The Metabolism of Artemether-lumefantrine Combination Therapy for Uncomplicated Plasmodium falciparum Malaria in Sudan

The Metabolism of Artemether-lumefantrine Combination Therapy for Uncomplicated Plasmodium falciparum Malaria in Sudan | 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 Research Article The Metabolism of Artemether-lumefantrine Combination Therapy for Uncomplicated Plasmodium falciparum Malaria in Sudan Hamad Alneel Albagir Ahmed, Ali Tajalsir Ali², Rayan Saifeldin Ali², and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8007310/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Artemether-lumefantrine (AL) is the first-line treatment for uncomplicated Plasmodium falciparum malaria in Sudan. Significant inter-individual variability in drug disposition suggests genetic influences on metabolism through cytochrome P450 enzymes. This study aimed to determine allele frequencies of key CYP450 variants affecting AL metabolism in the Sudanese population. Methods Whole exome sequencing data from 55 healthy Sudanese individuals were analyzed for CYP2C8*2 (rs11572103), CYP2C8*3 (rs10509681/rs11572080), CYP2B6*6 (rs3745274), CYP3A4*1B (rs2740574), CYP3A5*3 (rs776746), and CYP3A5*6 (rs10264272) using standard bioinformatics pipelines. Results Allele frequencies were: CYP2C8*2 (8.1%), CYP2C8*3 (4.5%), CYP2B6*6 (35.4%), CYP3A4*1B (0.9%), CYP3A5*3 (20.0%), and CYP3A5*6 (13.6%). The remarkably low CYP3A4*1B frequency contrasts sharply with other African populations (73–79%). High CYP2B6*6 frequency indicates 12.5% of the population are poor metabolizers at elevated risk for artemisinin toxicity. Conclusion Sudan exhibits a unique pharmacogenetic profile with dramatically low CYP3A4*1B and high CYP2B6*6 frequencies. These findings provide crucial insights for optimizing AL therapy and highlight the importance of population-specific pharmacogenetic data for malaria treatment in endemic regions. Pharmacogenetics Cytochrome P450 Artemether-Lumefantrine Sudan Malaria Plasmodium falciparum Drug Metabolism Figures Figure 1 Figure 2 Figure 3 Background Malaria remains a formidable public health challenge in Sudan, with the World Health Organization (WHO) reporting approximately 1.6 million confirmed cases annually, predominantly caused by Plasmodium falciparum [ 1 ]. The strategic implementation of artemisinin-based combination therapies (ACTs) constitutes the cornerstone of malaria control in endemic regions, aligned with WHO treatment recommendations [ 2 ]. Artemether-lumefantrine (AL) has been adopted as the first-line therapeutic regimen for uncomplicated malaria in Sudan, leveraging the rapid schizonticidal activity of artemether with the prolonged elimination half-life of lumefantrine to prevent recrudescence [ 3 ]. Despite the established efficacy of AL in clinical trials, significant inter-individual variability in therapeutic response has been consistently documented across diverse populations [ 4 ]. This heterogeneity manifests as variable treatment outcomes, including late clinical failures, early treatment failures, and divergent adverse event profiles, potentially compromising therapeutic efficacy and contributing to the emergence of antimalarial drug resistance [ 5 ]. The underlying mechanisms driving this pharmacokinetic variability are multifactorial, with host genetic factors emerging as significant determinants of drug disposition and response [ 6 ]. The cytochrome P450 (CYP) enzyme superfamily represents the principal enzymatic system responsible for Phase I metabolism of numerous therapeutic agents, including antimalarial drugs [ 7 ]. The biotransformation of AL involves complex metabolic pathways: artemether undergoes extensive primary metabolism primarily via CYP3A4 to its active metabolite dihydroartemisinin (DHA), with complementary involvement of CYP2B6, while lumefantrine metabolism is predominantly mediated by CYP3A4 [ 8 , 9 ]. Genetic polymorphisms within these CYP genes—including single nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations—can profoundly alter enzyme expression, structural conformation, and catalytic efficiency, resulting in discrete metabolic phenotypes classified as poor (PM), intermediate (IM), extensive (EM), or ultrarapid metabolizers (UM) [ 10 ]. The clinical implications of these pharmacogenetic variations are substantial and well-documented. For instance, impaired metabolism of artemether due to CYP2B6 polymorphisms may lead to drug accumulation and increased risk of artemisinin-related neurotoxicity [ 11 ]. Conversely, enhanced lumefantrine clearance mediated by CYP3A4/5 genetic variations may yield sub-therapeutic plasma concentrations, predisposing to treatment failure and potentially selecting for drug-resistant parasite populations [ 12 ]. The global distribution of these pharmacogenetic variants exhibits considerable ethnogeographic heterogeneity, reflecting complex evolutionary histories, population migrations, and diverse selective pressures [ 13 , 14 ]. This genetic diversity necessitates population-specific pharmacogenetic characterization rather than extrapolation from geographically or genetically distinct populations [ 15 ]. Previous investigations have revealed striking variations in CYP allele frequencies across different African populations. The CYP3A4*1B variant, for instance, demonstrates frequencies of 73–79% in West African populations but shows remarkable geographic patterning across the continent [ 16 ]. Similarly, CYP2B6*6 exhibits substantial inter-population variability, with implications for the metabolism of both antiretroviral and antimalarial medications [ 17 ]. Despite the central role of AL in Sudan's malaria control program and the country's unique genetic heritage at the