Clinical trials for predictive medicine

Area of Science:

• Biotechnology and Genomics
• Clinical Trial Design
• Precision Medicine

Background:

• Disease heterogeneity is evident clinically, now supported by biological insights from biotechnology and genomics.
• Molecular characterization of diseases offers opportunities for novel treatments but challenges clinical trial design.
• Current oncology practices are unsustainable, with broad treatments benefiting few patients, especially with expensive targeted therapies.

Purpose of the Study:

• To review prospective designs for developing new therapeutics and predictive biomarkers.
• To address the need for new paradigms in randomized clinical trial design and analysis for predictive medicine.
• To outline a prediction-based approach for analyzing clinical trials.

Main Methods:

• Review of prospective clinical trial designs for therapeutics and biomarkers.
• Exploration of designs ranging from single-biomarker development to genome-wide classifier discovery and validation.
• Development of a prediction-based analytical approach for randomized clinical trials.

Main Results:

• Proposed designs accommodate a spectrum of biomarker development, from single candidates to complex genomic classifiers.
• The prediction-based analysis preserves Type I error rates.
• The approach provides internally validated methods for predicting patient response to new regimens.

Conclusions:

• Advances in biotechnology and genomics necessitate adaptive clinical trial designs.
• A prediction-based analytical framework is crucial for effective personalized medicine.
• New trial designs and analysis methods ensure reliable identification of patient subgroups likely to benefit from novel treatments.