Mapping strategies towards improved external validity in preclinical translational research

ABSTRACT

Introduction

Translation is about successfully bringing findings from preclinical contexts into the clinic. This transfer is challenging as clinical trials frequently fail despite positive preclinical results. Limited robustness of preclinical research has been marked as one of the drivers of such failures. One suggested solution is to improve the external validity of in vitro and in vivo experiments via a suite of complementary strategies.

Areas covered

In this review, the authors summarize the literature available on different strategies to improve external validity in in vivo, in vitro, or ex vivo experiments; systematic heterogenization; generalizability tests; and multi-batch and multicenter experiments. Articles that tested or discussed sources of variability in systematically heterogenized experiments were identified, and the most prevalent sources of variability are reviewed further. Special considerations in sample size planning, analysis options, and practical feasibility associated with each strategy are also reviewed.

Expert opinion

The strategies reviewed differentially influence variation in experiments. Different research projects, with their unique goals, can leverage the strengths and limitations of each strategy. Applying a combination of these approaches in confirmatory stages of preclinical research putatively increases the chances of success in clinical studies.

KEYWORDS:

• Experimental design
• external validity
• preclinical research
• translational success
• systematic heterogenization

Article highlights

• Introducing variation in preclinical experiments has the potential to strengthen external validity and increase translational success. We highlight complementary strategies to achieve this goal in in vivo experiments as well as in vitro and ex vivo new approach methodologies (NAMs).

• Systematic heterogenization strategies add variation within a single experiment, e.g. through the use of multiple sexes, age groups, times of day, and cage enrichment.

• Generalization tests add variation between experiments and with that identify boundary conditions, for example, by varying species, strains, or time of year between replication experiments.

• Multicenter experiments add variation from unknown sources in the environment or experimental conditions from multiple laboratories. Similarly, multi-batch strategies are an alternative for unknown sources of variation within a single laboratory.

• Applying a sequential combination of these strategies is a promising approach to increase efficiency in pathways to clinical applications.

Acknowledgments

The authors thank Professor F Konietschke from Charité – Universitätsmedizin Berlin, for providing counsel on the sample size planning for factorial designs.

Declaration of interest

N Drude is an external consultant and animal welfare officer at Medizinisches Kompetenzzentrum c/o HCx Consulting, Brandenburg, Germany. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer Disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17460441.2023.2251886

Additional information

Funding

This work was supported by the Bundesministerium für Bildung und Forschung (BMBF) under Grant 01KC1901A; the Einstein Foundation under Grant EC3R EZ-2020-597-2; and by Charité 3 R|Replace - Reduce - Refine.