Evaluation of factors with potential influence on [18F]FDG radiochemical synthesis yield

Chochevska, Maja; Velichkovska, Maja; Lazareva, Marija Atanasova; Kolevska, Katerina; Jolevski, Filip; Razmoska, Jasmina; Filipovski, Zlatko; Nikolovski, Sasho; Kocovska, Marina Zdraveska; Ugrinska, Ana

doi:10.1016/j.apradiso.2023.110900

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…It has been found that during radiolabeling process, different reaction parameters directly affect the nal RCY [12,24]. The parameters which can notably affect the radiolabeling e ciency are the initial radioactivity of the radionuclide, pH, time and temperature of radiolabeling reaction [25].…”

Section: Resultsmentioning

confidence: 99%

“…The in uence of temperature is explained by the fact that the average kinetics of the radiolabeling reaction increases as the temperature of the system increases. In other words, it can be assumed that an increase in temperature leads to a higher effective collision rate between HA molecules and the 177 Lu isotope, thereby increasing the reaction e ciency [24]. The maximum radiochemical yield is found at the initial concentration of radionuclide = 0.128 (MB q ), pH = 6.1 and Temperature = 38.9 ( o C).…”

Section: Response Surface Plotsmentioning

confidence: 99%

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The Central Composite Design and Artificial Neural Network in Optimization and Modeling of Radiolabeling Process of 177Lu-hydroxyapatite as a Potential Radiosynovectomy Agent

Nosrati,

Salahinejad,

Aboudzadeh

et al. 2024

Preprint

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Background A promising material used in radiation synovectomy of small joints is hydroxyapatite which has been 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 a precise optimization. In this investigation, central composite design based on response surface methodology and artificial neural networks are applied to build predictive models and explore the effect of key parameters in the radiolabeling process of hydroxyapatite with 177Lu radionuclide. The variables that directly affected the labeling reaction were the initial 177Lu radionuclide concentration, 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). In addition, the maximum radiochemical yield is found at the initial concentration of 177Lu radionuclide = 0.128 megabecquerel (MBq), pH = 6.1, and temperature = 38.9 (oC), by performing the response surface methodology. 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 optimization and artificial neural networks modeling are successfully utilized to create prediction models and investigate the impact of critical variables in the radiolabeling of hydroxyapatite with 177Lu radionuclide.

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Section: Resultsmentioning

confidence: 99%

Section: Response Surface Plotsmentioning

confidence: 99%

The Central Composite Design and Artificial Neural Network in Optimization and Modeling of Radiolabeling Process of 177Lu-hydroxyapatite as a Potential Radiosynovectomy Agent

Nosrati,

Salahinejad,

Aboudzadeh

et al. 2024

Preprint

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[¹⁸F]Fluoromisonidazole synthesis method: development and optimization by cartridge purification

et al. 2023

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[18F]Fluoromisonidazole ([18F]FMISO) as nitroimidazole derivative with 18F radioisotope is a widely known and studied hypoxia marker for PET imaging. A number of automated synthesis modules and purification strategies for production of [18F]FMISO have been described in recent years. The goal of this work was to develop [18F]FMISO synthesis process with Synthera module with solid phase extraction (SPE) Sep-Pak purification cartridges. To adjust the reaction conditions we synthesized [18F]FMISO under different reaction conditions and using various reversed-phase (RP) purification cartridges (HLB light, HLB plus, tC18, C18 environmental, Chromafix PS-RP). The synthesis was performed by nucleophilic substitution of commercial 1-(2′-nitro-1′-imidazolyl)-2-O-tetrahydropyranyl-3-O-toluenesulfonylpropanediol precursor and subsequent acidic hydrolysis. Further, the product mixture was purified by passing through the SPE cartridge. The produced [18F]FMISO was retained on the cartridge, while the impurities passed through the cartridge into a waste. The retained [18F]FMISO was then eluted with small amounts of ethanol in water and eluates were collected in the final product vial. The product sample was subjected to quality control tests, while for waste sample chemical and radiochemical tests were performed. We have developed an efficient synthesis method of [18F]FMISO with cartridge purification with good radiochemical yield (RCY) and high chemical and radiochemical purity in accordance with the Ph. Eur. Monograph for Fluoromisonidazole (18F) injection.

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Evaluation of factors with potential influence on [18F]FDG radiochemical synthesis yield

Cited by 2 publications

References 15 publications

The Central Composite Design and Artificial Neural Network in Optimization and Modeling of Radiolabeling Process of 177Lu-hydroxyapatite as a Potential Radiosynovectomy Agent

The Central Composite Design and Artificial Neural Network in Optimization and Modeling of Radiolabeling Process of 177Lu-hydroxyapatite as a Potential Radiosynovectomy Agent

[¹⁸F]Fluoromisonidazole synthesis method: development and optimization by cartridge purification

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Evaluation of factors with potential influence on [18F]FDG radiochemical synthesis yield

Cited by 2 publications

References 15 publications

The Central Composite Design and Artificial Neural Network in Optimization and Modeling of Radiolabeling Process of 177Lu-hydroxyapatite as a Potential Radiosynovectomy Agent

The Central Composite Design and Artificial Neural Network in Optimization and Modeling of Radiolabeling Process of 177Lu-hydroxyapatite as a Potential Radiosynovectomy Agent

[18F]Fluoromisonidazole synthesis method: development and optimization by cartridge purification

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[¹⁸F]Fluoromisonidazole synthesis method: development and optimization by cartridge purification