Humane Endpoint Estimation in Laboratory Rodents Using Bayesian Time Series Forecasts

We present a system to aid humane endpoint decisions in laboratory rodents. The system derives the future probability distribution of any quantified parameter suitable as endpoint criterion. This provides experimenters with knowledge about the likelihood of a criterion exceeding a fixed threshold. Researchers thus gain valuable information about the individual progression of the recorded parameters, to reduce distress and avoidable loss of animals. We examine the system on the data of a xenograft model using body weight as an endpoint criterion.