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. 2018 Nov 8;13(11):e0206700.
doi: 10.1371/journal.pone.0206700. eCollection 2018.

HIV 2-LTR experiment design optimization

LaMont Cannon  1   2 , Cesar A Vargas-Garcia  3   4 , Aditya Jagarapu  1 , Michael J Piovoso  3 , Ryan Zurakowski  1   3
Affiliations

HIV 2-LTR experiment design optimization

LaMont Cannon et al. PLoS One. .

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.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: RZ is named as an inventor on US Patent 9874563, “Detecting and quantifying cryptic HIV replication.” RZ provides consulting to Merck, Sharp, and Dohme, Corp. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Sigma point patient dynamics.
Fig 2
Fig 2. 6 point sample schedules.
Fig 3
Fig 3. 6 point schedule analysis.
Fig 4
Fig 4. 4 point sample schedules.
Fig 5
Fig 5. 4 point schedule analysis.
Fig 6
Fig 6. qPCR vs. ddPCR measurement error.
Fig 7
Fig 7. 6 point ddPCR schedule analysis.
Fig 8
Fig 8. 4 point ddPCR schedule analysis.
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