Emergence and Spread of Plasmodium falciparum Kelch 13 Mutations in Selected Counties of Kenya: Implications for Responding to Artemisinin Partial Resistance.

Emergence and Spread of Plasmodium falciparum Kelch 13 Mutations in Selected Counties of Kenya: Implications for Responding to Artemisinin Partial Resistance. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Emergence and Spread of Plasmodium falciparum Kelch 13 Mutations in Selected Counties of Kenya: Implications for Responding to Artemisinin Partial Resistance. Hoseah Akala, Bernhards Ogutu, Benjamin Opot, Dennis Juma, Raphael Okoth, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6214166/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Background This study evaluated the polymorphisms of Pfk13 gene alongside other malaria drug resistance markers in clinical samples from eight geographically distinct locations in Kenya to determine the prevalence of mutations associated with partial artemisinin resistance. Methods Between 2018 and 2024, blood samples from individuals with symptoms of uncomplicated malaria at hospitals in eight hospitals in four of the five distinct malaria transmission zones of Kenya were sequenced for single nucleotide polymorphisms (SNPs) in Pfk13, and Pfmdr1, then a subset tested for in vitro susceptibility to selected antimalarials. Each individual was followed up on day 7 to monitor treatment outcomes Findings A total of 44/679 (6.5%) samples harbored 49 Pfk13 mutations. The mutations include 14 nonsynonymous at Pfk13 A675V 2.9% (n=20), A578S 0.6% (n=4), C469Y (n=3), V386A (n=1) at 0.44%, and P553L(n=1), R561H/P (n=1), S522C(n=1), K455E(n=1), S600F(n=1), E612D(n=1), N489K(n=1), F491L(n=1) plus A504V at 0.15% (n=1) alongside eleven synonymous mutations. Prevalence of the five validated markers of partial artemisinin resistance was 27/679 (4.0%). Most of Pfk13 675V mutations n=12 (1.8%) were detected in Baringo County of Kenya. 178/823 (21.6%) of the individuals tested positive by PCR on day 7 follow-up after treatment with artemether-lumefantrine. The median 50% inhibition concentration for lumefantrine against field samples was significantly higher than that of reference clones. Interpretation The detection of nonsynonymous mutations in 14 loci including five validated makers of partial artemisinin resistance in more samples than previously detected in Kenya and in diverse transmission zones suggests intense selective pressure consistent with emerging burden of partial artemisinin resistance. Funding Armed Forces Health Surveillance Branch and its Global Emerging Infections Surveillance Section. Health sciences/Medical research/Epidemiology Health sciences/Diseases/Infectious diseases/Malaria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Confirmed reports of artemisinin tolerance in Southeast Asia (SEA) threaten the continued use of artemisinin-based combination therapies (ACTs) for the treatment of uncomplicated malaria globally. 1 , 2 Studies show that mutations in the Plasmodium falciparum Kelch 13 gene ( Pfk13 ) associated with delayed parasite clearance in vitro and in vivo 3 are widespread in SEA. 4 , 5 The first cases of Pfk13 mutations-associated with artemisinin partial resistance were identified in 2013 in Africa. Initially reported in Rwanda and Ethiopia, these mutations were not associated to those previously detected in SEA, 6 , 7 before being detected in Eritrea, 8 Tanzania 9 and Kenya. 10 11 Specifically, artemisinin delayed parasite clearance in Africa has been linked to the Pfk13 622I mutation in Ethiopia, the R561H mutation, which is undergoing clonal proliferation southwards in Rwanda, 6 , 12 in to northern Tanzania, 13 and the C469Y and A675V mutations in Eastern Uganda 14 and western Kenya. 10 11 The most widely studied and well characterized ACTs in Africa are artemether-lumefantrine (AL), artesunate-amodiaquine (ASAQ), dihydroartemisinin-piperaquine (DHAPPQ), artesunate-mefloquine (ASMQ), and artesunate-pyronaridine 15 . Of these, AL is the most widely used treatment in Africa. Polymorphisms within the Pfmdr1 gene loci N86Y, Y184F, S1034C, N1042D have been implicated in modulating parasite response to chloroquine and ACTs partner drugs amodiaquine, lumefantrine and mefloquine. 16 Notably, AL and ASAQ exert inverse selective pressures whereby parasites with genotype 86Y, Y184 and 1246Y are selected by ASAQ treatment, while N86, 184F and D1246 are selected by AL treatment. 16 Since the strategy behind the deployment of ACTs relies on the slow acting partner drug to clear the residual parasitemia after initial rapid clearance of most of the parasite biomass by the short half-life, fast-acting artemisinin component of the combination, the delayed or partial parasite clearance by the artemisinin derivative will result in prolonged exposure of the parasite to the partner drug. This therefore renders the partner drug vulnerable to rapid development of resistance as a major concern for the combination therapy approach. Owing to initial reports in southeast Asia, 1 the emergence of resistance to artemisinin and partner drugs has been identified as a significant risk to the global effort to reduce the malaria burden as per the Global Technical Strategy (GTS) for malaria, 17 necessitating intensified surveillance for the early detection of resistance. This surveillance study sought to determine the frequency of polymorphisms in Pfk13 with attention to thevalidated malaria drug resistance markers from among clinical samples collected in eight hospitals located in geographically diverse regions across Kenya within four of the five malaria transmission zones of Kenya, between 2018 and 2024. Methods Study sites and sample collection Samples were collected between 01-July 2018 and June 2024 from eight health facilities located in seven counties spanning across four of the five malaria transmission zones comprising three sites in the Lake endemic zone (Busia – Busia County Referral Hospital; Kisumu- Kombewa Sub-County Hospital; and Kisumu County Referral Hospital), two sites in the highland zone (Kisii - Kisii County Referral and Kericho- Kericho County Referral), semi-arid seasonal transmission zone (Baringo – Marigat Sub-County Hospital) as well as low risk zone (Nakuru – Lanet Regional Hospital and Laikipia- Laikipia Hospital). These sites were chosen to span four of the five distinct malaria transmission zones across Kenya National Malaria Control Programme 2020. 18 , 19 Patients aged six months and above presenting to the outpatient departments and diagnosed with uncomplicated malaria by rapid diagnostic test (mRDT; Parascreen® (Pan/Pf), Zephyr Biomedicals, Verna Goa, India) were recruited into the study after providing written informed consent or assent. Pregnant women were also eligible for enrollment. Individuals were excluded from the study upon refusal to participate or unwillingness to give blood. The study also excluded prisoners, including children attending the Kenyan government’s correction and rehabilitation programme. Additionally, individuals under age 18 without available parent or legal guardian, volunteers who were previously enrolled in the study during the same calendar year, individuals weighing less than 5 kg and anyone who, in the opinion of the attending medical provider, would be adversely affected by the drawing of 2.5 mL of blood were excluded. Written informed assent for individuals aged 17 years and below was provided by their parent or legal guardian in accordance with the laws of the government of Kenya. Individuals who did not meet the inclusion criteria were excluded as previously described. 20 For all consenting and assenting individuals who were enrolled into the study, 2-3 ml of whole blood was collected and aliquots of 1.5ml in Vacutainer™ Glass Blood Collection Tubes with Acid Citrate Dextrose catalogue number BD 364816 from Becton, Dickinson U.K. Limited form Berkshire, United Kingdom for in vitro drug sensitivity testing. The remainder of sample was dispensed in the Ethylenediaminetetraacetic acid tube for molecular analyses. Both samples were swirled well in the anticoagulant to homogeneity, retained at room temperature for 30 min then stored at 2 to 8 degrees pending transportation at this same temperature to the central lab for processing. For case management, all Plasmodium positive cases were treated with Coartem® (Novartis, Basel, Switzerland), which contains artemether-lumefantrine (AL) in accordance with the Kenya Ministry of Health recommended case management guidelines for uncomplicated malaria. After drawing blood, the attending clinician administered and observed the first dose of Coartem® 20mg/120mg tablets, with individuals weighing 5 to <15 kg prescribed 1 tablet per dose, 15 to < 25kg 2 tablets per dose, 25 to <35kg 3 tablets per dose and individuals greater than 35kg 4 tablets per dose. Each patient was given the remaining 5 doses (i.e. 5, 10, 15, 20 and 25 tablets for each weight range) and advised to take the next dose after eight hours, followed by the remaining doses at 12 hourly intervals till completion of treatment. Further, each participant was required to come back on day 7 for clinical and laboratory evaluation comprising documentation of vital signs as well as parasitological diagnosis of malaria. Regardless of the outcomes of these evaluations, an additional blood draw was taken during the day 7 visit or any other visit date within 42days follow-up for molecular and in vitro testing at the central lab to monitor treatment outcomes. Importantly, all participants were encouraged to come back to the study clinician on any other day during the subsequent 42 days for review and further clinical management symptoms persist. This study was conducted under the approval of the Kenya Medical Research Institute (KEMRI), Scientific and Ethics Review Unit (SERU) and Walter Reed Army Institute of Research (WRAIR) institutional review boards, protocol numbers: WRAIR 2454, KEMRI 3628: “Epidemiology of malaria and drug sensitivity patterns in Kenya.” Parasite density estimation Three glass microscope slides for thick and thin smears were prepared from the whole blood sample obtained from each participant. One slide was read the same day and reported, the two remaining slides were batched and read at a later date. All positive Plasmodium infections were characterized for parasitemia. Parasites were typed counts per number of cells counted in either 200 or 2000 high power fields for thick and thin malaria blood smears, respectively, before expressing as either percent parasitemia or parasite density as previously described 21 . P. falciparum DNA extraction and sequencing DNA extraction from whole blood was done using Qiagen DNA Mini extraction spin protocol (Qiagen, Valencia, CA) as per the manufacturer’s guidelines and eluted with 150 µl of elution buffer. Molecular diagnosis of Plasmodium genus and species was done using a quantitative real-time PCR using primers targeting 18S ribosomal RNA as described elsewhere. 22 This procedure involved two reactions. The first detected the presence of Plasmodium genus using the real-time PCR assay on Applied Biosystem QuantStudio 6 flex (QuantStudio™ real-time PCR Applied Biosystems by Thermos Fisher Scientific Foster City, CA) as previously described. 22 , 23 The second reaction on the same platform, using the assay conditions and primers previously described, 24 , 25 identified the Plasmodium species present in the infection. Samples that were positive for the Plasmodium genus with cycle threshold values of at least 31 and below were further processed for Pfk13 and mutation analysis. Single nucleotide polymorphisms in the Pfk13, and Pfmdr1 genes A conventional PCR was done using primers as described by de Laurent and coworkers 26 to amplify the Pfk13 and Pfmdr1, genes for sequencing. This resulted in a detectable band size consistent with successful amplification of the Pfk13, and Pfmdr1 genes. Exo-SAP IT was used to purify the PCR products by removing excess nucleotides and primer remnants. Sanger sequencing was performed on purified PCR products using Big Dye terminator method version 3.1 on the 3500xL ABI genetic analyzer (Applied Biosystems, Foster City, CA). Sequencing data was then analyzed on the Qiagen CLC Main Workbench (Qiagen, Valencia, CA). Sequencing of the Pf1k13 , and Pfmdr1 genes amplicons Library preparation and sequencing A total of 927 isolates were subjected to amplicon sequencing. The amplicons were purified using Exo-SAP IT to remove primers and other short DNA fragments following manufacturer’s instructions. These amplicons were then normalized to a concentration of 2 ng/µL for DNA library preparation. Libraries were generated using the Nextera XT library preparation kit, following the manufacturer's protocol. Using Tape Station 4200 system (Agilent Technologies, Santa Clara, CA, USA) D1000 screen Tape was used to confirm adaptor ligation. The libraries were pooled for sequencing on Illumina iSeq 100 (Illumina Inc, San Diego, CA). Sequencing Data processing Data processing was performed as previously described by Niaré & Bailey (2023). Microhaplotype clustering was performed using the SeekDeep v3·0·0 workflows as previously described. 