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• W2938498627 abstract "The prediction of in vivo clearance from in vitro metabolism models such as liver microsomes is an established procedure in drug discovery. The potentials and limitations of this approach have been extensively evaluated in the pharmaceutical sector; however, this is not the case for the field of positron emission tomography (PET) radiotracer development. The application of PET radiotracers and classical drugs differs greatly with regard to the amount of substance administered. In typical PET imaging sessions, subnanomolar quantities of the radiotracer are injected, resulting in body concentrations that cannot be readily simulated in analytical assays. This raises concerns regarding the predictability of radiotracer clearance from in vitro data. We assessed the accuracy of clearance prediction for three prototypical PET radiotracers developed for imaging the A1 adenosine receptor (A1AR). Using the half-life (t1/2) approach and physiologically based scaling, in vivo clearance in the rat model was predicted from microsomal stability data. Actual clearance could be accurately predicted with an average fold error (AFE) of 0.78 and a root mean square error (RMSE) of 1.6. The observed slight underprediction (1.3-fold) is in accordance with the prediction accuracy reported for classical drugs. This result indicates that the prediction of radiotracer clearance is possible despite concentration differences of more than three orders of magnitude between in vitro and in vivo conditions. Consequently, in vitro metabolism models represent a valuable tool for PET radiotracer development." @default.
• W2938498627 created "2019-04-25" @default.
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• W2938498627 date "2019-04-14" @default.
• W2938498627 modified "2023-09-26" @default.
• W2938498627 title "Relevance of In Vitro Metabolism Models to PET Radiotracer Development: Prediction of In Vivo Clearance in Rats from Microsomal Stability Data" @default.
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• W2938498627 doi "https://doi.org/10.3390/ph12020057" @default.
• W2938498627 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6631687" @default.
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• W2938498627 hasPublicationYear "2019" @default.
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