Pharmacokinetic–pharmacodynamic models for categorical toxicity data

Diack, Cheikh; Bois, Frédéric Y.

doi:10.1016/j.yrtph.2004.09.007

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Further, PBPK models use toxicokinetic information to translate chemical exposure into an internal dose (tissues as well blood and urine) and allow extrapolation between different species and routes of exposure (Caldwell et al 2012;Fairman et al 2020;Kumar et al 2020). It is also common to use PBPK/PD models that include pharmacodynamic information, which can explain internal exposure and the mechanism of action of a chemical (Diack and Bois 2005;Mumtaz et al 2012).…”

Section: Introductionmentioning

confidence: 99%

EDC-induced mechanisms of immunotoxicity: a systematic review

Vidal

Deepika

Schuhmacher

et al. 2021

Critical Reviews in Toxicology

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Endocrine-disrupting chemicals (EDCs) refer to a group of chemicals that cause adverse effects in human health, impairing hormone production and regulation, resulting in alteration of homeostasis, reproductive, and developmental, and immune system impairments. The immunotoxicity of EDCs involves many mechanisms altering gene expression that depend on the activation of nuclear receptors such as the aryl hydrocarbon receptor (AHR), the estrogen receptor (ER), and the peroxisome proliferator-activated receptor (PPAR), which also results in skin and intestinal disorders, microbiota alterations and inflammatory diseases. This systematic review aims to review different mechanisms of immunotoxicity and immunomodulation of T cells, focusing on T regulatory (Treg) and Th17 subsets, B cells, and dendritic cells (DCs) caused by specific EDCs such as 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), bisphenols (BPs) and polyfluoroalkyl substances (PFASs). To achieve this objective, a systematic study was conducted searching various databases including PubMed and Scopus to find in-vitro, in-vivo, and biomonitoring studies that examine EDC-dependent mechanisms of immunotoxicity. While doing the systematic review, we found species-and cell-specific outcomes and a translational gap between in-vitro and in-vivo experiments. Finally, an adverse outcome pathway (AOP) framework is proposed, which explains mechanistically toxicity endpoints emerging from different EDCs having similar key events and can help to improve our understanding of EDCs mechanisms of immunotoxicity. In conclusion, this review provides insights into the mechanisms of immunotoxicity mediated by EDCs and will help to improve human health risk assessment.

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Section: Introductionmentioning

confidence: 99%

EDC-induced mechanisms of immunotoxicity: a systematic review

Vidal

Deepika

Schuhmacher

et al. 2021

Critical Reviews in Toxicology

View full text Add to dashboard Cite

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“…where F is the cumulative distribution function of a normal distribution with mean µ and standard deviation σ (Diack and Bois, 2005). In this formulation, each individual has a given threshold, log-normally distributed.…”

Section: Introductionmentioning

confidence: 99%

“…First, it is not possible to analyze data for which only one exposure time is available, because only two combinations of parameters (n/σ and (log(t)µ)/σ) could be identified in this case. This model has also been criticized for its lack of biological parameters by Diack and Bois (2005) who have proposed an alternative model (the so-called PKPD model). However, the model they propose has even more limitations in a risk assessment perspective.…”

Section: Introductionmentioning

confidence: 99%

Comparison of models to analyze mortality data and derive concentration–time response relationship of inhaled chemicals

Péry

Troise

Tissot

et al. 2010

Regulatory Toxicology and Pharmacology

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The derivation of thresholds for lethal effects for inhaled chemicals is a key issue in accidental risk management because they largely determine the outcome of land use planning, among which localization of habitations in the vicinity of a factory. This derivation is generally performed on the basis of rodent lethality data analyzed by statistical models able to extrapolate effects for different times and concentrations of exposure. A model commonly used in France is the standard probit model. In this model, effects is related to exposure concentration and duration according to the Haber's law and considers that individual thresholds, corresponding to the maximum tolerated effects before dying, are log-normally distributed among the population. This approach has been criticized for its lack of biological parameters and its inability to treat data characterized by only one time of exposure. In order to improve the current state of modeling, we proposed three alternative models. Two of them (DEBtox and Haber-TKTD models) incorporate the kinetics of the chemicals. The third one (Loguniform model) is a linearization of the standard probit model. We evaluated their performance by analyzing real data and simulated data generated with each model. For data characterized by several times of exposure, the standard probit model outperformed all other models in terms of goodness of fits and estimation of parameters. For data characterized by only one time of exposure, only DEBtox model was able to fit the data and estimate parameters, provided we dispose of several observation times, typically just after exposure and a long period afterwards.

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Pharmacodynamic Models for Discrete Data

2012

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Clinical outcomes are often described as events: death, stroke, epileptic seizure, multiple sclerosis lesions, recurrence of cancer, disease progression, pain, infection and bacterial/viral eradication, severe toxic adverse effect, resistance to treatment, etc. They may be quantified as time-to-event, counts of events per time interval (rates), their severity grade, or a combination of these. Such data are discrete and require specific modelling structures and methods. This article references the most common modelling approaches for categorical, count and time-to-event data, and reviews examples of such models applied in the analysis of pharmacodynamic data. Modelling is useful for identification of influential factors related to the clinical outcome, characterization and quantification of their impact, for making better informed predictions and clinical decisions, assessments of efficacy of therapeutic interventions, optimizing the individual treatments and drug development studies.

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Pharmacokinetic–pharmacodynamic models for categorical toxicity data

Cited by 7 publications

References 9 publications

EDC-induced mechanisms of immunotoxicity: a systematic review

EDC-induced mechanisms of immunotoxicity: a systematic review

Comparison of models to analyze mortality data and derive concentration–time response relationship of inhaled chemicals

Pharmacodynamic Models for Discrete Data

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