crossroads of African and Arabian populations, a comprehensive pharmacogenetic profile of the Sudanese population remains inadequately characterized [ 18 ]. The critical importance of population-specific pharmacogenetic data is further emphasized by emerging evidence of gene-environment interactions and the potential for pharmacogenetic variants to influence treatment outcomes in malaria-endemic settings [ 19 ]. Understanding the local allele frequencies represents the essential first step toward personalized malaria treatment, enabling the prediction of treatment outcomes, optimization of dosing strategies, and mitigation of adverse drug reactions [ 20 ]. This study was therefore designed to establish the allele frequency distribution of six functionally significant CYP polymorphismsCYP2C8*2 (rs11572103), CYP2C8*3 (rs10509681/rs11572080), CYP2B6*6 (rs3745274), CYP3A4*1B (rs2740574), CYP3A5*3 (rs776746), and CYP3A5*6 (rs10264272)—with established relevance to AL pharmacokinetics. The findings will provide a crucial evidence base for refining AL therapy and advancing precision medicine approaches for malaria treatment in Sudan. Methods Study Population and Ethical Considerations Whole exome sequencing data from 55 unrelated healthy Sudanese individuals were obtained from the Sudan Exome Project at the Institute of Endemic Diseases, University of Khartoum. The study protocol received ethical approval from the Institutional Ethics Committee, and all participants provided written informed consent prior to enrollment. The research was conducted in accordance with the principles outlined in the Declaration of Helsinki. Variant Selection and Genotyping Pharmacogenetically relevant single nucleotide polymorphisms (SNPs) were identified through systematic literature review and consultation of the Pharmacogenomics Knowledgebase (PharmGKB) [ 21 ]. The analysis focused on six well-characterized variants with established roles in antimalarial drug metabolism: CYP2C8*2 (rs11572103), CYP2C8*3 (rs10509681, rs11572080), CYP2B6*6 (rs3745274), CYP3A4*1B (rs2740574), CYP3A5*3 (rs776746), and CYP3A5*6 (rs10264272). Bioinformatics Analysis Variant calling was performed from whole exome sequencing data using established bioinformatics pipelines. Quality control measures included Hardy-Weinberg equilibrium testing and visual inspection of alignment files. Allele frequencies were calculated, and comparative analysis was conducted using data from the Genome Aggregation Database (gnomAD) [ 22 ] for population comparisons. Data Analysis and Visualization Statistical analyses were performed using LibreOffice Calc 6.0 with custom scripts for genetic analysis. Data visualization was created using Plotly online tools, and reference management was maintained using Mendeley. All genotype distributions were tested for deviation from Hardy-Weinberg equilibrium using exact tests. Results Allele Frequency Distribution in Sudanese Population Analysis of 55 Sudanese individuals revealed distinct allele frequencies for the six CYP variants investigated (Table 1 ). CYP2B6*6 demonstrated the highest frequency at 35.4%, while CYP3A4*1B showed the lowest frequency at 0.9%. Table 1 Allele frequencies and functional consequences of CYP variants in Sudanese population (n = 55) Variant SNP ID Function Allele Frequency 95% CI CYP2C8 2* rs11572103 Missense 0.081 (8.1%) 0.032–0.158 CYP2C8 3* rs10509681/rs11572080 Missense 0.045 (4.5%) 0.013–0.112 CYP2B6 6* rs3745274 Missense 0.354 (35.4%) 0.265–0.452 CYP3A4 1B* rs2740574 5' Flanking 0.009 (0.9%) 0.001–0.049 CYP3A5 3* rs776746 Intronic 0.200 (20.0%) 0.128–0.296 CYP3A5 6* rs10264272 Synonymous 0.136 (13.6%) 0.076–0.220 Expected Genotype Distributions and Carrier Frequencies Based on Hardy-Weinberg equilibrium calculations, the expected genotype distributions reveal significant population-level implications (Table 2 ). The CYP2B6*6 variant shows particularly concerning distribution with 12.5% of the population expected to be homozygous variant carriers. Table 2 Expected genotype frequencies and population impact Variant Homozygous Wild-type Heterozygous Homozygous Variant Carrier Frequency CYP2C8 2* 84.5% 14.9% 0.66% 15.5% CYP2C8 3* 91.2% 8.6% 0.20% 8.8% CYP2B6 6* 41.7% 45.8% 12.5% 58.3% CYP3A4 1B* 98.2% 1.8% 0.01% 1.8% CYP3A5 3* 64.0% 32.0% 4.00% 36.0% CYP3A5 6* 74.6% 23.6% 1.85% 25.4% Comparative Analysis with Other African Populations The CYP3A4*1B frequency in Sudan (0.9%) demonstrates a dramatic deviation from other African populations, which typically show frequencies between 73–79% (Fig. 2). This represents an 80–90 fold difference from expected African frequencies. Population Impact Projection Table 3 Projected population impact based on Sudanese population of 45 million Variant Carriers Homozygous Variant Clinical Priority CYP2B6 6* ~ 26.2 million ~ 5.6 million VERY HIGH CYP3A5 3* ~ 16.2 million ~ 1.8 million HIGH CYP3A5 6* ~ 11.4 million ~ 0.8 million MODERATE-HIGH CYP2C8 2* ~ 7.0 million ~ 0.3 million MODERATE CYP3A4 1B* ~ 0.8 million ~ 0.04 million LOW Discussion This comprehensive pharmacogenetic study reveals a distinctive CYP variant profile in the Sudanese population with significant implications for artemether-lumefantrine therapy. The most striking finding is the exceptionally low frequency of CYP3A4*1B (0.9%), which contrasts dramatically with reports from other African populations where frequencies typically range from 73–79% [ 23 , 24 ]. This represents one of the most substantial pharmacogenetic differences observed between African populations and challenges existing paradigms about CYP3A4 variation across the continent. The clinical implications of this finding are substantial. While CYP3A4*1B has been associated with altered metabolism of artemether and lumefantrine