Translating preclinical models to humans
Name
Brubaker Lauffenburger resubmission copy.pdf
Description
Accepted version
Size
226.89 KB
Format
Adobe PDF
Checksum (MD5)
faf1ac07abb4c041d4eeec0fdcf9d21e
Author(s)
Brubaker, Douglas
Lauffenburger, Douglas A.
Date Issued
February 2020
Journal
Science
Publisher
American Association for the Advancement of Science (AAAS)
Citation
Brubaker, Douglas K. and Douglas A. Lauffenburger. "Translating preclinical models to humans." Science 367, 6479 (February 2020): 742-743 © 2020 The Authors
Version
Author's final manuscript
Abstract
Generalizing results from animal models to human patients is a critical biomedical challenge. This problem is a key cause of the large proportion of failures encountered in moving therapeutics from preclinical studies to clinical trials (1). Direct translation of observations in rodents or nonhuman primates (NHPs) to humans frequently disappoints, for reasons including discrepancies in complexity and regulation between species. Because the experiments required to understand disease biology to the degree required for ascertaining effective treatments cannot be performed in human subjects, translation from animals to humans is necessary—and needs to be improved. Systems biology and machine learning (ML) can be used to translate relationships across species. Instead of attempting to “humanize” animal experimental models, which is possible to only a limited extent, greater success may be obtained by humanizing computational models derived from animal experiments.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1126/science.aay8086
