bioRxivPharmacology & ToxicologyPreprint
Non-invasive quantification of hepatic necrosis from circulating alanine aminotransferase kinetics in acetaminophen-treated mice in vivo
Chris HumphriesiD, Jennifer Ann CartwrightiD, Rhona E. AirdiD, Maria Elena CandelaiD, Anuruddika J FernandoiD, Philip J. Starkey LewisiD, Janet Man, Charlotte Potter, Kathleen ScullioniD, Justyna Cholewa-WaclawiD, James W. DeariD, Stuart John ForbesiD
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Abstract
Histological necrosis is the reference measure of hepatotoxicity but can only be obtained at terminal cull. A time course requires independent cohorts at every timepoint. Circulating alanine aminotransferase (ALT) can be sampled repeatedly in the same animal, and is released in liver injury. We asked whether serial ALT kinetics can be used to estimate histological necrosis in vivo. Forty-five fasted twelve-week-old male C57BL/6J mice received a single intraperitoneal dose of 350 mg/kg acetaminophen (paracetamol). Plasma ALT and microRNA-122 (miR-122) were measured serially from baseline to 48h as cumulative area-under-the-curve (AUC) to cull, against centrilobular necrosis on haematoxylin and eosin sections as reference (range 0 to 59%, mean 32%). A generalised additive model of necrosis on cumulative ALT AUC and time since dosing predicted necrosis with a leave-one-animal-out cross-validated mean absolute error of 7.7% necrotic area (95% CI 5.6 to 10.0; n=49). A panel of traditional regression and machine-learning models all gave equal or larger error, and adding an additional biomarker or regeneration information did not improve prediction. Serial ALT kinetics therefore provide a calibrated, longitudinal measure of hepatic necrosis in vivo and give a more stable estimate of within-group variance for study planning, while supporting reductions in animal use, because one serially-sampled cohort can replace separate cohorts at each timepoint.
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: benchmarking
- research focus: drug response / drug discovery
- approach: statistical model
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