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Predictive Models and Enrichment Study Design Strategies

From: Roberto Gomeni <roberto.gomeni>
Date: Wed, 3 Sep 2014 09:02:55 +0200


Registration is open for the:


 


Special Course on Predictive Models and Enrichment Study Design Strategies


 

October 20-22, 2014: The Carolina Inn on the Campus of the University of
North Carolina at Chapel Hill, NC (USA)

 

This is a 3-day advanced course designed to help scientists to develop and
use predictive models for implementing enrichment study design with
particular emphasis in CNS diseases.

 

The very high frequency of failed and negative clinical trials has been
recognized as a critical issue for the clinical development of novel
antidepressant treatments. A trial is considered failed when the reference
treatment does not differentiate from placebo. The level of placebo response
has been shown to strongly affect the probability of detecting active
treatment superiority. Furthermore, growing evidence indicates that placebo
responses in antidepressant and antipsychotic trials have been gradually
increasing over time. These findings indicate that there is an urgent need
for exploring, evaluating and implementing novel study design to counteract
the uncontrolled and time varying level of placebo response and for
improving the overall efficiency of clinical trials.

 

In this workshop, we will present and discuss different methodologies for
improving the efficiency of placebo-controlled clinical trial of
antidepressant drugs by implementing novel study designs and novel
methodologies for data analysis.

 

In particular we will discuss the use of enrichment strategies that have
been recommended as an effective methodology for improving the efficiency of
drug development [Guidance for Industry Enrichment Strategies for Clinical
Trials to Support Approval of Human Drugs and Biological Products. Draft
Guidance. December 2012].

 

Three study design strategies will be presented: the sequential parallel
comparison design (SPCD) recommended for reducing both the placebo response
and the required sample size, the lead-in study design to screen out
patients who are likely to be placebo responders using data collected in in
a short double blinded placebo lead-in phase, and the adaptive randomization
design (ARD) to identify, during the patients accrual process, uninformative
centers (centers with excessively low or excessively high placebo response)
in an ongoing clinical trial and, based on this information, to implement an
adaptive randomization scheme to stop the inclusion of patients in the
uninformative centers and increase the inclusion of patients in the
informative centers.

 

Two novel methodologies for data analyses will be discussed. The first one,
classified as a post-hoc analysis, is based on a novel analytic technique
called band-pass filter to minimize the interference of confounding signals
such as the ones associated with excessively high or low placebo response
characterizing the placebo response in the different recruitment centers.

 

The second methodology proposes a novel approach for estimating the
treatment effect (TE) using a Non-Linear Mixed-Effect Model for Repeated
Measures approach (NLMMRM). NLMMRM can be considered as the natural
generalization of the likelihood-based mixed-effects model for repeated
measures (MMRM) approach that is today recognized as the most efficient and
reliable method for conducting the primary analysis of continuous endpoints
in longitudinal clinical trials. The novel data analysis approach is based
on the use of the center-specific level of placebo response as a weighting
factor in the evaluation of TE assuming that centers with high placebo
response are less informative than the others for estimating the 'true'
treatment effect.

 

We will discuss how placebo response models can be used to build prior
knowledge on the expected time-course of placebo response and how disease
progression models can be used to predict the individual disease trajectory
deterioration shape. During the workshop we will illustrate how
probabilistic models can be used for classifying subjects as placebo
responders or slow/fast disease progressors using a Bayesian framework
applied to information collected in an early phase of the trial.

 


The workshop includes lectures and hands-on training on concepts,
applications, and software tools. Cases-study in Depression, Alzheimer's
disease and in a Rare Diseases (Amyotrophic Lateral Sclerosis) will be used
to implement study design where patients with more rapid progression are
selected based on a disease-progression model.


 


Additional details on the workshop are available at:
<http://www.pharmacometricaworkshop.webs.com>
www.pharmacometricaworkshop.webs.com


 


References


 

R. Gomeni and E. Merlo-Pich. Bayesian modeling and ROC analysis to predict
placebo responders using clinical score measured in the initial weeks of
treatment in depression trials. Br J Clin Pharmacol. May;63(5):595-613,
2007.

E Merlo-Pich and R Gomeni. Model-based Approach and Signal Detection Theory
to Evaluate the Performance of Recruitment Centers in Clinical Trials with
Antidepressant Drugs. Clin Pharmacol Ther. 2008 Sep;84(3):378-84.

E. Merlo-Pich, R. C. Alexander, M. Fava, R. Gomeni. A new population
enrichment strategy to improve efficiency of placebo-controlled clinical
trial in depression. Clin. Pharmacol. Ther., 2010 Nov;88(5):634-42.

R. Gomeni, E. Merlo-Pich. Trial Simulation to estimate Type I error when a
population window enrichment strategy is used to improve efficiency of
clinical trials in depression. Eur Neuropsychopharmacol. 2012
Jan;22(1):44-52.

R. Gomeni, M. Simeoni, M. Zvartau-Hind, M. Irizarry, M. Gold. Disease System
Analysis approach for modelling Alzheimer's disease progression in subjects
on stable acetylcholinesterase inhibitors therapy. Alzheimers Dement. 2012
Jan; 8(1):39-50.

N. Goyal and R. Gomeni. Exposure-Response modeling of anti-depressant
treatments: the confounding role of placebo effect. J Pharmacokinet
Pharmacodyn. 2013; 40(3):389-99.

R. Gomeni R, M. Fava. The Pooled Resource Open-Access ALS Clinical Trials
Consortium. Amyotrophic lateral sclerosis disease progression model.
Amyotroph Lateral Scler Frontotemporal Degener. 2014 Mar;15(1-2):119-29

R. Gomeni. Use of predictive models in CNS diseases. Current Opinion in
Pharmacology. 2014; 14: 23-29.

S. Yang, R. Gomeni, M. Beerahee. Does Short-Term Placebo Response Predict
the Long-Term Observation? Meta-Analysis on Forced Expiratory Volume in 1
Second From Asthma Trials. The Journal of Clinical Pharmacology, 2014, DOI:
10.1002/jcph.329 [Epub ahead of print].

R. Gomeni and N. Goyal. Adaptive Randomization Study Design in Clinical
Trials for Psychiatric Disorders. J Biomet Biostat 2014, 5:187,1-6

 


Received on Wed Sep 03 2014 - 03:02:55 EDT

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