Jocelyn Sietsma Penington

Artemisinin-based combination therapies (ACTs) remain the cornerstone of malaria treatment, but emerging resistance threatens their efficacy. The potential for the development of drug resistance against plasmepsin X (PMX)-selective inhibitors and dual plasmepsin IX/X (PMIX/X) inhibitors was investigated in Plasmodium falciparum . A series of PMX-selective (WM4, WM76, WM92) and PMIX/X dual inhibitors (WM382, WM09, WM42) were characterised for potency against parasite growth and enzyme inhibition. In vitro selection experiments showed that all compounds had a high barrier to resistance, although parasites with reduced sensitivity to PMX‑selective inhibitors could still be selected. Resistance mechanisms involved pmx gene amplification and point mutations (D245N, S315P, S359P, I363L) that alter inhibitor binding. Recombinant expression and Michaelis–Menten kinetics demonstrated that these mutations impair drug binding whilst preserving PMX catalytic function. Reverse genetics confirmed that introducing these mutations into the pmx gene resulted in decreased potency of the inhibitors. In this study, resistance to the PMIX/X dual inhibitors evaluated here could not be selected, despite prolonged selection pressure. Antimalarial Resistome Barcoding (AReBar) assays confirmed the absence of pre-existing resistance to either inhibitor class. Critically, PMIX/X dual inhibitors maintained efficacy against parasites with decreased sensitivity to PMX-selective compounds. These findings demonstrate that dual PMIX/X inhibitors present a substantially higher barrier to resistance than PMX-selective inhibitors, informing antimalarial drug development strategies and highlighting dual-target inhibition as a promising approach to mitigate resistance risks.

Supplementary Table 9 summarizes the characterization of 16 SNPs associated with 23 concordant dQTLs.

Supplementary Table 1 shows a feature-by-patient summary matrix. For each patient, this table provides version information of bioinformatics tools, summary sequencing statistics, mutational density metrics, clinical information and driver mutation status for all drivers detected using GISTIC and ActiveDriverWGS as or identified in Armenia and colleagues 2018. Two additional tables are provided for the clinical and mutation data for the discovery and replication cohorts of dQTL discovery.

Supplementary Table 4 shows driver selection for dQTL nomination and prevalence of drivers in cohorts

Supplementary Table 5 displays summary statistics from PRS and HOXB13 associated with somatic drivers. β and P-value from logistic regression correcting for five genetic principal components, age and somatic mutation burden. FDR = false discovery rate.

Supplementary Table 10 lists dQTL SNPs identified as eQTLs in prostate tissue in GTEx.

Supplementary Table 7 provides summary statistics from distal dQTL associations

Supplementary Table 6 displays the number of dQTLs identified for each somatic driver in each analysis strategy. Summary statistics from local dQTL associations. Statistics from logistic regression correcting for five genetic principal components, age and somatic mutation burden. OR = odds ratio; SE = standard error; L95 = lower 95% confidence interval; U95 = upper 95% confidence interval

Supplementary Table 8 shows summary statistics of 16 SNPs associated with 23 concordant dQTLs across cohorts

Supplementary Table 11 reports percentages of cross-individual contamination for each sample and sequencing lane.