Re-design and improvement of animal experiments, using Bayesian methods
2019
The pharmaceutical industry uses animal models in a variety of settings
including safety, pharmacodynamic modelling and efficacy. Conventionally,
a frequentist design of standard animal experiments of human asthma replication
includes repeatedly running treatment groups with the exact layout, focusing on
effect size rather than capturing historical data of animal responses on compounds
of interest. Here, we propose a Bayesian framework which is used as an alternative
to the frequentist approach. This allows for the incorporation of prior beliefs into
the experimental process. Specifically, non-informative, semi-informative and informative
prior distributions are assigned to Single-Level Normal models. Given the
priors aforementioned, it was found that using semi-informative priors leads to the
creation of consistent historical data. Given these beliefs, we combine the results
of all experiments by implementing a Two-Level Bayesian Meta-Analysis, achieved
by adding the extra level of experimental studies to the data structure. The end
point of this project showed that simulation trials of all experimental studies with
the assignment of semi-informative priors can result in the reduction of animals per
treatment group by a margin of 10 %.
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