Quantitative structure-pharmacokinetic relationships for hemodialysis clearance

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K16094
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 47060:Clinical pharmacy-related
• Research Institution: Nihon University
• Principal Investigator: AOYAMA Takahiko 日本大学, 薬学部, 講師 (70384633)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: 血液浄化療法 / 透析 / 定量的構造－薬物動態相関解析

Outline of Final Research Achievements

In this study, a mathematical model for predicting dialysis clearance (CLHD) from molecular structures was developed by quantitative structure-pharmacokinetic correlation analysis. 47 compounds and 457 CLHD values were included. 133 molecular descriptors representing molecular structures and physicochemical properties were calculated from the two-dimensional structural data of 47 compounds. A CLHD prediction model was constructed by machine learning with CLHD as the objective variable and blood flow, dialysate flow, dialysate membrane, and molecular descriptors as explanatory variables. The CLHD prediction model developed in this study predicts CLHD with high accuracy and is expected to be applied to clinical practice.

Free Research Field

臨床薬物動態学

Academic Significance and Societal Importance of the Research Achievements

血液浄化療法は、血液中の病因物質を除去する治療法であり、慢性腎不全、薬物中毒をはじめ様々な病態において施行されるが、治療上必要な薬物も除去される場合がある。そこで、血液浄化療法施行患者の薬物投与量決定を行うため、血液浄化療法に薬物除去について定量的な予測方法が求められている。本研究は、血液浄化療法による薬物の除去と、薬物の分子構造との関係を定量的に表し、分子構造から血液浄化療法による薬物除去を予測することを可能とした。