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A robust design for identification of the Parasite Clearance Estimator

Jamsen, Kris M., Duffell, Stephen B., Tarning, Joel, Price, Ric N. and Simpson, Julie A. (2013). A robust design for identification of the Parasite Clearance Estimator. Malaria Journal,12:410-1-410-6.

Document type: Journal Article
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IRMA ID 84373604xPUB11
NHMRC Grant No. 566855
Title A robust design for identification of the Parasite Clearance Estimator
Author Jamsen, Kris M.
Duffell, Stephen B.
Tarning, Joel
Price, Ric N.
Simpson, Julie A.
Journal Name Malaria Journal
Publication Date 2013
Volume Number 12
ISSN 1475-2875   (check CDU catalogue open catalogue search in new window)
Scopus ID 2-s2.0-84887344885
Start Page 410-1
End Page 410-6
Total Pages 6
Place of Publication United Kingdom
Publisher BioMed Central
HERDC Category C1 - Journal Article (DIISR)
Abstract Background
Anti-malarial efficacy needs to be monitored continually to ensure optimal dosing in the face of emerging anti-malarial drug resistance. The efficacy of artemisinin based combination therapies (ACT) is assessed by repeated measurements of parasite density in the blood of patients following treatment. Parasite density is measured from a capillary or venous blood sample, but this can be logistically and ethically challenging if multiple samples are required within a short time period. The aim of this work was to apply optimal design theory to derive clinically feasible blood sampling schedules from which parasite clearance could be defined using the Parasite Clearance Estimator (PCE), a recently developed tool to identify and quantify artemisinin resistance.

Methods
Robust T-optimal design methodology was applied to offer a sampling schedule that allows for discrimination across models that best describe an individual patient’s parasite-time profile. The design was based on typical parasite-time profiles derived from the literature combined with key sampling constraints of no more than six samples per patient within 48 hours of initial treatment. The design was evaluated with a simulation-estimation procedure that implemented the PCE.

Results
The optimal sampling times (sampling windows) were: 0 (0 to 1.1), 5.8 (4.0 to 6.0), 9.9 (8.4 to 11.5), 24.8 (24.0 to 24.9), 36.3 (34.8 to 37.2) and 48 (47.3, 48.0) hours post initial treatment. The simulation-estimation procedure showed that the design supported identification of the appropriate method by the PCE to determine an individual’s parasite clearance rate constant (the main output calculation from the PCE).

Conclusions
The proposed sampling design requires six samples per patient within the first 48 hours. The derived design requires validation in a real world setting, but should be considered for future studies that intend to employ the PCE.

DOI http://dx.doi.org/10.1186/1475-2875-12-410   (check subscription with CDU E-Gateway service for CDU Staff and Students  check subscription with CDU E-Gateway in new window)
Open access True
Additional Notes This is an Open Access article distributed under the terms of the Creative Commons Attribution License 2.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Description for Link Link to CC Attribution 2.0 License
URL http://creativecommons.org/licenses/by/2.0/au/legalcode


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