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

Abstract

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.