PF-04217903

The tyrosine kinase MET (hepatocyte growth factor receptor) is activated or mutated in a wide range of cancers and is often correlated with a poor prognosis. Precision medicine with positron emission tomography (PET) can potentially aid in the assessment of tumor biochemistry and heterogeneity, which can prompt the selection of the most effective therapeutic regimes. The selective MET inhibitor PF04217903 (1) formed the basis for a bioisosteric replacement, leading to the deoxyfluorinated analog [18F]2. [18F]2 could be synthesized with a “hydrous fluoroethylation” protocol in 6.3 ± 2.6% radiochemical yield and a molar activity of >50 GBq/μmol. In vitro autoradiography indicated that [18F]2 selectively binds to MET in PC3 tumor tissue, and in vivo biodistribution in mice showed predominantly a hepatobiliary excretion along with a low retention of radiotracer in other organs.

Prediction of drug clearance by aldehyde oxidase (AO) catalyzed metabolism has been of great interest in drug discovery.We report a fast, accurate, and fully-automated tool that combines semi-empirical quantum mech. (SQM) calculations and machine-learning (ML) technique to predict AO-mediated site of metabolism (SOM).We infer that calculated probability scores together with those based on approx. C(sp2) - H bond dissociation energies accomplish accurate and immediate prediction of the number and rank ordering of SOMs.Tests on a series of 150 AO substrates demonstrate that our computational strategy can be applicable to high-throughput screening of metabolically stable compounds