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Predict progression free survival and overall survival using objective response rate for anti-PD1/PDL1 therapy development

  • 0Sanofi Bridgewater, New Jersey, US.
Bmc Cancer +

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July 29, 2024

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July 29, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study predicts long-term survival outcomes (median PFS and median OS) using early-phase objective response rates (ORR) in cancer immunotherapy trials. The developed model optimizes trial design and success probability for anti-PD1/PDL1 therapies.

Area Of Science

  • Oncology
  • Clinical Trial Design
  • Biostatistics

Background

  • Objective response rate (ORR) is a key early endpoint in oncology anti-PD1/PDL1 therapy trials, while progression-free survival (PFS) and overall survival (OS) are critical late-phase endpoints.
  • Predicting late-phase outcomes from early-phase data can optimize trial design and probability of success (POS) evaluations.
  • Existing literature has limited data on the association between ORR and survival outcomes (mPFS, mOS).

Purpose Of The Study

  • To develop predictive models for median PFS (mPFS) and median OS (mOS) based on early-phase objective response rate (ORR) in solid tumor anti-PD1/PDL1 therapy development.
  • To optimize late-phase clinical trial design and evaluate the probability of success (POS).

Main Methods

  • Constructed a comprehensive quantitative clinical trial landscape database (QLD) by integrating data from multiple sources.
  • Developed a generalizable algorithm for systematic, accurate, and complete structured data extraction.
  • Utilized a tree-based machine learning regression model incorporating tumor type, stage, line of therapy, and treatment class to predict mPFS and mOS from ORR.

Main Results

  • Identified over 150 late-phase clinical trials for predictive model development, significantly expanding upon existing literature.
  • The developed tree-based model demonstrated robust predictions, accounting for heterogeneity and borrowing strength across trials.
  • Cross-validation showed the model's predictive mean square error was competitive with advanced machine learning methods and superior to linear regression models.

Conclusions

  • The developed predictive model offers a robust and interpretable method for estimating late-phase survival outcomes from early-phase ORR data in oncology.
  • This approach can significantly inform and optimize the design of late-phase clinical trials for anti-PD1/PDL1 therapies.
  • The model provides a valuable tool for evaluating the probability of success in late-phase trials, particularly for combination therapies.

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