Fangxin Hong1, Richard Simon. 1. Affiliations of authors: Departments of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA (FH); Biometric Research Branch, National Cancer Institute, Bethesda, MD (RS).
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.
RCT Entities:
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.
Authors: David Avigan; Baldev Vasir; Jianlin Gong; Virginia Borges; Zekui Wu; Lynne Uhl; Michael Atkins; James Mier; David McDermott; Therese Smith; Nancy Giallambardo; Carolyn Stone; Kim Schadt; Jennifer Dolgoff; Jean-Claude Tetreault; Marisa Villarroel; Donald Kufe Journal: Clin Cancer Res Date: 2004-07-15 Impact factor: 12.531
Authors: D S Echt; P R Liebson; L B Mitchell; R W Peters; D Obias-Manno; A H Barker; D Arensberg; A Baker; L Friedman; H L Greene Journal: N Engl J Med Date: 1991-03-21 Impact factor: 91.245
Authors: Cecilia Superchi; Florie Brion Bouvier; Chiara Gerardi; Montserrat Carmona; Lorena San Miguel; Luis María Sánchez-Gómez; Iñaki Imaz-Iglesia; Paula Garcia; Jacques Demotes; Rita Banzi; Raphaël Porcher Journal: BMJ Open Date: 2022-05-06 Impact factor: 3.006