scholarly journals A simple linearization method unveils hidden enzymatic assay interferences

2019 ◽  
Author(s):  
Maria Filipa Pinto ◽  
Jorge Ripoll-Rozada ◽  
Helena Ramos ◽  
Emma E. Watson ◽  
Charlotte Franck ◽  
...  
Keyword(s):  
Drug Targets ◽  
Caspase 3 ◽  
A Priori ◽  
Linearization Method ◽  
Dependent Inactivation ◽  
Highly Sensitive ◽  
Modified Coordinates ◽  
Important Drug ◽  
Selection Of ◽  
Signal Readout

Enzymes are among the most important drug targets in the pharmaceutical industry. The bioassays used to screen enzyme modulators can be affected by unaccounted interferences such as time-dependent inactivation and inhibition effects. Using procaspase-3, caspase-3, and α-thrombin as model enzymes, we show that some of these effects are not eliminated by merely ignoring the reaction phases that follow initial-rate measurements. We thus propose a linearization method (LM) for detecting spurious changes of enzymatic activity based on the representation of progress curves in modified coordinates. This method is highly sensitive to signal readout distortions, thereby allowing rigorous selection of valid kinetic data. The method allows the detection of assay interferences even when their occurrence is not suspected a priori. By knowing the assets and liabilities of the bioassay, enzymology results can be reported with enhanced reproducibility and accuracy. Critical analysis of full progress curves is expected to help discriminating experimental artifacts from true mechanisms of enzymatic inhibition.

Innovative Approach to Information Search by Example of a Patent Analysis of an Important Substitution Plan

2020 ◽  
pp. 143-157
Author(s):  
Maria A. Milkova
Keyword(s):  
Information Search ◽  
Topic Modeling ◽  
Cognitive Biases ◽  
A Priori ◽  
Import Substitution ◽  
Innovative Approach ◽  
Iterative Search ◽  
Comprehensive Picture ◽  
Priori Information ◽  
Selection Of

Nowadays the process of information accumulation is so rapid that the concept of the usual iterative search requires revision. Being in the world of oversaturated information in order to comprehensively cover and analyze the problem under study, it is necessary to make high demands on the search methods. An innovative approach to search should flexibly take into account the large amount of already accumulated knowledge and a priori requirements for results. The results, in turn, should immediately provide a roadmap of the direction being studied with the possibility of as much detail as possible. The approach to search based on topic modeling, the so-called topic search, allows you to take into account all these requirements and thereby streamline the nature of working with information, increase the efficiency of knowledge production, avoid cognitive biases in the perception of information, which is important both on micro and macro level. In order to demonstrate an example of applying topic search, the article considers the task of analyzing an import substitution program based on patent data. The program includes plans for 22 industries and contains more than 1,500 products and technologies for the proposed import substitution. The use of patent search based on topic modeling allows to search immediately by the blocks of a priori information – terms of industrial plans for import substitution and at the output get a selection of relevant documents for each of the industries. This approach allows not only to provide a comprehensive picture of the effectiveness of the program as a whole, but also to visually obtain more detailed information about which groups of products and technologies have been patented.

Discovery of an Unexpected Similarity in Ligand Binding Between BRD4 and PPARγ

2019 ◽  
Author(s):  
Lina Humbeck ◽  
Jette Pretzel ◽  
Saskia Spitzer ◽  
Oliver Koch
Keyword(s):  
Cancer Therapy ◽  
Drug Targets ◽  
Synergistic Effects ◽  
Complex Structure ◽  
Peroxisome Proliferator ◽  
Resistance Development ◽  
Peroxisome Proliferator Activated Receptor ◽  
Starting Point ◽  
Fundamental Research ◽  
Important Drug

Knowledge about interrelationships between different proteins is crucial in fundamental research for the elucidation of protein networks and pathways. Furthermore, it is especially critical in chemical biology to identify further key regulators of a disease and to take advantage of polypharmacology effects. A comprehensive scaffold-based analysis uncovered an unexpected relationship between bromodomain-containing protein 4 (BRD4) and peroxisome-proliferator activated receptor gamma (PPARγ). They are both important drug targets for cancer therapy and many more important diseases. Both proteins share binding site similarities near a common hydrophobic subpocket which should allow the design of a polypharmacology-based ligand targeting both proteins. Such a dual-BRD4-PPARγ-modulator could show synergistic effects with a higher efficacy or delayed resistance development in, for example, cancer therapy. Thereon, a complex structure of sulfasalazine was obtained that involves two bromodomains and could be a potential starting point for the design of a bivalent BRD4 inhibitor.

