Development of a Dynamic Physiologically Based Mechanistic Kidney Model to Predict Renal Clearance

Huang, Weize; Isoherranen, Nina

doi:10.1002/psp4.12321

Cited by 49Computational Drug Discovery MethodsDrug Transport and Resistance MechanismsPharmacogenetics and Drug Metabolism

Development of a Dynamic Physiologically Based Mechanistic Kidney Model to Predict Renal Clearance

Open access

Huang, Weize, Isoherranen, Nina·2018·CPT: Pharmacometrics & Systems Pharmacology

DOI 10.1002/psp4.12321PMC PMC6157663

Open this paper in CoChat — free account Open PDF

Abstract

Renal clearance is usually predicted via empirical approaches including quantitative structure activity relationship and allometric scaling. Recently, mechanistic prediction approaches using in silico kidney models have been proposed. However, empirical scaling factors are typically used to adjust for either passive diffusion or active secretion, to acceptably predict renal clearances. The goal of this study was to establish a renal clearance simulation tool that allows prediction of renal clearance (filtration and pH‐dependent passive reabsorption) from in vitro permeability data. A 35‐compartment physiologically based mechanistic kidney model was developed based on human physiology. The model was verified using 46 test compounds, including neutrals, acids, bases, and zwitterions. The feasibility of incorporating active secretion and pH‐dependent bidirectional passive diffusion into the model was demonstrated using para‐aminohippuric acid (PAH), cimetidine, memantine, and salicylic acid. The developed model enables simulation of renal clearance from in vitro permeability data, with predicted renal clearance within twofold of observed for 87% of the test drugs.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1002/psp4.12321

