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SUMMARY:PKPD modelling to optimize dose-escalation trials in Oncology - Sa
 velieva Praz\, M (Novartis)
DTSTART:20110819T100000Z
DTEND:20110819T104500Z
UID:TALK32414@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:The purpose of dose-escalation trials in Oncology is to determ
 ine the highest dose that would provide the desirable treatment effect wit
 hout unacceptable toxicity\, a so-called Maximum Tolerated Dose (MTD). Neu
 enschwander et al. [1] introduced a Bayesian model-based approach that pro
 vides realistic inferential statements about the probabilities of a Dose-L
 imiting Toxicity (DLT) at each dose level. After each patient cohort\, inf
 ormation is derived from the posterior distribution of the model parameter
 s. This model output helps the clinical team to define the dose for the ne
 xt patient cohort. The approach not only allows for more efficient patient
  allocation\, but also for inclusion of prior information regarding the sh
 ape of the dose-toxicity curve. However\, in its simplest form\, the metho
 d relies on an assumption that toxicity events are driven solely by the do
 se\, and that the patients' population is homogeneous w.r.t. the response.
  This is rarely the case\, in particular in a very heterogeneous cancer pa
 tients' population. Stratification of the response by covariates\, such as
  disease\, disease status\, baseline characteristics\, etc.\, could potent
 ially reduce the variability and allow to identify subpopulations that are
  more or less prone to experience an event. This stratification requires e
 nough data been available\, that is rarely the case when toxicity events a
 re 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 covariate
 s can be introduced into the model\, and predictions for patients' subgrou
 ps of interest could be done. Thus\, we will aim to reduce the number of p
 atients exposed to low and inefficient doses\, the number of cohorts and t
 he total number of patients required to define MTD. Finally we hope to rea
 ch MTD faster at a lower cost. We test the methodology on a concrete examp
 le and discuss the benefits and drawbacks of the approach. References [1] 
 Neuenschwander B.\, Branson M.\, Gsponer T. Critical aspects of the Bayesi
 an approach to Phase I cancer trials\, Statistics in Medicine 2008\, 27:24
 20-2439 [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 Partiti
 on Models with Regression on Covariates. Journal of Statistical Planning a
 nd Inference\, 140(10)\, 2801-2808 [4] Berry S.\, Carlin B.\, Lee J. and M
 ller P. Bayesian Adaptive Methods for Clinical Trials\, CRC Press\, 2010 \
 n\n\n
LOCATION:Seminar Room 1\, Newton Institute
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