27 GATK4 Haplotype Caller was also used to call variants with the following parameters described by 28 Niaré & Bailey (2023). Using BCFtools, SNPs with a DP > 100, minor allele frequency (MAF) 25% missingness were excluded. Alleles with a frequency below 0·5 were considered minor alleles, while those above were considered major alleles. Functional annotation of the SNPs was performed using SnpEff annotation tool. Haplotypes constructions were conducted by aligning PCR-amplified sequences to a reference genome using ClustalW to identify SNPs relative to the wildtype. After variant calling, we applied stringent quality control measures—including filters based on minor allele frequency and Hardy–Weinberg equilibrium—to ensure data reliability. The resulting SNP dataset was then processed using a Bayesian algorithm to infer haplotype phases, enabling the accurate assembly of both single and multiple mutation haplotypes. Finally, haplotype frequencies were determined by calculating the proportion of each haplotype relative to the total number of sequences analyzed. Results mapping Mutation mapping was achieved by compiling mutation data in Excel and mapping it onto an OpenStreetMap (OSM) basemap using Quantum GIS (QGIS), a free and open-source Geographic Information System (GIS) software used for mapping, spatial data analysis, and geospatial visualization. QGIS allows users to overlay datasets onto basemaps and apply various spatial analysis techniques. The final map can then be exported as an image. In vitro susceptibility testing A subset of the malaria positive samples was tested for in vitro/ ex vivo susceptibility to five antimalarial drugs using the malaria SYBR Green I-based assay as previously described. 29 The tested drugs include dihydroartemisinin (DHA), artemether (AT), chloroquine (CQ), Lumefantrine (LU) and Mefloquine (MQ) as earlier described. 30 , 31 , 32 Culture adapted clinical isolates and reference strains (W2 strain is chloroquine resistant /mefloquine sensitive, “D6” and “3D7” are chloroquine sensitive /mefloquine resistant) obtained from Biodefense and Emerging infectious Research Resources Repository (BEI Resources) were tested side by side as the assay quality controls. 20 Parasite replication inhibition was quantified by measuring the per-well relative fluorescence units (RFU) of SYBR Green I dye using the Tecan Genios Plus® (Tecan US. Inc., Durham, NC) with excitation and emission wavelengths of 485 nm and 535 nm. The IC50 values for each drug were calculated as previously described. 31 Data Management and Statistical Analyses Data were initially collected on standardized case report forms and subsequently entered into a secure, dedicated database. To ensure data integrity, we implemented double data entry, performed regular quality checks, and conducted range and consistency validations. Any missing or outlier data were carefully reviewed and managed according to pre-established protocols. For statistical analysis, categorical data including gene sequence polymorphisms were summarized as frequencies and proportions. Continuous variables, including in vitro drug susceptibility results, were first assessed for normality (using tests such as the Shapiro–Wilk test) and then expressed as median IC₅₀ values with their corresponding interquartile ranges (IQR) when non-normal distributions were observed. Additionally, proportions were reported for relevant parameters. Comparative analyses between groups were conducted using non-parametric tests when appropriate, given the data distribution. All statistical analyses were performed using GraphPad Prism version 8.0 (GraphPad Software, Inc., San Diego, CA, USA), with a significance level set at p < 0.05. Results are presented in detailed tables and figures, supplemented by additional materials that further clarify the data distributions and trends observed throughout the study. Raw data files will be available upon request, with the eventual intention of depositing all sequence data on the NCBI platform for unrestricted public access. Results Between July 2018 and June 2024, 2,805 study participants consisting of symptomatic patients presenting at the outpatient departments of eight healthcare facilities were screened, 2,563 met inclusion criteria and were enrolled in the epidemiology of malaria drug resistance surveillance study. Of these, 679/927 samples were successfully sequenced for polymorphisms in Pfk13, and Pfmdr1 genes. The successfully sequenced samples comprised those from hospitals namely Busia County Referral (n=193), Kisumu County (Kombewa Sub-County n= 164, Kisumu County Referral n=141), Kisii County Referral (n=64) Kericho County Referral (n=39), Nakuru county at Lanet Regional (n=1), Laikipia county’s Laikipia Air Base (n=2), Baringo county’s Marigat Sub-County (75) hospitals. Of these, 671 were from the initial day zero visits, and eight were from subsequent visits. The overall median parasitemia and interquartile range was 0.62 % [0.13 to 2.50; n=761/926]. Per county median parasitemia and interquartile range were Kericho 1.55% [0.30-5.83, n= 48]; comparable to Kisumu 1.5% [0.14- 4.00, n=323]; Busia 0.5 % [0.12-1.60, n=215]; Baringo 0.60% [0.09-1.50, n=81]; Kisii 0.18% [0.1-0.25], n=70]. The low malaria burden counties had 2 samples each at parasitemia of 0.03% and 1.00 % for Nakuru and 0.04% and 0.20% for Laikipia (Supplementary Table 1). Metadata available for 148 individuals showed observed lower mean age of participants, and more malaria episodes in the past 12 months in the lake endemic region than highland epidemic (Table 1). Table1: Study site demographics and clinical summaries Site (n) Region Age* Sex (% Female) Temperature* Malaria episodes in last 12 months Busia (61) Lake Endemic 11.38 [7.72-15.04] (60/61) 60.66% (37/61) 37.77 [37.50-38.04] (60/61) 37/61 Kisumu (13) Lake Endemic 11.85 [4.04-19.65] (13/13) 61.54% (8/13) 38.29 [37.66-38.92] (13/13) 6/13 Kombewa (32) Lake Endemic 10.96 [8.10-13.82] (27/32) 59.38% (19/32) 37.77 [37.46-38.09] (32/32) 25/27 Kisii (27) Highland Epidemic 13.38 [7.54-19.22] (26/27) 37.04% (10/27) 38.73 [38.30-39.17] (26/27) 8/26 Kericho (15) Highland Epidemic 23.46[17.17-29.75] (15/15) 46.67% (7/15) 37.67 [36.88-38.47] (15/15) 2/15 All (148) 13 [10 . 81-15 . 19] (141/148) 54 . 73% (81/148) 37 . 98 [37 . 79-38 . 16] (146/148) 78/142 *Mean [95% Confidence Interval] (n/N). P. falciparum single nucleotide polymorphism in Plasmodium falciparum Kelch 13 gene A total of distinct mutations in Pfk13 gene were detected in 44/679 (6.5%) samples using Sanger and amplicon sequencing techniques (Figure 1) and a subset confirmed by NGS using iSeq100 Sequencing System - Illumina. Samples that did not meet the inclusion threshold due to low depth were excluded. Those included in this study report comprised 13 nonsynonymous mutations in 36 samples including C469Y n=3, (0.5%), P553L n=1, (0.15%), R561H n=1 (0.15%) and A675V n=20 (2.9%) that are validated markers of partial artemisinin resistance. Other non-synonymous mutations included A578S n=4 (0.6%) as well as V386A, R561H/P, S522C, K455E, S600F, E612D, N489K, F491L plus A504V at n=1 (0.15%). A total of 11 synonymous mutations were also detected (Figure 1). Notably, seven of the nonsynonymous and six of the synonymous mutations had not previously been reported elsewhere. One infection from Kisii had triple Pfk13 G497G, P553L, L488L mutations while two infections from Busia had double Pfk13 mutations, A675V plus V386V and A675V plus C469Y. Pfk13 Temporal Trends Mutations were detected across different years between 2018 and 2024, with the earliest occurrences observed in 2018, including A675V, K455E, and N489K. No mutations were identified in 2019. In 2020, E612D, G690G, and S600F mutations were detected, followed by A504V, P553L, and A626A in 2021. In 2022, additional mutations such as A504A, C469C, E612D, F491L, and S522C were observed. A significant rise in mutation frequency occurred in 2023, with notable increases in A578S, A627A, A675A, and A675V, the latter appearing in 7 instances. By 2024, A675V was the most frequently observed mutation (13 occurrences), while other variants appeared at lower frequencies (Figure 2a). Temporally, Pfk13 675V was the first mutation to be detected in two samples from Busia and Kericho between January and June 2023. By December 2023, the mutation frequency increased by six additional samples, followed by an additional 14 by June 2024 bringing the frequency of this mutation to 3.1% (Figure 2b). Geographical Distribution Geographically, the Pfk13 alleles were distributed among the seven study sites where Busia had the highest number of allele variants with 17 different alleles present in 15 samples followed by Kisii with seven different alleles in five samples while Baringo County (Marigat) which had the least allele frequency with one allele in all the 12 samples sample that were polymorphic (Table 2, Figure 3). The WHO validated partial artemisinin resistance allele A675V was the most prevalent, occurring at 3.1% (n = 21/679) of the infections followed by A578S at 0.6% (n= 4), C469Y at 0.45% (n =3) while R561H, were at 0.15% (n=1) (Figure 1). Synonymous mutation C469C, was detected in two samples obtained from Kisii and Kisumu (Figure 1). Seven nonsynonymous and six synonymous mutations had not been reported previously. Assessment of the haplotype frequency is summarized in supplementary Table 2. Table 2: Frequency and distribution of Pfk13 mutation and samples in the eight study sites. CRH means County Referral Hospital, * High percentage due to the low number of samples analyzed from the county given the low malaria endemicity in the region despite lengthy surveillance duration. County Study site Non-synonymous mutation Synonymous mutation Alleles (N=49) Frequency per site Busia Busia CRH 12 5 17(34.7%) 17/193(8.8%) Kisumu Kombewa Sub- CRH and Kisumu CRH 6 2 8(16.3%) 8/305 (2.6%) Kericho Kericho CRH 3 0 3(6.1%) 3/39(7.7%) Kisii Kisii CRH 3 4 7(14.3%) 7/64(10.9%) Baringo Marigat Sub- CRH 12 0 12(24.5%) 12/72(16.7%) Nakuru Lanet regional Hospital 1 1 2(4.1%) 2/4(50%) * Laikipia Laikipia Air Base hospital 0 0 0(0) 0/2 (0%) * Single nucleotide polymorphisms in Plasmodium falciparum multidrug resistance 1 gene A subset of the 626/679 samples with complete Pfk13 sequences gave complete readouts of single nucleotide polymorphisms in the Pfmdr1 gene codon 86 (n=586) and 184 (n= 593). SNP frequencies for N86 and 86Y were 98.5% (577/586) and 1.5% (9/586) while those of Y184 and 184F were 51.6% (306/593) and 48.4% (287/593), respectively. 565 samples that had both calls at codons 86 and 184 showed that the Pfmdr1 86,184 the NY (51.0%) haplotype was most frequent followed by NF (47.4%), YF (0.9%) and YY (0.7%). In vitro drug susceptibility of the field isolates Up to 234/926 field isolates were successfully analyzed for in vitro susceptibility to lumefantrine, artemether, chloroquine, mefloquine and dihydroartemisinin. Their 50% inhibition concentrations (IC50s) in nanomolar (nm) are summarized in Table 3. The median IC50 for CQ against field isolate was significantly different from that of chloroquine resistant “W2 clone” and comparable with that of 3D7 clone, suggesting that field isolates were majorly sensitive to chloroquine. IC50s for lumefantrine, artemether, quinine and dihydroartemisinin against field isolates were comparable to those of the reference clones (Figure 4). Comparison of IC50s for samples with Pfmdr1 polymorphisms showed significantly higher chloroquine IC50s among samples with 184F than Y184 allele while the rest were comparable (Supplementary Figure 1). Table 3: In vitro responses of field isolates to selected antimalarial drugs Chloroquine Artemether Lumefantrine Mefloquine Field Isolates W2 3D7 Field Isolates W2 3D7 Field Isolates W2 3D7 Field Isolates W2 3D7 Number of values 234 9 4 217 6 3 49 5 2 149.00 4.00 3.00 Minimum 0.89 9.51 7.70 0.05 0.12 0.35 0.09 1.76 33.79 0.19 1.79 1.07 25% Percentile 3.85 17.35 7.77 0.46 0.25 0.35 0.42 5.08 33.79 2.25 1.82 1.07 Median 7.51 57.57 8.56 0.93 0.42 0.58 3.98 9.93 49.24 11.11 2.52 11.75 75% Percentile 15.38 142.94 14.04 1.78 0.50 3.58 21.46 25.71 64.69 22.07 3.18 13.25 Maximum 114.68 173.02 15.67 14.91 0.54 3.58 65.69 33.97 64.69 71.46 3.20 13.25 Treatment outcome Of the individuals 823/926 successfully followed up on day-7 to monitor treatment outcomes, 178/823 (21·6%) had PCR-amplification suggestive of residual parasite nucleic acids though data for both parasite and drug kinetics was not available. These included 12 samples that harbored validated artemisinin partial resistance markers Pfk13 P553L, C469Y, A675V (n=2), and those not previously validated V637D day zero. The 69/178 samples that had day zero SNP data available for both Pfmdr1 86 and 184 were mutant 1/69(2%) and 23/69 (33·3%) respectively (Table 4). The most frequent haplotype among day-7 positive was Pfmdr1 N/Y (Figure 5a, b). Table 4: Parasite residual parasite nucleic acids on day 7 and the Pf mdr1 86 and 184 alleles after administering artemether-lumefantrine dose. Parasite nucleic acids detected in comparable proportion in infections that had mutant alleles at day zero alike to those that were wildtype on day zero. Day 0 and 7 samples that amplified for presence of parasite nucleic acids (PLU) Number of samples (%) Pfmdr1_86, total 71 Day 0 Pfmdr1_N86 66(93%) Day 0 Pfmdr1_86Y 1 (1.5%) Day 0 Pfmdr1_N86Y 2(2.8%) Day 0 Pfmdr1_86, No sequence assembled 2(2.8%) Day 0 Pfmdr1_184, total 71 Day 0 Pfmdr1_Y184 46(64.8%) Day 0 Pfmdr1_184F 18(25.4%) Day 0 Pfmdr1_Y184F 5(7.0%) Day 0 Pfmdr1_184, No sequence assembled 2(2.8%) Discussion Evolution of mutations in P. falciparum K13 propeller domain that is associated with artemisinin partial resistance is on the rise in Africa, sixteen years after the implementation of artemisinin-based combination therapies. The earlier findings by 33 and 34 showing minimal polymorphisms in the Pfk13 without clinical implication have been updated by reports of mutations in Pfk13 C469Y, R561H, A657V and, SNPs mediating delayed parasite clearance after treatment with artemisinin-based combination therapies in east African countries implying emergence of resistance in Africa. 