Working in collaboration with you, we will develop novel and high-precision techniques that can evaluate and predict the pharmacokinetics and safety of drug candidate compounds in humans.

Pharmaceuticals Research Center
Laboratory for Safety Assessment & ADME


Creating for Tomorrow

“ Prediction of Pharmacokinetics and Toxicity ”


安全性・動態研究部 博士(薬学)ユニットリーダー 画像

Hirotomo Shimizu

Principal Scientist
Laboratory for Safety Assessment & ADME

We are seeking novel techniques for assessing and predicting, with much higher accuracy, the pharmacokinetics and safety of drug candidate compounds in humans. We have been trying to predict human pharmacokinetics more accurately using a variety of methods. These include the application of allometric principles and the efficacy prediction with multiple techniques (such as a mechanistic PK/PD model). As for safety, we aim to identify candidate compounds with increased safety at the early stages of drug discovery research. To achieve this goal, we have been developing high-content imaging-based in vitro toxicity assays and novel in silico toxicity prediction models.

In recent years, we are excited about the potential of new approaches to drug discovery. These approaches were made possible through the diversification of drug modalities (including intermediate-molecular-weight compounds) and the advancement of new analytical technologies (such as machine learning and AI). We look forward to proposals from researchers who can work with us in tackling the challenging task of developing novel techniques for investigating the pharmacokinetics and safety of drug candidates.

Recruitment theme 

6.1 A new technology enabling the effective oral delivery of middle size-molecules including cyclic peptides
6.2 In vitro or in silico methodology elucidating the clearance mechanisms including proteolytic elimination and target-mediated drug disposition of middle size-molecules, e.g. cyclic peptides
6.3 In silico technologies for predicting on-target toxicities of drug-target proteins
  • Can be utilized for the go/no-go decision or prioritization of potential drug-target proteins.
  • Applies pathway (or mapping) analysis techniques created based on gene expression, protein-protein interaction, [and/or] text mining.
  • Includes the datasets or model built from multiple publicly-available databases.


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