in other African populations [ 25 ], its minimal prevalence in Sudan suggests this variant contributes little to AL pharmacokinetic variability in this population. This contrasts with the high frequency of CYP2B6*6 (35.4%), which positions this variant as the primary pharmacogenetic concern for AL therapy in Sudan. The CYP2B6*6 variant demonstrates a carrier frequency of 58.3%, with 12.5% of the population expected to be homozygous variant carriers. This translates to approximately 5.6 million Sudanese at elevated risk for altered artemisinin metabolism, potentially leading to increased drug exposure and toxicity risk [ 26 ]. This finding necessitates urgent attention in clinical practice and malaria treatment guidelines, particularly given the central role of AL in Sudan's malaria control program. The combined impact of CYP3A5 variants further complicates the metabolic landscape. With 36.0% and 25.4% carrier frequencies for CYP3A5*3 and CYP3A5*6 respectively, a substantial proportion of the Sudanese population may experience variable metabolism of both artemether and lumefantrine components. This dual pathway involvement underscores the complexity of predicting AL pharmacokinetics based on single variants and highlights the need for comprehensive pharmacogenetic profiling. From a clinical perspective, our risk stratification identifies CYP2B6*6 as the highest priority for intervention. The high frequency of poor metabolizers, coupled with the known impact of this variant on artemisinin pharmacokinetics [ 27 ], suggests that a significant proportion of patients may be at risk for drug accumulation and associated toxicity. This is particularly concerning given the limited monitoring capabilities in many healthcare settings in Sudan. The comparative analysis with other African populations reveals both consistencies and striking differences. While CYP2B6*6 frequencies align with broader African patterns, the dramatic deviation in CYP3A4*1B prevalence underscores the genetic diversity within Africa and highlights the limitations of extrapolating pharmacogenetic data across different populations. This finding emphasizes the necessity of population-specific pharmacogenetic studies to inform treatment optimization in malaria-endemic regions. Clinical Implications and Public Health Recommendations Limitations and Research Implications This study has several limitations that should be considered when interpreting the results. The sample size of 55 individuals, while sufficient for initial frequency estimation, may limit the precision of estimates for rare variants. Additionally, the focus on six specific variants, while justified by their established roles in antimalarial metabolism, does not capture the full complexity of pharmacogenetic variation affecting AL disposition. Future research should prioritize several key areas. First, clinical correlation studies are urgently needed to establish the relationship between these genetic variants and actual treatment outcomes, including both efficacy and safety endpoints. Second, pharmacokinetic studies in genotyped individuals would provide crucial insights into the functional impact of these variants on AL disposition in the Sudanese population. Finally, expanded sampling across different regions of Sudan would help determine whether the observed frequencies are consistent nationwide or show regional variation. Public Health Implications The findings from this study have immediate implications for malaria treatment policy in Sudan. The high prevalence of CYP2B6*6 poor metabolizers suggests that enhanced monitoring for artemisinin-related toxicity may be warranted, particularly in settings where therapeutic drug monitoring is unavailable. Additionally, healthcare provider education about the potential for pharmacogenetic variability in AL response could improve clinical management. For policy makers, these results highlight the importance of considering population genetics in drug formulation and dosing strategies. The unique pharmacogenetic profile of the Sudanese population may necessitate tailored approaches to AL therapy that differ from those used in other African countries. Conclusion This study establishes that the Sudanese population possesses a distinctive pharmacogenetic profile characterized by exceptionally low CYP3A4*1B frequency and high CYP2B6*6 prevalence. These findings have profound implications for artemether-lumefantrine therapy optimization in Sudan, with nearly one-quarter of the population at potentially increased risk for artemisinin toxicity due to poor metabolizer status. The data provide a crucial foundation for developing personalized malaria treatment approaches in Sudan and highlight the critical importance of population-specific pharmacogenetic data for optimizing antimalarial therapy in endemic regions. Future clinical studies should validate these genetic findings with pharmacokinetic and treatment outcome data to translate this knowledge into improved patient care and inform evidence-based malaria treatment policies in Sudan. Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of the Institute of Endemic Diseases, University of Khartoum (Reference number: IEND-IRB-2017-045). Written informed consent was obtained from all participants prior to enrollment. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the Institute of Endemic Diseases, University of Khartoum. The funding body had no role in the design of the study, collection, analysis, interpretation of data, or in writing the manuscript. Author Contribution HAAAA contributed to data analysis, interpretation, and manuscript drafting. ATA contributed to data analysis and visualization. RSA contributed to data curation and methodology. MMO contributed to conceptualization and data interpretation. MI conceived and designed the study, supervised the research, and critically revised the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors thank the Sudan Exome Project at the Institute of Endemic Diseases for providing the data and the study participants for their contribution to this research. 