{"title":"td2pLL: An intuitive time-dose-response model for cytotoxicity data with varying exposure durations","authors":"Julia Duda , Jan G. Hengstler , 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 . 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.
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