Mechanistic Approaches to Volume of Distribution Predictions: Understanding the Processes
Abstract
Purpose To use recently developed mechanistic equations to predict tissue-to-plasma water partition coefficients (Kpus), apply these predictions to whole body unbound volume of distribution at steady state (Vu(ss)) determinations, and explain the differences in the extent of drug distribution both within and across the various compound classes. Materials and methods Vu(ss) values were predicted for 92 structurally diverse compounds in rats and 140 in humans by two approaches. The first approach incorporated Kpu values predicted for 13 tissues whereas the second was restricted to muscle. Results The prediction accuracy was good for both approaches in rats and humans, with 64-78% and 82-92% of the predicted Vu(ss) values agreeing with in vivo data to within factors of +/-2 and 3, respectively. Conclusions Generic distribution processes were identified as lipid partitioning and dissolution where the former is higher for lipophilic unionised drugs. In addition, electrostatic interactions with acidic phospholipids can predominate for ionised bases when affinities (reflected by binding to constituents within blood) are high. For acidic drugs albumin binding dominates when plasma protein binding is high. This ability to explain drug distribution and link it to physicochemical properties can help guide the compound selection process.
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The Kp values for each tissue can be calculated by three mechanistic models.
“The K p values for each tissue can be calculated by three mechanistic models (Table 2). 42 , 43 , 44”
From Does the choice of applied physiologically‐based pharmacokinetics platform matter? A case study on simvastatin disposition and drug–drug interaction (2022) · cites this paper as [44]
Cites
A Novel Strategy for Physiologically Based Predictions of Human Pharmacokinetics
Jones, H. M., Parrott, N., Jorga, K., & Lav??, T. (2006) · Clinical Pharmacokinetics · doi:10.2165/00003088-200645050-00006 · Cited by 352 · see citing works
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Physiologically-Based Pharmacokinetics in Drug Development and Regulatory Science
Rowland, M., Peck, C., & Tucker, G. (2011) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010510-100540 · Cited by 662 · see citing works
Self-citationBasic Concepts in Physiologically Based Pharmacokinetic Modeling in Drug Discovery and Development
Jones, H., & Rowland‐Yeo, K. (2013) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2013.41 · Cited by 626 · see citing works
Rostami-Hodjegan, A. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2012.65 · Cited by 331 · see citing works
Eissing, T. (2011) · Frontiers in Physiology · doi:10.3389/fphys.2011.00004 · Cited by 204 · see citing works
Poulin, P., Jones, R. D. O., Jones, H. M., Gibson, C. R., Rowland, M., Chien, J. Y., Ring, B. J., Adkison, K. K., Ku, M. S., He, H., et al. (2011) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.22550 · Cited by 174 · see citing works
Self-citationDavies, M., Jones, R. D. O., Grime, K., Jansson-Löfmark, R., Fretland, A. J., Winiwarter, S., Morgan, P., & McGinnity, D. F. (2020) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2020.03.004 · Cited by 123 · see citing works
Jones, H. M., Gardner, I. B., Collard, W. T., Stanley, P. J., Oxley, P., Hosea, N. A., Plowchalk, D., Gernhardt, S., Lin, J., Dickins, M., et al. (2011) · Clinical Pharmacokinetics · doi:10.2165/11539680-000000000-00000 · Cited by 110 · see citing works
Heikkinen, A. T., Baneyx, G., Caruso, A., & Parrott, N. (2012) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2012.06.013 · Cited by 76 · see citing works
Species Differences in Distribution and Prediction of Human Vss from Preclinical Data
Berry, L. M., Li, C., & Zhao, Z. (2011) · Drug Metabolism and Disposition · doi:10.1124/dmd.111.040766 · Cited by 69 · see citing works
Tissue lipids and drug distribution: Dog versus rat
Rodgers, T., Jones, H. M., & Rowland, M. (2012) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.23285 · Cited by 31 · see citing works
Self-citationPrieto Garcia, L., Lundahl, A., Ahlström, C., Vildhede, A., Lennernäs, H., & Sjögren, E. (2022) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12837 · Cited by 21 · see citing works
Schmitt, M. V., Reichel, A., Liu, X., Fricker, G., & Lienau, P. (2021) · Drug Metabolism and Disposition · doi:10.1124/dmd.120.000161 · Cited by 19 · see citing works
Korzekwa, K., & Nagar, S. (2017) · Pharmaceutical Research · doi:10.1007/s11095-016-2089-8 · Cited by 18 · see citing works
- Authors
- Rodgers, T., & Rowland, M.
- Affiliations
- University of Manchester (GB)
- Corresponding author
- Rodgers, Trudy
- Venue
- Pharmaceutical Research
- Published
- 2007-03-20
- Volume
- 24
- Issue
- 5
- Pages
- 918-933
- ISSN
- 0724-8741 (ISSN-L)
- 1573-904X
- OpenAlex
- W2028165836
- Cited by
- 420
- References
- 1
- Citations by year
- 2026: 19 · 2025: 27 · 2024: 26 · 2023: 30 · 2022: 35 · 2021: 30 · 2020: 25 · 2019: 29 · 2018: 20 · 2017: 14 · 2016: 21 · 2015: 20 · 2014: 12 · 2013: 23 · 2012: 26
- FWCI
- 16.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pharmaceutical Research (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
- Keywords
- volume of distribution, tissue-to-plasma partition coefficient, drug distribution, lipid partitioning, dissolution, albumin binding, plasma protein binding, electrostatic interactions, phospholipids, physicochemical properties of drugs, rats, human
- MeSH terms
- Algorithms, Animals, Computer Simulation, Pharmaceutical Preparations, Humans, Kinetics, Models, Biological, Technology, Pharmaceutical, Tissue Distribution, Drug Design, Rats
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