Insights and Current Perspectives on Pharmacogenomics of Antimalarial Drugs

Insights and Current Perspectives on Pharmacogenomics of Antimalarial Drugs | 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 Systematic Review Insights and Current Perspectives on Pharmacogenomics of Antimalarial Drugs Julius Olugbenga Soyinka, Charles Okeke Nnadi, Cyprian Ogbonna Onyeji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2030964/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 Malaria constitutes a major public health concern in tropical and other malaria-endemic regions. Genetic and non-genetic factors are known to influence the pharmacokinetics and/or pharmacodynamics of drugs including antimalarial drugs resulting in variability in drug responses. This article aimed to update perspectives on pharmacogenomics and also provide an updated appraisal of genetic variability in drug-metabolizing enzymes which alter the disposition of antimalarial drugs causing variations in treatment outcomes. Important literature databases such as Elsevier, IEEExplore, Pubmed, Scopus, Web of Science, Google Scholar, ProQuest, ScienceDirect, and BioMed Central were selected based on the quality, extant content, and broad area of the discipline. The specific keywords related to the study were identified and used for the study purposedly to identify related works. Advances in genetic research have facilitated the identification of Single Nucleotide Polymorphisms (SNPs) that alter the activity of drug-metabolizing enzymes that metabolize most antimalarial drugs. There is an association between isoforms of CYP450 gene variants and the efficacy of some antimalarial drugs, and this can be applied to the optimization of malarial therapy. Although identification of cytochrome P450 (CYP450) gene variants can be used for personalization of malaria treatment, several challenges are encountered in this process but some resources provide education and guidelines on how to use the pharmacogenetic results of specific drugs. Antimalarial drugs Cytochrome P450 Gene Variants Pharmacogenomics Figures Figure 1 Figure 2 Figure 3 Introduction Malaria, mostly caused by Plasmodium falciparum , continues to be a major public health problem in tropical and other malaria-endemic regions. The latest report by the World Health Organization (WHO) indicates that there were about 241 million malaria cases and about 627,000 malaria deaths in 2020, with the WHO African Region accounting for about 95% of this malaria cases [ 1 ]. The emergence of antimalarial drug resistance challenges the control and treatment of malaria, necessitating regular monitoring of drug efficacy [ 2 ]. The current mainstay and first-line treatment for the majority of cases of malaria is artemisinin-based combination therapy (ACT) in which a rapid-acting with short half-life artemisinin or its derivative is combined with a drug with a longer half-life [ 1 ]. Another strategy aimed at enhancing the effectiveness of antimalarial treatment is the application of findings on Pharmacogenetics/pharmacogenomics of antimalarial drugs. Pharmacogenetics involves the study of how inter-individual differences in a single gene can affect an individual’s response to particular drugs, while the term pharmacogenomics is much broader and it involves investigating the entire genome to assess their effects on drug responses. This field of study aims at developing effective and safe medications with doses that are tailored to variations in a person’s genes [ 3 , 4 ]. Plasma drug levels have been reported to vary as much as 1000-fold when the same drug dose is administered to different individuals with the same body weights, and this is due to variations in genes encoding cytochrome P450 (CYP450) and other Drug metabolizing enzymes [ 5 ]. It has been established that there are more than 14 million Single Nucleotide Polymorphisms (SNPs) in the entire human genome, and these variations in the human genome occur approximately in every 300–1000 nucleotides [ 6 ]. Most SNPs are attributable to independent single mutational events in the past [ 7 ]. Thus, where there are inter-individual variations in drug responses, the identification of a variant of the gene that mediates the variation would lead to optimization of the treatment efficacy and reduction of the adverse effect profiles of drugs in a given population [ 8 ]. However, it