14 , 35 The current study analyzed a large sample size of 679 parasite isolates collected from symptomatic patients between 2018 and 2024 from seven counties in Kenya for Pfk13 and other drug resistance mutations in addition to in vitro drug susceptibility profiles. The seven counties represent four of the five distinct malaria transmission zones across Kenya. A total of 24 mutations comprising 13 nonsynonymous including Pfk13 A675V (n=20), C469Y n=3, R561H, P553L and, M476I that are on the WHO list of validated artemisinin partial resistance markers previously reported in Africa and Southeast Asia as well as 11 synonymous were observed across all study sites. Importantly, three of the validated artemisinin partial resistance in SEA were detected at lower frequency than those previously reported across Africa. This observation appears to suggest that the resistance to artemisinin in the African region is being driven by local emergence of mutations rather than spread from Asia. Secondly, the frequency of Pfmdr1 N86 and 184F SNPs in day zero samples of the infections that tested positive on day-7 follow up was 98% and 33.3%, respectively. Median IC50s were below the resistance cut off threshold for resistance to chloroquine and somewhat elevated for lumefantrine based on in vitro drug susceptibility assays. The global strategy for combatting malaria relies on sustained drug resistance surveillance. The frequency of mutations conferring phenotypic resistance to artemisinin is rising across Africa, posing a public health threat given the weak healthcare infrastructure. This report from a surveillance study in four of the five malaria transmission zones of Kenya, depicting increased genomic activity on Pfk13 calls for largescale surveillance across diverse ecotypes to identify mutations as they arise and estimate their implications on treatment outcome. The C469Y mutations were reported in one infection from Busia and Kisumu in January 2024. The sample harboring this mutation in Busia also harbored Pfk13 gene A675V mutation that our surveillance had previously reported to be on the rise in Kenya. Three months after this initial detection, a second and third infection in-country, harboring this mutation was detected in Busia and Kisumu County. Busia town, where our surveillance site is located is the main transit town between Kenya and Uganda, connecting Kenya to the Great Lakes region by road. Notably, Pfk13 gene C469Y mutation has recently reported to confer artemisinin partial resistance in Eastern Uganda. The patient in question was five years old presenting with uncomplicated malaria, concurrently has a strain harboring Pfk 13 A675V mutation that is on the rise in Kenya, was successfully treated with artemether-lumefantrine confirmed by malaria rapid diagnostic test and PCR on Day-7. In October 2023, a study by Jeang and coworkers reported detection of Pfk13 gene A675V mutation in three field isolates collected from Kakamega County in western Kenya in 2021. 10 This surveillance study reports a cumulative 5-fold increase in frequency of Pfk13 A675V mutation between January 2023 and June 2024 from 2/312 (0.6%) to 21/679 (3.1%). The first mutation was detected in Kericho, a highland epidemic malaria zone in January 2023, and the second one in Busia in April 2023,194 kilometers away but along the same highway. Between October and November 2023, an additional four samples harboring Pfk13 gene A675V mutation were detected in Busia (n=1) Kisii (n=1) and Baringo (n=2) adding to 6/476 (1.2%). Between January and February, additional nine mutations detected in Busia (n=2), and Baringo (n=7) bringing the overall frequency of the mutation in Kenya to 3.1%. It is worth noting that 60% (12/20) of Pfk13 A675V mutations were detected in Baringo County, a semi-arid seasonal transmission zone, located over 100 kilometers away from the main road linking Kenya and Uganda 36 , 37 . This observation was somewhat new in two different ways. First, Kenya is stratified in to five malaria burden zones placing Baringo county semi-arid seasonal transmission zone, therefore inhabitants are inherently less likely to acquire Plasmodium species infections per year hence likely less immunity to malaria as compared to Kisumu and Busia that are very high burden. Artemisinin partial resistance was expected to follow the classical trend of chloroquine resistance that emerged in holoendemic Kisumu County 38 , before spreading and causing epidemics in the less immune individuals in Kisii county. Secondly, only one Pf k13 mutation, the A675V was detected in Baringo during the study period. The frequency of infections harboring this mutation increased in Baringo county, faster than in any other counties including those where our surveillance study detected the variant much earlier. Conversely, malaria holoendemic counties had higher number SNPs, Busia (n=9), Kisumu (n=8) while those from highland epidemic zone were moderately few Kisii (n=4). These observations appear to suggest that the infections in Baringo county were undergoing purifying selection similar to that reported in Southeast Asia given the low immunity in Baringo County, therefore presenting a unique dilemma to Kenya’s division of national malaria program’s innovativeness in solving the problem posed by resistant variants in both immune and less immune population. It is noteworthy that the number of participants enrolled per site was purely drawn from individuals presenting at the outpatient department, seeking treatment after giving written informed consent to participate in the study, then providing blood samples hence deemed proportionate to the population and disease burden. The 13 nonsynonymous and the 11 synonymous mutations included V386A, S600F, K455E, and F491L alongside A626A, A627A, and G690G respectively; that to the best of our knowledge are new. Functional annotation of these mutations revealed that clarifying the role of these mutations in a clinical trial would be worthwhile as a strategy for monitoring the therapeutic efficacy of artemisinin. The P553L mutation was also detected (1%). This mutation that was previously reported in SEA and Africa, including Kenya, 39 is associated with delayed parasite clearance by artemisinin and its derivatives 39 , 40 and validated as a marker of artemisinin partial resistance. 8 , 41 , 42 Occurrence of this mutation among other novel nonsynonymous mutations in Kenyan parasites is disconcerting and warrants intense surveillance in Sub-Saharan Africa and well-designed studies to ascertain their contribution to treatment outcome. It is noteworthy that all the patients had a successful treatment with artemether/lumefantrine documented based on WHO recommended parasitological diagnosis using the PAN band of the mRDT on day-7. However, 12 of the 44 infections that harbored Pfk13 mutations on day zero were among the 178 samples that amplified on the day-7 follow-up, depicting likelihood of residual parasite nucleic acids though this surveillance study was not structured to assess parasite and drug kinetics that would ascertain inherent tolerance across different polymorphisms compositions. pharmacokinetics data was not available to clarify adherence. These 12 samples harbored validated artemisinin partial resistance markers Pfk13 A578S, P553L, C469Y, A675V (n=2), and those not previously validated V637D – X, N531I A_T, G639R – S, F628L – TT, L571S – F, D559E – G, C437Y - I on day zero. Synonymous mutations, V637V, V637D, A504A, L488L, C469C, A626A, K455E and G497G were detected which corroborates earlier findings that SNP V637V exclusively occur in Africa, 41 albeit involving different nucleotides than those found in this investigation. A synonymous mutation C469C (0.3%) was noted in Kisii and Kisumu and likened to the WHO validated marker C469Y 14 . The C469C had previously been detected in Congo. 43 To the best of our knowledge, none of the mixed genotypes discovered by this study, including the synonymous mutations A504A and V386V, have never been reported before. The role of these mutations together with E612D, and non-synonymous SNPS F491L in parasite population prior to detection of widespread ACT resistance needs to be investigated in a large sample size. The large number of polymorphisms reported in this study is evocative of intense molecular events within the Pfk13 suggestive of increasing fitness of parasites harboring these mutations in the population. Further, absence of the mutations that are driving resistance in southeast Asia appear to underscore the role of increased de novo local genetic background of parasite in contributing to parasite fitness therefore driving susceptibility to drugs. Policy changes are recommended to any ACT regimen in use at the point in time when the frequency of resistance rises above 10% as confirmed using WHO approved clinical efficacy protocols. Therefore, there is an urgent need for early launch of containment efforts as in the Greater Mekong Subregion but adapted to a greater region of intense transmission in sub-Saharan Africa. Preliminarily, this study was not powered to assess the effect of these polymorphisms on responsiveness to artemisinin derivatives even though S600F was detected in a day 38 follow-up infection which had negative results on recrudescence analyses. In vitro drug susceptibility assays are important in validating drug resistance candidate markers and establishing the susceptibility of parasites. 14 Our study findings on the susceptibility of P. falciparum parasites revealed a 2-fold rise in Lumefantrine IC50, compared to a previous publication. 44 32 , 45 . Genotype data for response to partner drugs showed that Pfmdr1 184F frequency was higher than other SNPs in the gene suggesting that this SNP plays a role in parasite fitness during the period of widespread use of artemether-lumefantrine. Our surveillance study was designed for high throughput genomic epidemiology and in vitro drug susceptibility testing to cover diverse malaria transmission zones by assessing Day-7 treatment outcome after dosing with Coartem ® on day zero and it is therefore expectedly unable to detect delayed clearance. Timely characterization of the ring stage survival assay is still underway to reveal in vitro responsiveness of these isolates besides the classical Malaria SYBR Green assay. Despite these limitations, the study provides important insights into the trends in molecular markers of drug resistance in P. falciparum parasites. The occurrence of mutations coding for resistance to these drugs at a low prevalence is an early warning signal and hence sustained surveillance should be encouraged to contain a rise in frequencies of these mutations. Declarations Acknowledgments Funding for this study was provided by the Armed Forces Health Surveillance Branch and its Global Emerging Infections Surveillance Section (Grant P0209_15_KY). Material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the author, and are not to be construed as official, or as reflecting true views of the Department of the Army or the Department of Defense. The investigators have adhered to the policies for protection of human subjects as prescribed in AR 70–25. We thank the individuals who participated in this study for providing these invaluable samples and time to generate these findings. We also thank the county Governments of Busia, Kisumu, Kisii, Kericho, Nakuru, and Laikipia plus the Ministry of Health Kenya for giving us access to these samples. We thank the Director, Walter Reed Army Institute of Research-Africa, COL. Lacy Shannon, Director Walter Reed Army Institute of Research -Africa Kisumu field Stations, COL. Gerald Keller, Deputy Director General, Center for Clinical Research, Kenya Medical Research Institute Dr. Linus Ndegwa, Kenya for supporting this study and giving their permission to publish these data. We also thank all clinical staff at all the participating hospitals for their assistance. Author Contributions Hoseah M. Akala: Conceived and designed the study, coordinated data collection, contributed to data interpretation, and critically reviewed the manuscript. Benjamin H. Opot: Molecular analyses, data interpretation, manuscript preparation, and critical review of the manuscript. Dennis Juma: Conceived and designed the study, coordinated data collection, supervised laboratory analyses, contributed to data interpretation, and critically reviewed the manuscript. Raphael O. Okoth: Molecular analyses, data interpretation, manuscript preparation, and critical review of the manuscript. Maurine Mwalo: Molecular laboratory analyses, PCR and sequencing experiments. Farid A. Salim: In-vitro drug susceptibility testing and contributed to the analysis of drug sensitivity data. Risper Maisiba: Molecular laboratory analyses, PCR and sequencing experiments. Redemptah Yeda: Molecular laboratory analyses, PCR and sequencing experiments. Edwin W. Mwakio: Molecular analyses, in-vitro drug susceptibility testing, data interpretation, manuscript preparation, and critical review of the manuscript. Gladys Chemwor: Molecular analyses, data interpretation, manuscript preparation, and critical review of the manuscript. Jackline A. Juma: Molecular laboratory analyses, PCR and sequencing experiments. Charles O. Okudo: In-vitro drug susceptibility testing and contributed to the analysis of drug sensitivity data. Timothy E. Egbo: Manuscript preparation, and critical review of the manuscript. Doris Njoroge: Oversaw sample collection coordination, clinical implementation, provided critical insights into epidemiological interpretations. Michal M. Ohaga: Oversaw sample collection coordination, clinical implementation, provided critical insights into epidemiological interpretations. Agnes C. Cheruiyot: In-vitro drug susceptibility testing and contributed to the analysis of drug sensitivity data. Kristan A. Schneider: Statistical expertise, analyzed data, and contributed to manuscript preparation. Victor Osoti: Molecular analyses and interpretation of genomic data. Kevin Wamae: Molecular analyses and interpretation of genomic data. Milton Obilo: Provided critical insights, reviewed and revised manuscript drafts. Eric C. Garges: Provided critical insights, reviewed and revised manuscript drafts. Ben Andagalu: Study design, protocol implementation, manuscript preparation, and critical review of the manuscript. Edwin Kamau: Manuscript preparation, and critical review of the manuscript. Lynette Isabella Ochola-Oyier: Genetic data analyses, and contributed significantly to the manuscript review. Bernhards R. Ogutu: Oversaw study coordination, clinical implementation, provided critical insights into epidemiological interpretations, and contributed to manuscript review and editing. All authors critically revised the manuscript, approved the final version, and agreed to be accountable for all aspects of the work. Competing interests We declare no competing interests. Materials & Correspondence Dr. Hoseah M. Akala, Department of Emerging and Infectious Diseases Walter Reed Army Institute of Research-Africa/Centre for Clinical Research, Kenya Medical Research Institute, P. O. Box 54 – 40100, Kisumu, Kenya. Tel. +254722329845. E-mail: [email protected] ; [email protected] Supplementary Material (1) References Noedl H, Se Y, Schaecher K, Smith BL, Socheat D, Fukuda MM (2008) Evidence of artemisinin-resistant malaria in western Cambodia. N Engl J Med 359(24):2619–2620 Nosten F, White NJ (2007) Artemisinin-based combination treatment of falciparum malaria. 