scholarly journals Advances in Development of New Treatment for Leishmaniasis

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Juliana Perrone Bezerra de Menezes ◽  
Carlos Eduardo Sampaio Guedes ◽  
Antônio Luis de Oliveira Almeida Petersen ◽  
Deborah Bittencourt Mothé Fraga ◽  
Patrícia Sampaio Tavares Veras
Keyword(s):  
High Throughput ◽  
Drug Targets ◽  
Scientific Community ◽  
Large Scale ◽  
High Throughput Analysis ◽  
Large Scale Analysis ◽  
Parasite Biology ◽  
New Treatment ◽  
Novel Drug ◽  
Chemotherapeutic Interventions

Leishmaniasis is a neglected infectious disease caused by several different species of protozoan parasites of the genusLeishmania. Current strategies to control this disease are mainly based on chemotherapy. Despite being available for the last 70 years, leishmanial chemotherapy has lack of efficiency, since its route of administration is difficult and it can cause serious side effects, which results in the emergence of resistant cases. The medical-scientific community is facing difficulties to overcome these problems with new suitable and efficient drugs, as well as the identification of new drug targets. The availability of the complete genome sequence ofLeishmaniahas given the scientific community the possibility of large-scale analysis, which may lead to better understanding of parasite biology and consequent identification of novel drug targets. In this review we focus on how high-throughput analysis is helping us and other groups to identify novel targets for chemotherapeutic interventions. We further discuss recent data produced by our group regarding the use of the high-throughput techniques and how this helped us to identify and assess the potential of new identified targets.

scholarly journals High-Throughput Analysis of Genome-Wide Receptor Tyrosine Kinase Expression in Human Cancers Identifies Potential Novel Drug Targets

2004 ◽  
Vol 10 (4) ◽  
pp. 1241-1249 ◽  
Author(s):  
Carsten Müller-Tidow ◽  
Joachim Schwäble ◽  
Björn Steffen ◽  
Nicola Tidow ◽  
Burkhardt Brandt ◽  
...  
Keyword(s):  
Tyrosine Kinase ◽  
High Throughput ◽  
Receptor Tyrosine Kinase ◽  
Drug Targets ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Genome Wide ◽  
Kinase Expression ◽  
Novel Drug ◽  
Novel Drug Targets

scholarly journals Cytotoxic Profiling of Annotated and Diverse Chemical Libraries Using Quantitative High-Throughput Screening

2019 ◽  
Vol 25 (1) ◽  
pp. 9-20 ◽  
Author(s):  
Olivia W. Lee ◽  
Shelley Austin ◽  
Madison Gamma ◽  
Dorian M. Cheff ◽  
Tobie D. Lee ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
High Throughput ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Large Scale ◽  
Early Stage ◽  
Cancer Cell Line ◽  
Hek 293 ◽  
Cytotoxic Compounds

Cell-based phenotypic screening is a commonly used approach to discover biological pathways, novel drug targets, chemical probes, and high-quality hit-to-lead molecules. Many hits identified from high-throughput screening campaigns are ruled out through a series of follow-up potency, selectivity/specificity, and cytotoxicity assays. Prioritization of molecules with little or no cytotoxicity for downstream evaluation can influence the future direction of projects, so cytotoxicity profiling of screening libraries at an early stage is essential for increasing the likelihood of candidate success. In this study, we assessed the cell-based cytotoxicity of nearly 10,000 compounds in the National Institutes of Health, National Center for Advancing Translational Sciences annotated libraries and more than 100,000 compounds in a diversity library against four normal cell lines (HEK 293, NIH 3T3, CRL-7250, and HaCat) and one cancer cell line (KB 3-1, a HeLa subline). This large-scale library profiling was analyzed for overall screening outcomes, hit rates, pan-activity, and selectivity. For the annotated library, we also examined the primary targets and mechanistic pathways regularly associated with cell death. To our knowledge, this is the first study to use high-throughput screening to profile a large screening collection (>100,000 compounds) for cytotoxicity in both normal and cancer cell lines. The results generated here constitute a valuable resource for the scientific community and provide insight into the extent of cytotoxic compounds in screening libraries, allowing for the identification and avoidance of compounds with cytotoxicity during high-throughput screening campaigns.

scholarly journals Integrative pharmacogenomics to infer large-scale drug taxonomy

2016 ◽  
Author(s):  
Nehme El-Hachem ◽  
Deena M.A. Gendoo ◽  
Laleh Soltan Ghoraie ◽  
Zhaleh Safikhani ◽  
Petr Smirnov ◽  
...  
Keyword(s):  
Drug Targets ◽  
Large Scale ◽  
Prior Information ◽  
Structural Information ◽  
Drug Efficacy ◽  
Basic Drug ◽  
Flexible Tool ◽  
Experimental Drugs ◽  
New Compounds ◽  
Novel Drug

