The translational relevance of Drosophila in drug discovery.
Ontology highlight
ABSTRACT:
SUBMITTER: Fernandez-Hernandez I
PROVIDER: S-EPMC4818774 | biostudies-literature | 2016 Apr
REPOSITORIES: biostudies-literature
Publications
The translational relevance of Drosophila in drug discovery.
Fernández-Hernández Ismael I Scheenaard Eulalia E Pollarolo Giulia G Gonzalez Cayetano C
EMBO reports 20160216 4
Similar Datasets
Project description:Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.
| S-EPMC6448364 | biostudies-literature
Project description:The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.
| S-EPMC10302550 | biostudies-literature
Project description:Clinical translation of drug-drug interaction (DDI) studies is limited, and knowledge gaps across different types of DDI evidence make it difficult to consolidate and link them to clinical consequences. Consequently, we developed information retrieval (IR) models to retrieve DDI and drug-gene interaction (DGI) evidence from 25 million PubMed abstracts and distinguish DDI evidence into in vitro pharmacokinetic (PK), clinical PK, and clinical pharmacodynamic (PD) studies for US Food and Drug Administration (FDA) approved and withdrawn drugs. Additionally, information extraction models were developed to extract DDI-pairs and DGI-pairs from the IR-retrieved abstracts. An overlapping analysis identified 986 unique DDI-pairs between all 3 types of evidence. Another 2,157 and 13,012 DDI-pairs and 3,173 DGI-pairs were identified from known clinical PK/PD DDI, clinical PD DDI, and DGI evidence, respectively. By integrating DDI and DGI evidence, we discovered 119 and 18 new pharmacogenetic hypotheses associated with CYP3A and CYP2D6, respectively. Some of these DGI evidence can also aid us in understanding DDI mechanisms.
| S-EPMC7883394 | biostudies-literature
Project description:Protein post-translational modifications (PTMs), which are usually enzymatically catalyzed, are major regulators of protein activity and involved in almost all celluar processes. Dysregulation of PTMs is associated with various types of diseases. Therefore, PTM regulatory enzymes represent as an attractive and important class of targets in drug research and development. Inhibitors against kinases, methyltransferases, deacetyltransferases, ubiquitin ligases have achieved remarkable success in clinical application. Mass spectrometry-based proteomics technologies serve as a powerful approach for system-wide characterization of PTMs, which facilitates the identification of drug targets, elucidation of the mechanisms of action of drugs, and discovery of biomakers in personalized therapy. In this review, we summarize recent advances of proteomics-based studies on PTM targeting drugs and discuss how proteomics strategies facilicate drug target identification, mechanism elucidation, and new therapy development in precision medicine.
| S-EPMC9712763 | biostudies-literature
Project description:Molecular editing such as insertion, deletion, and single atom exchange in highly functionalized compounds is an aspirational goal for all chemists. Here, we disclose a photoredox protocol for the replacement of a single fluorine atom with hydrogen in electron-deficient trifluoromethylarenes including complex drug molecules. A robustness screening experiment shows that this reductive defluorination tolerates a range of functional groups and heterocycles commonly found in bioactive molecules. Preliminary studies allude to a catalytic cycle whereby the excited state of the organophotocatalyst is reductively quenched by the hydrogen atom donor, and returned in its original oxidation state by the trifluoromethylarene.
| S-EPMC7304874 | biostudies-literature
Project description:Inflammation and depression are closely inter-related; inflammation induces symptoms of depression and, conversely, depressed mood and stress favor an inflammatory phenotype. The mechanisms that mediate the ability of inflammation to induce symptoms of depression are intensively studied at the preclinical level. This review discusses how it has been possible to build animal models of inflammation-induced depression based on clinical data and to explore critical mechanisms downstream of inflammation. Namely, we focus on the ability of inflammation to increase the activity of the tryptophan-degrading enzyme, indoleamine 2,3 dioxygenase, which leads to the production of kynurenine and downstream neuroactive metabolites. By acting on glutamatergic neurotransmission, these neuroactive metabolites play a key role in the development of depression-like behaviors. An important outcome of the preclinical research on inflammation-induced depression is the identification of potential novel targets for antidepressant treatments, which include targeting the kynurenine system and production of downstream metabolites, altering transport of kynurenine into the brain, and modulating glutamatergic transmission.