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Malar J 21(1):369 Akala HM, Eyase FL, Cheruiyot AC et al (2011) Antimalarial drug sensitivity profile of western Kenya Plasmodium falciparum field isolates determined by a SYBR Green I in vitro assay and molecular analysis. Am J Trop Med Hyg 85(1):34 Eyase FL, Akala HM, Ingasia L et al (2013) The role of Pfmdr1 and Pfcrt in changing chloroquine, amodiaquine, mefloquine and lumefantrine susceptibility in western-Kenya P. falciparum samples during 2008–2011. PLoS ONE 8(5):e64299 Additional Declarations There is NO Competing Interest. None to declare Supplementary Files Supplementarymaterial.docx Emergence and Spread of Plasmodium falciparum Kelch 13 Mutations in Selected Counties of Kenya: Implications for Responding to Artemisinin Partial Resistance. 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Africa, Kisumu, Kenya.","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Andagalu","suffix":""},{"id":428485957,"identity":"f4c614e2-52c9-40a6-a379-fe56678cddf7","order_by":21,"name":"Edwin Kamau","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Edwin","middleName":"","lastName":"Kamau","suffix":""},{"id":428485958,"identity":"7f9f2950-bc96-421e-98e3-2ca7d6fd636e","order_by":22,"name":"Lynette Isabella Ochola-Oyier","email":"","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya","correspondingAuthor":false,"prefix":"","firstName":"Lynette","middleName":"Isabella","lastName":"Ochola-Oyier","suffix":""},{"id":428485959,"identity":"d7883026-b11f-4a7f-8cf4-e80c08b3f555","order_by":23,"name":"Milton Obilo","email":"","orcid":"","institution":"Kenya Ministry of Defense","correspondingAuthor":false,"prefix":"","firstName":"Milton","middleName":"","lastName":"Obilo","suffix":""}],"badges":[],"createdAt":"2025-03-12 17:50:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6214166/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6214166/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78639738,"identity":"fd4da0e5-0ff6-44ee-a07d-6c9c33260061","added_by":"auto","created_at":"2025-03-17 06:08:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":380414,"visible":true,"origin":"","legend":"\u003cp\u003eSynonymous and non-synonymous polymorphisms in \u003cem\u003ePlasmodium\u003c/em\u003e \u003cem\u003efalciparumPfK13\u003c/em\u003e gene in field isolate\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6214166/v1/a3a432636f76e31af63272a4.png"},{"id":78639755,"identity":"956403af-c87e-454b-b5a3-16a9a22d9958","added_by":"auto","created_at":"2025-03-17 06:08:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea: Temporal trends of the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePfk13\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e mutations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb: Predicted Change in frequency of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePfk13\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eA675V mutation in Kenya\u003c/strong\u003e. Plots show the estimated increase in frequency of \u003cem\u003ePfk13\u003c/em\u003e burden on y-axis based on number of infection cycles on x-axis spanning 15 days apart to give 30 infections per year.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6214166/v1/e81596f8f66784ca6f78c1f2.png"},{"id":78640225,"identity":"f3f73176-f217-4466-83d8-89115b7df06c","added_by":"auto","created_at":"2025-03-17 06:16:44","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":330384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of Kenya showing the number of Pfk13 mutations in selected Counties.\u003c/strong\u003e Color difference represents the relative frequency of validated artemisinin mutations for each County. Highest frequency in Baringo followed by Busia. Single nucleotide polymorphisms font red = validated, blue = nonsynonymous, black = synonymous mutations. A675A in Baringo represents a base change\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6214166/v1/9cd6934e2f5809a3a178bdeb.jpeg"},{"id":78639719,"identity":"a73a0479-7f54-47a8-ab7f-4722725ef89d","added_by":"auto","created_at":"2025-03-17 06:08:43","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":426370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAntimalarial IC50 comparison between field isolates and control clones. \u003c/strong\u003eThe antimalarial drugs tested for include chloroquine, artemether, lumefantrine, mefloquine, artesunate, and amodiaquine\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6214166/v1/1af29f5881e9b7ecb263c177.jpeg"},{"id":78639770,"identity":"6becb666-9826-4547-93c1-b211ffebf209","added_by":"auto","created_at":"2025-03-17 06:08:44","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":202425,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea, b\u003c/strong\u003e: \u003cem\u003ePlasmodium falciparum\u003c/em\u003emultidrug Resistance gene 1 codon 86 and 184 a) allele and b) haplotypes frequency. The Allele N86Y was majorly wildtype while Y184F was highly polymorphic. The NY and NF haplotypes were highest.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6214166/v1/01d58c8b5dd2bb9c0b789121.jpeg"},{"id":78783096,"identity":"c18b00d2-0b28-410a-8068-35b1a0619688","added_by":"auto","created_at":"2025-03-18 22:18:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2892578,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6214166/v1/49bc05e3-0df2-4496-8714-7e75990db00b.pdf"},{"id":78639707,"identity":"f2238507-6904-4efa-855d-df07a55ea1c4","added_by":"auto","created_at":"2025-03-17 06:08:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":140492,"visible":true,"origin":"","legend":"Emergence and Spread of Plasmodium falciparum Kelch 13 Mutations in Selected Counties of Kenya: Implications for Responding to Artemisinin Partial Resistance.","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6214166/v1/71f97e9f8f0e776edc63f45e.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.\nNone to declare","formattedTitle":"Emergence and Spread of Plasmodium falciparum Kelch 13 Mutations in Selected Counties of Kenya: Implications for Responding to Artemisinin Partial Resistance.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eConfirmed reports of artemisinin tolerance in Southeast Asia (SEA) threaten the continued use of artemisinin-based combination therapies (ACTs) for the treatment of uncomplicated malaria globally.\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e2\u003c/sup\u003eStudies show that mutations in the \u003cem\u003ePlasmodium\u003c/em\u003e \u003cem\u003efalciparum\u003c/em\u003e Kelch 13 gene (\u003cem\u003ePfk13\u003c/em\u003e) associated with delayed parasite \u0026nbsp;clearance \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u0026nbsp;\u003c/em\u003e\u003csup\u003e3\u003c/sup\u003e are widespread in SEA.\u003csup\u003e4\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e The first cases of \u003cem\u003ePfk13\u0026nbsp;\u003c/em\u003emutations-associated with artemisinin partial resistance were identified in 2013 in Africa. \u0026nbsp;Initially reported in Rwanda and Ethiopia, these mutations were not associated to those previously detected in \u0026nbsp;SEA,\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e7\u003c/sup\u003e before being detected in Eritrea,\u003csup\u003e8\u003c/sup\u003e Tanzania\u0026nbsp;\u003csup\u003e9\u003c/sup\u003e and Kenya.\u003csup\u003e10\u003c/sup\u003e \u003csup\u003e11\u003c/sup\u003e Specifically, artemisinin delayed parasite clearance in Africa has been linked to the \u003cem\u003ePfk13\u003c/em\u003e\u0026nbsp; \u0026nbsp;622I mutation in Ethiopia, \u0026nbsp;the R561H mutation, which is undergoing clonal proliferation southwards in Rwanda,\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e,\u0026nbsp;\u003c/sup\u003e\u003csup\u003e12\u003c/sup\u003e in to northern Tanzania,\u003csup\u003e13\u003c/sup\u003e and the C469Y and A675V mutations in Eastern Uganda\u003csup\u003e14\u003c/sup\u003e and western Kenya.\u003csup\u003e10\u003c/sup\u003e \u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe most widely studied and well characterized ACTs in Africa are artemether-lumefantrine (AL), artesunate-amodiaquine (ASAQ), dihydroartemisinin-piperaquine (DHAPPQ), artesunate-mefloquine (ASMQ), and artesunate-pyronaridine\u003csup\u003e15\u003c/sup\u003e. Of these, AL is the most widely used treatment in Africa. Polymorphisms within the \u003cem\u003ePfmdr1\u003c/em\u003e gene loci N86Y, Y184F, S1034C, N1042D have been implicated in modulating parasite response to chloroquine and ACTs partner drugs amodiaquine, lumefantrine and mefloquine.\u003csup\u003e16\u003c/sup\u003e Notably, AL and ASAQ exert inverse selective pressures whereby parasites with genotype 86Y, Y184 and 1246Y are selected by ASAQ treatment, while N86, 184F and D1246 are selected by AL treatment.\u003csup\u003e16\u003c/sup\u003e Since the strategy behind the deployment of ACTs relies on the slow acting partner drug to clear the residual parasitemia after initial rapid clearance of most of the parasite biomass by the short half-life, fast-acting artemisinin component of the combination, the delayed or partial parasite clearance by the artemisinin derivative will result in prolonged exposure of the parasite to the partner drug. This therefore renders the partner drug vulnerable to rapid development of resistance as a major concern for the combination therapy approach. Owing to initial reports in southeast Asia,\u003csup\u003e1\u003c/sup\u003e the emergence of resistance to artemisinin and partner drugs has been identified as a significant risk to the global effort to reduce the malaria burden as per the Global Technical Strategy (GTS) for malaria,\u003csup\u003e17\u003c/sup\u003e necessitating intensified surveillance for the early detection of resistance. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis surveillance study sought to determine the frequency of polymorphisms in \u003cem\u003ePfk13\u003c/em\u003e with attention to thevalidated malaria drug resistance markers from among clinical samples collected in eight hospitals located in geographically diverse regions across Kenya within four of the five malaria transmission zones of Kenya, between 2018 and 2024.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy sites and sample collection\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples were collected between 01-July 2018 and June 2024 from eight health facilities located in seven counties spanning across four of the five malaria transmission zones comprising three sites in the Lake endemic zone \u0026nbsp;(Busia – Busia County \u0026nbsp;Referral Hospital; Kisumu- Kombewa Sub-County \u0026nbsp;Hospital; and Kisumu County \u0026nbsp;Referral Hospital), two sites in the highland zone (Kisii \u0026nbsp;- Kisii County \u0026nbsp;Referral and Kericho- Kericho County \u0026nbsp;Referral), semi-arid seasonal transmission zone (Baringo – \u0026nbsp;Marigat Sub-County Hospital) as well as low risk zone (Nakuru – Lanet Regional Hospital and Laikipia- Laikipia Hospital). These sites were chosen to span four of the five distinct malaria transmission zones across Kenya National Malaria Control Programme 2020.\u003csup\u003e18\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e Patients aged six months and above presenting to the outpatient departments and diagnosed with uncomplicated malaria by rapid diagnostic test (mRDT; Parascreen® (Pan/Pf), Zephyr Biomedicals, Verna Goa, India) were recruited into the study after providing written informed consent or assent. Pregnant women were also eligible for enrollment. Individuals were excluded from the study upon refusal to participate or unwillingness to give blood. The study also excluded prisoners, including children attending the Kenyan government’s correction and rehabilitation programme. \u0026nbsp;Additionally, individuals under age 18 without available parent or legal guardian, volunteers who were previously enrolled in the study during the same calendar year, individuals weighing less than 5 kg and anyone who, in the opinion of the attending medical provider, would be adversely affected by the drawing of 2.5 mL of blood were excluded. Written informed assent for individuals aged 17 years and below was provided by their parent or legal guardian in accordance with the laws of the government of Kenya. Individuals who did not meet the inclusion criteria were excluded as previously described.