HIV 2-LTR experiment design optimization.

Lamont Cannon,Cesar A Vargas-Garcia,Aditya Jagarapu,Ryan Zurakowski,Michael J Piovoso,Yuntao Wu

doi:10.1371/journal.pone.0206700

Lamont Cannon, Cesar A Vargas-Garcia + Show 4 more

Open Access

https://doi.org/10.1371/journal.pone.0206700

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Journal: PloS one	Publication Date: Nov 8, 2018
Citations: 2	License type: CC BY 4.0

Affiliation: University of Pennsylvania, University of Delaware

Abstract

Clinical trials are necessary in order to develop treatments for diseases; however, they can often be costly, time consuming, and demanding to the patients. This paper summarizes several common methods used for optimal design that can be used to address these issues. In addition, we introduce a novel method for optimizing experiment designs applied to HIV 2-LTR clinical trials. Our method employs Bayesian techniques to optimize the experiment outcome by maximizing the Expected Kullback-Leibler Divergence (EKLD) between the a priori knowledge of system parameters before the experiment and the a posteriori knowledge of the system parameters after the experiment. We show that our method is robust and performs equally well if not better than traditional optimal experiment design techniques.

Highlights

Amid the increasing costs of carrying out experiments coupled with a decreasingly generous funding environment, there is an expanding charge to apply optimization methods to clinical trial design in order to maximize the amount of information that can be garnered from the resulting data [1,2,3,4]
In the previous integrase inhibitor intensification Human Immunodeficiency Virus (HIV) 2-LTR study done by Buzon et al, a total of six samples were taken per patient
The intent of this paper is to introduce our novel Expected Kullback-Leibler Divergence (EKLD) method for optimal experiment design

Summary

Introduction

Amid the increasing costs of carrying out experiments coupled with a decreasingly generous funding environment, there is an expanding charge to apply optimization methods to clinical trial design in order to maximize the amount of information that can be garnered from the resulting data [1,2,3,4]. Is the monetary cost a principal concern, when the study contains human subjects, the overall burden to the patient must be considered The latter is meticulously controlled under regulations imposed by the Institutional Review Board (IRB) [5].

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