Perspective | Published:

How to design preclinical studies in nanomedicine and cell therapy to maximize the prospects of clinical translation

Nature Biomedical Engineeringvolume 2pages797809 (2018) | Download Citation

Abstract

The clinical translation of promising products, technologies and interventions from the disciplines of nanomedicine and cell therapy has been slow and inefficient. In part, translation has been hampered by suboptimal research practices that propagate biases and hinder reproducibility. These include the publication of small and underpowered preclinical studies, suboptimal study design (in particular, biased allocation of experimental groups, experimenter bias and lack of necessary controls), the use of uncharacterized or poorly characterized materials, poor understanding of the relevant biology and mechanisms, poor use of statistics, large between-model heterogeneity, absence of replication, lack of interdisciplinarity, poor scientific training in study design and methods, a culture that does not incentivize transparency and sharing, poor or selective reporting, misaligned incentives and rewards, high costs of materials and protocols, and complexity of the developed products, technologies and interventions. In this Perspective, we discuss special manifestations of these problems in nanomedicine and in cell therapy, and describe mitigating strategies. Progress on reducing bias and enhancing reproducibility early on ought to enhance the translational potential of biomedical findings and technologies.

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Acknowledgements

We thank N. Boyd for helping create Fig. 2. We also acknowledge funding from the Mayo Clinic Center for Regenerative Medicine (B.Y.S.K.), the National Institute of Neurological Disorders and Stroke Grant R01 NS104315 (B.Y.S.K.) and the Laura and John Arnold Foundation for providing funding for the Meta-Research Innovation Center at Stanford (METRICS) (J.P.A.I.).

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  1. Departments of Medicine, Health Research and Policy, and Biomedical Data Science, Stanford University School of Medicine, and Department of Statistics, Stanford University School of Humanities and Sciences, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA

    • John P. A. Ioannidis
  2. Departments of Neurosurgery, Cancer Biology, and Neuroscience, Mayo Clinic College of Medicine, Jacksonville, FL, USA

    • Betty Y. S. Kim
  3. Monash University & Hudson Institute of Medical Research, Clayton, Victoria, Australia

    • Alan Trounson

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All authors contributed to writing the paper, revising it and approving the final version.

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The authors declare no competing interests.

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Correspondence to John P. A. Ioannidis.

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https://doi.org/10.1038/s41551-018-0314-y