\u003csup\u003e20\u003c/sup\u003e For all consenting and assenting individuals who were enrolled into the study, 2-3 ml of whole blood was collected and aliquots of 1.5ml in Vacutainer™ Glass Blood Collection Tubes with Acid Citrate Dextrose catalogue number BD 364816 from Becton, Dickinson U.K. Limited form Berkshire, United Kingdom for \u003cem\u003ein vitro\u003c/em\u003e drug sensitivity testing. The remainder of sample was dispensed in the Ethylenediaminetetraacetic acid tube for molecular analyses. Both samples were swirled well in the anticoagulant to homogeneity, retained at room temperature for 30 min then stored at 2 to 8 degrees pending transportation at this same temperature to the central lab for processing.\u003c/p\u003e\n\u003cp\u003eFor case management, all \u003cem\u003ePlasmodium\u003c/em\u003e positive cases were treated with Coartem® (Novartis, Basel, Switzerland), which contains artemether-lumefantrine (AL) in accordance with the Kenya Ministry of Health recommended case management guidelines for uncomplicated malaria. After drawing blood, the attending clinician administered and observed the first dose of Coartem® 20mg/120mg tablets, with individuals weighing 5 to \u0026lt;15 kg prescribed 1 tablet per dose, 15 to \u0026lt; 25kg 2 tablets per dose, 25 to \u0026lt;35kg 3 tablets per dose and individuals greater than 35kg 4 tablets per dose. Each patient was given the remaining 5 doses (i.e. 5, 10, 15, 20 and 25 tablets for each weight range) and advised to take the next dose after eight hours, followed by the remaining doses at 12 hourly intervals till completion of treatment.\u003c/p\u003e\n\u003cp\u003eFurther, each participant was required to come back on day 7 for clinical and laboratory evaluation comprising documentation of vital signs as well as parasitological diagnosis of malaria. Regardless of the outcomes of these evaluations, an additional blood draw was taken during the day 7 visit or any other visit date within 42days follow-up for molecular and in vitro testing at the central lab to monitor treatment outcomes. Importantly, all participants were encouraged to come back to the study clinician on any other day during the subsequent 42 days for review and further clinical management symptoms persist. This study was conducted under the approval of the Kenya Medical Research Institute (KEMRI), Scientific and Ethics Review Unit (SERU) and Walter Reed Army Institute of Research (WRAIR) institutional review boards, protocol numbers: WRAIR 2454, KEMRI 3628: “Epidemiology of malaria and drug sensitivity patterns in Kenya.”\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParasite density estimation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree glass microscope slides for thick and thin smears were prepared from the whole blood sample obtained from each participant. One slide was read the same day and reported, the two remaining slides were batched and read at a later date. All positive \u003cem\u003ePlasmodium\u003c/em\u003e infections were characterized for parasitemia. Parasites were typed counts per number of cells counted in either 200 or 2000 high power fields for thick and thin malaria blood smears, respectively, before expressing as either percent parasitemia or parasite density as previously described\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eP. falciparum\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eDNA extraction and sequencing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA extraction from whole blood was done using Qiagen DNA Mini extraction spin protocol (Qiagen, Valencia, CA) as per the manufacturer’s guidelines and eluted with 150 µl of elution buffer. Molecular diagnosis of \u0026nbsp;\u003cem\u003ePlasmodium\u003c/em\u003e genus and species \u0026nbsp;was done using a quantitative real-time PCR using primers targeting 18S ribosomal RNA as described elsewhere.\u003csup\u003e22\u003c/sup\u003e This procedure involved two reactions. The first detected the presence of \u003cem\u003ePlasmodium\u003c/em\u003e genus using the real-time PCR assay on Applied Biosystem QuantStudio 6 flex (QuantStudio™ real-time PCR Applied Biosystems by Thermos Fisher Scientific Foster City, CA) as previously described.\u003csup\u003e22\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e23\u003c/sup\u003e The second reaction on the same platform, using the assay conditions and primers previously described,\u003csup\u003e24\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e25\u003c/sup\u003e identified the \u003cem\u003ePlasmodium\u003c/em\u003e species present in the infection. Samples that were positive for the \u003cem\u003ePlasmodium\u003c/em\u003e genus with cycle threshold values of at least 31 and below were further processed for \u003cem\u003ePfk13\u003c/em\u003e and mutation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle nucleotide polymorphisms in the \u003cem\u003ePfk13,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Pfmdr1\u003c/em\u003egenes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA conventional PCR was done using primers as described by de Laurent and coworkers\u003csup\u003e26\u003c/sup\u003e to amplify the \u003cem\u003ePfk13\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Pfmdr1,\u0026nbsp;\u003c/em\u003egenes for sequencing. This resulted in a detectable band size consistent with successful amplification of the \u003cem\u003ePfk13, and Pfmdr1\u0026nbsp;\u003c/em\u003egenes. Exo-SAP IT was used to purify the PCR products by removing excess nucleotides and primer remnants. Sanger sequencing was performed on purified PCR products using Big Dye terminator method version 3.1 on the 3500xL ABI genetic analyzer (Applied Biosystems, Foster City, CA). Sequencing data was then analyzed on the Qiagen CLC Main Workbench (Qiagen, Valencia, CA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequencing of the \u003cem\u003ePf1k13\u003c/em\u003e, and \u003cem\u003ePfmdr1\u003c/em\u003e genes amplicons\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLibrary preparation and sequencing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 927 isolates were subjected to amplicon sequencing. The amplicons were purified using Exo-SAP IT to remove primers and other short DNA fragments following manufacturer’s instructions. These amplicons were then normalized to a concentration of 2 ng/µL for DNA library preparation. Libraries were generated using the Nextera XT library preparation kit, following the manufacturer's protocol. Using Tape Station 4200 system (Agilent Technologies, Santa Clara, CA, USA) D1000 screen Tape was used to confirm adaptor ligation. The libraries were pooled for sequencing on Illumina iSeq 100 (Illumina Inc, San Diego, CA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequencing Data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData processing was performed as previously described by Niaré \u0026amp; Bailey (2023). Microhaplotype clustering was performed using the \u0026nbsp;SeekDeep v3·0·0 workflows as previously described.\u003csup\u003e27\u003c/sup\u003e GATK4 Haplotype Caller was also used to call variants with the following parameters described by\u003csup\u003e28\u003c/sup\u003e\u0026nbsp; Niaré \u0026amp; Bailey (2023). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing BCFtools, SNPs with a DP \u0026gt; 100, minor allele frequency (MAF) \u0026lt; 0·02, and \u0026gt; 25% missingness were excluded. Alleles with a frequency below 0·5 were considered minor alleles, while those above were considered major alleles. Functional annotation of the SNPs was performed using SnpEff annotation tool.\u003c/p\u003e\n\u003cp\u003eHaplotypes constructions were conducted by aligning PCR-amplified sequences to a reference genome using ClustalW to identify SNPs relative to the wildtype. After variant calling, we applied stringent quality control measures—including filters based on minor allele frequency and Hardy–Weinberg equilibrium—to ensure data reliability. The resulting SNP dataset was then processed using a Bayesian algorithm to infer haplotype phases, enabling the accurate assembly of both single and multiple mutation haplotypes. Finally, haplotype frequencies were determined by calculating the proportion of each haplotype relative to the total number of sequences analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults mapping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMutation mapping was achieved by compiling mutation data in Excel and mapping it onto an OpenStreetMap (OSM) basemap using Quantum GIS (QGIS), a free and open-source Geographic Information System (GIS) software used for mapping, spatial data analysis, and geospatial visualization. QGIS allows users to overlay datasets onto basemaps and apply various spatial analysis techniques. \u0026nbsp;The final map can \u0026nbsp;then be exported as an image.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIn vitro\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;susceptibility testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA subset of the malaria positive samples was tested for \u003cem\u003ein vitro/ ex vivo\u0026nbsp;\u003c/em\u003esusceptibility to five antimalarial drugs using the malaria SYBR Green I-based assay as previously described.\u003csup\u003e29\u003c/sup\u003e\u0026nbsp; The tested drugs include dihydroartemisinin (DHA), artemether (AT), chloroquine (CQ), Lumefantrine (LU) and Mefloquine (MQ) as earlier described.\u003csup\u003e30\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e31\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e32\u003c/sup\u003e Culture adapted clinical isolates and reference strains (W2 strain is chloroquine resistant /mefloquine sensitive, “D6” and “3D7” are \u0026nbsp;chloroquine sensitive /mefloquine resistant) obtained from Biodefense and Emerging infectious \u0026nbsp; Research \u0026nbsp;Resources Repository (BEI Resources) were tested side by side as the assay quality controls.\u003csup\u003e20\u003c/sup\u003e Parasite replication inhibition was quantified by measuring the per-well relative fluorescence units (RFU) of SYBR Green I dye using the Tecan Genios Plus® (Tecan US. Inc., Durham, NC) with excitation and emission wavelengths of 485 nm and 535 nm. The IC50 values for each drug were calculated as previously described.\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Management and\u0026nbsp;Statistical Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were initially collected on standardized case report forms and subsequently entered into a secure, dedicated database. To ensure data integrity, we implemented double data entry, performed regular quality checks, and conducted range and consistency validations. Any missing or outlier data were carefully reviewed and managed according to pre-established protocols.\u003c/p\u003e\n\u003cp\u003eFor statistical analysis, categorical data including gene sequence polymorphisms were summarized as frequencies and proportions. Continuous variables, including in vitro drug susceptibility results, were first assessed for normality (using tests such as the Shapiro–Wilk test) and then expressed as median IC₅₀ values with their corresponding interquartile ranges (IQR) when non-normal distributions were observed. Additionally, proportions were reported for relevant parameters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparative analyses between groups were conducted using non-parametric tests when appropriate, given the data distribution. All statistical analyses were performed using GraphPad Prism version 8.0 (GraphPad Software, Inc., San Diego, CA, USA), with a significance level set at p \u0026lt; 0.05. Results are presented in detailed tables and figures, supplemented by additional materials that further clarify the data distributions and trends observed throughout the study.\u003c/p\u003e\n\u003cp\u003eRaw data files will be available upon request, with the eventual intention of depositing all sequence data on the NCBI platform for unrestricted public access.