Background

Traditionally, the purpose of a dose-finding design in cancer is to find the maximum tolerated dose based solely on toxicity. However, for molecularly targeted agents, little toxicity may arise within the therapeutic dose range and the dose–response curves may not be monotonic. This challenges the principle that more is better, which is widely accepted for conventional chemotherapy.

Methods

We propose three adaptive dose-finding designs for trials evaluating molecularly targeted agents, for which the dose–response curves are unimodal or plateaued. The goal of these designs is to find the optimal biological dose, which is defined as the lowest dose with the highest rate of efficacy while safe. The first proposed design is parametric and assumes a logistic dose–efficacy curve for dose finding, the second design is nonparametric and uses the isotonic regression to identify the optimal biological dose, and the third design has the spirit of a ‘semiparametric’ approach by assuming a logistic model only locally around the current dose.

Results

We conducted extensive simulation studies to investigate the operating characteristics of the proposed designs. Simulation studies show that the nonparametric and semiparametric designs have good operating characteristics for finding the optimal biological dose.

Limitations

The proposed designs assume a binary endpoint. Extension of the proposed designs to ordinal and time-to-event endpoints is worth further investigation.

Conclusion

Among the three proposed designs, the nonparametric and semiparametric designs yield consistently good operating characteristics and thus are recommended for practical use. The software to implement these two designs is available for free download at http://odin.mdacc.tmc.edu/~yyuan/.

Search results

Modelling of repeated ordered measurements by isotonic sequential regression

Adaptive designs for identifying optimal biological dose for molecularly targeted agents