Large-Scale Assessment of Animal-to-Human Drug Translation Using Natural Language Processing
Journal:
bioRxiv
Published Date:
May 22, 2026
Abstract
Background: Large-scale estimates of animal-to-human drug translation and the study characteristics associated with successful translation remain limited. The expanding preclinical literature also challenges manual evidence synthesis. We developed a natural language processing (NLP) pipeline to structure and link preclinical and clinical evidence at scale. Methods: In this retrospective meta-research study, we analysed more than 500,000 neuroscience-related animal drug studies from PubMed and linked them to clinical trial and regulatory approval data. NLP methods extracted drug, disease, and experimental design characteristics from abstracts and full texts. Translation was defined as progression to completed phase III/IV trials or regulatory approval. Logistic regression assessed associations between preclinical study characteristics and successful translation. Findings: Among 291,624 drug entities identified in animal studies, 6.7% entered clinical development and 3.1% reached phase III/IV trials or regulatory approval. At the drug-disease level, 4.4% entered clinical development and 1.9% achieved translation. Restricting analyses to successfully linked ontology entities increased estimates to 11.3% and 4.1%, respectively. Male-only animal studies predominated, whereas reporting of randomisation, blinding, and sample size calculations remained limited. Testing across multiple species and reporting blinding were associated with higher odds of successful translation. Interpretation: Only a minority of interventions tested in animals progress to advanced clinical development or regulatory approval. Greater species diversity and blinding were associated with improved translational success. NLP-based evidence synthesis may support scalable evaluation of translational research and identification of potentially modifiable research practices.