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBetween July 2018 and June 2024, 2,805 study participants consisting of symptomatic patients presenting at the outpatient departments of eight healthcare facilities were screened, 2,563 met inclusion criteria and were enrolled in the epidemiology of malaria drug resistance surveillance study. Of these, 679/927 samples were successfully sequenced for polymorphisms in \u003cem\u003ePfk13,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Pfmdr1\u0026nbsp;\u003c/em\u003egenes. The successfully sequenced samples comprised those from hospitals namely Busia County Referral (n=193), Kisumu County (Kombewa Sub-County n= 164, Kisumu County Referral n=141), Kisii County Referral (n=64) Kericho County Referral (n=39), Nakuru county at Lanet Regional (n=1), Laikipia county\u0026rsquo;s Laikipia Air Base (n=2), Baringo county\u0026rsquo;s Marigat Sub-County (75) hospitals. Of these, 671 were from the initial day zero visits, and eight were from subsequent visits. The overall median parasitemia and interquartile range was 0.62 % [0.13 to 2.50; n=761/926]. Per county median parasitemia and interquartile range were Kericho 1.55% [0.30-5.83, n= 48]; comparable to Kisumu 1.5% [0.14- 4.00, n=323]; Busia 0.5 % [0.12-1.60, n=215]; Baringo 0.60% [0.09-1.50, n=81]; Kisii 0.18% [0.1-0.25], n=70]. The low malaria burden counties had 2 samples each at parasitemia of 0.03% and 1.00 % for Nakuru and 0.04% and 0.20% for Laikipia (Supplementary Table 1). Metadata available for 148 individuals showed observed lower mean age of participants, and more malaria episodes in the past 12 months in the lake endemic region than highland epidemic (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable1: Study site demographics and clinical summaries\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eSite (n)\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eRegion\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eAge*\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eSex\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e(% Female)\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6868%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eTemperature*\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 36.6082%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eMalaria episodes in last 12 months\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eBusia (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eLake Endemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e11.38 [7.72-15.04]\u003c/p\u003e\n \u003cp\u003e(60/61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e60.66%\u003c/p\u003e\n \u003cp\u003e(37/61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 24.8157%;\"\u003e\n \u003cp\u003e37.77 [37.50-38.04]\u003c/p\u003e\n \u003cp\u003e(60/61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4793%;\"\u003e\n \u003cp\u003e37/61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eKisumu (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eLake Endemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e11.85 [4.04-19.65]\u003c/p\u003e\n \u003cp\u003e(13/13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e61.54%\u003c/p\u003e\n \u003cp\u003e(8/13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 24.8157%;\"\u003e\n \u003cp\u003e38.29 [37.66-38.92]\u003c/p\u003e\n \u003cp\u003e(13/13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4793%;\"\u003e\n \u003cp\u003e6/13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eKombewa (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eLake Endemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e10.96 [8.10-13.82]\u003c/p\u003e\n \u003cp\u003e(27/32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e59.38%\u003c/p\u003e\n \u003cp\u003e(19/32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 24.8157%;\"\u003e\n \u003cp\u003e37.77 [37.46-38.09]\u003c/p\u003e\n \u003cp\u003e(32/32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4793%;\"\u003e\n \u003cp\u003e25/27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eKisii (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eHighland Epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e13.38 [7.54-19.22]\u003c/p\u003e\n \u003cp\u003e(26/27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e37.04%\u003c/p\u003e\n \u003cp\u003e(10/27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 24.8157%;\"\u003e\n \u003cp\u003e38.73 [38.30-39.17]\u003c/p\u003e\n \u003cp\u003e(26/27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4793%;\"\u003e\n \u003cp\u003e8/26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eKericho (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eHighland Epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e23.46[17.17-29.75]\u003c/p\u003e\n \u003cp\u003e(15/15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e46.67%\u003c/p\u003e\n \u003cp\u003e(7/15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 24.8157%;\"\u003e\n \u003cp\u003e37.67 [36.88-38.47]\u003c/p\u003e\n \u003cp\u003e(15/15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4793%;\"\u003e\n \u003cp\u003e2/15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eAll (148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e13 [10\u003cstrong\u003e.\u003c/strong\u003e81-15\u003cstrong\u003e.\u003c/strong\u003e19]\u003c/p\u003e\n \u003cp\u003e(141/148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e54\u003cstrong\u003e.\u003c/strong\u003e73%\u003c/p\u003e\n \u003cp\u003e(81/148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 24.8157%;\"\u003e\n \u003cp\u003e37\u003cstrong\u003e.\u003c/strong\u003e98 [37\u003cstrong\u003e.\u003c/strong\u003e79-38\u003cstrong\u003e.\u003c/strong\u003e16]\u003c/p\u003e\n \u003cp\u003e(146/148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4793%;\"\u003e\n \u003cp\u003e78/142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Mean [95% Confidence Interval] (n/N).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eP. falciparum\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;single nucleotide polymorphism in \u003cem\u003ePlasmodium falciparum\u0026nbsp;\u003c/em\u003eKelch 13 gene\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of distinct mutations in \u003cem\u003ePfk13\u003c/em\u003e gene were detected in 44/679 (6.5%) samples using Sanger and amplicon sequencing techniques (Figure 1) and a subset confirmed by NGS using iSeq100 Sequencing System - Illumina. Samples that did not meet the inclusion threshold due to low depth were excluded. Those included in this study report comprised 13 nonsynonymous mutations in 36 samples including C469Y n=3, (0.5%), P553L n=1, (0.15%), R561H n=1 (0.15%) and A675V n=20 (2.9%) that are validated markers of partial artemisinin resistance. Other non-synonymous mutations included A578S n=4 (0.6%) as well as V386A, R561H/P, S522C, K455E, S600F, E612D, N489K, F491L plus A504V at n=1 (0.15%). A total of 11 synonymous mutations were also detected (Figure 1). Notably, seven of the nonsynonymous and six of the synonymous mutations had not previously been reported elsewhere. One infection from Kisii had triple \u003cem\u003ePfk13\u0026nbsp;\u003c/em\u003eG497G, P553L, L488L mutations while two infections from Busia had double \u003cem\u003ePfk13\u003c/em\u003e mutations, A675V plus V386V and A675V plus C469Y.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePfk13 Temporal Trends\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMutations were detected across different years between 2018 and 2024, with the earliest occurrences observed in 2018, including A675V, K455E, and N489K. No mutations were identified in 2019. In 2020, E612D, G690G, and S600F mutations were detected, followed by A504V, P553L, and A626A in 2021. In 2022, additional mutations such as A504A, C469C, E612D, F491L, and S522C were observed. A significant rise in mutation frequency occurred in 2023, with notable increases in A578S, A627A, A675A, and A675V, the latter appearing in 7 instances. By 2024, A675V was the most frequently observed mutation (13 occurrences), while other variants appeared at lower frequencies (Figure 2a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTemporally, \u003cem\u003ePfk13\u003c/em\u003e 675V was the first mutation to be detected in two samples from Busia and Kericho between January and June 2023. By December 2023, the mutation frequency increased by six additional samples, followed by an additional 14 by June 2024 bringing the frequency of this mutation to 3.1% (Figure 2b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGeographical Distribution\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeographically, the \u003cem\u003ePfk13\u003c/em\u003e alleles were distributed among the seven study sites where Busia had the highest number of allele variants with 17 different alleles present in 15 samples followed by Kisii with seven different alleles in five samples while Baringo County (Marigat) which had the least allele frequency with one allele in all the 12 samples sample that were polymorphic (Table 2, Figure 3). The WHO validated partial artemisinin resistance allele A675V was the most prevalent, occurring at 3.1% (n = 21/679) of the infections followed by A578S at 0.6% (n= 4), C469Y at 0.45% (n =3) while R561H, were at 0.15% (n=1) (Figure 1). Synonymous mutation C469C, was detected in two samples obtained from Kisii and Kisumu (Figure 1). Seven nonsynonymous and six synonymous mutations had not been reported previously. Assessment of the haplotype frequency is summarized in supplementary Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Frequency and distribution of \u003cem\u003ePfk13\u003c/em\u003e mutation and samples in the eight study sites.\u0026nbsp;\u003c/strong\u003eCRH means County Referral Hospital, * High percentage due to the low number of samples analyzed from the county given the low malaria endemicity in the region despite lengthy surveillance duration.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;County\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-synonymous\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003emutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSynonymous mutation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles (N=49)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency per site\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eBusia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eBusia CRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e17(34.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e17/193(8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eKisumu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eKombewa Sub- CRH and Kisumu CRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e8(16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e8/305 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eKericho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eKericho CRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3(6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e3/39(7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eKisii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eKisii CRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e7(14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e7/64(10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eBaringo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eMarigat Sub- CRH\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e12(24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e12/72(16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eNakuru\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eLanet regional Hospital\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e2(4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2/4(50%) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eLaikipia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eLaikipia Air Base hospital\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0/2 (0%) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSingle nucleotide polymorphisms in \u003cem\u003ePlasmodium falciparum multidrug resistance 1 gene\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA subset of the 626/679 samples with complete \u003cem\u003ePfk13\u003c/em\u003e sequences gave complete readouts of single nucleotide polymorphisms in the \u003cem\u003ePfmdr1\u003c/em\u003e gene codon 86 (n=586) and 184 (n= 593). SNP frequencies for N86 and 86Y were 98.5% (577/586) and 1.5% (9/586) while those of Y184 and 184F were 51.6% (306/593) and 48.4% (287/593), respectively. 565 samples that had both calls at codons 86 and 184 showed that the \u003cem\u003ePfmdr1\u003c/em\u003e 86,184 the NY (51.0%) haplotype was most frequent followed by NF (47.4%), YF (0.9%) and YY (0.7%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn vitro drug susceptibility of the field isolates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUp to 234/926 field isolates were successfully analyzed for \u003cem\u003ein vitro\u003c/em\u003e susceptibility to lumefantrine, artemether, chloroquine, mefloquine and dihydroartemisinin. Their 50% inhibition concentrations (IC50s) in nanomolar (nm) are summarized in Table 3. The median IC50 for CQ against field isolate was significantly different from that of chloroquine resistant \u0026ldquo;W2 clone\u0026rdquo; and comparable with that of 3D7 clone, suggesting that field isolates were majorly sensitive to chloroquine. IC50s for lumefantrine, artemether, quinine and dihydroartemisinin against field isolates were comparable to those of the reference clones (Figure 4). Comparison of IC50s for samples with \u003cem\u003ePfmdr1\u003c/em\u003e polymorphisms showed significantly higher chloroquine IC50s among samples with 184F than Y184 allele while the rest were comparable (Supplementary Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eIn vitro\u003c/em\u003e responses of field isolates to selected antimalarial drugs \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChloroquine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArtemether\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLumefantrine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMefloquine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eField Isolates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eW2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3D7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eField Isolates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eW2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3D7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eField Isolates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eW2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3D7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eField Isolates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eW2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3D7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eNumber of values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e149.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e9.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e7.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e33.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e25% Percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e17.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e33.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e7.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e57.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e9.