Embracing variation: a multi-batch study delivers robust estimates of efficacy

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Presented by

Natasha A Karp, PhD, Principal Biostatistician in Discovery Sciences, AstraZeneca

About this talk

Phenotypic plasticity, the ability of a living organism to respond to the environment, can lead to conclusions from experiments that are idiosyncratic to a particular environment. The level of environmental responsiveness can result in difficulties in reproducing studies from the same institute with the same standardised environment. Here we present a multi-batch approach to in-vivo studies to improve replicability of the results for a defined environment. These multi-batch experiments consist of small independent mini-experiments where the data are combined in an integrated data analysis to appropriately assess the treatment effect after accounting for the structure in the data. We demonstrate the method on two case studies with syngeneic tumour models which are challenging due to high variability both within and between studies. Through simulations and discussions, we explore the optimum design that balances practical constraints of working with animals versus sensitivity and replicability. Through the increased confidence from the multi-batch design, we reduce the need to replicate the experiment, which can reduce the total number of animals used. 3 take home messages: • The historic definition of Reduction, focused on absolute number of animals within a single highly standardised experiment, is not fit for purpose as it leads to experiments with highly context dependent conclusions which are not robust. • A multi-batch design embraces the concept of replication and through integrated data analysis allows a reduction in animal usage by planning to integrate studies from the offset. • Immuno-oncology tumour studies provide a compelling example of the confidence obtained from exploring a treatment effect across three separate experiments where findings were seen with independent batches of animals, cell cultures etc.

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