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11:46:46","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78644,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8007310/v1/a06076bd7f9f8ee7087a024f.html"},{"id":96283372,"identity":"4eb3fc38-5264-4cb2-9cdc-93356a55f9fc","added_by":"auto","created_at":"2025-11-19 11:46:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74772,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8007310/v1/bd3c082068a165a47662f8ca.png"},{"id":96283373,"identity":"ef24d408-863a-44c6-ac18-067e6bf7bc93","added_by":"auto","created_at":"2025-11-19 11:46:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102150,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8007310/v1/403656f10233e392705ae7bb.png"},{"id":96283376,"identity":"f3e7c09d-b0d1-4dbe-b9b4-1ce54b2d6561","added_by":"auto","created_at":"2025-11-19 11:46:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73152,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Results section.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8007310/v1/c02dddc9c89a675672c05585.png"},{"id":104012635,"identity":"0daeec55-5603-41ac-9800-b78345f79bfe","added_by":"auto","created_at":"2026-03-05 16:10:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":928245,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8007310/v1/a294f73f-b0a3-483a-ab7e-84d5f1518876.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Metabolism of Artemether-lumefantrine Combination Therapy for Uncomplicated Plasmodium falciparum Malaria in Sudan","fulltext":[{"header":"Background","content":"\u003cp\u003eMalaria remains a formidable public health challenge in Sudan, with the World Health Organization (WHO) reporting approximately 1.6\u0026nbsp;million confirmed cases annually, predominantly caused by \u003cem\u003ePlasmodium falciparum\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The strategic implementation of artemisinin-based combination therapies (ACTs) constitutes the cornerstone of malaria control in endemic regions, aligned with WHO treatment recommendations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Artemether-lumefantrine (AL) has been adopted as the first-line therapeutic regimen for uncomplicated malaria in Sudan, leveraging the rapid schizonticidal activity of artemether with the prolonged elimination half-life of lumefantrine to prevent recrudescence [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the established efficacy of AL in clinical trials, significant inter-individual variability in therapeutic response has been consistently documented across diverse populations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This heterogeneity manifests as variable treatment outcomes, including late clinical failures, early treatment failures, and divergent adverse event profiles, potentially compromising therapeutic efficacy and contributing to the emergence of antimalarial drug resistance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The underlying mechanisms driving this pharmacokinetic variability are multifactorial, with host genetic factors emerging as significant determinants of drug disposition and response [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe cytochrome P450 (CYP) enzyme superfamily represents the principal enzymatic system responsible for Phase I metabolism of numerous therapeutic agents, including antimalarial drugs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The biotransformation of AL involves complex metabolic pathways: artemether undergoes extensive primary metabolism primarily via CYP3A4 to its active metabolite dihydroartemisinin (DHA), with complementary involvement of CYP2B6, while lumefantrine metabolism is predominantly mediated by CYP3A4 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Genetic polymorphisms within these CYP genes\u0026mdash;including single nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations\u0026mdash;can profoundly alter enzyme expression, structural conformation, and catalytic efficiency, resulting in discrete metabolic phenotypes classified as poor (PM), intermediate (IM), extensive (EM), or ultrarapid metabolizers (UM) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe clinical implications of these pharmacogenetic variations are substantial and well-documented. For instance, impaired metabolism of artemether due to CYP2B6 polymorphisms may lead to drug accumulation and increased risk of artemisinin-related neurotoxicity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Conversely, enhanced lumefantrine clearance mediated by CYP3A4/5 genetic variations may yield sub-therapeutic plasma concentrations, predisposing to treatment failure and potentially selecting for drug-resistant parasite populations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The global distribution of these pharmacogenetic variants exhibits considerable ethnogeographic heterogeneity, reflecting complex evolutionary histories, population migrations, and diverse selective pressures [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This genetic diversity necessitates population-specific pharmacogenetic characterization rather than extrapolation from geographically or genetically distinct populations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious investigations have revealed striking variations in CYP allele frequencies across different African