Facile radiolabeling optimization process via design of experiments and an intelligent optimization algorithm: Application for omeprazole radioiodination

The major uses of radiopharmaceuticals (RP) in clinical areas are diagnosis and/or therapy. The present study aimed to utilize the application of fractional factorial design analysis (FFDA) coupled with particle swarm optimization algorithm (PSO) to assess the optimization of RP production process. In this regard, omeprazole (OMP), which is gastric parietal cell proton pump inhibitor (PPI), was radiolabeled with iodine‐125 (¹²⁵I) isotope in order to be used as a radiotracer for stomach imaging. Different factors that affect radiolabeling process were studied. According to the proposed design, just 16 experimental runs of radiolabeling process were performed using the extremes of each factor. In addition, one run was executed at the mean point of each factor. Undesirable maximum radiolabeling yield (RY) of radioiodinated omeprazole (¹²⁵I‐OMP) was deduced from application of FFDA (88.4%). Furthermore, after applying PSO with changing limits of one factor, the maximum RY of ¹²⁵I‐OMP was found to be 93.78%. Moreover, the practically verification from optimum conditions, which obtained from PSO, was found to give an RY of 93.99%. Overall, the findings of this study confirmed the potential use of that hybrid design for optimization of radiolabeling processes.