Run-in phase III trial design with pharmacodynamics predictive biomarkers

Abstract

Background: Developments in biotechnology have stimulated the use of predictive biomarkers to identify patients who are likely to benefit from a targeted therapy. Several randomized phase III designs have been introduced for development of a targeted therapy using a diagnostic test. Most such designs require biomarkers measured before treatment. In many cases, it has been very difficult to identify such biomarkers. Promising candidate biomarkers can sometimes be effectively measured after a short run-in period on the new treatment.

Methods: We introduce a new design for phase III trials with a candidate predictive pharmacodynamic biomarker measured after a short run-in period. Depending on the therapy and the biomarker performance, the trial would either randomize all patients but perform a separate analysis on the biomarker-positive patients or only randomize marker-positive patients after the run-in period. We evaluate the proposed design compared with the conventional phase III design and discuss how to design a run-in trial based on phase II studies.

Results: The proposed design achieves a major sample size reduction compared with the conventional randomized phase III design in many cases when the biomarker has good sensitivity (≥0.7) and specificity (≥0.7). This requires that the biomarker be measured accurately and be indicative of drug activity. However, the proposed design loses some of its advantage when the proportion of potential responders is large (>50%) or the effect on survival from run-in period is substantial.

Conclusions: Incorporating a pharmacodynamic biomarker requires careful consideration but can expand the capacity of clinical trials to personalize treatment decisions and enhance therapeutics development.

Figure 1.

The schema of run-in design with three motivating examples: 1) immunological biomarker in vaccine therapy, 2) imaging biomarkers for early response, and 3) mechanistic markers for drug resistance. PD = pharmacodynamics.

Figure 2.

With sample sizes that give 80% power for the standard design, the trial-level power with the run-in design (solid lines) is shown when randomizing all patients, for a series of sensitivity and specificity of the biomarker, under 25%, 50%, and 75% prevalence of true responders, with no run-in effect (= 1).

Figure 3.

Average simulated number of events needed to have 80% trial-level power in the standard design (dotted line) and then run-in design when randomizing all patients (solid lines, top panels) or randomizing only M+ patients (solid lines, bottom panels), by sensitivity and specificity of the marker, under 25%, 50%, and 75% true responders, with no run-in effect (= 1). The standard design needs 1440, 420, and 210 events to have 80% power under 25%, 50%, and 75% true responders, respectively.