Quantification of parasite clearance in Plasmodium knowlesi infections

• Published in: Malaria journal 2023-02, Vol.22 (1), p.54-54, Article 54
• Main Authors: T Thurai Rathnam, Jeyamalar, Grigg, Matthew J, Dini, Saber, William, Timothy, Sakam, Sitti Saimah, Cooper, Daniel J, Rajahram, Giri S, Barber, Bridget E, Anstey, Nicholas M, Haghiri, Ali, Rajasekhar, Megha, Simpson, Julie A
• Format: Article
• Language: English
• Subjects: Animals; Antimalarials; Antimalarials - pharmacology; Artemisinin; Artemisinins - therapeutic use; Bayes Theorem; Bayesian analysis; Bayesian hierarchical modelling; Bayesian theory; Bias; Blood; Care and treatment; Case Study; Chloroquine; Chloroquine - pharmacology; Clinical trials; Control; Diagnosis; Dosage and administration; Drug efficacy; Drug resistance; Human diseases; Humans; Identification and classification; Infections; Malaria; Malaria - drug therapy; Malaria - parasitology; Methods; Modelling; Parasite clearance rate; Parasite resistance; Parasitemia - drug therapy; Parasitemia - parasitology; Parasites; Patients; Plasmodium; Plasmodium falciparum; Plasmodium knowlesi; Population characteristics; Prevention; Probability theory; Vector-borne diseases; Zoonoses
• ISSN: 1475-2875; 1475-2875
• DOI: 10.1186/s12936-023-04483-9

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. 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. 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. 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 th