Abstract

Sepsis is a major worldwide healthcare issue with unmet clinical need. Despite extensive animal research in this area, successful clinical translation has been largely unsuccessful.We propose one reason for this is that, sometimes, the experimental question is misdirected or unrealistic expectations are being made of the animal model.As sepsis models can lead to a rapid and substantial suffering – it is essential that we continually review experimental approaches and undertake a full harm:benefit impact assessment for each study. In some instances, this may require refinement of existing sepsis models. In other cases, it may be replacement to a different experimental system altogether, answering a mechanistic question whilst aligning with the principles of reduction, refinement and replacement (3Rs).We discuss making better use of patient data to identify potentially useful therapeutic targets which can subsequently be validated in preclinical systems. This may be achieved through greater use of construct validity models, from which mechanistic conclusions are drawn. We argue that such models could provide equally useful scientific data as face validity models, but with an improved 3Rs impact. Indeed, construct validity models may not require sepsis to be modelled, per se. We propose that approaches that could support and refine clinical translation of research findings, whilst reducing the overall welfare burden on research animals.

Highlights

  • What is the clinical need?Sepsis arises from a dysregulated host response to a microbial infection and can lead to septic shock

  • Harm relates to welfare experience of animal whilst benefit relates to value of scientific data

  • Data should be interpreted in the context of what the model can deliver, avoiding over-translation

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Summary

Introduction

What is the clinical need?Sepsis arises from a dysregulated host response to a microbial infection and can lead to septic shock. Harm relates to welfare experience of animal whilst benefit relates to value of scientific data. Data should be interpreted in the context of what the model can deliver, avoiding over-translation. Focussing on construct rather than face validity, has the same potential to increase clinical translation, but with reduced levels of animal suffering.

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