Pathogenic Genes Selection Model of Genetic Disease based on Network Motifs Slicing Feedback

2019 ◽  
Vol 16 (5) ◽  
pp. 392-401
Author(s):  
Shengli Zhang ◽  
Zekun Tong ◽  
Haoyu Yin ◽  
Yifan Feng
Keyword(s):  
Genetic Disease ◽  
Drug Targets ◽  
Selection Model ◽  
Network Motifs ◽  
Gene Set ◽  
Pathway Network ◽  
Gene Pathway ◽  
Pathogenic Genes ◽  
Pathogenic Gene ◽  
Selection Of

Background: Finding the pathogenic gene is very important for understanding the pathogenesis of the disease, locating effective drug targets and improving the clinical level of medical treatment. However, the existing methods for finding the pathogenic genes still have limitations, for instance the computational complexity is high, and the combination of multiple genes and pathways has not been considered to search for highly related pathogenic genes and so on. Methods: We propose a pathogenic genes selection model of genetic disease based on Network Motifs Slicing Feedback (NMSF). We find a point set which makes the conductivity of the motif minimum then use it to substitute for the original gene pathway network. Based on the NMSF, we propose a new pathogenic genes selection model to expand pathogenic gene set. Results: According to the gene set we have obtained, selection of key genes will be more accurate and convincing. Finally, we use our model to screen the pathogenic genes and key pathways of liver cancer and lung cancer, and compare the results with the existing methods. Conclusion: The main contribution is to provide a method called NMSF which simplifies the gene pathway network to make the selection of pathogenic gene simple and feasible. The fact shows our result has a wide coverage and high accuracy and our model has good expeditiousness and robustness.

scholarly journals Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

2021 ◽  
Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
A Priori ◽  
External Validation ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Clinical Validation ◽  
Biological Processes ◽  
Imaging Data ◽  
Selection Of

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

scholarly journals Improving the precision of the selection of piecewise linear useful signal component under conditions of a priori uncertainty

2021 ◽  
Vol 1029 ◽  
pp. 012102
Author(s):  
I S Shrayfel ◽  
V I Marchuk ◽  
A B Mihailov ◽  
I M Malcev ◽  
A V Fisunov
Keyword(s):  
Piecewise Linear ◽  
A Priori ◽  
Signal Component ◽  
Useful Signal ◽  
A Priori Uncertainty ◽  
Selection Of

Selection of an aptamer against Muscovy duck parvovirus for highly sensitive rapid visual detection by label-free aptasensor

Talanta ◽  
2018 ◽  
Vol 176 ◽  
pp. 214-220 ◽  
Author(s):  
Taofeng Lu ◽  
Qin Ma ◽  
Wenzhuo Yan ◽  
Yuanzhi Wang ◽  
Yuanyuan Zhang ◽  
...  
Keyword(s):  
Visual Detection ◽  
Muscovy Duck ◽  
Label Free ◽  
Highly Sensitive ◽  
Muscovy Duck Parvovirus ◽  
Selection Of

Selection of a DNA Aptamer against Zearalenone and Docking Analysis for Highly Sensitive Rapid Visual Detection with Label-Free Aptasensor

2018 ◽  
Vol 66 (45) ◽  
pp. 12102-12110 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Taofeng Lu ◽  
Yue Wang ◽  
Chenxi Diao ◽  
Yan Zhou ◽  
...  
Keyword(s):  
Visual Detection ◽  
Dna Aptamer ◽  
Label Free ◽  
Docking Analysis ◽  
Highly Sensitive ◽  
Selection Of

Toward a priori selection of ellipsometry angles and wavelengths using a high performance semantic database

1998 ◽  
Vol 313-314 ◽  
pp. 119-123 ◽  
Author(s):  
F.K. Urban III ◽  
David Barton
Keyword(s):  
High Performance ◽  
A Priori ◽  
Semantic Database ◽  
Selection Of
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