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e49.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e11.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e11.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e75% Percentile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e15.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e142.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e14.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e21.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e25.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e64.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e22.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e13.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e114.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e173.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e15.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e14.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e65.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e33.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e64.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e71.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e13.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTreatment outcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the individuals 823/926 successfully followed up on day-7 to monitor treatment outcomes, 178/823 (21\u0026middot;6%) had PCR-amplification suggestive of residual parasite nucleic acids though data for both parasite and drug kinetics was not available. These included 12 samples that harbored validated artemisinin partial resistance markers \u003cem\u003ePfk13\u003c/em\u003e P553L, C469Y, A675V (n=2), and those not previously validated V637D day zero. The 69/178 samples that had day zero SNP data available for both \u003cem\u003ePfmdr1\u003c/em\u003e 86 and 184 were mutant 1/69(2%) and 23/69 (33\u0026middot;3%) respectively (Table 4). The most frequent haplotype among day-7 positive was \u003cem\u003ePfmdr1\u003c/em\u003e N/Y (Figure 5a, b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Parasite residual parasite nucleic acids on day 7 and the \u003cem\u003ePf\u003c/em\u003emdr1 86 and 184 alleles after administering artemether-lumefantrine dose.\u0026nbsp;\u003c/strong\u003eParasite nucleic acids detected in comparable proportion in infections that had mutant alleles at day zero alike to those that were wildtype on day zero.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDay 0 and 7 samples that amplified for presence of parasite nucleic acids (PLU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of samples (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePfmdr1_86, total\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e71\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eDay 0 Pfmdr1_N86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e66(93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eDay 0 Pfmdr1_86Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eDay 0 Pfmdr1_N86Y\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2(2.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eDay 0 Pfmdr1_86, No sequence assembled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2(2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDay 0 Pfmdr1_184, total\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e71\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eDay 0 Pfmdr1_Y184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e46(64.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eDay 0 Pfmdr1_184F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e18(25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eDay 0 Pfmdr1_Y184F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e5(7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 282px;\"\u003e\n \u003cp\u003eDay 0 Pfmdr1_184, No sequence assembled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2(2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEvolution of mutations in \u003cem\u003eP. falciparum\u003c/em\u003e K13 propeller domain that is associated with artemisinin partial resistance is on the rise in Africa, sixteen years after the implementation of artemisinin-based combination therapies. The earlier findings by\u0026nbsp;\u003csup\u003e33\u003c/sup\u003e and\u0026nbsp;\u003csup\u003e34\u003c/sup\u003e showing minimal polymorphisms in the \u003cem\u003ePfk13\u003c/em\u003e without clinical implication have been updated by reports of mutations in \u003cem\u003ePfk13\u003c/em\u003e C469Y, R561H, A657V and, SNPs mediating delayed parasite clearance after treatment with artemisinin-based combination therapies in east African countries implying emergence of resistance in Africa.\u003csup\u003e14\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e35\u003c/sup\u003e The current study analyzed a large sample size \u0026nbsp;of 679 parasite isolates collected from symptomatic patients between 2018 and 2024 from seven counties in Kenya for \u003cem\u003ePfk13\u003c/em\u003e and other drug resistance mutations in addition to \u003cem\u003ein vitro\u003c/em\u003e drug susceptibility profiles. \u0026nbsp;The seven counties represent four of the five distinct malaria transmission zones across Kenya. A total of\u0026nbsp;24 mutations comprising 13 nonsynonymous including \u003cem\u003ePfk13\u0026nbsp;\u003c/em\u003eA675V (n=20), C469Y n=3, R561H, P553L and, M476I that are on the WHO list of validated artemisinin partial resistance markers previously reported in Africa and Southeast Asia as well as 11 synonymous were observed across all study sites. Importantly, three of the validated artemisinin partial resistance in SEA were detected at lower frequency than those previously reported across Africa. This observation appears to suggest that the resistance to artemisinin in the African region is being driven by local emergence of mutations rather than spread from Asia. Secondly, the frequency of\u003cem\u003e\u0026nbsp;Pfmdr1\u003c/em\u003e N86 and 184F SNPs in day zero samples of the infections that tested positive on day-7 follow up was 98% and 33.3%, respectively. Median IC50s were below the resistance cut off threshold for resistance to chloroquine and somewhat elevated for lumefantrine based on in vitro drug susceptibility assays.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe global strategy for combatting malaria relies on sustained drug resistance surveillance. The frequency of mutations conferring phenotypic resistance to artemisinin is rising across Africa, posing a public health threat given the weak healthcare infrastructure. This report from a surveillance study in four of the five malaria transmission zones of Kenya, depicting increased genomic activity on \u003cem\u003ePfk13\u003c/em\u003e calls for largescale surveillance across diverse ecotypes to identify mutations as they arise and estimate their implications on treatment outcome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe C469Y mutations were reported in one infection from Busia and Kisumu in January 2024. The sample harboring this mutation in Busia also harbored \u003cem\u003ePfk13 gene\u003c/em\u003e A675V mutation that our surveillance had previously reported to be on the rise in Kenya. Three months after this initial detection, a second and third infection in-country, harboring this mutation was detected in Busia and Kisumu County. Busia town, where our surveillance site is located is the main transit town between Kenya and Uganda, connecting Kenya to the Great Lakes region by road. Notably, \u003cem\u003ePfk13 gene\u003c/em\u003e C469Y mutation has recently reported to confer artemisinin partial resistance in Eastern Uganda. The patient in question was five years old presenting with uncomplicated malaria, concurrently has a strain harboring Pfk\u003cem\u003e13\u003c/em\u003e A675V mutation that is on the rise in Kenya, was successfully treated with artemether-lumefantrine confirmed by malaria rapid diagnostic test and PCR on Day-7.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn October 2023, a study by Jeang and coworkers reported detection of \u003cem\u003ePfk13 gene\u003c/em\u003e A675V mutation in three field isolates collected from Kakamega County in western Kenya in 2021.\u003csup\u003e10\u003c/sup\u003e This surveillance study reports a cumulative 5-fold increase in frequency of \u003cem\u003ePfk13\u003c/em\u003e A675V mutation between January 2023 and June 2024 from 2/312 (0.6%) to 21/679 (3.1%). The first mutation was detected in Kericho, a highland epidemic malaria zone in January 2023, and the second one in Busia in April 2023,194 kilometers away but along the same highway. Between October and November 2023, an additional four samples harboring \u003cem\u003ePfk13 gene\u003c/em\u003e A675V mutation were detected in Busia (n=1) Kisii (n=1) and Baringo (n=2) adding to 6/476 (1.2%). Between January and February, additional nine mutations detected in Busia (n=2), and Baringo (n=7) bringing the overall frequency of the mutation in Kenya to 3.1%. It is worth noting that 60% (12/20) of \u003cem\u003ePfk13\u003c/em\u003e A675V mutations were detected in Baringo County, a semi-arid seasonal transmission zone, located over 100 kilometers away from the main road linking Kenya and Uganda\u003csup\u003e36\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e37\u003c/sup\u003e. This observation was somewhat new in two different ways. First, Kenya is stratified in to five malaria burden zones placing Baringo county semi-arid seasonal transmission zone, therefore inhabitants are inherently less likely to acquire \u003cem\u003ePlasmodium\u003c/em\u003e species infections per year hence likely less immunity to malaria as compared to Kisumu and Busia that are very high burden. Artemisinin partial resistance was expected to follow the classical trend of chloroquine resistance that emerged in holoendemic Kisumu County\u003csup\u003e38\u003c/sup\u003e, before spreading and causing epidemics in the less immune individuals in Kisii county. Secondly, only one \u003cem\u003ePf\u003c/em\u003ek13 mutation, the A675V was detected in Baringo during the study period. The frequency of infections harboring this mutation increased in Baringo county, faster than in any other counties including those where our surveillance study detected the variant much earlier. Conversely, malaria holoendemic counties had higher number SNPs, Busia (n=9), Kisumu (n=8) while those from highland epidemic zone were moderately few Kisii (n=4). These observations appear to suggest that the infections in Baringo county were undergoing purifying selection similar to that reported in Southeast Asia given the low immunity in Baringo County, therefore presenting a unique dilemma to Kenya\u0026rsquo;s division of national malaria program\u0026rsquo;s innovativeness in solving the problem posed by resistant variants in both immune and less immune population. It is noteworthy that the number of participants enrolled per site was purely drawn from individuals presenting at the outpatient department, seeking treatment after giving written informed consent to participate in the study, then providing blood samples hence deemed proportionate to the population and disease burden. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 13 nonsynonymous and the 11 synonymous mutations included V386A, S600F, K455E, and F491L alongside A626A, A627A, and G690G respectively; that to the best of our knowledge are new. Functional annotation of these mutations revealed that clarifying the role of these mutations in a clinical trial would be worthwhile as a strategy for monitoring the therapeutic efficacy of artemisinin. The P553L mutation was also detected (1%). This mutation that was previously reported in SEA and Africa, including Kenya,\u003csup\u003e39\u003c/sup\u003e is associated with delayed parasite clearance by artemisinin and its derivatives\u0026nbsp;\u003csup\u003e39\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e40\u003c/sup\u003e and validated as a marker of artemisinin partial resistance.\u0026nbsp;\u003csup\u003e8\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e41\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e42\u003c/sup\u003e Occurrence of this mutation among other novel nonsynonymous mutations in Kenyan parasites is disconcerting and warrants intense surveillance in Sub-Saharan Africa and well-designed studies to ascertain their contribution to treatment outcome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt is noteworthy that all the patients had a successful treatment with artemether/lumefantrine documented based on WHO recommended parasitological diagnosis using the PAN band of the mRDT on day-7. However, 12 of the 44 infections that harbored \u003cem\u003ePfk13\u003c/em\u003e mutations on day zero were among the 178 samples that amplified on the day-7 follow-up, depicting likelihood of residual parasite nucleic acids though this surveillance study was not structured to assess parasite and drug kinetics that would ascertain inherent tolerance across different polymorphisms compositions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003epharmacokinetics data was not available to clarify adherence. These 12 samples harbored validated artemisinin partial resistance markers Pfk13 A578S, P553L, C469Y, A675V (n=2), and those not previously validated V637D \u0026ndash; X, N531I A_T, G639R \u0026ndash; S, F628L \u0026ndash; TT, L571S \u0026ndash; F, D559E \u0026ndash; G, C437Y - I on day zero.\u003c/p\u003e\n\u003cp\u003eSynonymous mutations, V637V, V637D, A504A, L488L, C469C, A626A, K455E and G497G were detected which corroborates earlier findings that SNP V637V exclusively occur in Africa,\u0026nbsp;\u003csup\u003e41\u003c/sup\u003e albeit involving different nucleotides than those found in this investigation. A synonymous mutation C469C (0.3%) was noted in Kisii and Kisumu and likened to the WHO validated marker C469Y \u003csup\u003e14\u003c/sup\u003e . The C469C had previously been detected in Congo.