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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:PKPD modelling to optimize dose-escalation trials
in Oncology - Savelieva Praz\, M (Novartis)
DTSTART;TZID=Europe/London:20110819T110000
DTEND;TZID=Europe/London:20110819T114500
UID:TALK32414AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/32414
DESCRIPTION:The purpose of dose-escalation trials in Oncology
is to determine the highest dose that would provid
e the desirable treatment effect without unaccepta
ble toxicity\, a so-called Maximum Tolerated Dose
(MTD). Neuenschwander et al. [1] introduced a Baye
sian 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 derive
d from the posterior distribution of the model par
ameters. This model output helps the clinical team
to define the dose for the next patient cohort. T
he approach not only allows for more efficient pat
ient allocation\, but also for inclusion of prior
information regarding the shape of the dose-toxici
ty curve. However\, in its simplest form\, the met
hod relies on an assumption that toxicity events a
re driven solely by the dose\, and that the patien
ts' population is homogeneous w.r.t. the response.
This is rarely the case\, in particular in a very
heterogeneous cancer patients' population. Strati
fication of the response by covariates\, such as d
isease\, disease status\, baseline characteristics
\, etc.\, could potentially reduce the variability
and allow to identify subpopulations that are mor
e or less prone to experience an event. This strat
ification requires enough data been available\, th
at is rarely the case when toxicity events are use
d as a response variable. We propose to use a PKPD
approach to model the mechanistic process underly
ing the toxicity. In such a way\, all the data\, a
lso including those from patients that have not (y
et) experienced a toxicity event\, are taken into
account. Furthermore\, various covariates can be i
ntroduced into the model\, and predictions for pat
ients' subgroups of interest could be done. Thus\,
we will aim to reduce the number of patients expo
sed to low and inefficient doses\, the number of c
ohorts and the total number of patients required t
o define MTD. Finally we hope to reach MTD faster
at a lower cost. We test the methodology on a conc
rete example and discuss the benefits and drawback
s of the approach. References [1] Neuenschwander B
.\, Branson M.\, Gsponer T. Critical aspects of th
e Bayesian approach to Phase I cancer trials\, Sta
tistics in Medicine 2008\, 27:2420-2439 [2] Pianta
dosi S. and Liu G\, Improved Designs for Dose Esca
lation Studies Using Pharmacokinetic measurements\
, Statistics in Medicine 1996\, 15\, 1605-1618 [3]
Mller\, P. and Quintana\, F. A. (2010) Random Par
tition Models with Regression on Covariates. Journ
al of Statistical Planning and Inference\, 140(10)
\, 2801-2808 [4] Berry S.\, Carlin B.\, Lee J. and
Mller P. Bayesian Adaptive Methods for Clinical T
rials\, CRC Press\, 2010 \n\n\n
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:Mustapha Amrani
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