populations. The CYP3A4*1B variant, for instance, demonstrates frequencies of 73\u0026ndash;79% in West African populations but shows remarkable geographic patterning across the continent [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, CYP2B6*6 exhibits substantial inter-population variability, with implications for the metabolism of both antiretroviral and antimalarial medications [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Despite the central role of AL in Sudan's malaria control program and the country's unique genetic heritage at the crossroads of African and Arabian populations, a comprehensive pharmacogenetic profile of the Sudanese population remains inadequately characterized [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe critical importance of population-specific pharmacogenetic data is further emphasized by emerging evidence of gene-environment interactions and the potential for pharmacogenetic variants to influence treatment outcomes in malaria-endemic settings [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Understanding the local allele frequencies represents the essential first step toward personalized malaria treatment, enabling the prediction of treatment outcomes, optimization of dosing strategies, and mitigation of adverse drug reactions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study was therefore designed to establish the allele frequency distribution of six functionally significant CYP polymorphismsCYP2C8*2 (rs11572103), CYP2C8*3 (rs10509681/rs11572080), CYP2B6*6 (rs3745274), CYP3A4*1B (rs2740574), CYP3A5*3 (rs776746), and CYP3A5*6 (rs10264272)\u0026mdash;with established relevance to AL pharmacokinetics. The findings will provide a crucial evidence base for refining AL therapy and advancing precision medicine approaches for malaria treatment in Sudan.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population and Ethical Considerations\u003c/h2\u003e\u003cp\u003eWhole exome sequencing data from 55 unrelated healthy Sudanese individuals were obtained from the Sudan Exome Project at the Institute of Endemic Diseases, University of Khartoum. The study protocol received ethical approval from the Institutional Ethics Committee, and all participants provided written informed consent prior to enrollment. The research was conducted in accordance with the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eVariant Selection and Genotyping\u003c/h3\u003e\n\u003cp\u003ePharmacogenetically relevant single nucleotide polymorphisms (SNPs) were identified through systematic literature review and consultation of the Pharmacogenomics Knowledgebase (PharmGKB) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The analysis focused on six well-characterized variants with established roles in antimalarial drug metabolism: CYP2C8*2 (rs11572103), CYP2C8*3 (rs10509681, rs11572080), CYP2B6*6 (rs3745274), CYP3A4*1B (rs2740574), CYP3A5*3 (rs776746), and CYP3A5*6 (rs10264272).\u003c/p\u003e\n\u003ch3\u003eBioinformatics Analysis\u003c/h3\u003e\n\u003cp\u003eVariant calling was performed from whole exome sequencing data using established bioinformatics pipelines. Quality control measures included Hardy-Weinberg equilibrium testing and visual inspection of alignment files. Allele frequencies were calculated, and comparative analysis was conducted using data from the Genome Aggregation Database (gnomAD) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] for population comparisons.\u003c/p\u003e\n\u003ch3\u003eData Analysis and Visualization\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were performed using LibreOffice Calc 6.0 with custom scripts for genetic analysis. Data visualization was created using Plotly online tools, and reference management was maintained using Mendeley. All genotype distributions were tested for deviation from Hardy-Weinberg equilibrium using exact tests.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eAllele Frequency Distribution in Sudanese Population\u003c/h2\u003e\u003cp\u003eAnalysis of 55 Sudanese individuals revealed distinct allele frequencies for the six CYP variants investigated (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). CYP2B6*6 demonstrated the highest frequency at 35.4%, while CYP3A4*1B showed the lowest frequency at 0.9%.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAllele frequencies and functional consequences of CYP variants in Sudanese population (n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSNP ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFunction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAllele Frequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP2C8\u003c/em\u003e2*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers11572103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMissense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.081 (8.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.032\u0026ndash;0.158\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP2C8\u003c/em\u003e3*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers10509681/rs11572080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMissense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.045 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.013\u0026ndash;0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP2B6\u003c/em\u003e6*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers3745274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMissense\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.354 (35.