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Cited by 28Pharmaceutical studies and practicesPharmacogenetics and Drug MetabolismAnimal testing and alternatives

The DOI matches its Crossref record; 75 of the 78 references it cites were found in a registry or an index, and 3 are not checked yet.

Estimation of Human Drug Clearance Using Multiexponential Techniques

Goteti, Kosalaram, Brassil, Patrick J., Good, Steven S., Garner, C. Edwin·2008·The Journal of Clinical Pharmacology
DOI 10.1177/0091270008320369
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Abstract

A multiexponential allometry (MA) method was developed to predict human drug clearance from preclinical data. Separate data sets containing clearances from human and preclinical species were chosen for the study. Human clearance was estimated using the MA technique according to the equation: CL = aBWb + cBWd, where CL is clearance in milliliters/minute, and a, b, c, and d are constants derived from preclinical pharmacokinetic data. Simple allometry (SA) gave the poorest prediction using any data set, and the percentage outliers remained larger than MA or monkey liver blood flow within 1.5-, 2-, and 3-fold error. Analysis of compounds common to both data sets suggested that MA could accurately predict human clearances within approximately 10% of 3-fold error. The analysis also showed that monkey is an important species for scaling, and MA is a better predictor of human clearance when the slope of SA is >0.7.

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Figures

Figure 1 Schematic depicting the strategy employed for evaluating each technique and data set. dg, dog; MA, multiexponential allometry; mk, monkey; MLBF, monkey liver blood flow; rt, rat; SA, simple allometry.
Figure 1 Schematic depicting the strategy employed for evaluating each technique and data set. dg, dog; MA, multiexponential allometry; mk, monkey; MLBF, monkey liver blood flow; rt, rat; SA, simple allometry.From the PDF
Figure 2 Organ weight (wt), flow rate, and volume of 2 major excretory organs (liver and kidney) across species (mouse, rat, rabbit, dog, monkey, and human). The slope of the relationship is given in parentheses. GFR, glomerular filtration rate.
Figure 2 Organ weight (wt), flow rate, and volume of 2 major excretory organs (liver and kidney) across species (mouse, rat, rabbit, dog, monkey, and human). The slope of the relationship is given in parentheses. GFR, glomerular filtration rate.From the PDF
Figure 3 Comparison of error in the estimation of human drug clearance by simple allometry (SA), monkey liver blood flow (MLBF) technique, and multiexponential allometry (MA) using 50 compounds from data set I and 102 compounds from data set II. Error was characterized as the percentages of outliers outside 1.5fold (0.67-1.5), 2-fold (0.5-2), and 3-fold (0.33-3) of actual measured values.
Figure 3 Comparison of error in the estimation of human drug clearance by simple allometry (SA), monkey liver blood flow (MLBF) technique, and multiexponential allometry (MA) using 50 compounds from data set I and 102 compounds from data set II. Error was characterized as the percentages of outliers outside 1.5fold (0.67-1.5), 2-fold (0.5-2), and 3-fold (0.33-3) of actual measured values.From the PDF
Figure 4 Comparison of error in the estimation of human drug clearance by simple allometry (SA), monkey liver blood flow (MLBF) technique, and multiexponential allometry (MA) using 33 compounds common to both data sets I and II. Error was characterized as with the percentages of outliers outside 1.5-fold (0.671.5), 2-fold (0.5-2), and 3-fold (0.33-3) of actual measured values. Comparison of SA, MLBF technique, and MA with the percentages of outliers outside 1.5-fold (0.67-1.5), 2-fold (0.5-2), and 3fold (0.33-3).
Figure 4 Comparison of error in the estimation of human drug clearance by simple allometry (SA), monkey liver blood flow (MLBF) technique, and multiexponential allometry (MA) using 33 compounds common to both data sets I and II. Error was characterized as with the percentages of outliers outside 1.5-fold (0.671.5), 2-fold (0.5-2), and 3-fold (0.33-3) of actual measured values. Comparison of SA, MLBF technique, and MA with the percentages of outliers outside 1.5-fold (0.67-1.5), 2-fold (0.5-2), and 3fold (0.33-3).From the PDF
Figure 5 Species selection for 33 compounds common to both data sets I and II. dg, dog; mk, monkey; rt, rat.
Figure 5 Species selection for 33 compounds common to both data sets I and II. dg, dog; mk, monkey; rt, rat.From the PDF
Figure 6 Comparison of simple allometry (SA), monkey liver blood flow (MLBF) technique, and multiexponential allometry (MA) on 27 compounds (with and without monkey data from data set I).
Figure 6 Comparison of simple allometry (SA), monkey liver blood flow (MLBF) technique, and multiexponential allometry (MA) on 27 compounds (with and without monkey data from data set I).From the PDF
Figure 7 Error analysis of data set I using simple allometry (SA) and multiexponential allometry (MA) grouped by allometric slopes.
Figure 7 Error analysis of data set I using simple allometry (SA) and multiexponential allometry (MA) grouped by allometric slopes.From the PDF

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Research works printed but not linked: 1

  1. Mahmood I, 2005, Interspecies Pharmacokinetic Scaling

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Metadata

Authors
Goteti, K., Brassil, P. J., Good, S. S., & Garner, C. E.
Affiliations
AstraZeneca (United States) (US)
Corresponding author
Goteti, Kosalaram
ORCID
Venue
The Journal of Clinical Pharmacology
Publisher
Wiley
Published
2008-10
Volume
48
Issue
10
Pages
1226-1236
ISSN
  • 0021-9754 (ISSN-L)
  • 0091-2700
  • 1552-4604
DOI
10.1177/0091270008320369
OpenAlex
W2126304490
Cited by
28
References
74
Citations by year
2026: 1 · 2025: 2 · 2024: 2 · 2020: 1 · 2019: 1 · 2018: 1 · 2016: 1 · 2014: 1 · 2013: 2 · 2012: 5
FWCI
2.97
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 1618 d)
Locations
  • The Journal of Clinical Pharmacology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
  • Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
Keywords
allometric scaling, clearance prediction, interspecies extrapolation
MeSH terms
Animals, Haplorhini, Body Weight, Data Interpretation, Statistical, Drug Evaluation, Preclinical, Pharmaceutical Preparations, Humans, Liver Circulation, Models, Biological, Pharmacokinetics, Species Specificity

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CoChat. (2026). Citation check: Estimation of Human Drug Clearance Using Multiexponential Techniques. https://app.cochat.ai/papers/10.1177/0091270008320369
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Goteti, K., Brassil, P. J., Good, S. S., & Garner, C. E. (2008). Estimation of Human Drug Clearance Using Multiexponential Techniques. The Journal of Clinical Pharmacology. https://doi.org/10.1177/0091270008320369

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.