Large-Scale Assessment of Animal-to-Human Drug Translation Using Natural Language Processing

Abstract

BackgroundLarge-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. MethodsIn 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. FindingsAmong 291,624 drug entities identified in animal studies, 6{middle dot}7% entered clinical development and 3{middle dot}1% reached phase III/IV trials or regulatory approval. At the drug-disease level, 4{middle dot}4% entered clinical development and 1{middle dot}9% achieved translation. Restricting analyses to successfully linked ontology entities increased estimates to 11{middle dot}3% and 4{middle dot}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. InterpretationOnly 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. FundingSwiss National Science Foundation, UZH Digital Entrepreneurship Fellowship, Universities Federation for Animal Welfare. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched the literature for studies quantifying large-scale animal-to-human translation and factors associated with successful translation. Existing work was mainly limited to specific diseases, interventions, or manually curated datasets, and large-scale linkage of animal and clinical evidence remained limited. Added value of this studyWe developed a natural language processing pipeline linking more than 500,000 animal studies to clinical trial and regulatory approval data. The study provides large-scale estimates of translation and identifies experimental characteristics associated with successful translation. Implications of all the available evidenceThe findings suggest that only a minority of interventions tested in animals progress to advanced clinical development or regulatory approval. Greater species diversity and reporting of blinding were associated with improved translation. Automated evidence synthesis may support more systematic evaluation of translational research practices.