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.
Original language | English |
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Article number | 54 |
Journal | Malaria Journal |
Volume | 22 |
Issue number | 1 |
DOIs | |
Publication status | Published - Dec 2023 |
Bibliographical note
Funding Information:JTTR is funded by the University of Melbourne’s Research Scholarship. This work was supported by the Australian National Health and Medical Research Council (Leadership Investigator Grant (#1196068) to JAS, Fellowships to NMA (#1135820) and MJG (#1138860), the Australian Centre for Research Excellence in Malaria Elimination (#1134989), and grant numbers 1037304 and 1045156), and the National Institutes of Health, USA (R01AI160457-01 and R01AI160457-02). MJG is supported by the Australian Centre for International Agricultural Research, Australian Government (LS-2019-116). The funders were not involved in the analysis, interpretation and the writing of this manuscript.
Funding Information:
The authors would like to thank all participants from the ACT KNOW, CAN KNOW and PACKNOW randomised clinical trials as well as the clinical/laboratory staff involved. We extend our appreciation to the hospital directors, medical and nursing staff, as well as the hospitals involved for providing logistics support. We acknowledge any other party that provided help or assistance throughout the duration of the clinical trials. We also appreciate the colleagues and staff from the UK Medical Research Council. We would like to thank the Director-General, Ministry of Health, Malaysia for permission to publish this manuscript.
Publisher Copyright:
© 2023, The Author(s).