scholarly journals Addition of erlotinib to fluoropyrimidine-oxaliplatin-based chemotherapy with or without bevacizumab: Two sequential phase I trials

2011 ◽  
Vol 2 (3) ◽  
pp. 449-455 ◽  
Author(s):  
CHIARA CARLOMAGNO ◽  
GENNARO DANIELE ◽  
ROBERTO BIANCO ◽  
ROBERTA MARCIANO ◽  
VINCENZO DAMIANO ◽  
...  
Author(s):  
Burak Kürsad Günhan ◽  
Sebastian Weber ◽  
Abdelkader Seroutou ◽  
Tim Friede

Abstract Background: Phase I dose-escalation trials constitute the first step in investigating the safety of potentially promising drugs in humans. Conventional methods for phase I dose-escalation trials are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial.Methods: Here, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing dose-escalation trial to inform decisions on the dose level for the next dose cohort. For this purpose, we adapted the time-to-event pharmacokinetics (TITE-PK) model, which were originally developed for simultaneous investigation of multiple schedules. TITE-PK integrates information from multiple schedules using a pharmacokinetics (PK) model. Results: In a simulation study, the developed appraoch is compared to the bridging continual reassessment method and the Bayesian logistic regression model using a meta-analytic-prior. TITE-PK results in better performance than comparators in terms of recommending acceptable dose and avoiding overly toxic doses for sequential phase I trials in most of the scenarios considered. Furthermore, better performance of TITE-PK is achieved while requiring similar number of patients in the simulated trials. For the scenarios involving one schedule, TITE-PK displays similar performance with alternatives in terms of acceptable dose recommendations. The R and Stan code for the implementation of an illustrative sequential phase I trial example is publicly available (https://github.com/gunhanb/TITEPK sequential).Conclusion: In sequential phase I dose-escalation trials, the use of all relevant information is of great importance. For these trials, the adapted TITE-PK which combines information using PK principles is recommended.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Burak Kürsad Günhan ◽  
Sebastian Weber ◽  
Abdelkader Seroutou ◽  
Tim Friede

Abstract Background Conventional methods for phase I dose-escalation trials in oncology are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial. Methods Here, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing schedules to inform decisions on the dose level for the next dose cohort. For this purpose, we adapted the time-to-event pharmacokinetics (TITE-PK) model, which were originally developed for simultaneous investigation of multiple schedules. TITE-PK integrates information from multiple schedules using a pharmacokinetics (PK) model. Results In a simulation study, the developed approach is compared to the bridging continual reassessment method and the Bayesian logistic regression model using a meta-analytic-predictive prior. TITE-PK results in better performance than comparators in terms of recommending acceptable dose and avoiding overly toxic doses for sequential phase I trials in most of the scenarios considered. Furthermore, better performance of TITE-PK is achieved while requiring similar number of patients in the simulated trials. For the scenarios involving one schedule, TITE-PK displays similar performance with alternatives in terms of acceptable dose recommendations. The and code for the implementation of an illustrative sequential phase I trial example in oncology is publicly available (https://github.com/gunhanb/TITEPK_sequential). Conclusion In phase I oncology trials with sequential multiple schedules, the use of all relevant information is of great importance. For these trials, the adapted TITE-PK which combines information using PK principles is recommended.


1999 ◽  
Vol 35 ◽  
pp. S283
Author(s):  
C. Twelves ◽  
J.L. Misset ◽  
M. Villalona-Calero ◽  
D. Ryan ◽  
J. Clark ◽  
...  

1996 ◽  
Vol 7 (7) ◽  
pp. 728-733 ◽  
Author(s):  
Richard Pazdur ◽  
Yvonne Lassere ◽  
Enrique Diaz-Canton ◽  
Beth Bready ◽  
Dah H Ho

PLoS ONE ◽  
2012 ◽  
Vol 7 (12) ◽  
pp. e51039 ◽  
Author(s):  
Christophe Le Tourneau ◽  
Hui K. Gan ◽  
Albiruni R. A. Razak ◽  
Xavier Paoletti

2016 ◽  
Vol 43 (4) ◽  
pp. E153-E160 ◽  
Author(s):  
Denise Weiss ◽  
Laurel Northouse ◽  
Sonia Duffy ◽  
Berit Ingersoll-Dayton ◽  
Maria Katapodi ◽  
...  

Author(s):  
Pavel Mozgunov ◽  
Rochelle Knight ◽  
Helen Barnett ◽  
Thomas Jaki

There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial.


Author(s):  
Dixie-Lee W. Esseltine ◽  
David P. Schenkein
Keyword(s):  
Phase I ◽  

2011 ◽  
Vol 17 (3) ◽  
pp. 200-203 ◽  
Author(s):  
Matthew Macaluso ◽  
Michael Krams ◽  
Sheldon H. Preskorn
Keyword(s):  
Phase I ◽  

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