A Three-Stage Algorithm to Make Toxicologically Relevant Activity Calls from Quantitative High Throughput Screening Data.
Background: The ability of a substance to induce a toxicological response is better understood by analyzing the response profile over a broad range of concentrations than at a single concentration. In vitro quantitative high throughput screening (qHTS) assays are multiple-concentration experiments w...
| Publicado en: | Environmental Health Perspectives Vol. 120; no. 8; pp. 1107 - 1116 |
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| Autor principal: | |
| Formato: | research tables/charts Journal Article |
| Publicado: |
National Institute of Environmental Health Sciences
Aug2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104493792&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104493792 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00916765 3B5 jtl: Environmental Health Perspectives issn: 00916765 maglogo: N pubinfo: dt: Aug2012 vid: 120 iid: 8 pid: 56539 pub: National Institute of Environmental Health Sciences place: Research Triangle Park, North Carolina artinfo: ui: 104493792 78434155 10.1289/ehp.1104688 NLM22575717 104493792 ppf: 1107 ppct: 9 formats: fmt: @attributes: type: P tig: atl: A Three-Stage Algorithm to Make Toxicologically Relevant Activity Calls from Quantitative High Throughput Screening Data. aug: au: Shockley, Keith R. affil: Biostatistics Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA sug: subj: Algorithms Technology, Pharmaceutical Toxicology T-Tests Linear Regression Toxicity Tests ROC Curve Regression Descriptive Statistics Fluorouracil Amines Cadmium Compounds Simulations Receptors, Cell Surface Androgens Data Analysis Software Funding Source ab: Background: The ability of a substance to induce a toxicological response is better understood by analyzing the response profile over a broad range of concentrations than at a single concentration. In vitro quantitative high throughput screening (qHTS) assays are multiple-concentration experiments with an important role in the National Toxicology Program's (NTP) efforts to advance toxicology from a predominantly observational science at the level of disease-specific models to a more predictive science based on broad inclusion of biological observations. Objective: We developed a systematic approach to classify substances from large-scale concentration-response data into statistically supported, toxicologically relevant activity categories. Methods: The first stage of the approach finds active substances with robust concentration-response profiles within the tested concentration range. The second stage finds substances with activity at the lowest tested concentration not captured in the first stage. The third and final stage separates statistically significant (but not robustly statistically significant) profiles from responses that lack statistically compelling support (i.e., "inactives"). The performance of the proposed algorithm was evaluated with simulated qHTS data sets. Results: The proposed approach performed well for 14-point-concentration-response curves with typical levels of residual error (σ ≤ 25%) or when maximal response (|RMAX|) was > 25% of the positive control response. The approach also worked well in most cases for smaller sample sizes when |RMAX| ≥50%, even with as few as four data points. Conclusions: The three-stage classification algorithm performed better than one-stage classification approaches based on overall F-tests, t-tests, or linear regression. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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