CTP-LLM: Clinical Trial Phase Transition Prediction Using Large Language Models (2408.10995v1)

Abstract: New medical treatment development requires multiple phases of clinical trials. Despite the significant human and financial costs of bringing a drug to market, less than 20% of drugs in testing will make it from the first phase to final approval. Recent literature indicates that the design of the trial protocols significantly contributes to trial performance. We investigated Clinical Trial Outcome Prediction (CTOP) using trial design documents to predict phase transitions automatically. We propose CTP-LLM, the first LLM based model for CTOP. We also introduce the PhaseTransition (PT) Dataset; which labels trials based on their progression through the regulatory process and serves as a benchmark for CTOP evaluation. Our fine-tuned GPT-3.5-based model (CTP-LLM) predicts clinical trial phase transition by analyzing the trial's original protocol texts without requiring human-selected features. CTP-LLM achieves a 67% accuracy rate in predicting trial phase transitions across all phases and a 75% accuracy rate specifically in predicting the transition from Phase~III to final approval. Our experimental performance highlights the potential of LLM-powered applications in forecasting clinical trial outcomes and assessing trial design.

• The paper introduces a GPT-3.5 fine-tuned CTP-LLM model that predicts clinical trial phase transitions using protocol data.
• It leverages a novel PhaseTransition dataset to achieve 67% overall accuracy and 75% accuracy for Phase III approval predictions.
• The study highlights key trial design factors like participant criteria and study descriptions to inform better clinical trial strategies.

CTP-LLM: Clinical Trial Phase Transition Prediction Using LLMs

The paper "CTP-LLM: Clinical Trial Phase Transition Prediction Using LLMs" addresses the critical issue of predicting the outcomes of clinical trials based on protocol design documents. The research explores Clinical Trial Outcome Prediction (CTOP), leveraging a unique approach involving LLMs to predict trial phase transitions automatically. Given the significant human and financial costs associated with bringing drugs to market and the low success rates of clinical trial advancements, this paper proposes a novel model named CTP-LLM, which aims to enhance predictive accuracy using LLM techniques.

Methodological Overview

The research presents a comprehensive framework for predicting clinical trial outcomes. Central to this framework is the CTP-LLM model, a fine-tuned version of GPT-3.5. The authors introduce the PhaseTransition (PT) dataset, a novel resource specifically designed for CTOP containing labeled information about trial phase transitions. The PT dataset links trial protocols with data indicating whether a trial successfully advanced to the next phase.

Key components of the methodology include:

• Data Acquisition and Labeling: The compilation of a dataset from ClinicalTrials.gov and Biomedtracker, focusing on trial protocols and their phase transitions through the regulatory process.
• Model Development: Two models are proposed: CTP-LLM, a GPT-3.5-based model fine-tuned on the PT dataset, and BERT+RF, which combines clinical BERT embeddings with a Random Forest classifier.
• Validation: The models undergo rigorous evaluation to predict phase transitions across all trial phases, with the model's effectiveness measured by accuracy and F1 scores.

Experimental Results

The experimental setup involves splitting the data into training, validation, and testing sets based on trial modification dates to prevent temporal bias. The results indicate that:

• CTP-LLM Performance: The CTP-LLM model achieves a 67% accuracy rate for general phase transition predictions and 75% accuracy for predicting transitions from Phase III to final approval.
• Comparison with Baselines: CTP-LLM outperforms baseline models like Longformer and Llama 2, reflecting the enhanced capability of fine-tuned LLM in the specific domain of clinical trial prediction.

Implications and Future Directions

The paper underscores several significant implications:

• Data-Driven Decision Making: The ability to predict trial outcomes based on protocol documentation allows for better resource allocation and trial design, potentially reducing high attrition rates.
• Feature Importance: An analysis of feature importance reveals that participant selection criteria, paper descriptions, and the drug itself are critical factors influencing trial success, highlighting areas to focus on for improving trial designs.

Future research could explore the integration of additional trial phases, enhancing dataset quality, and potentially introducing explainability features to the CTP-LLM model. The possibility of integrating external variables such as economic factors, real-world evidence, and ongoing trial metrics could further refine predictive capabilities.

Conclusion

The paper introduces a significant advancement in clinical trial phase transition prediction by leveraging LLMs. The research demonstrates that fine-tuned models like CTP-LLM can provide accurate predictions and valuable insights into trial design factors that influence success rates. This work lays the foundation for future developments in predictive modeling within the clinical trial domain, promising to enhance efficiency and effectiveness in the drug development pipeline.