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Related Experiment Video

Updated: May 29, 2026

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Integrating predictive biomarkers and classifiers into oncology clinical development programmes.

Robert A Beckman¹, Jason Clark, Cong Chen

¹Clinical Research Oncology, Daiichi-Sankyo Pharmaceutical Development, 399 Thornall Street, Edison, New Jersey 08837, USA. eniac1@snip.net

Nature Reviews. Drug Discovery

|October 1, 2011

Summary

This summary is machine-generated.

Predictive biomarkers in oncology drug development can improve clinical trials, but failures increase costs. This study introduces adaptive, data-driven methods to integrate biomarkers, maximizing their value and optimizing oncology drug development.

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Last Updated: May 29, 2026

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Area of Science:

Oncology
Biomarker Discovery
Clinical Trial Design

Background:

Predictive biomarkers are crucial for personalized oncology drug development.
Biomarker failures can increase clinical trial costs, complexity, and duration.
Optimizing biomarker integration is essential for efficient drug development.

Purpose of the Study:

To present adaptive, data-driven methods for integrating predictive biomarkers into oncology clinical programs.
To ensure biomarkers are utilized proportionally to their validated predictive value.
To maximize the value derived from predictive biomarkers in drug development.

Main Methods:

Adaptive integration of predictive biomarkers.
Data-driven emphasis based on evidence of clinical predictive value.
Optimizing clinical program design for biomarker value.

Main Results:

Development of a framework for adaptive biomarker integration.
Demonstration of methods to proportionally emphasize biomarkers based on evidence.
A program designed to optimally harvest biomarker value.

Conclusions:

Adaptive, data-driven integration of predictive biomarkers enhances oncology drug development.
This approach mitigates risks associated with biomarker failures.
Optimized biomarker utilization leads to more efficient and valuable drug development programs.