TKTDsimulation.jl and tktdjl2r: innovative packages for High Performance Computing of survival predictions in support of environmental risk assessment under time-variable scenarios

Abstract

Predictive environmental risk scenarios are today of major interest for environmental risk assessment as they provide plausible and consistent descriptions of possible effects of chemical in natura. In particular, they can be used for predictions of the future as consistent descriptions of pathways towards desired targets to protect. One single scenario would therefore be meaningless, as it could not capture all the variability and uncertainty involved in natural phenomenon combined with socio-economical events. A set of environmental risk scenarios is then a key asset to address sustainable and collaborative decision making associated with appropriate actions. Toxicokinetics-Toxicodynamics (TKTD) models are increasingly used for the assessment and the prediction of environmental risk assessment due to chemical products. This mechanistic modelling approach offers many advantages as the possibility to perform simulations under non-observed realistic situations with time-variable exposure profiles embedded in environmental risk scenarios. TKTD simulations can also be linked with other types of models (e.g., Individual Based Model) within a pipeline of computing inference as for example Bayesian inference or Machine Learning. To handle such challenges within the particular framework of TKTD models for survival, we present an innovative simulation tool written in the new programming language Julia, called TKTDsimulations.jl. Given that TKTD models for survival usually require high performance computing due to the numerical integration of differential equations, our tool strongly benefits from Julias facilities, in particular a code that is fast to compile and easy to maintain. In addition, to ease the link with the already developed R-package morse dedicated to the statistical handling of ecotoxicity data, we also developed a new R-package, called tktdjl2r, interfacing morse with our new simulation tool TKTDsimulations.jl that considerably faster predictions with the corresponding ready-to-use morse functions.