Using the hepatotoxicity endpoint values estimated from LRTD values, we investigated if the prediction of hepatotoxicity could be improved by combining structural models with gene expression data and metabolic models using the LINCS database (Duan et al.
First, we determined LRTD doses for each histopathological finding and assigned the compounds into toxicity classes.
2: Correlation between pairs of compounds for hepatotoxicity endpoints as a function of chemical similarity a) Correlation between pairs of similar compounds as a function of the similarity threshold for hepatotoxicity endpoints (HT) quantified as LRTD, representing the lowest dose at which they were positive in any study and grouped by controlled terms: morphological findings (MF); clinical chemistry (CC); hepatobiliary injury (HB); and hepatocellular injury (HC).
3: Percentage of positives for the four main hepatotoxicity categories We show positives for LRTD values <10, <50, <100, and <500 mg/kg.
a) LRTD values generated from eTOX compared with estimated network elasticity perturbation according to gene regulation data.
The density function curves of the simulated LRTD and experimental GRTD at varying screw speed (N) are shown in Fig.
Figure 11 shows the density function curves of the simulated LRTD and experimental GRTD with different screw configurations.
With the indirect verification method, the simulated LRTD and the experimental GRTD, and the simulated LART and the experimental GART were compared to prove the reliability.
The trends of experimental GRTD and simulated LRTD were similar, and based on this, the GART and LART at different process conditions are in similar trends, which indirectly verified the reliability of simulation.
The overall and partial RTDs were measured in-line directly and LRTDs were calculated according to a statistical theory.