In the pharmaceutical industry, the development of new drugs requires pre-clinical testing for efficacy and safety. Organ-Chips can be used in this process to improve models that reproduce the effects of drugs at patient level. Organ-Chips are 3D micro-physiological systems that aim to recapitulate the complex physiology and microenvironment of an in vivo organ. Using Organ-Chips in the drug discovery process means that more drugs are likely to reach late stage clinical testing and that fewer animals are needed in the testing process.

In this project, the IMI worked with industry partners AstraZeneca and Emulate to develop a statistical best-practice framework for studies involving Organ-Chip data. As part of this project, we developed an intelligent scanning workflow for a Liver-Chip model by Emulate, allowing for the first time to image Organ-Chips reproducibly at scale. Here, high-content confocal imaging generates spatiotemporal data of multiple imaging endpoints simultaneously, leading to a much richer dataset than the traditional 2D cell culture without flow. Due to the novelty of the imaging pipeline, there were no standard practices for analysing such data. We developed a best practice framework which included randomisation of chip imaging locations to remove biases as well as Bayesian methods and sample size analysis for optimising experiments. Results were fed back to the experimental team to improve the experimental settings. This best practice standard now guides the experimental design and analysis of all Organ-Chip studies at AstraZeneca.

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