is known that apart from genetic factors other factors can influence the outcome of drug therapy and these include environmental factors (exposure to some chemicals in the environment), physiological factors (age, sex, hepatic and renal functions, pregnancy), lifestyle factors (smoking, drinking alcohol, exercise) and concomitant drug use [ 9 , 10 ]. Pharmacogenomics is a part of personalized medicine or precision medicine that individualizes therapy by using SNPs and tailor-making medicines to each patient for effective therapy [ 11 ]. We have carried out numerous studies in our laboratories on pharmacokinetics and pharmacogenomics of different antimalarial drugs and diverse anti-infective agents to generate data for optimization of the drug efficacies [ 12 – 14 ]. An earlier review on the pharmacogenetics of antimalarial drugs by Kerb and co-workers was reported when, as acknowledged by the authors, pharmacogenetic research into antimalarial drugs was still in its infancy [ 15 ]. Within more than 12 years since the publication of this review article, 15 a deluge of publications have appeared in the literature on pharmacogenetic studies into antimalarial drugs. A later review article by Elewa and Wilby on pharmacogenetics of antimalarial drugs reported only on pharmacogenetic studies in malarial patients with associated clinical outcomes [ 16 ] Thus, only a few studies (about 10) met the inclusion criteria and this limited the coverage of studies on the pharmacogenetics of diverse antimalarial drugs. We found it necessary to present an updated report on current perspectives on pharmacogenomics in general, and also provide up-to-date insights into polymorphisms within the genes encoding for drug-metabolizing enzymes which alter the pharmacokinetics of antimalarial drugs, with the potential to cause variations in antimalarial drug responses. Methods Review Strategy and study selection This review is designed to study the insights and current perspectives of pharmacogenomics of antimalarial drugs. It identifies the dominant roles of pharmacogenomics in antimalarial drugs. As a result, unstructured literature review (ULR) as a method is adopted in this study to summarize research findings transparently (Fig. 1 ). Studies show that ULR has been popularised in medical studies. Key literature databases were selected based on the quality, extant content, and broad area of the discipline. These include Elsevier, IEEExplore, Pubmed, Scopus, Web of Science, Google Scholar, ProQuest, ScienceDirect, and BioMed Central. The keywords related to the study were identified and used for the study, purposedly to identify related works. These are “antimalarial drugs”, “personalized medicine”, “pharmacogenomics”, “pharmacogenetics”, “metabolism”, “cytochromes”, “amodiaquine”, “artesunate”, “artemisinin” “artemether”, “chloroquine”, “chlorproguanil”, “lumefantrine”, “mefloquine” “piperaquine”, “primaquine”, “proguanil”, “pyronaridine”, “quinine”, “tafenoquine”. These keywords were explicitly used to search in the selected database. Data extraction To identify eligible papers, 6 criteria were put in place: The paper must be peer-reviewed. Must be written in the English language. The paper must be in the pharmacogenomics/pharmacogenetics discipline. The paper is investigating the pharmacogenomics of antimalarial drugs The paper described the metabolism of antimalarial drugs. The paper identified at least one cytochrome P450 metabolizing enzyme. Before accepting any of the papers for investigation, further screening was performed using these conditions: The Paper is not available for download. The Paper’s findings are a repetition of an earlier reviewed work. An extended journal paper from a conference is preferred to the conference paper. After performing the search query, each paper’s abstract and keywords were manually sieved to exclude papers not related to the study. Results And Discussion Evolution of Pharmacogenetics and Genes of Importance The concept of pharmacogenetics/pharmacogenomics has been evolving along with remarkable strides made in the completion of the map of human genome sequence by the International Human Genome Sequencing Consortium [ 17 , 18 ]. Following the completion of the human genome project in the 2000s, numerous researchers have gone into studying the impact of genetic variation, especially SNPs, on drug response. The