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}
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.