Projecting human pharmacokinetics of therapeutic antibodies from nonclinical data: What have we learned?

Deng, Rong; Iyer, Suhasini; Theil, Frank-Peter; Mortensen, Deborah L.; Fielder, Paul J.; Prabhu, Saileta

doi:10.4161/mabs.3.1.13799

Cited by 288Monoclonal and Polyclonal Antibodies ResearchBiosimilars and Bioanalytical MethodsPharmaceutical studies and practices

Projecting human pharmacokinetics of therapeutic antibodies from nonclinical data: What have we learned?

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Deng, Rong, Iyer, Suhasini, Theil, Frank-Peter, Mortensen, Deborah L., Fielder, Paul J., Prabhu, Saileta·2011·mAbs

DOI 10.4161/mabs.3.1.13799PMC PMC3038012

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Abstract

The pharmacokinetics (PK) of therapeutic antibodies is determined by target and non-target mediated mechanisms. These antibody-specific factors need to be considered during prediction of human PK based upon preclinical information. Principles of allometric scaling established for small molecules using data from multiple animal species cannot be directly applied to antibodies. Here, different methods for projecting human clearance (CL) from animal PK data for 13 therapeutic monoclonal antibodies (mAbs) exhibiting linear PK over the tested dose ranges were examined: simple allometric scaling (CL versus body weight), allometric scaling with correction factors, allometric scaling based on rule of exponent and scaling from only cynomolgus monkey PK data. A better correlation was obtained between the observed human CL and the estimated human CL based on cynomolgus monkey PK data and an allometric scaling exponent of 0.85 for CL than other scaling approaches. Human concentration-time profiles were also reasonably predicted from the cynomolgus monkey data using species-invariant time method with a fixed exponent of 0.85 for CL and 1.0 for volume of distribution. In conclusion, we expanded our previous work and others and further confirmed that PK from cynomolgus monkey alone can be successfully scaled to project human PK profiles within linear range using simplify allometry and Dedrick plots with fixed exponent.

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Using simplified allometry, the volume of distribution scales proportionally with body weight and clearance often follows an exponent between 0.8-0.9 along with body weight scaling.
“Using this method, the volume of distribution scales proportionally with body weight and clearance often follows an exponent between 0.8–0.9 along with body weight scaling.66”
From Tutorial on monoclonal antibody pharmacokinetics and its considerations in early development (2018) · cites this paper as [66]

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Authors: Deng, R., Iyer, S., Theil, F.-P., Mortensen, D. L., Fielder, P. J., & Prabhu, S.
Affiliations: Genentech (US)
Corresponding author: Deng, Rong
ORCID: Deng, Rong
Iyer, Suhasini
Theil, Frank-Peter
Prabhu, Saileta
Venue: mAbs
Publisher: Informa UK Limited
Published: 2011-01
Volume: 3
Issue: 1
Pages: 61-66
ISSN: 1942-0862 (ISSN-L)
1942-0870
DOI: 10.4161/mabs.3.1.13799
PMC: PMC3038012
OpenAlex: W1555185942
Cited by: 285
References: 24
Citations by year: 2026: 16 · 2025: 13 · 2024: 25 · 2023: 10 · 2022: 17 · 2021: 27 · 2020: 15 · 2019: 19 · 2018: 18 · 2017: 19 · 2016: 23 · 2015: 24 · 2014: 14 · 2013: 21 · 2012: 14
FWCI: 14.18
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc
Locations: mAbs (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: Genentech
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
Life Sciences › Immunology and Microbiology › Immunology › Biosimilars and Bioanalytical Methods
Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
Keywords: therapeutic monoclonal antibodies, human pharmacokinetics, allometric scaling, clearance prediction, Macaca fascicularis, volume of distribution
MeSH terms: Algorithms, Animals, Antibodies, Monoclonal, Drug Evaluation, Preclinical, Humans, Macaca fascicularis, Metabolic Clearance Rate, Models, Biological, Species Specificity, Mice, Rats, Antibodies, Monoclonal, Humanized

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Projecting human pharmacokinetics of therapeutic antibodies from nonclinical data: What have we learned?

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