BACKGROUND: The phase I program of anticancer agents usually consists of multiple dose escalation studies to select a safe dose for various administration schedules. We hypothesized that pharmacokinetic and pharmacodynamic (PK-PD) modeling of an initial phase I study (stage 1) can be used for selection of an optimal starting dose for subsequent studies (stage 2) and that a post-hoc PK-PD analysis enhances the selection of a recommended dose for phase II evaluation. The aim of this analysis was to demonstrate that this two-stage model-based design, which does not interfere in the conduct of trials, is safe, efficient and effective. METHODS: PK and PD data of dose escalation studies were simulated for nine compounds and for five administration regimens (stage 1) for drugs with neutropenia as dose-limiting toxicity. PK-PD models were developed for each simulated study and were used to determine a starting dose for additional phase I studies (stage 2). The model-based design was compared to a conventional study design regarding safety (number of dose-limiting toxicities (DLTs)), efficiency (number of patients treated with a dose below the recommended dose) and effectiveness (precision of dose selection). Retrospective data of the investigational anticancer drug indisulam were used to show the applicability of the model-based design. RESULTS: The model-based design was as safe as the conventional design (median number of DLTs = 3) and resulted in a reduction of the number of patients who were treated with a dose below the recommended dose (-27%, power 89%). A post-hoc model-based determination of the recommended dose for future phase II studies was more precise than the conventional selection of the recommended dose (root mean squared error 8.3% versus 30%). CONCLUSIONS: A two-stage model-based phase I design is safe for anticancer agents with dose-limiting myelosuppression and may enhance the efficiency of dose escalation studies by reducing the number of patients treated with a dose below the recommended dose and by increasing the precision of dose selection for phase II evaluation.
BACKGROUND: The phase I program of anticancer agents usually consists of multiple dose escalation studies to select a safe dose for various administration schedules. We hypothesized that pharmacokinetic and pharmacodynamic (PK-PD) modeling of an initial phase I study (stage 1) can be used for selection of an optimal starting dose for subsequent studies (stage 2) and that a post-hoc PK-PD analysis enhances the selection of a recommended dose for phase II evaluation. The aim of this analysis was to demonstrate that this two-stage model-based design, which does not interfere in the conduct of trials, is safe, efficient and effective. METHODS: PK and PD data of dose escalation studies were simulated for nine compounds and for five administration regimens (stage 1) for drugs with neutropenia as dose-limiting toxicity. PK-PD models were developed for each simulated study and were used to determine a starting dose for additional phase I studies (stage 2). The model-based design was compared to a conventional study design regarding safety (number of dose-limiting toxicities (DLTs)), efficiency (number of patients treated with a dose below the recommended dose) and effectiveness (precision of dose selection). Retrospective data of the investigational anticancer drug indisulam were used to show the applicability of the model-based design. RESULTS: The model-based design was as safe as the conventional design (median number of DLTs = 3) and resulted in a reduction of the number of patients who were treated with a dose below the recommended dose (-27%, power 89%). A post-hoc model-based determination of the recommended dose for future phase II studies was more precise than the conventional selection of the recommended dose (root mean squared error 8.3% versus 30%). CONCLUSIONS: A two-stage model-based phase I design is safe for anticancer agents with dose-limiting myelosuppression and may enhance the efficiency of dose escalation studies by reducing the number of patients treated with a dose below the recommended dose and by increasing the precision of dose selection for phase II evaluation.
Authors: D Moneta; C Geroni; O Valota; P Grossi; M J A de Jonge; M Brughera; E Colajori; M Ghielmini; C Sessa Journal: Eur J Cancer Date: 2003-03 Impact factor: 9.162
Authors: C Terret; S Zanetta; H Roché; J H M Schellens; M N Faber; J Wanders; M Ravic; J P Droz Journal: Eur J Cancer Date: 2003-05 Impact factor: 9.162
Authors: E Raymond; W W ten Bokkel Huinink; J Taïeb; J H Beijnen; S Faivre; J Wanders; M Ravic; P Fumoleau; J P Armand; J H M Schellens Journal: J Clin Oncol Date: 2002-08-15 Impact factor: 44.544
Authors: A Pessina; B Albella; M Bayo; J Bueren; P Brantom; S Casati; C Croera; G Gagliardi; P Foti; R Parchment; D Parent-Massin; G Schoeters; Y Sibiril; R Van Den Heuvel; L Gribaldo Journal: Toxicol Sci Date: 2003-07-25 Impact factor: 4.849
Authors: Ch Van Kesteren; R A A Mathôt; E Raymond; J P Armand; Ch Dittrich; H Dumez; H Roché; J P Droz; C Punt; M Ravic; J Wanders; J H Beijnen; P Fumoleau; J H M Schellens Journal: J Clin Oncol Date: 2002-10-01 Impact factor: 44.544
Authors: Elena Soto; Ron J Keizer; Iñaki F Trocóniz; Alwin D R Huitema; Jos H Beijnen; Jan H M Schellens; Jantien Wanders; Josep María Cendrós; Rosendo Obach; Concepción Peraire; Lena E Friberg; Mats O Karlsson Journal: Invest New Drugs Date: 2010-05-07 Impact factor: 3.850
Authors: Jian-Feng Lu; Erik Rasmussen; Beth Y Karlan; Ignace B Vergote; Lynn Navale; Mita Kuchimanchi; Rebeca Melara; Daniel E Stepan; David M Weinreich; Yu-Nien Sun Journal: Cancer Chemother Pharmacol Date: 2012-01-01 Impact factor: 3.333
Authors: Ron J Keizer; Anthe S Zandvliet; Jos H Beijnen; Jan H M Schellens; Alwin D R Huitema Journal: Invest New Drugs Date: 2011-05-28 Impact factor: 3.850
Authors: J G C van Hasselt; A Gupta; Z Hussein; J H Beijnen; J H M Schellens; A D R Huitema Journal: CPT Pharmacometrics Syst Pharmacol Date: 2015-06-30