Literature DB >> 32461840

Channel Interactions and Robust Inference for Ratiometric β-lactamase Assay Data: a Tox21 Library Analysis.

Fjodor Melnikov1, Jui-Hua Hsieh2, Nisha S Sipes3, Paul T Anastas1,4.   

Abstract

Ratiometric β-lactamase (BLA) reporters are widely used to study transcriptional responses in a high-throughput screening (HTS) format. Typically, a ratio readout (background/target fluorescence) is used for toxicity assessment and structure-activity modeling efforts from BLA HTS data. This ratio readout may be confounded by channel-specific artifacts. To maximize the utility of BLA HTS data, we analyzed the relationship between individual channels and ratio readouts after fitting 10,000 chemical titration series screened in seven BLA stress-response assays from the Tox21 initiative. Similar to previous observations, we found that activity classifications based on BLA ratio readout alone are confounded by interference patterns for up to 85% (50 % on average) of active chemicals. Most Tox21 analyses adjust for this issue by evaluating target and ratio readout direction. In addition, we found that the potency and efficacy estimates derived from the ratio readouts may not represent the target channel effects and thus complicates chemical activity comparison. From these analyses we recommend a simpler approach using a direct evaluation of the target and background channels as well as the respective noise levels when using BLA data for toxicity assessment. This approach eliminates the channel interference issues and allows for straightforward chemical assessment and comparisons.

Entities:  

Keywords:  Tox21; concentration-response; in vitro; qHTS; β-lactamase

Year:  2018        PMID: 32461840      PMCID: PMC7252516          DOI: 10.1021/acssuschemeng.7b03394

Source DB:  PubMed          Journal:  ACS Sustain Chem Eng        ISSN: 2168-0485            Impact factor:   8.198


  39 in total

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Journal:  J Biomol Screen       Date:  2004-04

2.  Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries.

Authors:  James Inglese; Douglas S Auld; Ajit Jadhav; Ronald L Johnson; Anton Simeonov; Adam Yasgar; Wei Zheng; Christopher P Austin
Journal:  Proc Natl Acad Sci U S A       Date:  2006-07-24       Impact factor: 11.205

3.  Benchmarking organic micropollutants in wastewater, recycled water and drinking water with in vitro bioassays.

Authors:  Beate I Escher; Mayumi Allinson; Rolf Altenburger; Peter A Bain; Patrick Balaguer; Wibke Busch; Jordan Crago; Nancy D Denslow; Elke Dopp; Klara Hilscherova; Andrew R Humpage; Anu Kumar; Marina Grimaldi; B Sumith Jayasinghe; Barbora Jarosova; Ai Jia; Sergei Makarov; Keith A Maruya; Alex Medvedev; Alvine C Mehinto; Jamie E Mendez; Anita Poulsen; Erik Prochazka; Jessica Richard; Andrea Schifferli; Daniel Schlenk; Stefan Scholz; Fujio Shiraishi; Shane Snyder; Guanyong Su; Janet Y M Tang; Bart van der Burg; Sander C van der Linden; Inge Werner; Sandy D Westerheide; Chris K C Wong; Min Yang; Bonnie H Y Yeung; Xiaowei Zhang; Frederic D L Leusch
Journal:  Environ Sci Technol       Date:  2013-12-26       Impact factor: 9.028

4.  Screening Chemicals for Estrogen Receptor Bioactivity Using a Computational Model.

Authors:  Patience Browne; Richard S Judson; Warren M Casey; Nicole C Kleinstreuer; Russell S Thomas
Journal:  Environ Sci Technol       Date:  2015-06-26       Impact factor: 9.028

5.  High-throughput screening using beta-lactamase reporter-gene technology for identification of low-molecular-weight antagonists of the human gonadotropin releasing hormone receptor.

Authors:  Julia Oosterom; Els J P van Doornmalen; Sendy Lobregt; Marion Blomenröhr; Guido J R Zaman
Journal:  Assay Drug Dev Technol       Date:  2005-04       Impact factor: 1.738

6.  A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents.

Authors:  Hao Zhu; Lin Ye; Ann Richard; Alexander Golbraikh; Fred A Wright; Ivan Rusyn; Alexander Tropsha
Journal:  Environ Health Perspect       Date:  2009-04-03       Impact factor: 9.031

7.  A beta-lactamase-dependent Gal4-estrogen receptor beta transactivation assay for the ultra-high throughput screening of estrogen receptor beta agonists in a 3456-well format.

Authors:  Norbert T Peekhaus; Marc Ferrer; Tina Chang; Oleg Kornienko; Jonathan E Schneeweis; Todd S Smith; Ira Hoffman; Lyndon J Mitnaul; Jayne Chin; Paul A Fischer; Tim A Blizzard; Elizabeth T Birzin; Wanda Chan; James Inglese; Berta Strulovici; Susan P Rohrer; James M Schaeffer
Journal:  Assay Drug Dev Technol       Date:  2003-12       Impact factor: 1.738

8.  Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods.

Authors:  Qingda Zang; Daniel M Rotroff; Richard S Judson
Journal:  J Chem Inf Model       Date:  2013-12-11       Impact factor: 4.956

9.  Chemical genomics profiling of environmental chemical modulation of human nuclear receptors.

Authors:  Ruili Huang; Menghang Xia; Ming-Hsuang Cho; Srilatha Sakamuru; Paul Shinn; Keith A Houck; David J Dix; Richard S Judson; Kristine L Witt; Robert J Kavlock; Raymond R Tice; Christopher P Austin
Journal:  Environ Health Perspect       Date:  2011-05-04       Impact factor: 9.031

10.  Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?

Authors:  Dávid Bajusz; Anita Rácz; Károly Héberger
Journal:  J Cheminform       Date:  2015-05-20       Impact factor: 5.514

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