A great deal of the news that reaches the public about biomedical advances based on animal experimentation does not always highlight the difficulties in translating the achievements obtained in animals to human research. This could create false expectations. We, therefore, believe it is important to be aware of the difficulties entailed in translating a successful outcome obtained from animal experimentation into practical clinical applications. In this respect, a recent article published in the journal Science, entitled, “Translating preclinical models to humans” (February 14, 2020), shows the complexity of the matter and proposes the development of a promising new system.
Results of animal experimentation into practical clinical applications
Translating the findings of experiments in animals, both rodents and non-human primates, to humans is challenging. This constitutes a major problem because animal experiments are often a prerequisite for many human experiments and their translation to clinical practice. The authors believe that this problem is primarily due to the large number of failures encountered when translating preclinical results. Systems for emulating animal and human experimentation must, therefore, be improved. Accordingly, they suggest that, “Instead of attempting to ‘humanize’ animal experimental models, greater success may be obtained by humanizing computational models derived from animal experiments” (see more HERE).
Humanizing computational animal models
“A need to move from descriptive approaches to predictive models that incorporate cross-species differences in data types and phenotypes into translation”
The authors continue, briefly explaining this proposed computational model, based on sequencing data, “High-throughput DNA and RNA sequencing has made it possible to compare large animal and human data sets to search for translatable features and assess the representativeness of animal models.
Computational humanization shifts perspective from comparisons to translating predictive models of biological associations across species, incorporating diverse molecular and phenotypic data from animals and humans
“This comparative approach is vulnerable to how phenotypic and molecular similarity (our emphasis) are defined, factors that influence apparent translatability. For example, two independent analyses of the same mouse and human transcriptomic datasets came to opposite conclusions about the utility of mice in inflammatory disease research. The discrepancy in the conclusions of these studies derived from differences in the statistical methods and selection of mouse data and phenotypes to compare with that of humans. Such comparative studies that use animal-to-human dataset pairs, called cross-species pairs (CSPs), are subject to these pitfalls, demonstrating a need to move from descriptive approaches to predictive models that incorporate cross-species differences in data types and phenotypes into translation. Although CSP comparisons are potentially problematic, they can highlight biology that is challenging to translate. In a recent study, transcriptomic profiles from humans and animal models were used to identify the cross-species expression of genes (read HERE crossing-species analysis) according to sex in 12 tissues and 4 species. The authors showed that sex-specific differences may have evolved after speciation and therefore may not be translatable to humans. An example that uses CSPs to identify representative animal disease models is PhenoDigm, a computational method that ranks animal models by assigning similarity scores to animal and human disease phenotypes. These studies expand the knowledge base of both gene-phenotype associations and animal-human phenotype associations, aiding experimental design and interpretation. By contrast, computational humanization shifts perspective from comparisons to translating predictive models of biological associations across species, incorporating diverse molecular and phenotypic data from animals and humans. These approaches span from translation of disease gene or disease-pathway associations in comparable data types and phenotypes to a more complex signaling network, mechanistic, or data-driven computational models that integrate multiple data types and phenotypes.”
Expanding biomedicine could be a promising way forward to put animal experimentation into the human clinic investigation
The article concluded with a synthesis of their proposal to resolve the relevant issue: “A promising way forward is to use ML [machine learning] approaches for discovery purposes and network, mechanistic, or emerging computational approaches to study context-specific biology. Because context-specific predictive models will necessarily use less data than will generalized approaches, new methods are needed to integrate these models with data from biological knowledge bases of orthology, network topology, and cross-species phenotypic similarity. These considerations motivate the participation of researchers who bring approaches from various disciplines—including clinical, engineering, and biological sciences—into what must become an expanding area of biomedicine.”