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Quantification of parasite clearance in Plasmodium knowlesi infections

Authors :
Jeyamalar T. Thurai Rathnam
Matthew J. Grigg
Saber Dini
Timothy William
Sitti Saimah Sakam
Daniel J. Cooper
Giri S. Rajahram
Bridget E. Barber
Nicholas M. Anstey
Ali Haghiri
Megha Rajasekhar
Julie A. Simpson
Source :
Malaria Journal, Vol 22, Iss 1, Pp 1-13 (2023)
Publication Year :
2023
Publisher :
BMC, 2023.

Abstract

Abstract Background The incidence of zoonotic Plasmodium knowlesi infections in humans is rising in Southeast Asia, leading to clinical studies to monitor the efficacy of anti-malarial treatments for knowlesi malaria. One of the key outcomes of anti-malarial drug efficacy is parasite clearance. For Plasmodium falciparum, parasite clearance is typically estimated using a two-stage method, that involves estimating parasite clearance for individual patients followed by pooling of individual estimates to derive population estimates. An alternative approach is Bayesian hierarchical modelling which simultaneously analyses all parasite-time patient profiles to determine parasite clearance. This study compared these methods for estimating parasite clearance in P. knowlesi treatment efficacy studies, with typically fewer parasite measurements per patient due to high susceptibility to anti-malarials. Methods Using parasite clearance data from 714 patients with knowlesi malaria and enrolled in three trials, the Worldwide Antimalarial Resistance Network (WWARN) Parasite Clearance Estimator (PCE) standard two-stage approach and Bayesian hierarchical modelling were compared. Both methods estimate the parasite clearance rate from a model that incorporates a lag phase, slope, and tail phase for the parasitaemia profiles. Results The standard two-stage approach successfully estimated the parasite clearance rate for 678 patients, with 36 (5%) patients excluded due to an insufficient number of available parasitaemia measurements. The Bayesian hierarchical estimation method was applied to the parasitaemia data of all 714 patients. Overall, the Bayesian method estimated a faster population mean parasite clearance (0.36/h, 95% credible interval [0.18, 0.65]) compared to the standard two-stage method (0.26/h, 95% confidence interval [0.11, 0.46]), with better model fits (compared visually). Artemisinin-based combination therapy (ACT) is more effective in treating P. knowlesi than chloroquine, as confirmed by both methods, with a mean estimated parasite clearance half-life of 2.5 and 3.6 h, respectively using the standard two-stage method, and 1.8 and 2.9 h using the Bayesian method. Conclusion For clinical studies of P. knowlesi with frequent parasite measurements, the standard two-stage approach (WWARN’s PCE) is recommended as this method is straightforward to implement. For studies with fewer parasite measurements per patient, the Bayesian approach should be considered. Regardless of method used, ACT is more efficacious than chloroquine, confirming the findings of the original trials.

Details

Language :
English
ISSN :
14752875
Volume :
22
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Malaria Journal
Publication Type :
Academic Journal
Accession number :
edsdoj.9264b50a5c56427db4cdcce28b8e8fa0
Document Type :
article
Full Text :
https://doi.org/10.1186/s12936-023-04483-9