Development of Predictive Biomarkers for Molecular Targeted Therapy

Development of treatments with companion diagnostics requires major changes in the standard paradigms for the design and analysis of clinical trials. The standard clinical trial paradigm of employing broad eligibility criteria and focusing design and analysis on testing the null hypothesis of no overall average effect has led to large clinical trials that identified small average treatment effects and resulted in approval of drugs that do not benefit most patients. This problem has become exacerbated with the development of expensive molecularly targeted therapeutics. The established molecular heterogeneity of human cancer and the availability of genomic technologies for characterizing the molecular basis of individual tumors require the development of new paradigms for clinical trials that provide a reliable basis for predictive personalized oncology.

I will review two paradigms for the development of molecularly targeted drugs. The first is the Enrichment Design paradigm for settings where the drug effect is specific and the role of the target in disease pathogenesis is well understood. For settings where there is greater uncertainty, I will outline a prediction based approach to the analysis of randomized clinical trials that both preserves the type I error and provides a reliable internally validated indication classifier for identifying which patients are most likely or unlikely to benefit from the new treatment. This is a very structured approach which requires careful prospective planning but may provide the kinds of reliable individualized information which physicians and patients have long sought, but which have not been available from post-hoc subset analysis.

Developing new treatments with predictive biomarkers for identifying the patients who are most likely or least likely to benefit makes drug development more complex. For many new oncology drugs it is a science based approach which should increase the chance of success, may lead to more consistency in results among trials and reduce the number of patients who ultimately receive expensive drugs without benefit.