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.265\u0026ndash;0.452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP3A4\u003c/em\u003e1B*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers2740574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5' Flanking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u0026ndash;0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP3A5\u003c/em\u003e3*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers776746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.200 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.128\u0026ndash;0.296\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP3A5\u003c/em\u003e6*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers10264272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSynonymous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.136 (13.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.076\u0026ndash;0.220\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExpected Genotype Distributions and Carrier Frequencies\u003c/h3\u003e\n\u003cp\u003eBased on Hardy-Weinberg equilibrium calculations, the expected genotype distributions reveal significant population-level implications (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The CYP2B6*6 variant shows particularly concerning distribution with 12.5% of the population expected to be homozygous variant carriers.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExpected genotype frequencies and population impact\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHomozygous Wild-type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHeterozygous\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHomozygous Variant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCarrier Frequency\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP2C8\u003c/em\u003e2*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.66%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP2C8\u003c/em\u003e3*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e91.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP2B6\u003c/em\u003e6*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP3A4\u003c/em\u003e1B*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e98.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP3A5\u003c/em\u003e3*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP3A5\u003c/em\u003e6*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.85%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eComparative Analysis with Other African Populations\u003c/h3\u003e\n\u003cp\u003eThe CYP3A4*1B frequency in Sudan (0.9%) demonstrates a dramatic deviation from other African populations, which typically show frequencies between 73\u0026ndash;79% (Fig.\u0026nbsp;2). This represents an 80\u0026ndash;90 fold difference from expected African frequencies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePopulation Impact Projection\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eProjected population impact based on Sudanese population of 45 million\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCarriers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHomozygous Variant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClinical Priority\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP2B6\u003c/em\u003e6*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e~\u0026thinsp;26.2 million\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e~\u0026thinsp;5.6 million\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eVERY HIGH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP3A5\u003c/em\u003e3*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e~\u0026thinsp;16.2 million\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e~\u0026thinsp;1.8 million\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eHIGH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP3A5\u003c/em\u003e6*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e~\u0026thinsp;11.4 million\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e~\u0026thinsp;0.8 million\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eMODERATE-HIGH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP2C8\u003c/em\u003e2*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e~\u0026thinsp;7.0 million\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e~\u0026thinsp;0.3 million\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eMODERATE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCYP3A4\u003c/em\u003e1B*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e~\u0026thinsp;0.8 million\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e~\u0026thinsp;0.04 million\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eLOW\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis comprehensive pharmacogenetic study reveals a distinctive CYP variant profile in the Sudanese population with significant implications for artemether-lumefantrine therapy. The most striking finding is the exceptionally low frequency of CYP3A4*1B (0.9%), which contrasts dramatically with reports from other African populations where frequencies typically range from 73\u0026ndash;79% [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This represents one of the most substantial pharmacogenetic differences observed between African populations and challenges existing paradigms about CYP3A4 variation across the continent.\u003c/p\u003e\u003cp\u003eThe clinical implications of this finding are substantial. While CYP3A4*1B has been associated with altered metabolism of artemether and lumefantrine in other African populations [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], its minimal prevalence in Sudan suggests this variant contributes little to AL pharmacokinetic variability in this population. This contrasts with the high frequency of CYP2B6*6 (35.4%), which positions this variant as the primary pharmacogenetic concern for AL therapy in Sudan.