Quantitative Structure-Pharmacokinetic Relationships for the Prediction of Renal Clearance in Humans

Abstract

Renal clearance (CL R ), a major route of elimination for many drugs and drug metabolites, represents the net result of glomerular filtration, active secretion and reabsorption, and passive reabsorption. The aim of this study was to develop quantitative structurepharmacokinetic relationships (QSPKR) to predict CL R of drugs or drug-like compounds in humans. Human CL R data for 382 compounds were obtained from the literature.Step-wise multiple linear regression was used to construct QSPKR models for training sets and their predictive performance was evaluated using internal validation (leave-one-out method). All qualified models were validated externally using test sets. QSPKR models were also constructed for compounds in accordance with their 1) net elimination pathways (net secretion, extensive net secretion, net reabsorption, and extensive net reabsorption), 2) net elimination clearances (net secretion clearance, CL SEC ; or net reabsorption clearance, CL REAB ), 3) ion status, and 4) substrate/inhibitor specificity for renal transporters. We were able to predict 1) CL REAB Moreover, compounds undergoing net reabsorption/extensive net reabsorption predominantly belonged to Biopharmaceutics Drug Disposition Classification System classes 1 and 2. In conclusion, constructed parsimonious QSPKR models can be used to predict CL R of compounds that 1) undergo net reabsorption/extensive net reabsorption and 2) are substrates and/or inhibitors of human renal transporters.

Delineating the Role of Various Factors in Renal Disposition of Digoxin through Application of Physiologically Based Kidney Model to Renal Impairment Populations

“…There are mixed reports from clinical studies suggesting possible urine flow-dependent CL R of digoxin (Steiness, 1974; Halkin et al, 1975; Steiness et al, 1982) and potential role of passive tubular reabsorption in vivo (although minor). Although prediction of tubular reabsorption clearance from physicochemical properties was recently reported, the validated quantitative structure-pharmacokinetic property relationship model does not allow for prediction of passive diffusion clearance (CL PD ) values in different regions of the nephron (Dave and Morris, 2015). Recently, a static model for prediction of tubular reabsorption was reported that considered regional differences in physiologic parameters for prediction of fraction reabsorbed; this model has not been validated in the MechKiM using appropriate range of model compounds (Scotcher et al, 2016c).…”

Key to Opening Kidney for In Vitro–In Vivo Extrapolation Entrance in Health and Disease: Part I: In Vitro Systems and Physiological Data

“…Passive renal tubular reabsorption of drugs has been correlated with drug lipophilicity and other physico-chemical properties (23,24). Recently, a quantitative structurepharmacokinetic relationship (QSPKR) model has been developed for prediction of reabsorption clearance, although prior information on the dominant process (reabsorption or secretion) and/or Biopharmaceutical Drug Disposition and Classification System (BDDCS) class is required (25).…”

A quantitative threshold for high/low extent of urinary excretion of compounds in humans

“…Regression models were evaluated for (1) all 834 compounds, (2) compounds in the ROC‐HUE and ROC‐LUE groups and (3) compounds with unique ion status – anions, cations, zwitterions and neutral compounds. A regression model with R 2 adj > 0.5 would be considered to have strong predictive power .…”

“…(1) The measured LogP (MLogP) and (2) the measured LogD (pH=7.4) (MLogD) were obtained from Benet et al [7]. We further obtained (3) calculated LogP (cLogP), and (4) polar surface area (PSA), (5) ACD labs cLogP, (6) ACD labs cLogD (pH=7.4) , (7) pKa, (8) ionization status (anions, cations, zwitterions and neutral) at a pH of 7.4, (9) water accessible surface area, (10) polarizability, (11) molar refractivity, (12) molecular volume, (13) molecular weight, (14) number of free rotatable bonds, (15) number of rings, (16) dreiding energy, (17) minimum projection area and (18) maximum projection areas for all compounds from a ChemSpider search and share chemistry online database [13] and a ChEMBL database [14]. 'cLogP' descriptor values (3) obtained from the ChemSpider and ChEMBL databases were calculated using ChemAxon physicochemical properties calculator program.…”

“…All 18 physicochemical properties were evaluated to determine a threshold value that could be used to distinguish between compounds in the ROC-LUE and ROC-HUE groups using the ROC curve analysis for a priori determination of the extent of urinary excretion. In the absence of a threshold value, statistically significantly different medians could potentially be used as two unique A regression model with R 2 adj > 0.5 would be considered to have strong predictive power [18].…”