Related Experiment Video
Updated: Jun 21, 2026

Assessment of Chemical Toxicity in Adult Drosophila Melanogaster
Published on: March 24, 2023
Bayesian multilevel discrete interval hazard analysis to predict dichlorodiphenyldichloroethylene mortality in
Jong-Hyeon Lee1, Craig A Stow, Peter F Landrum
1Neo Environmental Business, Institute of Environmental Protection and Safety, Room 904, Byuksan Digital Valley II, Gasan dong 481-10, Geumcheon gu, Seoul 153-783, Korea.
Abstract:
We exposed Hyalella azteca to p,p'-dichlorodiphenyldichloroethylene for intervals of 1 to 4 d and followed mortality out to 10 d. Mortality was determined as the cessation of heartbeat; dead organism body residue was determined daily. To model mortality probability, body residues of the living organisms were estimated using published kinetic data with concentration-dependent rate constants. The estimated residues compared favorably with measured residues in the dead organisms (predicted body residue = 1.302 ± 0.142 measured body residue + 10.351 ± 15.766, r² = 0.64, n = 50). The response data were collected at discrete intervals; thus, it was not possible to determine the exact time of death for organisms. Consequently, we analyzed the mortality data using discrete interval analysis, in a Bayesian hierarchical framework, with body residue as the dose metric. The predicted body residues to produce mortality were similar across the duration of exposure when postexposure mortality was considered. The concentration for 50% mortality was 0.47 μmol/g (148.6 tg/g, range 0.32-0.66 μmol/g), and predictions of response indicted 95% (range 73-99.9%) mortality at 0.79 μmol/g (250 μg/g) and 4% (range 1.2-9.6%) mortality at 0.16 μmol/g (50 μg/g). The lethal residue for 50% mortality based on interval analysis for short-term exposures with postexposure mortality resulted in values similar to long-term continuous exposures for exposure durations of more than 600 h.
Related Concept Videos
Hazard Rate
Toxicity Testing in Animals
Mechanistic Models: Compartment Models in Individual and Population Analysis
