es6b05326_si_002.txt (14.42 kB)
Robust Fit of Toxicokinetic–Toxicodynamic Models Using Prior Knowledge Contained in the Design of Survival Toxicity Tests
dataset
posted on 2017-03-08, 00:00 authored by Marie Laure Delignette-Muller, Philippe Ruiz, Philippe VeberToxicokinetics–toxicodynamic
(TKTD) models have emerged
as a powerful means to describe survival as a function of time and
concentration in ecotoxicology. They are especially powerful to extrapolate
survival observed under constant exposure conditions to survival predicted
under realistic fluctuating exposure conditions. But despite their
obvious benefits, these models have not yet been adopted as a standard
to analyze data of survival toxicity tests. Instead simple dose–response
models are still often used although they only exploit data observed
at the end of the experiment. We believe a reason precluding a wider
adoption of TKTD models is that available software still requires
strong expertise in model fitting. In this work, we propose a fully
automated fitting procedure that extracts prior knowledge on parameters
of the model from the design of the toxicity test (tested concentrations
and observation times). We evaluated our procedure on three experimental
and 300 simulated data sets and showed that it provides robust fits
of the model, both in the frequentist and the Bayesian framework,
with a better robustness of the Bayesian approach for the sparsest
data sets.