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Renal and biliary elimination involving active transport are challenging to model mechanistically from in-vitro data, and prediction of human pharmacokinetics for transported molecules is difficult.
“Renal [39, 40] and biliary [41] elimination involving active transport are also challenging to model mechanistically from in-vitro data, and, in general, prediction of human pharmacokinetics for transported molecules is difficult because absorption, distribution and elimination can all be affected [18].”
From Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies (2019) · cites this paper as [40]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Correlation between oral drug absorption in humans and apparent drug permeability coefficients in human intestinal epithelial (Caco-2) cells
Artursson, P., & Karlsson, J. E. (1991) · Biochemical and Biophysical Research Communications · doi:10.1016/0006-291x(91)91647-u · Cited by 1,899 · see citing works
Caco-2 monolayers in experimental and theoretical predictions of drug transport1PII of original article: S0169-409X(96)00415-2. The article was originally published in Advanced Drug Delivery Reviews 22 (1996) 67–84.1
Artursson, P., Palm, K., & Luthman, K. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00128-9 · Cited by 1,549 · see citing works
In Vitro Permeability Across Caco-2 Cells (Colonic) Can Predict In Vivo (Small Intestinal) Absorption in Man—Fact or Myth
Yee, S. (1997) · Pharmaceutical Research · doi:10.1023/a:1012102522787 · Cited by 1,035 · see citing works
MDCK (Madin-Darby Canine Kidney) Cells: A Tool for Membrane Permeability Screening
Irvine, J. D., Takahashi, L. H., Lockhart, K., Cheong, J., Tolan, J. W., Selick, H. E., & Grove, J. R. (1999) · Journal of Pharmaceutical Sciences · doi:10.1021/js9803205 · Cited by 984 · see citing works
DRUG-DRUG INTERACTIONS FOR UDP-GLUCURONOSYLTRANSFERASE SUBSTRATES: A PHARMACOKINETIC EXPLANATION FOR TYPICALLY OBSERVED LOW EXPOSURE (AUCI/AUC) RATIOS
Williams, J. A., Hyland, R., Jones, B., Smith, D. A., Hurst, S. I., Goosen, T. C., Peterkin, V. C., Koup, J. R., & Ball, S. E. (2004) · Drug Metabolism and Disposition · doi:10.1124/dmd.104.000794 · Cited by 929 · see citing works
Optimized conditions for prediction of intestinal drug permeability using Caco-2 cells
Yamashita, S., Furubayashi, T., Kataoka, M., Sakane, T., Sezaki, H., & Tokuda, H. (2000) · European Journal of Pharmaceutical Sciences · doi:10.1016/s0928-0987(00)00076-2 · Cited by 570 · see citing works
Correlating Partitioning and Caco-2 Cell Permeability of Structurally Diverse Small Molecular Weight Compounds
Yazdanian, M. A., Glynn, S. L., Wright, J. L., & Hawi, A. A. (1998) · Pharmaceutical Research · doi:10.1023/a:1011930411574 · Cited by 454 · see citing works
Development of a new permeability assay using low‐efflux MDCKII cells
Di, L., Whitney‐Pickett, C., Umland, J. P., Zhang, H., Zhang, X., Gebhard, D. F., Lai, Y., Federico, J. J., Davidson, R. E., Smith, R., et al. (2011) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.22674 · Cited by 328 · see citing works
Correlation Between Oral Drug Absorption in Humans, and Apparent Drug Permeability in TC-7 Cells, A Human Epithelial Intestinal Cell Line: Comparison with the Parental Caco-2 Cell Line
Grès, M., Julian, B., Bourrié, M., et al. (1998) · Pharmaceutical Research · doi:10.1023/a:1011919003030 · Cited by 242 · see citing works
Compilation of 222 drugs’ plasma protein binding data and guidance for study designs
Zhang, F., Xue, J., Shao, J., & Li, J. (2011) · Drug Discovery Today · doi:10.1016/j.drudis.2011.12.018 · Cited by 220 · see citing works
Physicochemical Determinants of Human Renal Clearance
Varma, M. V. S., Feng, B., Obach, R. S., Troutman, M. D., Chupka, J., Miller, H., & El‐Kattan, A. F. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm900403j · Cited by 214 · see citing works
The Application of Allometric Scaling Principles to Predict Pharmacokinetic Parameters across Species
Huang, Q., & Riviere, J. E. (2014) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1517/17425255.2014.934671 · Cited by 149 · see citing works
p-Aminohippuric Acid Transport at Renal Apical Membrane Mediated by Human Inorganic Phosphate Transporter NPT1
Uchino, H., Tamai, I., Yamashita, K., Minemoto, Y., Sai, Y., Yabuuchi, H., Miyamoto, K., Takeda, E., & Tsuji, A. (2000) · Biochemical and Biophysical Research Communications · doi:10.1006/bbrc.2000.2407 · Cited by 148 · see citing works
Contribution of Multidrug Resistance Protein 2 (MRP2/ABCC2) to the Renal Excretion of p-aminohippurate (PAH) and Identification of MRP4 (ABCC4) as a Novel PAH Transporter
Smeets, P. H. E., van Aubel, R. A. A. A. M. H., Wouterse, A. C., et al. (2004) · Journal of the American Society of Nephrology · doi:10.1097/01.asn.0000143473.64430.ac · Cited by 146 · see citing works
Metformin and cimetidine: Physiologically based pharmacokinetic modelling to investigate transporter mediated drug–drug interactions
Burt, H. J., Neuhoff, S., Almond, L. M., et al. (2016) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2016.03.020 · Cited by 117 · see citing works
Development of a 7-Day, 96-Well Caco-2 Permeability Assay with High-Throughput Direct UV Compound Analysis
Alsenz, J., & Haenel, E. (2003) · Pharmaceutical Research · doi:10.1023/b:pham.0000008043.71001.43 · Cited by 110 · see citing works
Interspecies Scaling and Prediction of Human Clearance: Comparison of Small- and Macro-Molecule Drugs
Huh, Y., Smith, D. E., & Rose Feng, M. (2011) · Xenobiotica · doi:10.3109/00498254.2011.598582 · Cited by 98 · see citing works
Prediction of Human Renal Clearance from Preclinical Species for a Diverse Set of Drugs That Exhibit Both Active Secretion and Net Reabsorption
Paine, S. W., Ménochet, K., Denton, R., McGinnity, D. F., & Riley, R. J. (2011) · Drug Metabolism and Disposition · doi:10.1124/dmd.110.037267 · Cited by 97 · see citing works
Pharmacokinetics of memantine in rats and mice
Beconi, M. G., Howland, D., Park, L., et al. (2011) · PLoS Currents · doi:10.1371/currents.rrn1291 · Cited by 87 · see citing works
The International Transporter Consortium: A Collaborative Group of Scientists From Academia, Industry, and the FDA
Sm, H., Zhang, L., & Giacomini, K. M. (2009) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2009.236 · Cited by 82 · see citing works
Effects of Hypothermia on the Disposition of Morphine, Midazolam, Fentanyl, and Propofol in Intensive Care Unit Patients
Bjelland, T. W., Klepstad, P., Haugen, B. O., Nilsen, T., & Dale, O. (2012) · Drug Metabolism and Disposition · doi:10.1124/dmd.112.045567 · Cited by 78 · see citing works
