通过药物诱导耐药性数学模型了解治疗耐受性

Eduardo D. Sontag, Jana L. Gevertz, James Greene, Natacha Comandante-Lou, Samantha Prosperi
{"title":"通过药物诱导耐药性数学模型了解治疗耐受性","authors":"Eduardo D. Sontag, Jana L. Gevertz, James Greene, Natacha Comandante-Lou, Samantha Prosperi","doi":"10.1101/2024.09.04.611211","DOIUrl":null,"url":null,"abstract":"There is growing recognition that phenotypic plasticity enables cancer cells to adapt to various environmental conditions. An example of this adaptability is the persistence of an initially sensitive population of cancer cells in the presence of therapeutic agents. Understanding the implications of this drug-induced resistance is essential for predicting transient and long-term tumor tumor dynamics subject to treatment. This paper introduces a mathematical model of this phenomenon of drug-induced resistance which provides excellent fits to time-resolved <em>in vitro</em> experimental data. From observational data of total numbers of cells, the model unravels the relative proportions of sensitive and resistance subpopulations, and quantifies their dynamics as a function of drug dose. The predictions are then validated using data on drug doses which were not used when fitting parameters. The model is then used, in conjunction with optimal control techniques, in order to discover dosing strategies that might lead to better outcomes as quantified by lower total cell volume.","PeriodicalId":501213,"journal":{"name":"bioRxiv - Systems Biology","volume":"35 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Understanding therapeutic tolerance through a mathematical model of drug-induced resistance\",\"authors\":\"Eduardo D. Sontag, Jana L. Gevertz, James Greene, Natacha Comandante-Lou, Samantha Prosperi\",\"doi\":\"10.1101/2024.09.04.611211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There is growing recognition that phenotypic plasticity enables cancer cells to adapt to various environmental conditions. An example of this adaptability is the persistence of an initially sensitive population of cancer cells in the presence of therapeutic agents. Understanding the implications of this drug-induced resistance is essential for predicting transient and long-term tumor tumor dynamics subject to treatment. This paper introduces a mathematical model of this phenomenon of drug-induced resistance which provides excellent fits to time-resolved <em>in vitro</em> experimental data. From observational data of total numbers of cells, the model unravels the relative proportions of sensitive and resistance subpopulations, and quantifies their dynamics as a function of drug dose. The predictions are then validated using data on drug doses which were not used when fitting parameters. The model is then used, in conjunction with optimal control techniques, in order to discover dosing strategies that might lead to better outcomes as quantified by lower total cell volume.\",\"PeriodicalId\":501213,\"journal\":{\"name\":\"bioRxiv - Systems Biology\",\"volume\":\"35 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"bioRxiv - Systems Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2024.09.04.611211\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"bioRxiv - Systems Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.09.04.611211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

越来越多的人认识到,表型可塑性使癌细胞能够适应各种环境条件。这种适应性的一个例子是,最初敏感的癌细胞群在治疗药物的作用下会持续存在。了解这种由药物诱导的抗药性的影响,对于预测接受治疗的肿瘤瞬时和长期动态变化至关重要。本文介绍了这种药物诱导抗药性现象的数学模型,该模型与时间分辨体外实验数据的拟合效果极佳。根据细胞总数的观测数据,该模型揭示了敏感亚群和耐药亚群的相对比例,并量化了它们作为药物剂量函数的动态变化。然后使用拟合参数时未使用的药物剂量数据对预测结果进行验证。然后将该模型与优化控制技术结合使用,以发现可通过降低细胞总体积来量化的更佳治疗效果的剂量策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Understanding therapeutic tolerance through a mathematical model of drug-induced resistance
There is growing recognition that phenotypic plasticity enables cancer cells to adapt to various environmental conditions. An example of this adaptability is the persistence of an initially sensitive population of cancer cells in the presence of therapeutic agents. Understanding the implications of this drug-induced resistance is essential for predicting transient and long-term tumor tumor dynamics subject to treatment. This paper introduces a mathematical model of this phenomenon of drug-induced resistance which provides excellent fits to time-resolved in vitro experimental data. From observational data of total numbers of cells, the model unravels the relative proportions of sensitive and resistance subpopulations, and quantifies their dynamics as a function of drug dose. The predictions are then validated using data on drug doses which were not used when fitting parameters. The model is then used, in conjunction with optimal control techniques, in order to discover dosing strategies that might lead to better outcomes as quantified by lower total cell volume.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Decoding Cytokine Networks in Ulcerative Colitis to Identify Pathogenic Mechanisms and Therapeutic Targets High-content microscopy and machine learning characterize a cell morphology signature of NF1 genotype in Schwann cells Tissue-specific metabolomic signatures for a doublesex model of reduced sexual dimorphism Sequential design of single-cell experiments to identify discrete stochastic models for gene expression. Environment-mediated interactions cause an externalized and collective memory in microbes
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1