Discovery Phase Agrochemical Predictive Safety Assessment Using High Content In Vitro Data to Estimate an In Vivo Toxicity Point of Departure.
Enrica BianchiEduardo CostaJoshua A HarrillPaul DefordJessica LaRoccaWei ChenZachary SutakeAudrey LehmanAnthony Pappas-GartonEric ShererConnor MoreillonShreedharan SriramAndi DhrosoKamin J JohnsonPublished in: Journal of agricultural and food chemistry (2024)
Utilization of in vitro (cellular) techniques, like Cell Painting and transcriptomics, could provide powerful tools for agrochemical candidate sorting and selection in the discovery process. However, using these models generates challenges translating in vitro concentrations to the corresponding in vivo exposures. Physiologically based pharmacokinetic (PBPK) modeling provides a framework for quantitative in vitro to in vivo extrapolation (IVIVE). We tested whether in vivo (rat liver) transcriptomic and apical points of departure (PODs) could be accurately predicted from in vitro (rat hepatocyte or human HepaRG) transcriptomic PODs or HepaRG Cell Painting PODs using PBPK modeling. We compared two PBPK models, the ADMET predictor and the httk R package, and found httk to predict the in vivo PODs more accurately. Our findings suggest that a rat liver apical and transcriptomic POD can be estimated utilizing a combination of in vitro transcriptome-based PODs coupled with PBPK modeling for IVIVE. Thus, high content in vitro data can be translated with modest accuracy to in vivo models of ultimate regulatory importance to help select agrochemical analogs in early stage discovery program.
Keyphrases
- single cell
- high throughput
- rna seq
- small molecule
- early stage
- oxidative stress
- electronic health record
- endothelial cells
- molecular docking
- big data
- cell therapy
- transcription factor
- machine learning
- induced pluripotent stem cells
- bone marrow
- mesenchymal stem cells
- data analysis
- molecular dynamics simulations
- mass spectrometry
- drug induced