Monitoring Plasmodium vivax resistance to antimalarials: Persisting challenges and future directions

Abstract

Emerging antimalarial drug resistance may undermine current efforts to control and eliminate Plasmodium vivax, the most geographically widespread yet neglected human malaria parasite. Endemic countries are expected to assess regularly the therapeutic efficacy of antimalarial drugs in use in order to adjust their malaria treatment policies, but proper funding and trained human resources are often lacking to execute relatively complex and expensive clinical studies, ideally complemented by ex vivo assays of drug resistance. Here we review the challenges for assessing in vivo P. vivax responses to commonly used antimalarials, especially chloroquine and primaquine, in the presence of confounding factors such as variable drug absorption, metabolism and interaction, and the risk of new infections following successful radical cure. We introduce a simple modeling approach to quantify the relative contribution of relapses and new infections to recurring parasitemias in clinical studies of hypnozoitocides. Finally, we examine recent methodological advances that may render ex vivo assays more practical and widely used to confirm P. vivax drug resistance phenotypes in endemic settings and review current approaches to the development of robust genetic markers for monitoring chloroquine resistance in P. vivax populations.

Keywords: Chloroquine; Clinical studies; Drug resistance; Ex vivo assays; Molecular markers; Plasmodium vivax; Primaquine.

Graphical abstract

Fig. 1

Global distribution of chloroquine (CQ)-resistant P. vivax infections documented in therapeutic efficacy studies and clinical trials. Bar heights are directly proportional to failure rates. Orange bars represent patients treated with CQ alone who presented parasite recurrences until day 28 (n = 116 CQ treatment arms; treatment failure rate ranging between 0 and 100% among studies). Blue bars represent patients treated with CQ and primaquine who presented recurrences (n = 75 CQ-PQ treatment arms; treatment failure rate ranging between 0 and 22.7% among studies). Source: WorldWide Antimalarial Resistance Network (WWARN), available at: http://www.wwarn.org/vivax/surveyor/#0. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2

Global distribution of amplification at the multidrug resistance 1 (mdr-1) locus of Plasmodium vivax that is associated with resistance to mefloquine. Bar heights are directly proportional to the percentage of parasite samples from each location with two or more pvmdr-1 gene copies (n = 14 studies), ranging between 0 and 59%. Data compiled from: Auburn et al. (2016); Chaorattanakawee et al. (2017); Costa et al. (2017); Htun et al. (2017); Imwong et al. (2008); Joy et al. (2018); Li et al. (2020); Lo et al. (2017); Musset et al. (2019); Roesch et al. (2020); Silva et al. (2018); Suwanarusk et al. (2007), and Vargas-Rodríguez et al. (2012).

Fig. 3

Competing risk survival model applied to real-life data from vivax malaria patients who received standard chloroquine regimens with (A) or without (B) concomitant primaquine. Continuous lines represent the non-parametric Kaplan-Meier survival function and dashed lines represent the fitting of a competing risk survival model (Equation (3)) to empirical data. The red area represents the cumulative proportion of patients experiencing PQ-preventable relapses/late recrudescences following treatment (fast dynamics) and the yellow area represents the cumulative proportion of patients experiencing new infections (slow dynamics). Redrawn from Corder et al. (2020a). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4

Workflow for blood sample processing in the field, cryopreservation and thawing (if deemed necessary) and Plasmodium vivax enrichment prior to ex vivo schizont maturation assays.

Fig. 5

Amino acid substitutions found in the Plasmodium vivax chloroquine resistance transporter protein (PvCRT-O). The 10 transmembrane domains (TMDs) are represented by orange boxes: amino acid positions 58–78 (TMD1), 95–112 (TMD2), 129–148 (TMD3), 157–174 (TMD4), 183–200 (TMD5), 209–228 (TMD6), 247–265 (TMD7), 317–336 (TMD8), 345–365 (TMD9), and 374–390 (TMD10). TMD position and sizes as per Nomura et al. (2001). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6

Amino acid substitutions found in the Plasmodium vivax multidrug resistance 1 protein (PvMDR-1). The 12 transmembrane domains (TMDs) are represented by orange boxes: amino acid positions 63–85 (TMD1), 100–122 (TMD2), 171–193 (TMD3), 197–218 (TMD4), 285–307 (TMD5), 323–345 (TMD6), 813–835 (TMD7), 839–860 (TMD8), 868–890 (TMD9), 935–957 (TMD10), 965–986 (TMD11), and 1018–1040 (TMD12). TMD position and sizes as per Sá et al. (2005). Two common nonsynonymous substitutions found in PvMDR-1, Y976F and F1076L, are highlighted in red. . (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)