\u003c/p\u003e\u003cp\u003eThe CYP2B6*6 variant demonstrates a carrier frequency of 58.3%, with 12.5% of the population expected to be homozygous variant carriers. This translates to approximately 5.6\u0026nbsp;million Sudanese at elevated risk for altered artemisinin metabolism, potentially leading to increased drug exposure and toxicity risk [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This finding necessitates urgent attention in clinical practice and malaria treatment guidelines, particularly given the central role of AL in Sudan's malaria control program.\u003c/p\u003e\u003cp\u003eThe combined impact of CYP3A5 variants further complicates the metabolic landscape. With 36.0% and 25.4% carrier frequencies for CYP3A5*3 and CYP3A5*6 respectively, a substantial proportion of the Sudanese population may experience variable metabolism of both artemether and lumefantrine components. This dual pathway involvement underscores the complexity of predicting AL pharmacokinetics based on single variants and highlights the need for comprehensive pharmacogenetic profiling.\u003c/p\u003e\u003cp\u003eFrom a clinical perspective, our risk stratification identifies CYP2B6*6 as the highest priority for intervention. The high frequency of poor metabolizers, coupled with the known impact of this variant on artemisinin pharmacokinetics [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], suggests that a significant proportion of patients may be at risk for drug accumulation and associated toxicity. This is particularly concerning given the limited monitoring capabilities in many healthcare settings in Sudan.\u003c/p\u003e\u003cp\u003eThe comparative analysis with other African populations reveals both consistencies and striking differences. While CYP2B6*6 frequencies align with broader African patterns, the dramatic deviation in CYP3A4*1B prevalence underscores the genetic diversity within Africa and highlights the limitations of extrapolating pharmacogenetic data across different populations. This finding emphasizes the necessity of population-specific pharmacogenetic studies to inform treatment optimization in malaria-endemic regions.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eClinical Implications and Public Health Recommendations\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Research Implications\u003c/h2\u003e\u003cp\u003eThis study has several limitations that should be considered when interpreting the results. The sample size of 55 individuals, while sufficient for initial frequency estimation, may limit the precision of estimates for rare variants. Additionally, the focus on six specific variants, while justified by their established roles in antimalarial metabolism, does not capture the full complexity of pharmacogenetic variation affecting AL disposition.\u003c/p\u003e\u003cp\u003eFuture research should prioritize several key areas. First, clinical correlation studies are urgently needed to establish the relationship between these genetic variants and actual treatment outcomes, including both efficacy and safety endpoints. Second, pharmacokinetic studies in genotyped individuals would provide crucial insights into the functional impact of these variants on AL disposition in the Sudanese population. Finally, expanded sampling across different regions of Sudan would help determine whether the observed frequencies are consistent nationwide or show regional variation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003ePublic Health Implications\u003c/h2\u003e\u003cp\u003eThe findings from this study have immediate implications for malaria treatment policy in Sudan. The high prevalence of CYP2B6*6 poor metabolizers suggests that enhanced monitoring for artemisinin-related toxicity may be warranted, particularly in settings where therapeutic drug monitoring is unavailable. Additionally, healthcare provider education about the potential for pharmacogenetic variability in AL response could improve clinical management.\u003c/p\u003e\u003cp\u003eFor policy makers, these results highlight the importance of considering population genetics in drug formulation and dosing strategies. The unique pharmacogenetic profile of the Sudanese population may necessitate tailored approaches to AL therapy that differ from those used in other African countries.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study establishes that the Sudanese population possesses a distinctive pharmacogenetic profile characterized by exceptionally low CYP3A4*1B frequency and high CYP2B6*6 prevalence. These findings have profound implications for artemether-lumefantrine therapy optimization in Sudan, with nearly one-quarter of the population at potentially increased risk for artemisinin toxicity due to poor metabolizer status.\u003c/p\u003e\u003cp\u003eThe data provide a crucial foundation for developing personalized malaria treatment approaches in Sudan and highlight the critical importance of population-specific pharmacogenetic data for optimizing antimalarial therapy in endemic regions. Future clinical studies should validate these genetic findings with pharmacokinetic and treatment outcome data to translate this knowledge into improved patient care and inform evidence-based malaria treatment policies in Sudan.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eThe study was approved by the Ethics Committee of the Institute of Endemic Diseases, University of Khartoum (Reference number: IEND-IRB-2017-045). Written informed consent was obtained from all participants prior to enrollment.