scholarly journals Hierarchical dose-response modeling for high-throughput toxicity screening of environmental chemicals

Biometrics ◽  
2014 ◽  
Vol 70 (1) ◽  
pp. 237-246 ◽  
Author(s):  
Ander Wilson ◽  
David M. Reif ◽  
Brian J. Reich
Keyword(s):  
High Throughput ◽  
Dose Response ◽  
Environmental Chemicals ◽  
Toxicity Screening ◽  
Response Modeling

scholarly journals Nonlinear mixed effects dose response modeling in high throughput drug screens: application to melanoma cell line analysis

Oncotarget ◽  
2017 ◽  
Vol 9 (4) ◽  
pp. 5044-5057 ◽  
Author(s):  
Kuan-Fu Ding ◽  
Emanuel F. Petricoin ◽  
Darren Finlay ◽  
Hongwei Yin ◽  
William P.D. Hendricks ◽  
...  
Keyword(s):  
Cell Line ◽  
Melanoma Cell ◽  
High Throughput ◽  
Dose Response ◽  
Melanoma Cell Line ◽  
Mixed Effects ◽  
Nonlinear Mixed Effects ◽  
Response Modeling ◽  
Line Analysis

scholarly journals Nonlinear Dose–Response Modeling of High-Throughput Screening Data Using an Evolutionary Algorithm

Dose-Response ◽  
2020 ◽  
Vol 18 (2) ◽  
pp. 155932582092673
Author(s):  
Jun Ma ◽  
Eric Bair ◽  
Alison Motsinger-Reif
Keyword(s):  
High Throughput ◽  
Dose Response ◽  
High Throughput Screening ◽  
Model Fitting ◽  
Logistic Models ◽  
Toxicity Testing ◽  
Nonlinear Least Squares ◽  
New Method ◽  
Chemical Screening ◽  
Response Modeling

Nonlinear dose–response relationships exist extensively in the cellular, biochemical, and physiologic processes that are affected by varying levels of biological, chemical, or radiation stress. Modeling such responses is a crucial component of toxicity testing and chemical screening. Traditional model fitting methods such as nonlinear least squares (NLS) are very sensitive to initial parameter values and often had convergence failure. The use of evolutionary algorithms (EAs) has been proposed to address many of the limitations of traditional approaches, but previous methods have been limited in the types of models they can fit. Therefore, we propose the use of an EA for dose–response modeling for a range of potential response model functional forms. This new method can not only fit the most commonly used nonlinear dose–response models (eg, exponential models and 3-, 4-, and 5-parameter logistic models) but also select the best model if no model assumption is made, which is especially useful in the case of high-throughput curve fitting. Compared with NLS, the new method provides stable and robust solutions without sensitivity to initial values.

High-Throughput Transcriptomics Platform for Screening Environmental Chemicals

2021 ◽  
Author(s):  
Joshua A Harrill ◽  
Logan J Everett ◽  
Derik E Haggard ◽  
Thomas Sheffield ◽  
Joseph L Bundy ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Gene Expression Signature ◽  
Environmental Chemicals ◽  
Hazard Evaluation ◽  
Response Analysis ◽  
Chemical Treatments ◽  
Response Modeling ◽  
Reference Samples

Abstract New approach methodologies (NAMs) that efficiently provide information about chemical hazard without using whole animals are needed to accelerate the pace of chemical risk assessments. Technological advancements in gene expression assays have made in vitro high-throughput transcriptomics (HTTr) a feasible option for NAMs-based hazard characterization of environmental chemicals. In this study, we evaluated the Templated Oligo with Sequencing Readout (TempO-Seq) assay for HTTr concentration-response screening of a small set of chemicals in the human-derived MCF7 cell model. Our experimental design included a variety of reference samples and reference chemical treatments in order to objectively evaluate TempO-Seq assay performance. To facilitate analysis of these data, we developed a robust and scalable bioinformatics pipeline using open-source tools. We also developed a novel gene expression signature-based concentration-response modeling approach and compared the results to a previously implemented workflow for concentration-response analysis of transcriptomics data using BMDExpress. Analysis of reference samples and reference chemical treatments demonstrated highly reproducible differential gene expression signatures. In addition, we found that aggregating signals from individual genes into gene signatures prior to concentration-response modeling yielded in vitro transcriptional biological pathway altering concentrations (BPACs) that were closely aligned with previous ToxCast high-throughput screening assays. Often these identified signatures were associated with the known molecular target of the chemicals in our test set as the most sensitive components of the overall transcriptional response. This work has resulted in a novel and scalable in vitro HTTr workflow that is suitable for high-throughput hazard evaluation of environmental chemicals.

The developmental toxicity of inhaled methanol in the CD-1 mouse, with quantitative dose—response modeling for estimation of benchmark doses

Teratology ◽  
1993 ◽  
Vol 47 (3) ◽  
pp. 175-188 ◽  
Author(s):  
John M. Rogers ◽  
M. Leonard Mole ◽  
Neil Chernoff ◽  
Brenda D. Barbee ◽  
Christine I. Turner ◽  
...  
Keyword(s):  
Dose Response ◽  
Developmental Toxicity ◽  
Benchmark Doses ◽  
Response Modeling

TK/TD dose–response modeling of toxicity

2007 ◽  
Vol 18 (5) ◽  
pp. 515-525
Author(s):  
Munni Begum ◽  
Pranab K. Sen
Keyword(s):  
Dose Response ◽  
Response Modeling

scholarly journals T1000: A reduced toxicogenomics gene set for improved decision making

2019 ◽  
Author(s):  
Othman Soufan ◽  
Jessica Ewald ◽  
Charles Viau ◽  
Doug Crump ◽  
Markus Hecker ◽  
...  
Keyword(s):  
Dose Response ◽  
Gene Clusters ◽  
Gene Set ◽  
Chemical Hazards ◽  
Gene Sets ◽  
Toxicological Research ◽  
Multiple Species ◽  
Response Modeling

There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets.Though several gene sets have been defined for toxicological applications, few of these were purposefully derived using toxicogenomics data. Here, we developed and applied a systematic approach to identify 1000 genes (called Toxicogenomics-1000 or T1000) highly responsive to chemical exposures. First, a co-expression network of 11,210genes was built by leveraging microarray data from the Open TG-GATEs program. This network was then re-weighted based on prior knowledge of their biological (KEGG, MSigDB) and toxicological (CTD) relevance. Finally, weighted correlation network analysis was applied to identify 258 gene clusters. T1000 was defined by selecting genes from each cluster that were most associated with outcome measures. For model evaluation, we compared the performance of T1000 to that of other gene sets (L1000, S1500, Genes selected by Limma, and random set) using two external datasets. Additionally, a smaller (T384) and a larger version (T1500) of T1000 were used for dose-response modeling to test the effect of gene set size. Our findings demonstrated that the T1000 gene set is predictive of apical outcomes across a range of conditions (e.g.,in vitroand in vivo, dose-response, multiple species, tissues, and chemicals), and generally performs as well, or better than other gene sets available.

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