ABSTRACTIdentification of drug targets and mechanism of action (MoA) for new and uncharacterlzed drugs is important for optimization of drug efficacy. Current MoA prediction approaches largely rely on prior information including side effects, therapeutic indication and/or chemo-informatics. Such information is not transferable or applicable for newly identified, previously uncharacterlzed small molecules. Therefore, a shift in the paradigm of MoA predictions is necessary towards development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. We propose a new integrative computational pharmacogenomlc approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that relies only on basic drug characteristics towards elucidating drug-drug relationships. DNF is the first framework to integrate drug structural information, high-throughput drug perturbation and drug sensitivity profiles, enabling drug classification of new experimental compounds with minimal prior information. We demonstrate that the DNF taxonomy succeeds in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. We highlight how the scalability of DNF facilitates identification of key drug relationships across different drug categories, and poses as a flexible tool for potential clinical applications in precision medicine. Our results support DNF as a valuable resource to the cancer research community by providing new hypotheses on the compound MoA and potential insights for drug repurposlng.

Abstract 1925: High-throughput drug library screening identifies potent drugs and novel drug targets for high-risk acute leukemia in children

2018 ◽  
Author(s):  
Priscilla Wander ◽  
Sandra S. Pinhanços ◽  
Bianca Koopmans ◽  
M. Emmy M. Dolman ◽  
Pauline Schneider ◽  
...  
Keyword(s):  
High Risk ◽  
Acute Leukemia ◽  
High Throughput ◽  
Drug Targets ◽  
Library Screening ◽  
Novel Drug ◽  
Novel Drug Targets ◽  
Leukemia In Children

scholarly journals ZF-AutoML: An Easy Machine-Learning-Based Method to Detect Anomalies in Fluorescent-Labelled Zebrafish

Inventions ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 72
Author(s):  
Ryota Sawaki ◽  
Daisuke Sato ◽  
Hiroko Nakayama ◽  
Yuki Nakagawa ◽  
Yasuhito Shimada
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Fluorescent Protein ◽  
Learning Program ◽  
High Throughput Analysis ◽  
Easy Method ◽  
High Fecundity

Background: Zebrafish are efficient animal models for conducting whole organism drug testing and toxicological evaluation of chemicals. They are frequently used for high-throughput screening owing to their high fecundity. Peripheral experimental equipment and analytical software are required for zebrafish screening, which need to be further developed. Machine learning has emerged as a powerful tool for large-scale image analysis and has been applied in zebrafish research as well. However, its use by individual researchers is restricted due to the cost and the procedure of machine learning for specific research purposes. Methods: We developed a simple and easy method for zebrafish image analysis, particularly fluorescent labelled ones, using the free machine learning program Google AutoML. We performed machine learning using vascular- and macrophage-Enhanced Green Fluorescent Protein (EGFP) fishes under normal and abnormal conditions (treated with anti-angiogenesis drugs or by wounding the caudal fin). Then, we tested the system using a new set of zebrafish images. Results: While machine learning can detect abnormalities in the fish in both strains with more than 95% accuracy, the learning procedure needs image pre-processing for the images of the macrophage-EGFP fishes. In addition, we developed a batch uploading software, ZF-ImageR, for Windows (.exe) and MacOS (.app) to enable high-throughput analysis using AutoML. Conclusions: We established a protocol to utilize conventional machine learning platforms for analyzing zebrafish phenotypes, which enables fluorescence-based, phenotype-driven zebrafish screening.

Seed genomics: germinating opportunities

2002 ◽  
Vol 12 (3) ◽  
pp. 145-153 ◽  
Author(s):  
A. (Lonneke) ◽  
H.M. van der Geest
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Plant Biology ◽  
Sequence Information ◽  
Molecular Tools ◽  
Seed Biology ◽  
Physiological Processes ◽  
Large Scale Analysis ◽  
Quantum Leap ◽  
Mutant Phenotypes

With the sequencing of theArabidopsis thalianagenome, the field of plant biology has made a quantum leap. The sequence information available to the community has greatly facilitated the identification of genes responsible for mutant phenotypes and the large-scale analysis of gene expression inArabidopsis. High-throughput laboratory tools for DNA sequencing (genomics), mutant analysis (functional genomics), mRNA quantification (transcriptomics) and protein analysis (proteomics) are being used to scrutinize this model plant. For seed physiologists, the challenge lies in translating this information into physiological processes in seeds from other plant species. Combining more traditional seed biology with the new high-throughput molecular tools should yield breakthroughs in seed science.

scholarly journals Determination of Clonality and Relatedness of Vibrio cholerae Isolates by Genomic Fingerprinting, Using Long-Range Repetitive Element Sequence-Based PCR

2008 ◽  
Vol 74 (17) ◽  
pp. 5392-5401 ◽  
Author(s):  
Nipa Chokesajjawatee ◽  
Young-Gun Zo ◽  
Rita R. Colwell
Keyword(s):  
Vibrio Cholerae ◽  
Long Range ◽  
High Throughput ◽  
Large Scale ◽  
Rapid Screening ◽  
Genomic Fingerprinting ◽  
High Throughput Analysis ◽  
Consensus Sequences ◽  
Specialized Equipment ◽  
Long Range Pcr

