Abstract
Recent advancements in 18F radiochemistry, such as the advent of copper-mediated radiofluorination (CMRF) chemistry, have provided unprecedented access to novel chemically diverse PET probes; however, these multicomponent reactions have come with a new set of complex optimization problems. Design of experiments (DoE) is a statistical approach to process optimization that is used across a variety of industries. It possesses a number of advantages over the traditionally employed “one variable at a time” (OVAT) approach, such as increased experimental efficiency as well as an ability to resolve factor interactions and provide detailed maps of a process’s behavior. Here we demonstrate the utility of DoE to the development and optimization of new radiochemical methodologies and novel PET tracer synthesis. Using DoE to construct experimentally efficient factor screening and optimization studies, we were able to identify critical factors and model their behavior with more than two-fold greater experimental efficiency than the traditional OVAT approach. Additionally, the use of DoE allowed us to glean new insights into the behavior of the CMRF of a number of arylstannane precursors. This information has guided our decision-making efforts while developing efficient reaction conditions that suit the unique process requirements of 18F PET tracer synthesis.
Highlights
New 18F labeling methodologies have been published that have begun to push the field forward, opening new avenues for radiotracer design and synthesis[2,5,6,7,8]
In order to assess the benefit of investigating the Design of Experiments” (DoE) approach for radiochemical process optimization, we studied the supplementary information of the original paper disclosing the copper-mediated radiofluorinations (CMRF) of arylstannanes by Makaravage et al[11]
While their study successfully led to the development of a scalable and automatable procedure for the production of a number of PET tracers from aryltrialkylstannnes, it was done with great experimental effort
Summary
New 18F labeling methodologies have been published that have begun to push the field forward, opening new avenues for radiotracer design and synthesis[2,5,6,7,8]. 1a,b) in order to screen a large number of continuous (temperature, reagent stoichiometry, concentration, time, etc.) or discrete (atmosphere, solvent, reagent identity, etc.) variables that may affect the investigated response (%RCC, specific activity (SA), etc.) These “factor screening” (FS) experiments are designed to ascertain which factors have the largest influence on the response, give limited information on the presence of factor interactions and eliminate non-significant factors in as few runs as possible. They are usually not detailed enough to provide an accurate, predictive model of the system in question. These designs usually contain more experimental points (per factor) and are intended to produce a detailed mathematical model of the process’s behavior
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