Statistical NLP for Optimization of Clinical Trial Success Prediction in Pharmaceutical R&D
Open Access DepositedPharmaceutical research and development is plagued by high attrition rates and enormous clinical trial costs, particularly within neuroscience, where overall industry success rates are below 10%. Timely identification of promising programs can streamline resource allocation and reduce financial risk. This praxis presents the development and evaluation of an NLP‐enabled probabilistic classifier designed to estimate the probability of technical and regulatory success (pTRS) for clinical trials in the field of neuroscience. Leveraging data from the ClinicalTrials.gov database and success labels from the recently developed Clinical Trial Outcome dataset, the classifier extracts text‐based clinical trial features using statistical NLP techniques. These features were integrated into several non-LLM frameworks (logistic regression, gradient boosting, and random forest) to generate calibrated probability scores. Model performance was assessed on a retrospective dataset of 101,145 completed clinical trials spanning 1976–2024, achieving an overall ROC-AUC of 0.64. An LLM-based predictive model was then built using BioBERT, a domain-specific language representation encoder. The BioBERT-based model achieved an overall ROC-AUC of 0.74 and a Brier Score of 0.185, indicating its predictions had, on average, 40% less squared error than would be observed using industry benchmarks. The BioBERT-based model also made trial outcome predictions that were superior than benchmark values 70% of the time, overall. By integrating NLP‐driven insights into drug development decision‐making, this work aims to enhance strategic planning and optimize investment allocation in neuroscience programs.
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