Abstract
Regulatory authorities require animal toxicity tests for new chemical entities. Organ weight changes are accepted as a sensitive indicator of chemically induced organ damage, but can be difficult to interpret because changes in organ weight might reflect chemically-induced changes in overall body weight. A common solution is to calculate the relative organ weight (organ to body weight ratio), but this inadequately controls for the dependence on body weight – a point made by statisticians for decades, but which has not been widely adopted. The recommended solution is an analysis of covariance (ANCOVA), but it is rarely used, possibly because both the method of statistical correction and the interpretation of the output may be unclear to those with minimal statistical training. Using relative organ weights can easily lead to incorrect conclusions, resulting in poor decisions, wasted resources, and an ethically questionable use of animals. We propose to cast the problem into a causal modelling framework as it directly assesses questions of scientific interest, the results are easy to interpret, and the analysis is simple to perform with freely available software. Furthermore, by taking a Bayesian approach we can model unequal variances, control for multiple testing, and directly provide evidence of safety.
Highlights
Regulatory authorities require animal toxicity tests for new chemical entities
The results of the analysis of covariance (ANCOVA) model suggest that sodium dichromate dihydrate (SDD) does not affect liver weight at the highest dose of 1000 mg/L, based on the adjusted p-value of 0.16
Bringing our biological knowledge into play when interpreting the results, we would conclude that if we observe an effect at the second highest dose of 500 mg/L, it is likely that the effect exists at a higher dose, especially when the graphs suggest a large effect (Fig. 6A)
Summary
Regulatory authorities require animal toxicity tests for new chemical entities. Organ weight changes are accepted as a sensitive indicator of chemically induced organ damage, but can be difficult to interpret because changes in organ weight might reflect chemically-induced changes in overall body weight. We propose to cast the problem into a causal modelling framework as it directly assesses questions of scientific interest, the results are easy to interpret, and the analysis is simple to perform with freely available software. The primary scientific question is whether a compound directly effects an organ, not indirectly through changes in body weight. To overcome this problem, researchers frequently calculate a ratio – called the “relative organ weight” – by dividing each animal’s organ weight by their body weight. The relative organ weights are plotted, analysed, and interpreted, and researchers assume that this approach will provide correct conclusions. The Bayesian approach can distinguish between evidence for safety versus “insufficient evidence to make a conclusion”
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