ABSTRACT A high-throughput method which is applicable for rapid screening, identification, and delineation of isolates of Vibrio cholerae, sensitive to genome variation, and capable of providing phylogenetic inferences enhances environmental monitoring of this bacterium. We have developed and optimized a method for genomic fingerprinting of V. cholerae based on long-range PCR. The method uses a primer set directed to enterobacterial repetitive intergenic consensus sequences, a high-fidelity DNA polymerase, and analysis via conventional agarose gel electrophoresis. Long (∼10 kb), highly reproducible amplicons were generated from V. cholerae isolates, including those from different geographical locations and historical strains isolated during the period 1931-2000. The amplicons yielded reduced variability in their densitometric band patterns to ≤10% and clonal distinction at <90% similarity. Rapid band-matching analysis was accomplished for fingerprints with ≥90% similarity, discriminating O serotypes and biotypes (classical versus El Tor) as well as pathogenic and nonpathogenic strains. Compared to genome similarity measured by DNA-DNA hybridization, the results showed good correlation (r = 0.7; P < 0.001), with five times less measurement error and without bias. The method permits both phylogenetic inference and clonal differentiation of individual V. cholerae strains, enables robust, high-throughput analysis, and does not require specialized equipment to perform. With access to a curated public database furnished with appropriate analytical software applications, the method should prove useful in large-scale multilaboratory surveys, especially those designed to detect specific pathogens in the natural environment.

scholarly journals Cytotoxic Profiling of Annotated and Diverse Chemical Libraries Using Quantitative High-Throughput Screening

2018 ◽  
Author(s):  
Olivia W. Lee ◽  
Shelley Austin ◽  
Madison Gamma ◽  
Dorian M. Cheff ◽  
Tobie D. Lee ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
High Throughput ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Large Scale ◽  
Early Stage ◽  
Cancer Cell Line ◽  
Hek 293 ◽  
Cytotoxic Compounds

AbstractCell-based phenotypic screening is a commonly used approach to discover biological pathways, novel drug targets, chemical probes and high-quality hit-to-lead molecules. Many hits identified from high-throughput screening campaigns are ruled out through a series of follow-up potency, selectivity/specificity, and cytotoxicity assays. Prioritization of molecules with little or no cytotoxicity for downstream evaluation can influence the future direction of projects, so cytotoxicity profiling of screening libraries at an early stage is essential for increasing the likelihood of candidate success. In this study, we assessed cell-based cytotoxicity of nearly 10,000 compounds in NCATS annotated libraries, and over 100,000 compounds in a diversity library, against four ‘normal’ cell lines (HEK 293, NIH 3T3, CRL-7250 and HaCat) and one cancer cell line (KB 3-1, a HeLa subline). This large-scale library profiling was analyzed for overall screening outcomes, hit rates, pan-activity and selectivity. For the annotated library, we also examined the primary targets and mechanistic pathways regularly associated with cell death. To our knowledge, this is the first study to use high-throughput screening to profile a large screening collection (>100,000 compounds) for cytotoxicity in both normal and cancer cell lines. The results generated here constitutes a valuable resource for the scientific community and provides insight on the extent of cytotoxic compounds in screening libraries, identifying and avoiding compounds with cytotoxicity during high-throughput screening campaigns.

Computational Methods for the Prediction of GPCRs Coupling Selectivity

2009 ◽  
pp. 167-181
Author(s):  
Nikolaos G. Sgourakis ◽  
Pantelis G. Bagos ◽  
Stavros J. Hamodrakas
Keyword(s):  
G Proteins ◽  
Drug Targets ◽  
Large Scale ◽  
Single Receptor ◽  
Pharmacological Significance ◽  
Experimental Works ◽  
Orphan Gpcrs ◽  
Coupling Specificity ◽  
Novel Drug ◽  
Genome Projects

GPCRs comprise a wide and diverse class of eukaryotic transmembrane proteins with well-established pharmacological significance. As a consequence of recent genome projects, there is a wealth of information at the sequence level that lacks any functional annotation. These receptors, often quoted as orphan GPCRs, could potentially lead to novel drug targets. However, typical experiments that aim at elucidating their function are hampered by the lack of knowledge on their selective coupling partners at the interior of the cell, the G-proteins. Up-to-date, computational efforts to predict properties of GPCRs have been focused mainly on the ligand-binding specificity, while the aspect of coupling has been less studied. Here, we present the main motivations, drawbacks, and results from the application of bioinformatics techniques to predict the coupling specificity of GPCRs to G-proteins, and discuss the application of the most successful methods in both experimental works that focus on a single receptor and large-scale genome annotation studies.

Sign in / Sign up

Export Citation Format

Share Document