\u003csup\u003e43\u003c/sup\u003e To the best of our knowledge, none of the mixed genotypes discovered by this study, including the synonymous mutations A504A and V386V, have never been reported before. The role of these mutations together with E612D, and non-synonymous SNPS F491L in parasite population prior to detection of widespread ACT resistance needs to be investigated in a large sample size.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe large number of polymorphisms reported in this study is evocative of intense molecular events within the \u003cem\u003ePfk13\u003c/em\u003e suggestive of increasing fitness of parasites harboring these mutations in the population. Further, absence of the mutations that are driving resistance in southeast Asia appear to underscore the role of increased \u003cem\u003ede novo\u003c/em\u003e local genetic background of parasite in contributing to parasite fitness therefore driving susceptibility to drugs. Policy changes are recommended to any ACT regimen in use at the point in time when the frequency of resistance rises above 10% as confirmed using WHO approved clinical efficacy protocols. Therefore, there is an urgent need for early launch of containment efforts as in the Greater Mekong Subregion but adapted to a greater region of intense transmission in sub-Saharan Africa. Preliminarily, this study was not powered to assess the effect of these polymorphisms on responsiveness to artemisinin derivatives even though S600F was detected in a day 38 follow-up infection which had negative results on recrudescence analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e drug susceptibility assays are important in validating drug resistance candidate markers and establishing the susceptibility of parasites.\u003csup\u003e14\u003c/sup\u003e Our study findings on the susceptibility of \u003cem\u003eP. falciparum\u003c/em\u003e parasites revealed a 2-fold rise in Lumefantrine IC50, compared to a previous publication.\u0026nbsp;\u003csup\u003e44\u003c/sup\u003e \u003csup\u003e32\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e45\u003c/sup\u003e. Genotype data for response to partner drugs showed that Pfmdr1 184F frequency was higher than other SNPs in the gene suggesting that this SNP plays a role in parasite fitness during the period of widespread use of artemether-lumefantrine. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur surveillance study was designed for high throughput genomic epidemiology and \u003cem\u003ein vitro\u003c/em\u003e drug susceptibility testing to cover diverse malaria transmission zones by assessing Day-7 treatment outcome after dosing with Coartem\u003csup\u003e\u0026reg;\u003c/sup\u003e on day zero and it is therefore expectedly unable to detect delayed clearance. Timely characterization of the ring stage survival assay is still underway to reveal \u003cem\u003ein vitro\u003c/em\u003e responsiveness of these isolates besides the classical Malaria SYBR Green assay. Despite these limitations, the study provides important insights into the trends in molecular markers of drug resistance in \u003cem\u003eP. falciparum\u003c/em\u003e parasites. The occurrence of mutations coding for resistance to these drugs at a low prevalence is an early warning signal and hence sustained surveillance should be encouraged to contain a rise in frequencies of these mutations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this study was provided by the Armed Forces Health Surveillance Branch and its Global Emerging Infections Surveillance Section (Grant P0209_15_KY). Material has been reviewed by the Walter Reed Army Institute of Research. \u0026nbsp;There is no objection to its presentation and/or publication. \u0026nbsp;The opinions or assertions contained herein are the private views of the author, and are not to be construed as official, or as reflecting true views of the Department of the Army or the Department of Defense. The investigators have adhered to the policies for protection of human subjects as prescribed in AR 70\u0026ndash;25. We thank the individuals who participated in this study for providing these invaluable samples and time to generate these findings. We also thank the county Governments of Busia, Kisumu, Kisii, Kericho, Nakuru, and Laikipia plus the Ministry of Health Kenya for giving us access to these samples. We thank the Director, Walter Reed Army Institute of Research-Africa, COL. Lacy Shannon, Director Walter Reed Army Institute of Research -Africa Kisumu field Stations, COL. Gerald Keller, Deputy Director General, Center for Clinical Research, Kenya Medical Research Institute Dr. Linus Ndegwa, Kenya for supporting this study and giving their permission to publish these data. We also thank all clinical staff at all the participating hospitals for their assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eHoseah M. Akala: Conceived and designed the study, coordinated data collection, contributed to data interpretation, and critically reviewed the manuscript.\u003c/li\u003e\n \u003cli\u003eBenjamin H. Opot: Molecular analyses, data interpretation, manuscript preparation, and critical review of the manuscript.\u003c/li\u003e\n \u003cli\u003eDennis Juma: Conceived and designed the study, coordinated data collection, supervised laboratory analyses, contributed to data interpretation, and critically reviewed the manuscript.\u003c/li\u003e\n \u003cli\u003eRaphael O. Okoth: Molecular analyses, data interpretation, manuscript preparation, and critical review of the manuscript.\u003c/li\u003e\n \u003cli\u003eMaurine Mwalo: Molecular laboratory analyses, PCR and sequencing experiments.\u003c/li\u003e\n \u003cli\u003eFarid A. Salim: In-vitro drug susceptibility testing and contributed to the analysis of drug sensitivity data.\u003c/li\u003e\n \u003cli\u003eRisper Maisiba: Molecular laboratory analyses, PCR and sequencing experiments.\u003c/li\u003e\n \u003cli\u003eRedemptah Yeda: Molecular laboratory analyses, PCR and sequencing experiments.\u003c/li\u003e\n \u003cli\u003eEdwin W. Mwakio: Molecular analyses, in-vitro drug susceptibility testing, data interpretation, manuscript preparation, and critical review of the manuscript.\u003c/li\u003e\n \u003cli\u003eGladys Chemwor: Molecular analyses, data interpretation, manuscript preparation, and critical review of the manuscript.\u003c/li\u003e\n \u003cli\u003eJackline A. Juma: Molecular laboratory analyses, PCR and sequencing experiments.\u003c/li\u003e\n \u003cli\u003eCharles O. Okudo: In-vitro drug susceptibility testing and contributed to the analysis of drug sensitivity data.\u003c/li\u003e\n \u003cli\u003eTimothy E. Egbo: Manuscript preparation, and critical review of the manuscript.\u003c/li\u003e\n \u003cli\u003eDoris Njoroge: Oversaw sample collection coordination, clinical implementation, provided critical insights into epidemiological interpretations.\u003c/li\u003e\n \u003cli\u003eMichal M. Ohaga: Oversaw sample collection coordination, clinical implementation, provided critical insights into epidemiological interpretations.\u003c/li\u003e\n \u003cli\u003eAgnes C. Cheruiyot: In-vitro drug susceptibility testing and contributed to the analysis of drug sensitivity data.\u003c/li\u003e\n \u003cli\u003eKristan A. Schneider: Statistical expertise, analyzed data, and contributed to manuscript preparation.\u003c/li\u003e\n \u003cli\u003eVictor Osoti: Molecular analyses and interpretation of genomic data.\u003c/li\u003e\n \u003cli\u003eKevin Wamae: Molecular analyses and interpretation of genomic data.\u003c/li\u003e\n \u003cli\u003eMilton Obilo: Provided critical insights, reviewed and revised manuscript drafts.\u003c/li\u003e\n \u003cli\u003eEric C. Garges: Provided critical insights, reviewed and revised manuscript drafts.\u003c/li\u003e\n \u003cli\u003eBen Andagalu: Study design, protocol implementation, manuscript preparation, and critical review of the manuscript.\u003c/li\u003e\n \u003cli\u003eEdwin Kamau: Manuscript preparation, and critical review of the manuscript.\u003c/li\u003e\n \u003cli\u003eLynette Isabella Ochola-Oyier: Genetic data analyses, and contributed significantly to the manuscript review.\u003c/li\u003e\n \u003cli\u003eBernhards R. Ogutu: Oversaw study coordination, clinical implementation, provided critical insights into epidemiological interpretations, and contributed to manuscript review and editing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll authors critically revised the manuscript, approved the final version, and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials \u0026amp; Correspondence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. Hoseah M. Akala, Department of Emerging and Infectious Diseases Walter Reed Army Institute of Research-Africa/Centre for Clinical Research, Kenya Medical Research Institute, P. O. Box 54 \u0026ndash; 40100, Kisumu, Kenya. Tel. +254722329845. E-mail: [email protected] ; [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Material (1)\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNoedl H, Se Y, Schaecher K, Smith BL, Socheat D, Fukuda MM (2008) Evidence of artemisinin-resistant malaria in western Cambodia. N Engl J Med 359(24):2619\u0026ndash;2620\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNosten F, White NJ (2007) Artemisinin-based combination treatment of falciparum malaria. \u003cem\u003eDefining and Defeating the Intolerable Burden of Malaria III: Progress and Perspectives: Supplement to 77 (6) of American\u003c/em\u003e. J Trop Med Hygiene\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAriey F, Witkowski B, Amaratunga C et al (2014) A molecular marker of artemisinin-resistant Plasmodium falciparum malaria. 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Malar J 21(1):369\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkala HM, Eyase FL, Cheruiyot AC et al (2011) Antimalarial drug sensitivity profile of western Kenya Plasmodium falciparum field isolates determined by a SYBR Green I in vitro assay and molecular analysis. Am J Trop Med Hyg 85(1):34\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEyase FL, Akala HM, Ingasia L et al (2013) The role of Pfmdr1 and Pfcrt in changing chloroquine, amodiaquine, mefloquine and lumefantrine susceptibility in western-Kenya P. falciparum samples during 2008\u0026ndash;2011. PLoS ONE 8(5):e64299\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6214166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6214166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study evaluated the polymorphisms of \u003cem\u003ePfk13\u003c/em\u003egene alongside other malaria drug resistance markers in clinical samples from eight geographically distinct locations in Kenya to determine the prevalence of mutations associated with partial artemisinin resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween 2018 and 2024, blood samples from individuals with symptoms of uncomplicated malaria at hospitals in eight hospitals in four of the five distinct malaria transmission zones of Kenya were sequenced for single nucleotide polymorphisms (SNPs) in \u003cem\u003ePfk13, \u003c/em\u003eand\u003cem\u003e Pfmdr1,\u003c/em\u003ethen a subset tested for \u003cem\u003ein vitro\u003c/em\u003e susceptibility to selected antimalarials. Each individual was followed up on day 7 to monitor treatment outcomes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 44/679 (6.5%) samples harbored 49 \u003cem\u003ePfk13\u003c/em\u003emutations. The mutations include 14 nonsynonymous at \u003cem\u003ePfk13\u003c/em\u003e A675V 2.9% (n=20), A578S 0.6% (n=4), C469Y (n=3), V386A (n=1) at 0.44%, and P553L(n=1), R561H/P (n=1), S522C(n=1), K455E(n=1), S600F(n=1), E612D(n=1), N489K(n=1), F491L(n=1) plus A504V at 0.15% (n=1) alongside eleven synonymous mutations. Prevalence of the five validated markers of partial artemisinin resistance was 27/679 (4.0%). Most of \u003cem\u003ePfk13 \u003c/em\u003e675V mutations n=12 (1.8%) were detected in Baringo County of Kenya. 178/823 (21.6%) of the individuals tested positive by PCR on day 7 follow-up after treatment with artemether-lumefantrine. The median 50% inhibition concentration for lumefantrine against field samples was significantly higher than that of reference clones.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe detection of nonsynonymous mutations in 14 loci including five validated makers of partial artemisinin resistance in more samples than previously detected in Kenya and in diverse transmission zones suggests intense selective pressure consistent with emerging burden of partial artemisinin resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArmed Forces Health Surveillance Branch and its Global Emerging Infections Surveillance Section.\u003c/p\u003e","manuscriptTitle":"Emergence and Spread of Plasmodium falciparum Kelch 13 Mutations in Selected Counties of Kenya: Implications for Responding to Artemisinin Partial Resistance.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 06:08:17","doi":"10.21203/rs.3.rs-6214166/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db0ff68e-d91d-4b00-89c1-3a4e36e0ca67","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":45652105,"name":"Health sciences/Medical research/Epidemiology"},{"id":45652106,"name":"Health sciences/Diseases/Infectious diseases/Malaria"}],"tags":[],"updatedAt":"2025-04-03T08:26:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-17 06:08:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6214166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6214166","identity":"rs-6214166","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}