Plasmodium falciparum artemisinin resistance monitoring in Sabah, Malaysia: in vivo therapeutic efficacy and kelch13 molecular marker surveillance

Single cell transcriptomics shows that malaria promotes unique regulatory responses across multiple immune cell subsets

Plasmodium falciparum malaria drives immunoregulatory responses across multiple cell subsets, which protects from immunopathogenesis, but also hampers the development of effective anti-parasitic immunity. Understanding malaria induced tolerogenic responses in specific cell subsets may inform development of strategies to boost protective immunity during drug treatment and vaccination. Here, we analyse the immune landscape with single cell RNA sequencing during P. falciparum malaria. We identify cell type specific responses in sub-clustered major immune cell types. Malaria is associated with an increase in immunosuppressive monocytes, alongside NK and γδ T cells which up-regulate tolerogenic markers. IL-10-producing Tr1 CD4 T cells and IL-10-producing regulatory B cells are also induced. Type I interferon responses are identified across all cell types, suggesting Type I interferon signalling may be linked to induction of immunoregulatory networks during malaria. These findings provide insights into cell-specific and shared immunoregulatory changes during malaria and provide a data resource for further analysis.

Quantification of parasite clearance in Plasmodium knowlesi infections

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.

New insights into the spread of resistance to artemisinin and its analogues

Plasmodium falciparum, the causative agent of malaria, has been developing resistance to several drugs worldwide for more than five decades. Initially, resistance was against drugs such as chloroquine, pyrimethamine, sulfadoxine, mefloquine and quinine. Research studies are now reporting parasites with resistance to the most effective and novel drug used against malaria infection worldwide, namely artemisinin. For this reason, the first-line treatment strategy of artemisinin-based combination therapy is becoming unsuccessful in areas where drug resistance is highly prevalent. The increase in artemisinin-resistant P. falciparum strains has threatened international efforts to eliminate malarial infections and to reduce the disease burden. Detection of several phenotypes that display artemisinin resistance, specification of basic genetic factors, the discovery of molecular pathways, and evaluation of its clinical outcome are possible by the current series of research on genomics and transcriptomic levels in Asia and Africa. In artemisinin resistance, slow parasite clearance among malaria-infected patients and enhanced in vitro survival of parasites occurs at the early ring stage. This resistance is due to single nucleotide polymorphisms within the Kelch 13 gene of the parasite and is related to significantly upregulated resistance signalling pathways; thus, the pro-oxidant action of artemisinins can be antagonised. New strategies are required to halt the spread of artemisinin-resistant malarial parasites.

Malaria distribution and performance of malaria diagnostic methods in Malaysia (1980-2019): a systematic review

BACKGROUND

Malaysia has already achieved remarkable accomplishments in reaching zero indigenous human malaria cases in 2018. Prompt malaria diagnosis, surveillance and treatment played a key role in the country's elimination success. Looking at the dynamics of malaria distribution during the last decades might provide important information regarding the potential challenges of such an elimination strategy. This study was performed to gather all data available in term of prevalence or incidence on Plasmodium infections in Malaysia over the last four decades.

METHODS

A systematic review of the published English literature was conducted to identify malaria distribution from 1980 to June 2019 in Malaysia. Two investigators independently extracted data from PubMed, Scopus, Web of Science and Elsevier databases for original papers.

RESULTS

The review identified 46 epidemiological studies in Malaysia over the 39-year study period, on which sufficient information was available. The majority of studies were conducted in Malaysia Borneo (31/46; 67.4%), followed by Peninsular Malaysia (13/46; 28.3%) and in both areas (2/46; 4.3%). More than half of all studies (28/46; 60.9%) were assessed by both microscopy and PCR. Furthermore, there was a clear trend of decreases of all human malaria species with increasing Plasmodium knowlesi incidence rate throughout the year of sampling period. The summary estimates of sensitivity were higher for P. knowlesi than other Plasmodium species for both microscopy and PCR. Nevertheless, the specificities of summary estimates were similar for microscopy (40-43%), but varied for PCR (2-34%).

CONCLUSIONS

This study outlined the epidemiological changes in Plasmodium species distribution in Malaysia. Malaria cases shifted from predominantly caused by human malaria parasites to simian malaria parasites, which accounted for the majority of indigenous cases particularly in Malaysia Borneo. Therefore, malaria case notification and prompt malaria diagnosis in regions where health services are limited in Malaysia should be strengthened and reinforced to achieving the final goal of malaria elimination in the country.

Malaria Parasite Clearance: What Are We Really Measuring?

Antimalarial drugs are vital for treating malaria and controlling transmission. Measuring drug efficacy in the field requires large clinical trials and thus we have identified proxy measures of drug efficacy such as the parasite clearance curve. This is often assumed to measure the rate of drug activity against parasites and is used to predict optimal treatment regimens required to completely clear a blood-stage infection. We discuss evidence that the clearance curve is not measuring the rate of drug killing. This has major implications for how we assess optimal treatment regimens, as well as how we prioritise new drugs in the drug development pipeline.