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported by the Institute of Endemic Diseases, University of Khartoum. The funding body had no role in the design of the study, collection, analysis, interpretation of data, or in writing the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHAAAA contributed to data analysis, interpretation, and manuscript drafting. ATA contributed to data analysis and visualization. RSA contributed to data curation and methodology. MMO contributed to conceptualization and data interpretation. MI conceived and designed the study, supervised the research, and critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors thank the Sudan Exome Project at the Institute of Endemic Diseases for providing the data and the study participants for their contribution to this research. We also acknowledge the technical support provided by the molecular biology laboratory staff.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Summary data are included in this published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. World malaria report 2022. Geneva: World Health Organization; 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Guidelines for the treatment of malaria. 3rd ed. Geneva: World Health Organization; 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElmardi KA, Malik EM, Abdelgadir T, Ali SH, Elsyed AH, et al. Feasibility and acceptability of home-based management of malaria strategy adapted to Sudan's conditions using artemisinin-based combination therapy. Malar J. 2009;8:39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSugiarto SR, Davis TME, Salman S. Pharmacokinetic considerations for use of artemisinin-based combination therapies against falciparum malaria in different ethnic populations. Expert Opin Drug Metab Toxicol. 2017;13:1115\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDondorp AM, Nosten F, Yi P, Das D, Phyo AP, et al. Artemisinin resistance in \u003cem\u003ePlasmodium falciparum\u003c/em\u003e malaria. N Engl J Med. 2009;361(5):455\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKerb R, Fux R, M\u0026ouml;rike K, Kremsner PG, Gil JP, Gleiter CH, et al. Pharmacogenetics of antimalarial drugs: effect on metabolism and transport. Lancet Infect Dis. 2009;9:760\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZanger UM, Schwab M. Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol Ther. 2013;138:103\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIlett KF, Batty KT. Artemisinin and its derivatives. In: Yu VL, Edwards G, McKinnon PS, Peloquin C, Morse GD, editors. Antimicrobial Therapy and Vaccines. 2nd ed. Pittsburgh: ESun Technologies; 2005. pp. 1017\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEzzet F, van Vugt M, Nosten F, Looareesuwan S, White NJ. Pharmacokinetics and pharmacodynamics of lumefantrine (benflumetol) in acute falciparum malaria. Antimicrob Agents Chemother. 2000;44(3):697\u0026ndash;704.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIngelman-Sundberg M. Genetic polymorphisms of cytochrome P450 2D6 (CYP2D6): Clinical consequences, evolutionary aspects and functional diversity. Pharmacogenomics J. 2005;5(1):6\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHien TT, White NJ, Qinghaosu. Lancet. 1993;341(8845):603\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGerman PI, Aweeka FT. Clinical pharmacology of artemisinin-based combination therapies. 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Lancet Infect Dis. 2009;9:760\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pharmacogenetics, Cytochrome P450, Artemether-Lumefantrine, Sudan, Malaria, Plasmodium falciparum, Drug Metabolism","lastPublishedDoi":"10.21203/rs.3.rs-8007310/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8007310/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eArtemether-lumefantrine (AL) is the first-line treatment for uncomplicated \u003cem\u003ePlasmodium falciparum\u003c/em\u003e malaria in Sudan. Significant inter-individual variability in drug disposition suggests genetic influences on metabolism through cytochrome P450 enzymes. This study aimed to determine allele frequencies of key CYP450 variants affecting AL metabolism in the Sudanese population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWhole exome sequencing data from 55 healthy Sudanese individuals were analyzed for CYP2C8*2 (rs11572103), CYP2C8*3 (rs10509681/rs11572080), CYP2B6*6 (rs3745274), CYP3A4*1B (rs2740574), CYP3A5*3 (rs776746), and CYP3A5*6 (rs10264272) using standard bioinformatics pipelines.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAllele frequencies were: CYP2C8*2 (8.1%), CYP2C8*3 (4.5%), CYP2B6*6 (35.4%), CYP3A4*1B (0.9%), CYP3A5*3 (20.0%), and CYP3A5*6 (13.6%). The remarkably low CYP3A4*1B frequency contrasts sharply with other African populations (73\u0026ndash;79%). High CYP2B6*6 frequency indicates 12.5% of the population are poor metabolizers at elevated risk for artemisinin toxicity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSudan exhibits a unique pharmacogenetic profile with dramatically low CYP3A4*1B and high CYP2B6*6 frequencies. These findings provide crucial insights for optimizing AL therapy and highlight the importance of population-specific pharmacogenetic data for malaria treatment in endemic regions.\u003c/p\u003e","manuscriptTitle":"The Metabolism of Artemether-lumefantrine Combination Therapy for Uncomplicated Plasmodium falciparum Malaria in Sudan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 11:46:42","doi":"10.21203/rs.3.rs-8007310/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"938d514e-8f44-49c4-bdb3-852a30259096","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T16:10:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 11:46:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8007310","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8007310","identity":"rs-8007310","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}