiques sur la biologie cellulaire.The emergence of Big Data in molecular biology, especially through the study of
transcriptomics, has revolutionized the way we look at various disciplines, such as drug
development and cancer research. Big data analysis is an important part of the concept of
precision medicine, which primary purpose is to understand the molecular mechanisms
leading to better therapeutic response in patients.
This thesis is halfway between pharmaco-toxicogenomics experimental studies, and clinical
and translational studies. The aim of this thesis is mainly to show the potential and limitations
of these studies and their relevance, especially for the discovery of drug response biomarkers
and understanding the drug mechanisms (targets, toxicities). This thesis is an original work
since it proposes a global approach to analyzing the largest pharmaco-toxicogenomic datasets
available to date. The key aims were: 1) Addressing the challenge of reproducibility for
biomarker discovery in cancer pharmacogenomics, by comparing two large
pharmacogenomics studies published in 2012; 2) Understanding drugs mechanism of action
using an integrative approach to generate a superior drug-taxonomy; and 3) Evaluating the
conservation of toxicogenomic responses in primary hepatocytes vs. in vivo liver samples in
order to check the feasability of cell models in toxicology studies. My main contributions can be summarized as follow:
- I developed a bioinformatics pipeline to study the factors that trigger (in)consistency between
two major pharmacogenomic studies. I suggested that the observed differences emerged from
the non-standardization of pharmacological measurements, which could limit the validation of
drug response biomarker.
- I implemented a bioinformatics pipeline that demonstrated the superiority of the integrative
approach, since it takes into account different parameters for the drug (structure, cytotoxicity,
transcriptional perturbation) to elucidate the mechanism of action (MoA).
- I developed a bioinformatics pipeline to study the level of conservation of toxicity
mechanisms between the in vivo and in vitro system, showing that human hepatocytes is a
reliable model for hepatocarcinogens testing. Overall, our studies have provided a unique framework to leverage various types of
transcriptomic data in order to understand the impact of chemicals on cell biology
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Associating adverse drug effects with protein targets by integrating adverse event, in vitro bioactivity, and pharmacokinetic data
Adverse drug effects are unintended and undesirable effects of medicines, causing attrition of molecules in drug development and harm to patients. To anticipate potential adverse effects early, drug candidates are commonly screened for pharmacological activity against a panel of protein targets. However, there is a lack of large-scale, quantitative information on the links between routinely screened proteins and the reporting of adverse events (AEs). This work describes a systematic analysis of associations between AEs observed in humans and bioactivities of drugs while taking into account drug plasma concentrations.
In the first chapter, post-marketing drug-AE associations are derived from the United States Food and Drug Administration Adverse Event Reporting System using disproportionality methods, while applying Propensity Score Matching to reduce confounding factors. The resulting drug-AE associations are compared to those from the Side Effect Resource, which are primarily derived from clinical trials. The analysis reveals that the datasets generally share less than 10% of reported AEs for the same drug and have different distributions of AEs across System Organ Classes (SOCs).
Using the drugs from the two AE datasets described in the first chapter, the second chapter integrates corresponding bioactivities, i.e. measured potencies and affinities from the ChEMBL database and ligand-based target predictions obtained with the tool PIDGIN, with drug plasma concentrations compiled from literature, such as Cmax. Compared to a constant bioactivity cut-off of 1 uM, using the ratio of the unbound drug plasma concentration over the drug potency, i.e. Cmax/XC50, results in different binary activity calls for protein targets. Whether deriving activity calls in this way results in the selection of targets with greater relevance to human AEs is investigated in the third chapter, which computes relationships between targets and AEs using different measures of statistical association. Using the Cmax/XC50 ratio results in higher Likelihood Ratios and Positive Predictive Values (PPVs) for target-AE associations that were previously reported in the context of secondary pharmacology screening, at the cost of a lower recall, possibly due to the smaller size of the dataset with available plasma concentrations. Furthermore, a large-scale quantitative assessment of bioactivities as indicators of AEs reveals a trade-off between the PPV and how many AE-associated drugs can potentially be detected from in vitro screening, although using combinations of targets can improve the detection rate in ~40% of cases at limited cost to the PPV. The work highlights AEs most strongly related to bioactivities and their SOC distribution.
Overall, this thesis contributes to knowledge of the relationships between in vitro bioactivities and empirical evidence of AEs in humans. The results can inform the selection of proteins for secondary pharmacology screening and the development of computational models to predict AEs.Lhasa Limite
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