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Potential Sources of Inter-Subject Variability in Monoclonal Antibody Pharmacokinetics
Abstract
Understanding inter-subject variability in drug pharmacokinetics and pharmacodynamics is important to ensure that all patients attain suitable drug exposure to achieve efficacy and avoid toxicity. Inter-subject variability in the pharmacokinetics of therapeutic monoclonal antibodies (mAbs) is generally moderate to high; however, the factors responsible for the high inter-subject variability have not been comprehensively reviewed. In this review, the extent of inter-subject variability for mAb pharmacokinetics is presented and potential factors contributing to this variability are explored and summarised. Disease status, age, sex, ethnicity, body size, genetic polymorphisms, concomitant medication, co-morbidities, immune status and multiple other patient-specific details have been considered. The inter-subject variability for mAb pharmacokinetics most likely depends on the complex interplay of multiple factors. However, studies aimed at investigating the reasons for the inter-subject variability are sparse. Population pharmacokinetic models and physiologically based pharmacokinetic models are useful tools to identify important covariates, aiding in the understanding of factors contributing to inter-subject variability. Further understanding of inter-subject variability in pharmacokinetics should aid in development of dosing regimens that are more appropriate.
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“Many other factors, such as antibody properties (hydrophobicity, charge,38 glycosylation patterns39), intersubject variability (disease status, body size, genetic polymorphisms, concomitant medication, comorbidities, etc.40), and immune‐mediated response generated by mAbs administration can also impact mAb clearance.”
From Tutorial on monoclonal antibody pharmacokinetics and its considerations in early development (2018) · cites this paper as [40]
In the citing paper
“Once in clinical development, clinical pharmacology studies can identify the covariates and potential sources of intersubject variability; establish exposure–response relationships in patients, and aid in development of a dosing regimen.40”
From Tutorial on monoclonal antibody pharmacokinetics and its considerations in early development (2018) · cites this paper as [40]
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Metadata
- Authors
- Gill, K. L., Machavaram, K. K., Rose, R. H., & Chetty, M.
- Affiliations
- Simcyp (United Kingdom) (GB)
- Corresponding author
- Chetty, Manoranjenni
- Venue
- Clinical Pharmacokinetics
- Published
- 2016-01-27
- Volume
- 55
- Issue
- 7
- Pages
- 789-805
- ISSN
- 0312-5963 (ISSN-L)
- 1179-1926
- OpenAlex
- W2296252503
- Cited by
- 82
- References
- 210
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 9 · 2023: 11 · 2022: 11 · 2021: 7 · 2020: 5 · 2019: 6 · 2018: 10 · 2017: 7 · 2016: 1
- FWCI
- 1.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Clinical Pharmacokinetics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › 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
- monoclonal antibodies, interindividual variability, pharmacokinetics, covariates, genetic polymorphism, population pharmacokinetic model, physiologically based pharmacokinetic modeling, dosing regimen, disease status, immune status, concomitant medications, comorbidities
- MeSH terms
- Age Factors, Antibodies, Monoclonal, Body Weights and Measures, Dose-Response Relationship, Drug, Drug Interactions, Ethnicity, Humans, Lymphatic System, Models, Biological, Polymorphism, Genetic, Receptors, Fc, Severity of Illness Index, Sex Factors, Histocompatibility Antigens Class I, Comorbidity
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 ›
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