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PKPD modelling to optimize dose-escalation trials in Oncology

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Design and Analysis of Experiments

The purpose of dose-escalation trials in Oncology is to determine the highest dose that would provide the desirable treatment effect without unacceptable toxicity, a so-called Maximum Tolerated Dose (MTD). Neuenschwander et al. [1] introduced a Bayesian model-based approach that provides realistic inferential statements about the probabilities of a Dose-Limiting Toxicity (DLT) at each dose level. After each patient cohort, information is derived from the posterior distribution of the model parameters. This model output helps the clinical team to define the dose for the next patient cohort. The approach not only allows for more efficient patient allocation, but also for inclusion of prior information regarding the shape of the dose-toxicity curve. However, in its simplest form, the method relies on an assumption that toxicity events are driven solely by the dose, and that the patients’ population is homogeneous w.r.t. the response. This is rarely the case, in particular in a very heterogeneous cancer patients’ population. Stratification of the response by covariates, such as disease, disease status, baseline characteristics, etc., could potentially reduce the variability and allow to identify subpopulations that are more or less prone to experience an event. This stratification requires enough data been available, that is rarely the case when toxicity events are used as a response variable. We propose to use a PKPD approach to model the mechanistic process underlying the toxicity. In such a way, all the data, also including those from patients that have not (yet) experienced a toxicity event, are taken into account. Furthermore, various covariates can be introduced into the model, and predictions for patients’ subgroups of interest could be done. Thus, we will aim to reduce the number of patients exposed to low and inefficient doses, the number of cohorts and the total number of patients required to define MTD . Finally we hope to reach MTD faster at a lower cost. We test the methodology on a concrete example and discuss the benefits and drawbacks of the approach. References [1] Neuenschwander B., Branson M., Gsponer T. Critical aspects of the Bayesian approach to Phase I cancer trials, Statistics in Medicine 2008, 27:2420-2439 [2] Piantadosi S. and Liu G, Improved Designs for Dose Escalation Studies Using Pharmacokinetic measurements, Statistics in Medicine 1996, 15, 1605-1618 [3] Mller, P. and Quintana, F. A. (2010) Random Partition Models with Regression on Covariates. Journal of Statistical Planning and Inference, 140(10), 2801-2808 [4] Berry S., Carlin B., Lee J. and Mller P. Bayesian Adaptive Methods for Clinical Trials, CRC Press, 2010

This talk is part of the Isaac Newton Institute Seminar Series series.

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