Salicylate clearance, the resultant of protein binding and metabolism
Furst, D. E., Tozer, T. N., & Melmon, K. L. (1979) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt1979263380 · Cited by 76 · see citing works
Accounting for Transporters in Renal Clearance: Towards a Mechanistic Kidney Model (Mech KiM)
Neuhoff, S., Gaohua, L., Burt, H. J., Jamei, M., Li, L., Tucker, G. T., & Rostami‐Hodjegan, A. (2013) · AAPS advances in the pharmaceutical sciences series · doi:10.1007/978-1-4614-8229-1_7 · Cited by 74 · see citing works
Molecular Model of the Microvillar Cytoskeleton and Organization of the Brush Border
Brown, J. W., & McKnight, C. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009406 · Cited by 65 · see citing works
Delineating the Role of Various Factors in Renal Disposition of Digoxin through Application of Physiologically Based Kidney Model to Renal Impairment Populations
Scotcher, D., Jones, C. R., Galetin, A., & Rostami‐Hodjegan, A. (2017) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.116.237438 · Cited by 65 · see citing works
Influence of urine pH and urinary flow on the renal excretion of memantine
Freudenthaler, Meineke, Schreeb, et al. (1998) · British Journal of Clinical Pharmacology · doi:10.1046/j.1365-2125.1998.00819.x · Cited by 63 · see citing works
Novel minimal physiologically-based model for the prediction of passive tubular reabsorption and renal excretion clearance
Scotcher, D., Jones, C., Rostami-Hodjegan, A., & Galetin, A. (2016) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2016.03.018 · Cited by 61 · see citing works
Prediction of Renal Transporter Mediated Drug-Drug Interactions for Pemetrexed Using Physiologically Based Pharmacokinetic Modeling
Posada, M. M., Bacon, J. A., Schneck, K. B., et al. (2014) · Drug Metabolism and Disposition · doi:10.1124/dmd.114.059618 · Cited by 59 · see citing works
Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor
Watanabe, R., Ohashi, R., Esaki, T., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-55325-1 · Cited by 55 · see citing works
Predicting tubular reabsorption with a human kidney proximal tubule tissue-on-a-chip and physiologically-based modeling
Sakolish, C., Chen, Z., Dalaijamts, C., Mitra, K., Liu, Y., Fulton, T., Wade, T. L., Kelly, E. J., Rusyn, I., & Chiu, W. A. (2019) · Toxicology in Vitro · doi:10.1016/j.tiv.2019.104752 · Cited by 53 · see citing works
A rapid computational filter for predicting the rate of human renal clearance
Paine, S. W., Barton, P., Bird, J., et al. (2010) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2010.10.003 · Cited by 51 · see citing works
Drug Delivery Studies in Caco-2 Monolayers. Synthesis, Hydrolysis, and Transport of O-Cyclopropane Carboxylic Acid Ester Prodrugs of Various β-Blocking Agents
Hovgaard, L., BRØNDSTED, H. V., Buur, A., et al. (1995) · Pharmaceutical Research · doi:10.1023/a:1016204602471 · Cited by 50 · see citing works
THE EXCRETION OF SALICYLATE
Macpherson, C. R., Milne, M. D., & Evans, B. M. (1955) · British Journal of Pharmacology and Chemotherapy · doi:10.1111/j.1476-5381.1955.tb00109.x · Cited by 50 · see citing works
Emerging Role of Organ‐on‐a‐Chip Technologies in Quantitative Clinical Pharmacology Evaluation
Isoherranen, N., Madabushi, R., & Huang, S. M. (2019) · Clinical and Translational Science · doi:10.1111/cts.12627 · Cited by 50 · see citing works
Self-citation
In Vitro–In Vivo Extrapolation Method to Predict Human Renal Clearance of Drugs
Kunze, A., Huwyler, J., Poller, B., Gutmann, H., & Camenisch, G. (2014) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.23851 · Cited by 48 · see citing works
In Silico Renal Clearance Model Using Classical Volsurf Approach
Doddareddy, M. R., Cho, Y. S., Koh, H. Y., et al. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci0503309 · Cited by 41 · see citing works
Investigating Transporter‐Mediated Drug‐Drug Interactions Using a Physiologically Based Pharmacokinetic Model of Rosuvastatin
Wang, Q., Zheng, M., & Leil, T. A. (2017) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12168 · Cited by 40 · see citing works
A Hierarchical QSAR Model for Urinary Excretion of Drugs in Humans as a Predictive Tool for Biotransformation
Manga, N., Duffy, J. C., Rowe, P., et al. (2003) · QSAR & Combinatorial Science · doi:10.1002/qsar.200390021 · Cited by 33 · see citing works
Contribution of MATE1 to Renal Secretion of the NMDA Receptor Antagonist Memantine
Müller, F., Weitz, D., Derdau, V., et al. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00179 · Cited by 31 · see citing works
Secretory transport of p-aminohippuric acid across intestinal epithelial cells in Caco-2 cells and isolated intestinal tissue
Naruhashi, K., Tamai, I., Sai, Y., et al. (2001) · Journal of Pharmacy and Pharmacology · doi:10.1211/0022357011775217 · Cited by 30 · see citing works
Relationship Between the Urinary Excretion Mechanisms of Drugs and Their Physicochemical Properties
Ito, S., Ando, H., Ose, A., et al. (2013) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.23599 · Cited by 29 · see citing works
Physiologically based pharmacokinetic–pharmacodynamic modeling to predict concentrations and actions of sodium‐dependent glucose transporter 2 inhibitor canagliflozin in human intestines and renal tubules
Mori, K., Saito, R., Nakamaru, Y., Shimizu, M., & Yamazaki, H. (2016) · Biopharmaceutics & Drug Disposition · doi:10.1002/bdd.2040 · Cited by 28 · see citing works
More Power to OATP1B1: An Evaluation of Sample Size in Pharmacogenetic Studies Using a Rosuvastatin PBPK Model for Intestinal, Hepatic, and Renal Transporter‐Mediated Clearances
Riedmaier, A. E., Burt, H. J., Abduljalil, K., & Neuhoff, S. (2016) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.669 · Cited by 25 · see citing works
Relationship between structure and function in renal proximal tubule
Welling, L. W., & Welling, D. J. (1988) · Journal of Electron Microscopy Technique · doi:10.1002/jemt.1060090205 · Cited by 24 · see citing works
Sampling Site Has a Critical Impact on Physiologically Based Pharmacokinetic Modeling
Huang, W., & Isoherranen, N. (2019) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.119.262154 · Cited by 23 · see citing works
Self-citation
Organic Anion Transporter 1 Is Inhibited by Multiple Mechanisms and Shows a Transport Mode Independent of Exchange
Hotchkiss, A. G., Gao, T., Khan, U., Berrigan, L., Li, M., Ingraham, L., & Pelis, R. M. (2015) · Drug Metabolism and Disposition · doi:10.1124/dmd.115.065748 · Cited by 17 · see citing works

