PBPK modelling of inter-individual variability in the pharmacokinetics of environmental chemicals
Open accessAbstract
Generic PBPK models, applicable to a large number of substances, coupled to parameter databases and QSAR modules, are now available for predictive modelling of inter-individual variability in the absorption, distribution, metabolism and excretion of environmental chemicals. When needed, Markov chain Monte Carlo methods and multilevel population models can be jointly used for a Bayesian calibration of a PBPK model, to improve our understanding of the determinants of population heterogeneity and differential susceptibility. This article reviews those developments and illustrates them with recent applications to environmentally relevant questions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Another significant source of uncertainty in half-life estimation could be due to the possible ongoing and/or unaccounted background exposures, and to the possible role of individual determinants (such as sex, age, genetics and general health), which may contribute to interindividual variation.
“Another significant source of uncertainty in half-life estimation could be due to the possible ongoing and/or unaccounted background exposures (Dourson and Gadagbui, 2021), and to the possible role of individual determinants (such as sex, age, genetics and general health), which may contribute to interindividual variation (Bois et al., 2010; Chiu et al., 2022).”
From Estimation of per- and polyfluoroalkyl substances (PFAS) half-lives in human studies: a systematic review and meta-analysis (2024) · cites this paper as Bois et al., 2010
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 173 · Cited by 194
- Authors
- Bois, F. Y., Jamei, M., & Clewell, H. J.
- Affiliations
- Université de Technologie de Compiègne (FR)
- Simcyp (United Kingdom) (GB)
- Corresponding author
- Bois, Frédéric Y.
- Venue
- Toxicology
- Publisher
- Elsevier BV
- Published
- 2010-12
- Volume
- 278
- Issue
- 3
- Pages
- 256-267
- ISSN
- 0300-483X (ISSN-L)
- OpenAlex
- W2055526313
- Cited by
- 194
- References
- 173
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 6 · 2023: 7 · 2022: 7 · 2021: 8 · 2020: 25 · 2019: 13 · 2018: 16 · 2017: 17 · 2016: 15 · 2015: 9 · 2014: 13 · 2013: 14 · 2012: 17
- FWCI
- 1.26
- Type
- journal article
- Language
- en
- Open access
- green
- ineris.hal.science
- Licence
- Locations
- Toxicology (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Toxic Organic Pollutants Impact
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- physiologically based pharmacokinetic modelling, interindividual variability, environmental chemicals, population heterogeneity, differential susceptibility, Bayesian calibration, Markov chain Monte Carlo, QSAR, parameter database, ADME
- MeSH terms
- Animals, Drug Interactions, Environmental Pollutants, Humans, Models, Biological, Monte Carlo Method, Risk Assessment
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 ›
Are you an author, or do you see a problem with this page? Request a review
