Incorporating pharmacokinetic information in phase I studies in small populations

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Description:	Ursino, M (INSERM, Paris) Tuesday 7th July 2015, 15:00 - 15:30


Created:	2015-07-10 13:26
Collection:	Design and Analysis of Experiments
Publisher:	Isaac Newton Institute
Copyright:	Ursino, M
Language:	eng (English)


Abstract:	Objectives: To review and extend existing methods which take into account PK measurements in sequential adaptive designs for early dose-finding studies in small populations, and to evaluate the impact of PK measurements on the selection of the maximum tolerated dose (MTD). Methods: This work is set in the context of phase I dose-finding studies in oncology, where the objective is to determine the MTD while limiting the number of patients exposed to high toxicity. We assume toxicity to be related to a PK measure of exposure, and consider 6 possible dose levels. Three Bayesian phase I methods from the literature were modified and compared to the standard continual reassessment method (CRM) through simulations. In these methods PK measurement, more precisely the AUC, is present as covariate for a link function of probability of toxicity (Piantadosi and Liu (1996), Whitehead et al. (2007)) and/or as dependent variable in linear regression versus dose (Patterson et al. (1999), Whitehead et al. (2007)). We simulated trials based on a model for the TGF- inhibitor LY2157299 in patients with glioma (Gueorguieva et al., 2014). The PK model was reduced to a one-compartment model with first-order absorption as in Lestini et al. (2014) in order to achieve a close solution for the probability of toxicity. Toxicity was assumed to occur when the value of a function of AUC was above a given threshold, either in the presence or without inter-individual variability (IIV). For each scenario, we simulated 1000 trials with 30, 36 and 42 patients. Results: Methods which incorporate PK measurements had good performance when informative prior knowledge was available in term of Bayesian prior distribution on parameters. On the other hand, keeping fixed the priors information, methods that included PK values as covariate were less flexible and lead to trials with more toxicities than the same trials with CRM. Conclusion: Incorporating PK values as covariate did not alter the efficiency of estimation of MTD when the prior was well specified. The next step will be to assess the impact on the estimation of the dose-concentration-toxicity curve for the different approaches and to explore the introduction of fully model-based PK/PD in dose allocation rules.

Abstract:

Objectives: To review and extend existing methods which take into account PK measurements in sequential adaptive designs for early dose-finding studies in small populations, and to evaluate the impact of PK measurements on the selection of the maximum tolerated dose (MTD). Methods: This work is set in the context of phase I dose-finding studies in oncology, where the objective is to determine the MTD while limiting the number of patients exposed to high toxicity. We assume toxicity to be related to a PK measure of exposure, and consider 6 possible dose levels. Three Bayesian phase I methods from the literature were modified and compared to the standard continual reassessment method (CRM) through simulations. In these methods PK measurement, more precisely the AUC, is present as covariate for a link function of probability of toxicity (Piantadosi and Liu (1996), Whitehead et al. (2007)) and/or as dependent variable in linear regression versus dose (Patterson et al. (1999), Whitehead et al. (2007)). We simulated trials based on a model for the TGF- inhibitor LY2157299 in patients with glioma (Gueorguieva et al., 2014). The PK model was reduced to a one-compartment model with first-order absorption as in Lestini et al. (2014) in order to achieve a close solution for the probability of toxicity. Toxicity was assumed to occur when the value of a function of AUC was above a given threshold, either in the presence or without inter-individual variability (IIV). For each scenario, we simulated 1000 trials with 30, 36 and 42 patients. Results: Methods which incorporate PK measurements had good performance when informative prior knowledge was available in term of Bayesian prior distribution on parameters. On the other hand, keeping fixed the priors information, methods that included PK values as covariate were less flexible and lead to trials with more toxicities than the same trials with CRM. Conclusion: Incorporating PK values as covariate did not alter the efficiency of estimation of MTD when the prior was well specified. The next step will be to assess the impact on the estimation of the dose-concentration-toxicity curve for the different approaches and to explore the introduction of fully model-based PK/PD in dose allocation rules.

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