Evidence map›Paper›PMID 31218267›Full record

ArticleComputational toxicology (Amsterdam, Netherlands)2019

Role of Physiologically Based Kinetic modelling in addressing environmental chemical mixtures - A review.

Anteneh Desalegn, Stephanie Bopp, David Asturiol, Lara Lamon, Andrew Worth, Alicia Paini

Abstract read
In one paragraph

Article in Computational toxicology (Amsterdam, Netherlands), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Anteneh DesalegnEuropean Commission, Joint Research Centre, Ispra, VA, Italy.
Stephanie BoppEuropean Commission, Joint Research Centre, Ispra, VA, Italy.
David AsturiolEuropean Commission, Joint Research Centre, Ispra, VA, Italy.
Lara LamonEuropean Commission, Joint Research Centre, Ispra, VA, Italy.
Andrew WorthEuropean Commission, Joint Research Centre, Ispra, VA, Italy.
Alicia PainiEuropean Commission, Joint Research Centre, Ispra, VA, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The role of Physiologically Based Kinetic (PBK) modelling in assessing mixture toxicology has been growing for the last three decades. It has been widely used to investigate and address interactions in mixtures. This review describes the current state-of-the-art of PBK models for chemical mixtures and to evaluate the applications of PBK modelling for mixtures with emphasis on their role in chemical risk assessment. A total of 35 mixture PBK models were included after searching web resources (Scopus, PubMed, Web of Science, and Google Scholar), screening for duplicates, and excluding articles based on eligibility criteria. Binary mixtures and volatile organic compounds accounted for two-thirds of the chemical mixtures identified. The most common exposure route and modelled system were found to be inhalation and rats respectively. Twenty two (22) models were for binary mixtures, 5 for ternary mixtures, 3 for quaternary mixtures, and 5 for complex mixtures. Both bottom-up and top-down PBK modelling approaches are described. Whereas bottom-up approaches are based on a series of binary interactions, top-down approaches are based on the lumping of mixture components. Competitive inhibition is the most common type of interaction among the various types of mixtures, and usually becomes a concern at concentrations higher than environmental exposure levels. It leads to reduced biotransformation that either means a decrease in the amount of toxic metabolite formation or an increase in toxic parent chemical accumulation. The consequence is either lower or higher toxicity compared to that estimated for the mixture based on the additivity principle. Therefore, PBK modelling can play a central role in predicting interactions in chemical mixture risk assessment.

Indexed as

BiokineticsInteractionMixturePBPKPharmacokineticsPhysiologically based pharmacokinetic modellingToxicokinetic interaction

Identifiers

PMID31218267
PMCPMC6559215

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.