Clinical evaluation of [18F]pitavastatin for quantitative analysis of hepatobiliary transporter activity.

It is widely accepted that uptake and efflux transporters on clearance organs play crucial roles in drug disposition. Although in vitro transporter assay system can identify the intrinsic properties of the target transporters, it is not so easy to precisely predict in vivo pharmacokinetic parameters from in vitro data. Positron emission tomography (PET) imaging is a useful tool to directly assess the activity of drug transporters in humans. We recently developed a practical synthetic method for fluorine-18-labeled pitavastatin ([18F]PTV) as a PET probe for quantitative evaluation of hepatobiliary transport. In the present study, we conducted clinical PET imaging with [18F]PTV and compared the pharmacokinetic properties of the probe for healthy subjects with or without rifampicin pretreatment. Rifampicin pretreatment significantly suppressed the hepatic maximum concentration and biliary excretion of the probe to 52% and 34% of the control values, respectively. Rifampicin treatment markedly decreased hepatic uptake clearance (21% of the control), and moderately canalicular efflux clearance with regard to hepatic concentration (52% of the control). These results demonstrate that [18F]PTV is a useful probe for clinical investigation of the activities of hepatobiliary uptake/efflux transporters in humans.

Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning.

Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which indicates the capacity of the entire body to process a drug is one of the most important parameters, many methods have been developed. However, there are still rooms to be improved for practical use in drug discovery research; "improving CL prediction accuracy" and "understanding the chemical structure of compounds in terms of pharmacokinetics". To improve those, this research proposes a multimodal learning method based on deep learning that takes not only the chemical structure of a drug but also rat CL as inputs. Good results were obtained compared with the conventional animal scale-up method; the geometric mean fold error was 2.68 and the proportion of compounds with prediction errors of 2-fold or less was 48.5%. Furthermore, it was found to be possible to infer the partial structure useful for CL prediction by a structure contributing factor inference method. The validity of these results of structural interpretation of metabolic stability was confirmed by chemists.