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Conference Papers Year : 2018

Integrating pharmacokinetics and pharmacodynamics in AOPs for next generation risk assessments


Quantitative analysis and modelling of data are some of the most important aspects of risk analysis and assessment. Relevant modelling activities are often divided into pharmacokinetics (PK; related to exposure assessment) and pharmacodynamics (PD; related to dose–response) in a rather simplistic way. We tend towards a fusion of the two disciplines into systems toxicology, at the point where they meet. In any case, modelling has always been important for low-dose extrapolation, exposure route adjustments or assessing the impact of inter-individual variability. Yet, new challenges are emerging that we will focus on in this talk: quantitative in vitro to in vivo extrapolation, highthroughput and high-content data integration, and integration within the adverse outcome pathway (AOP) framework. In response to the need for in vitro data integration and extrapolation, PK modelling has definitely taken a physiological (PBPK) turn in the last 10 years. Models of drug distribution of chemicals in animal and human bodies have dramatically improved, but new models are now being developed to address the complexity of the new in vitro systems. We have the example of a zebrafish model, useable for human and ecological risk assessments. A whole series of PK models of in vitro systems is also being developed in ongoing projects such as EU-ToxRisk. In parallel, the methods for fast simulations and calibration of complex models with experimental data have also been considerably improved over the last decade. AOP models are also being actively developed. Given their potential number and complexity, the best mathematical tools to use are not precisely known at the present time. For extrapolation purposes, we would probably favour systems toxicology models, which are fundamentally mechanistic, like physiologically based PK models. Yet, they can be extremely complex and data hungry. Statistical models (such as linked non-linear regression relationships or Bayesian networks) might be simpler to develop, but they may have more restricted applications. In-between, there is a whole range of pharmacodynamic models, such as the effect compartment model, often used in pharmacology, but much less so in toxicology. Research is very active in those areas and it is likely that, for quite a while, the various approaches will co-exist. To illustrate the above considerations, I will present our recent PK/PD modelling of the effects of random mixtures of aromatase inhibitors on the dynamics of women’s menstrual cycles. Using high-speed computer code, we simulated random exposures to millions of potential mixtures of 86 aromatase inhibitors present both in the US EPA ToxCast and ExpoCast databases. A PK model of intake and disposition of the chemicals was used to predict their internal concentration as a function of time (up to 2 years). In vitro concentration–inhibition relationships for aromatase were collected from ToxCast and corrected for cytotoxicity. The resulting total aromatase inhibition was input into a mathematical model of the hormonal hypothalamus–pituitary–ovarian control of ovulation in women. At aromatase inhibitor concentrations leading to over 10% inhibition of estradiol synthesis, noticeable (eventually reversible) effects on ovulation were predicted. Exposures to single chemicals never led to such effects. However, a few per cent of the combined exposure scenarios were predicted to have potential impacts on ovulation, and hence fertility. These results demonstrate the possibility to predict large-scale mixture effects for endocrine disrupters with a predictive toxicology approach, suitable for high-throughput ranking and risk assessment.


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ineris-03239323 , version 1 (27-05-2021)


  • HAL Id : ineris-03239323 , version 1


Frédéric Y. Bois. Integrating pharmacokinetics and pharmacodynamics in AOPs for next generation risk assessments. EFSA Conference 2018, Sep 2018, Parme, Italy. ⟨ineris-03239323⟩


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