td2pLL: An intuitive time-dose-response model for cytotoxicity data with varying exposure durations

IF 3.1 Q2 TOXICOLOGY
Julia Duda , Jan G. Hengstler , Jörg Rahnenführer
{"title":"td2pLL: An intuitive time-dose-response model for cytotoxicity data with varying exposure durations","authors":"Julia Duda ,&nbsp;Jan G. Hengstler ,&nbsp;Jörg Rahnenführer","doi":"10.1016/j.comtox.2022.100234","DOIUrl":null,"url":null,"abstract":"<div><p>Statistical modeling approaches for dose-response or concentration-response analyses are often required in toxicological applications, especially for cytotoxicity assays. By fitting a concentration-response curve, one can derive target concentrations, such as the <span><math><mrow><msub><mrow><mi>EC</mi></mrow><mrow><mn>50</mn></mrow></msub></mrow></math></span>. In practice, concentration-response data for different exposure durations might be available and the target concentration for each or some exposure duration(s) are of interest. In this work, we propose a statistical modeling approach that improves the precision of the target concentration estimation at a given exposure duration by extrapolating the concentration-response data from other exposure durations. The method further enables target concentration estimation at exposure durations that were not conducted in the experiment. For practitioners, the proposed model yields additional complexity compared to the simple approach of a single concentration-response curve for all exposure durations. It would only be used if it improves the estimation of the target concentration compared to the simple approach. We propose a two-step pipeline to decide between using the complex and the simple approach to result in a precise target concentration estimation.</p><p>The methods were evaluated using a simulation study and a real data set. The models are accessible for practitioners through the R package td2pLL.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111322000226/pdfft?md5=c58793832fbf242eeca603afc36da78a&pid=1-s2.0-S2468111322000226-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111322000226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Statistical modeling approaches for dose-response or concentration-response analyses are often required in toxicological applications, especially for cytotoxicity assays. By fitting a concentration-response curve, one can derive target concentrations, such as the EC50. In practice, concentration-response data for different exposure durations might be available and the target concentration for each or some exposure duration(s) are of interest. In this work, we propose a statistical modeling approach that improves the precision of the target concentration estimation at a given exposure duration by extrapolating the concentration-response data from other exposure durations. The method further enables target concentration estimation at exposure durations that were not conducted in the experiment. For practitioners, the proposed model yields additional complexity compared to the simple approach of a single concentration-response curve for all exposure durations. It would only be used if it improves the estimation of the target concentration compared to the simple approach. We propose a two-step pipeline to decide between using the complex and the simple approach to result in a precise target concentration estimation.

The methods were evaluated using a simulation study and a real data set. The models are accessible for practitioners through the R package td2pLL.

td2pLL:一个直观的时间-剂量-反应模型,用于不同暴露时间的细胞毒性数据
剂量-反应或浓度-反应分析的统计建模方法在毒理学应用中经常需要,特别是在细胞毒性分析中。通过拟合浓度-响应曲线,可以得到目标浓度,如EC50。在实践中,不同暴露持续时间的浓度-反应数据可能是可用的,并且每个或某些暴露持续时间的目标浓度是感兴趣的。在这项工作中,我们提出了一种统计建模方法,通过外推其他暴露持续时间的浓度-响应数据来提高给定暴露持续时间下目标浓度估计的精度。该方法还可以在实验中未进行的暴露持续时间内估计目标浓度。对于从业者来说,与所有暴露持续时间的单一浓度-响应曲线的简单方法相比,所提出的模型产生了额外的复杂性。与简单的方法相比,只有当它能提高对目标浓度的估计时才会被使用。我们提出了一个两步流程来决定使用复杂和简单的方法来获得精确的目标浓度估计。通过模拟研究和实际数据集对这些方法进行了评估。从业者可以通过R包td2pLL访问这些模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
文献相关原料
公司名称 产品信息 采购帮参考价格
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信