A Bayesian neural ordinary differential equations framework to study the effects of chemical mixtures on survival.

ABSTRACT: In the field of Plant Protection Products (PPP), the combination of multiple active ingredients is a common strategy to improve the efficacy against target species. However, these mixtures may expose non-target species to untested chemical combinations due to varied application and degradation pathways. Developing a method to predict the effects of such mixtures without extensive testing is crucial for enhancing risk assessment for non-target organisms. Toxicokinetic (TK) and toxicodynamic (TD) models currently provide predictive power for the toxicity of mixture over time, utilizing the concentration addition and independent action principles. However, their capacity to capture complex, non-linear interactions - such as synergistic and antagonistic effects - remains limited. To overcome t

SUBMITTER: Baudrot V 

PROVIDER: S-EPMC12674509 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature