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.