中心复合设计与人工神经网络结合遗传算法对177lu -羟基磷灰石作为潜在放射滑膜切除药物的放射标记过程进行优化建模。

IF 1.2 4区 医学 Q3 PHARMACOLOGY & PHARMACY
Sima Attar Nosrati, Maryam Salahinejad, Mohammad Reza Aboudzadeh, Mojtaba Amiri, Ali Roozbahani
{"title":"中心复合设计与人工神经网络结合遗传算法对177lu -羟基磷灰石作为潜在放射滑膜切除药物的放射标记过程进行优化建模。","authors":"Sima Attar Nosrati, Maryam Salahinejad, Mohammad Reza Aboudzadeh, Mojtaba Amiri, Ali Roozbahani","doi":"10.2174/0118744710336283250227020659","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>A promising material used in radiation synovectomy of small joints is hydroxyapatite, labeled with <sup>177</sup>Lu. During the design and production of radiopharmaceuticals, the condition of the radiolabeling process directly influences the radiochemical yield and consequently the quality of the final product so this process necessitates precise optimization.</p><p><strong>Methods: </strong>In this investigation, a central composite design based on response surface methodology and artificial neural networks modeling coupled with genetic algorithm technique is applied to build predictive models and explore key parameters' effect in hydroxyapatite's radiolabeling process with <sup>177</sup>Lu radionuclide. The variables that directly affected the labeling reaction were the initial <sup>177</sup>Lu radioactivity, pH, radiolabeling reaction time, and temperature.</p><p><strong>Results: </strong>Based on the validation data set, the statistical values demonstrate that the artificial neural networks model performs better than the response surface methodology model. The artificial neural networks model has a small mean squared error (9.08 artificial neural networks < 12.36 response surface methodology) and a high coefficient of determination (R<sup>2</sup>: 0.99 artificial neural networks > 0.93 response surface methodology). The optimum conditions to achieve maximum radiochemical yield based on response surface methodology using artificial neural networks modeling coupled with genetic algorithm were at the initial radioactivity of <sup>177</sup>Lu radionuclide = 0.082 Gigabecquerel (GBq), pH = 6.75, time= 22 (min), and temperature = 37.8 (<sup>o</sup>C).</p><p><strong>Conclusion: </strong>The ability to generate more data with fewer experiments for optimization and improved production is a pertinent advantage of multivariate optimization methods over traditional methods in radiation-related activities. The central composite design and artificial neural network- genetic algorithm optimization approaches are successfully utilized to create prediction models and investigate the impact of critical variables in the radiolabeling of hydroxyapatite with <sup>177</sup>Lu radionuclide.</p>","PeriodicalId":10991,"journal":{"name":"Current radiopharmaceuticals","volume":" ","pages":"201-215"},"PeriodicalIF":1.2000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Central Composite Design and Artificial Neural Network Coupled with Genetic Algorithm in Optimization and Modeling of the Radiolabeling Process of <sup>177</sup>Lu-hydroxyapatite as a Potential Radiosynovectomy Agent.\",\"authors\":\"Sima Attar Nosrati, Maryam Salahinejad, Mohammad Reza Aboudzadeh, Mojtaba Amiri, Ali Roozbahani\",\"doi\":\"10.2174/0118744710336283250227020659\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>A promising material used in radiation synovectomy of small joints is hydroxyapatite, labeled with <sup>177</sup>Lu. During the design and production of radiopharmaceuticals, the condition of the radiolabeling process directly influences the radiochemical yield and consequently the quality of the final product so this process necessitates precise optimization.</p><p><strong>Methods: </strong>In this investigation, a central composite design based on response surface methodology and artificial neural networks modeling coupled with genetic algorithm technique is applied to build predictive models and explore key parameters' effect in hydroxyapatite's radiolabeling process with <sup>177</sup>Lu radionuclide. The variables that directly affected the labeling reaction were the initial <sup>177</sup>Lu radioactivity, pH, radiolabeling reaction time, and temperature.</p><p><strong>Results: </strong>Based on the validation data set, the statistical values demonstrate that the artificial neural networks model performs better than the response surface methodology model. The artificial neural networks model has a small mean squared error (9.08 artificial neural networks < 12.36 response surface methodology) and a high coefficient of determination (R<sup>2</sup>: 0.99 artificial neural networks > 0.93 response surface methodology). The optimum conditions to achieve maximum radiochemical yield based on response surface methodology using artificial neural networks modeling coupled with genetic algorithm were at the initial radioactivity of <sup>177</sup>Lu radionuclide = 0.082 Gigabecquerel (GBq), pH = 6.75, time= 22 (min), and temperature = 37.8 (<sup>o</sup>C).</p><p><strong>Conclusion: </strong>The ability to generate more data with fewer experiments for optimization and improved production is a pertinent advantage of multivariate optimization methods over traditional methods in radiation-related activities. The central composite design and artificial neural network- genetic algorithm optimization approaches are successfully utilized to create prediction models and investigate the impact of critical variables in the radiolabeling of hydroxyapatite with <sup>177</sup>Lu radionuclide.</p>\",\"PeriodicalId\":10991,\"journal\":{\"name\":\"Current radiopharmaceuticals\",\"volume\":\" \",\"pages\":\"201-215\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current radiopharmaceuticals\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2174/0118744710336283250227020659\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current radiopharmaceuticals","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/0118744710336283250227020659","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0