| S-EPMC5043641 | biostudies-literature
Project description:canSAR (http://cansar.icr.ac.uk) is the largest, public, freely available, integrative translational research and drug discovery knowledgebase for oncology. canSAR integrates vast multidisciplinary data from across genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and more. It also provides unique data, curation and annotation and crucially, AI-informed target assessment for drug discovery. canSAR is widely used internationally by academia and industry. Here we describe significant developments and enhancements to the data, web interface and infrastructure of canSAR in the form of the new implementation of the system: canSARblack. We demonstrate new functionality in aiding translation hypothesis generation and experimental design, and show how canSAR can be adapted and utilised outside oncology.
| S-EPMC7778970 | biostudies-literature
Project description:canSAR (https://cansar.ai) is the largest public cancer drug discovery and translational research knowledgebase. Now hosted in its new home at MD Anderson Cancer Center, canSAR integrates billions of experimental measurements from across molecular profiling, pharmacology, chemistry, structural and systems biology. Moreover, canSAR applies a unique suite of machine learning algorithms designed to inform drug discovery. Here, we describe the latest updates to the knowledgebase, including a focus on significant novel data. These include canSAR's ligandability assessment of AlphaFold; mapping of fragment-based screening data; and new chemical bioactivity data for novel targets. We also describe enhancements to the data and interface.
| S-EPMC9825411 | biostudies-literature
Project description:Omics data from clinical samples are the predominant source of target discovery and drug development. Typically, hundreds or thousands of differentially expressed genes or proteins can be identified from omics data. This scale of possibilities is overwhelming for target discovery and validation using biochemical or cellular experiments. Most of these proteins and genes have no corresponding drugs or even active compounds. Moreover, a proportion of them may have been previously reported as being relevant to the disease of interest. To facilitate translational drug discovery from omics data, we have developed a new classification tool named Omics and Text driven Translational Medicine (OTTM). This tool can markedly narrow the range of proteins or genes that merit further validation via drug availability assessment and literature mining. For the 4489 candidate proteins identified in our previous proteomics study, OTTM recommended 40 FDA-approved or clinical trial drugs. Of these, 15 are available commercially and were tested on hepatocellular carcinoma Hep-G2 cells. Two drugs-tafenoquine succinate (an FDA-approved antimalarial drug targeting CYC1) and branaplam (a Phase 3 clinical drug targeting SMN1 for the treatment of spinal muscular atrophy)-showed potent inhibitory activity against Hep-G2 cell viability, suggesting that CYC1 and SMN1 may be potential therapeutic target proteins for hepatocellular carcinoma. In summary, OTTM is an efficient classification tool that can accelerate the discovery of effective drugs and targets using thousands of candidate proteins identified from omics data. The online and local versions of OTTM are available at http://otter-simm.com/ottm.html.
| S-EPMC10516341 | biostudies-literature
Project description:This review advances the case that bidirectional, cross-species translation of findings from experimental animals to and from humans is an important strategy for drug discovery. Animal models of mental disorders require appropriate behavioural or cognitive outcome variables that can be generalized cross-species. One example is the treatment of impulsive behaviour in attention deficit hyperactivity disorder (ADHD) with stimulant drugs. Performance on the stop signal reaction task as an index of impulsivity is improved both in healthy human volunteers and in patients with adult ADHD by stimulant drugs and also by the selective noradrenaline reuptake blocker atomoxetine. Functional neuroimaging evidence suggests a modulation of circuitry including the inferior prefrontal cortex by this drug. Parallel work in rats had shown that atomoxetine improves stop signal performance by affecting possibly homologous regions of the rodent prefrontal cortex. This parallel effect of atomoxetine in rodents and humans could potentially be exploited in other disorders in which impulsivity plays a role, such as stimulant abuse and Parkinson's disease. A contrasting relative lack of involvement of 5-HT mechanisms in the stop signal reaction time task will also be described. Research in humans and experimental animals that demonstrate effects of serotoninergic agents such as the selective serotonin (5-HT) reuptake inhibitor citalopram on probabilistic learning and reversal (upon which atomoxetine has little effect) will also be reviewed, possibly relevant to the treatment of clinical depression, Finally, other promising examples of parallel studies of behavioural effects of CNS-active drugs in animals and humans will also be described. Linked Articles This article is part of a themed section on Pharmacology of Cognition: a Panacea for Neuropsychiatric Disease? To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.19/issuetoc.
| S-EPMC5595762 | biostudies-literature