Flagged first, then by citations

Research works printed but not linked: 1

[38]Rowland, M.. & Tozer, T. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (Lippincott Williams & Wilkins, New York, NY, 2012).

Sorted by citations

Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies
Miller, N. A., Reddy, M. B., Heikkinen, A. T., Lukacova, V., & Parrott, N. (2019) · Clinical Pharmacokinetics · doi:10.1007/s40262-019-00741-9 · Cited by 142 · see citing works

Authors: Huang, W., & Isoherranen, N.
Affiliations: University of Washington (US)
Corresponding author: Isoherranen, Nina
ORCID: Huang, Weize
Isoherranen, Nina
Venue: CPT: Pharmacometrics & Systems Pharmacology
Publisher: Wiley
Published: 2018-08-11
Volume: 7
Issue: 9
Pages: 593-602
ISSN: 2163-8306 (ISSN-L)
DOI: 10.1002/psp4.12321
PMC: PMC6157663
OpenAlex: W2884215650
Cited by: 49
References: 46
Citations by year: 2026: 4 · 2025: 3 · 2024: 5 · 2023: 5 · 2022: 4 · 2021: 8 · 2020: 10 · 2019: 8 · 2018: 1 · 2017: 1
FWCI: 5.05
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/4.0/ (vor)
Locations: CPT Pharmacometrics & Systems Pharmacology (publishedVersion, cc-by-nc)
Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Institutes of Health (P01 DA032507)
Topics: Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
Keywords: renal clearance, physiologically based pharmacokinetic modeling, kidney model, active secretion, in vitro permeability, drug clearance prediction, para-aminohippuric acid, cimetidine, memantine, salicylic acid
MeSH terms: Humans, Kidney, Metabolic Clearance Rate, Models, Biological

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Development of a Dynamic Physiologically Based Mechanistic Kidney Model to Predict Renal Clearance

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks