Novel minimal physiologically-based model for the prediction of passive tubular reabsorption and renal excretion clearance
Open accessAbstract
Develop a minimal mechanistic model based on in vitro–in vivo extrapolation (IVIVE) principles to predict extent of passive tubular reabsorption. Assess the ability of the model developed to predict extent of passive tubular reabsorption (Freab) and renal excretion clearance (CLR) from in vitro permeability data and tubular physiological parameters. Model system parameters were informed by physiological data collated following extensive literature analysis. A database of clinical CLR was collated for 157 drugs. A subset of 45 drugs was selected for model validation; for those, Caco-2 permeability (Papp) data were measured under pH 6.5–7.4 gradient conditions and used to predict Freab and subsequently CLR. An empirical calibration approach was proposed to account for the effect of inter-assay/laboratory variation in Papp on the IVIVE of Freab. The 5-compartmental model accounted for regional differences in tubular surface area and flow rates and successfully predicted the extent of tubular reabsorption of 45 drugs for which filtration and reabsorption were contributing to renal excretion. Subsequently, predicted CLR was within 3-fold of the observed values for 87% of drugs in this dataset, with an overall gmfe of 1.96. Consideration of the empirical calibration method improved overall prediction of CLR (gmfe = 1.73 for 34 drugs in the internal validation dataset), in particular for basic drugs and drugs with low extent of tubular reabsorption. The novel 5-compartment model represents an important addition to the IVIVE toolbox for physiologically-based prediction of renal tubular reabsorption and CLR. Physiological basis of the model proposed allows its application in future mechanistic kidney models in preclinical species and human.
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A mechanistic model for passive tubular reabsorption was verified with a large dataset of drugs.
“For renal secretion, measurements in organic anion transporter-transfected human embryonic kidney cells successfully predicted renal clearance of 31 diverse drugs [42] while a mechanistic model for passive tubular reabsorption was verified with a large dataset of drugs [43].”
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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
Mathialagan, S., Piotrowski, M. A., Tess, D. A., Feng, B., Litchfield, J., & Varma, M. V. (2017) · Drug Metabolism and Disposition · doi:10.1124/dmd.116.074294 · Cited by 128 · see citing works
Maass, C., Stokes, C. L., Griffith, L. G., & Cirit, M. (2017) · Integrative Biology · doi:10.1039/c6ib00243a · Cited by 72 · see citing works
Development of a Dynamic Physiologically Based Mechanistic Kidney Model to Predict Renal Clearance
Huang, W., & Isoherranen, N. (2018) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12321 · Cited by 49 · see citing works
- Authors
- Scotcher, D., Jones, C., Rostami-Hodjegan, A., & Galetin, A.
- Affiliations
- University of Manchester (GB)
- AstraZeneca (United Kingdom) (GB)
- Corresponding author
- Galetin, Aleksandra
- Venue
- European Journal of Pharmaceutical Sciences
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 94
- Pages
- 59-71
- ISSN
- 0928-0987 (ISSN-L)
- PMC
- PMC5074076
- OpenAlex
- W2313644778
- Cited by
- 61
- References
- 1
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 4 · 2023: 4 · 2022: 3 · 2021: 9 · 2020: 9 · 2019: 6 · 2018: 5 · 2017: 5 · 2016: 3
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- European Journal of Pharmaceutical Sciences (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion, cc-by)
- Funders
- AstraZeneca
- University of Manchester
- Directorate for Biological Sciences
- Biotechnology and Biological Sciences Research Council (BB/J500379/1, 1156739)
- Topics
- Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmaceutical Science › Drug Solubulity and Delivery Systems
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- in vitro-in vivo extrapolation, physiologically based pharmacokinetic modeling, Caco-2 permeability, empirical calibration, inter-assay variation, basic drugs
- MeSH terms
- Clinical Trials as Topic, Pharmaceutical Preparations, Glomerular Filtration Rate, Humans, Kidney Tubules, Metabolic Clearance Rate, Models, Biological, Renal Elimination
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