Application of a hemodialysis clearance prediction model using quantitative structure‐pharmacokinetic relationship analysis. Issue 6 (3rd February 2020)

• Record Type: Journal Article
• Authors: Suzuki, Daisuke; Aoyama, Takahiko; Nakajima, Junki; Miyamoto, Aoi; Ako, Yumina; Kikkawa, Akiyoshi; Hiraki, Kouichi; Matsumoto, Yoshiaki
• Is Part Of: Therapeutic apheresis and dialysis. Volume 24:Issue 6(2020)
• Page Start: 655
• Page End: 667
• Publication Date: 2020-02-03
• Subjects: clearance -- hemodialysis -- quantitative structure‐pharmacokinetic relationship -- nonlinear mixed effect model -- logarithm of the brain/blood partition coefficient; Hemapheresis -- Periodicals; Dialysis -- Periodicals; Blood Component Removal -- Periodicals; Renal Dialysis -- Periodicals; Hémaphérèse -- Périodiques; Dialyse -- Périodiques; Sang -- Collecte et conservation -- Périodiques; 616
• DOI: 10.1111/1744-9987.13472
• Languages: English
• ISSNs: 1744-9979

Abstract

Abstract: Hemodialysis (HD) is a method used to remove biogenic substances or blood components that cause disease and some drugs used by patients to treat their diseases. Therefore, dosing schedule must be planned according to HD clearance (CLHD ) when medical treatment is provided to patients receiving HD. We aimed to clarify the physical properties (eg, octanol‐water partition coefficient and molecular electronegativity) or pharmacokinetic parameters (eg, volume of distribution) of compounds affecting CLHD and to construct a mathematical model to predict CLHD . The analysis covered individual CLHD data for nine compounds from the literature. The molecular descriptors which are physical properties or pharmacokinetic parameters were calculated using the structural formula of each compound, and searched for factors related to CLHD among the calculated 148 molecular descriptors. Nonlinear mixed‐effects model analysis with CLHD as objective variable and molecular descriptors as explanatory variable was conducted to examine the factor affecting CLHD and develop a model for predicting CLHD . The logarithm of the brain/blood partition coefficient was detected as a factor affecting CLHD . The predictive accuracy of CLHD using the constructed mathematical model with the logarithm of the brain/blood partition coefficient as explanatory variable was adequate.