摘要

背景:羟基磷灰石是一种很有前途的材料,用于小关节的放射滑膜切除术,标记为177Lu。在放射性药物的设计和生产过程中,放射性标记过程的条件直接影响到放射化学产率,从而影响到最终产品的质量,因此这一过程需要精确的优化。方法:采用响应面法和人工神经网络建模结合遗传算法的中心复合设计,建立预测模型,探讨关键参数对177Lu放射性核素羟基磷灰石放射性标记过程的影响。直接影响标记反应的变量是初始177Lu放射性、pH、放射性标记反应时间和温度。结果:基于验证数据集,统计值表明人工神经网络模型优于响应面方法模型。人工神经网络模型均方误差小(9.08人工神经网络< 12.36响应面方法),决定系数高(R2: 0.99人工神经网络bb0 0.93响应面方法)。基于人工神经网络建模与遗传算法相结合的响应面法,得到177Lu核素初始放射性= 0.082 GBq, pH = 6.75,时间= 22 (min),温度= 37.8 (oC)时获得最大放化学产率的最佳条件。在与辐射相关的活动中,多元优化方法比传统方法具有更大的优势,即能够以更少的实验产生更多的数据,从而进行优化和提高产量。利用中心复合设计和人工神经网络-遗传算法优化方法建立了预测模型,并研究了177Lu放射性核素对羟基磷灰石放射性标记的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Central Composite Design and Artificial Neural Network Coupled with Genetic Algorithm in Optimization and Modeling of the Radiolabeling Process of 177Lu-hydroxyapatite as a Potential Radiosynovectomy Agent.

Introduction: A promising material used in radiation synovectomy of small joints is hydroxyapatite, labeled with 177Lu. During the design and production of radiopharmaceuticals, the condition of the radiolabeling process directly influences the radiochemical yield and consequently the quality of the final product so this process necessitates precise optimization.

Methods: In this investigation, a central composite design based on response surface methodology and artificial neural networks modeling coupled with genetic algorithm technique is applied to build predictive models and explore key parameters' effect in hydroxyapatite's radiolabeling process with 177Lu radionuclide. The variables that directly affected the labeling reaction were the initial 177Lu radioactivity, pH, radiolabeling reaction time, and temperature.

Results: Based on the validation data set, the statistical values demonstrate that the artificial neural networks model performs better than the response surface methodology model. The artificial neural networks model has a small mean squared error (9.08 artificial neural networks < 12.36 response surface methodology) and a high coefficient of determination (R2: 0.99 artificial neural networks > 0.93 response surface methodology). The optimum conditions to achieve maximum radiochemical yield based on response surface methodology using artificial neural networks modeling coupled with genetic algorithm were at the initial radioactivity of 177Lu radionuclide = 0.082 Gigabecquerel (GBq), pH = 6.75, time= 22 (min), and temperature = 37.8 (oC).

Conclusion: The ability to generate more data with fewer experiments for optimization and improved production is a pertinent advantage of multivariate optimization methods over traditional methods in radiation-related activities. The central composite design and artificial neural network- genetic algorithm optimization approaches are successfully utilized to create prediction models and investigate the impact of critical variables in the radiolabeling of hydroxyapatite with 177Lu radionuclide.

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
Current radiopharmaceuticals
Current radiopharmaceuticals PHARMACOLOGY & PHARMACY-
CiteScore
3.20
自引率
4.30%
发文量
43
×
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:604180095
Book学术官方微信