current status of pharmacogenetics development can be seen at PharmGKB website https://www.pgrn.org/pharmgkb.html developed by the US-based Pharmacogenomics Research Network (PGRN) that provides general information on individual polymorphisms and the impact of pharmacogenetics on response to specific drugs [ 19 ]. PharmGKB collates drug dosing guidelines based on variations in pharmacogenetics which are published by the Clinical Pharmacogenetics Implementation Consortium (CPIC), and other Pharmacogenetics Working Groups in different countries. With the availability of more validated scientific reports and data, drug regulatory agencies of several countries have listed hundreds of drugs requiring the determination of genomic biomarkers for optimization of drug efficacy and safety through dosage adjustment [ 20 ]. Cytochrome P450s (CYP450s), a superfamily of haemoproteins, play a very important role in phase I drug metabolism as they are known to metabolize about 80 to 90% of clinically used drugs [ 21 ]. The CYP450 isoforms that are very important in human drug metabolism include CYP1A2, CYP2A6, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4 (Fig. 2 ), and are each encoded by different genes [ 19 , 22 ]. Alterations in enzyme function such as increased or decreased activity result from mutations in a CYP gene that encodes for the enzyme. A mutant allele that occurs at a frequency of not less than one percent in a population is recognized as a pharmacogenetic polymorphism. Generally, polymorphisms can be identified in a population through genetic studies (identifying the mutant allele) and/or through determining altered enzyme function (ie phenotype studies) [ 23 ]. In terms of the extent of variations in drug metabolism in different ethnic groups and the number of drugs that are metabolized by each CYP450, it has been shown that the most important polymorphic CYPs are 1A2, 2C9, 2C19, and 2D6 [ 23 ]. However, widespread polymorphisms have also been observed in other CYP genes, such as CYP1A1, 2A6, 2C8, 3A4, and 3A5 [ 24 ]. Pharmacogenetic studies have shown that individuals can be classified into one of four general metabolizer types - Poor Metabolizer (PM), Intermediate Metabolizer (IM), Extensive Metabolizer (EM) and Ultra-rapid Metabolizer (UM). In the poor metabolizer group, the enzyme activity is abolished because they have a gene variant in which there are two nonfunctional alleles or the entire gene is deleted such that adverse drug effects at standard doses may be experienced due to drug accumulation. An intermediate metabolizer phenotype has decreased enzyme activity and is usually found in individuals carrying one nonfunctional allele and another allele with reduced function. The extensive metabolizer (now called normal metabolizer) phenotype is characterized by normal enzyme activity because one or two alleles have normal function, while the Ultra-rapid Metabolizer has increased enzyme activity as they carry more than one extra functional gene [ 25 ]. The variant alleles of CYP genes are distributed differently among different ethnic populations [ 25 ]. The enzymes involved in Phase II biotransformation also have isoforms that exhibit genetic polymorphisms which can influence the outcome of drug therapy. Generally, there are fewer instances in which drug clearance is influenced by variations in the genes that encode for the phase II enzymes because phase I metabolic reactions are usually the rate-limiting steps in the overall drug pharmacokinetic process [ 19 ]. It is pertinent to note that genetic variations resulting in inter-individual drug responses are not only due to the presence of variants of genes encoding drug-metabolizing enzymes, genetic variation in drug transporters and drug targets (e.g., receptors) can also have a great effect on drug efficacy, with several examples already identified [ 26 ]. Impact of Pharmacogenetics on Antimalarial Treatment Efficacy Pre-emptive genotyping of actionable genetic variants could be a good tool to optimize pharmacotherapy in patients [ 8 ]. The impact of genetic variants on antimalarial drugs and their clinical implications are outlined below and summarized in Table 1. Table 1 Antimalarial drugs and associated CYP450 Enzyme variants with Phenotype frequencies in different ethnic populations Antimalarial Drug/Enzyme Variants Phenotype Phenotype Frequency in Africans (%) Phenotype Frequency in Caucasians (%) Phenotype Frequency in Asians (%) Clinical Implication Amodiaquine, Chloroquine Amodiaquine: Treatment outcomes with amodiaquine do not to vary with the CYP2C8 genetic variants. There is increased risk of amodiaquine related ADR in PM Chloroquine : Wild-type CYP2C8 individuals achieved greater reduction of gametocytes than PM CYP2C8*2 PM 11 -22 <1 0 CYP2C8*3 PM 0 – 2.1 15 <0.1 Artesunate Increased adverse effect due to accumulation of active metabolite in UM Possible reduction in antimalarial activity in PM CYP2A6*1B UM 11 - 18 28 - 35 26 - 57 CYP2A6*2 PM 0-1 1-5 0 CYP2A6*4 PM 0.5-3 0.1-4 5-24 CYP2A6*7 PM 0 0-3 2-13 CYP2A6*9 PM 6-10 5-8 16-22 CYPA6*10 PM 0 0 0.4-4 Artemisinin, Artemether Artemisinin : No documented association of CYP2B6 variants with drug efficacy Artemether: CYP2B6 and CYP3A4/5 polymorphisms have no obvious effects on artemether treatment outcome CYP2B6*4 UM 0 4 - 6 3 - 40 CYP2B6*6 PM 25 – 50 15 – 25 12 - 19 CYP2B6*9 PM 20 - 50 2 – 29 2 - 47 CYP2B6*18 PM 2 - 8 0 0 Proguanil, Chlorproguanil Treatment failure may or may not be associated with decreased formation of cycloguanil in PM phenotypes. Further studies are required to clarify the clinical significance of CYP2C19 polymorphism in Proguanil efficacy CYP2C19*2 PM 15 – 25 12 29 – 35 CYP2C19*3 PM 0-1 <1 2 - 9 CYP2C19*17 UM 16 21 3 Mefloquine, Artemether, Lumefantrine, Piperaquine, Quinine CYP3A5*3 PM 12 – 40 82 - 95 40 – 80 Mefloquine: ABCB1 (TT) variant were found to have a three-times greater chance of successful treatment outcome compared with other genotypes (CC and CT). Lumefantrine : Most studies demonstrated that CYP3A4 and CYP3A5 gene variants showed no relationship with pharmacokinetic profiles of lumefantrine or treatment outcomes. Piperaquine: CYP450 genetic variants were found to have no significant effect piperaquine elimination. Quinine : There is a significant influence of few CYP3A5 variant alleles (CYP3A5*3,*4,*6, *7, and *9) on quinine metabolism CYP3A5*6 PM 7-17 0 0 CYP3A5*7 PM 10 0 0 CYP3A4*1B EM 77 2.7 11 CYP3A4*1G EM 85 8.2 40 CYP3A4*22 PM 0.08 5 2.6 Primaquine, Tafenoquine Primaquine : PM phenotypes of CYP2D6 variants have been shown to lead to therapeutic failure with primaquine. Tafenoquine : No association of CYP2D6 polymorphism with tafenoquine efficacy has been observed CYP2D6*1xN UM 0 -1 0 – 3 0 – 3 CYP2D6*2xN UM 1-2 0-3 0-1 CYP2D6*2 PM 14-19 20-25 13-29 CYP2D6*4 PM 3-6 10-18 1-12 CYP2D6*5 PM 4-5 1-3 1-4 CYP2D6*10 PM 3-7 2-5 1-10 CYP2D6*17 PM 17-20 0-1 0-2 CYP2D6*41 PM 6-12 8-17 2-13 Amodiaquine Amodiaquine, a 4-aminoquinoline (Fig. 3 ), is rapidly metabolized by CYP2C8 to N-desethylamodiaquine (DEAQ) which is 3-times less active than the parent drug but has a slower rate of elimination [ 27 ]. The CYP2C8 gene has primarily two major alleles, CYP2C8*2 and CYP2C* 3 , and both are associated with slow metabolizer phenotype, with *3 identified to result in significantly impaired metabolism. The prevalence of CYP2C8 gene varies in different ethnic populations as shown in Table 1 [ 28 , 29 ]. A recent study has shown that CYP2C8*2 and *3 frequencies among Eritreans are intermediate between the values documented for Caucasians and Africans [ 30 ]. Studies have been undertaken to determine whether the efficacy of amodiaquine is affected by CYP2C8 polymorphisms. It has been shown that treatment outcomes with amodiaquine do not vary with the CYP2C8 genetic variants, but there was an increased risk of non-serious adverse events in CYP2C8*2 or CYP2C8*3 allele carriers compared to the wild-type [ 27 , 28 , 31 ]. Artesunate Artesunate, a sesquiterpene lactone derivative (Fig. 3 ), is metabolized to an active dihydroartemisinin (DHA) primarily by CYP2A6 [ 32 ]. The CYP2A6 gene is reported to be highly polymorphic, with over 35 different CYP2A6 alleles described and the majority of them have been shown to alter CYP2A6 enzyme activity. The most common of these CYP2A6 variant alleles include CYP2A6*1B, CYP2A6*2, CYP2A6*4, CYP2A6*7, CYP2A6*9 and CYP2A6*10 [ 32 ]. Across ancestral groups, a wide variation in the frequency of CYP2A6 alleles is observed, as summarized in Table 1 [ 15 , 33 , 34 ]. A study assessing the influence of CYP2A6 gene on the incidence of adverse effects in healthy Malaysian volunteers receiving single doses of artesunate with amodiaquine showed that significantly more adverse effects were noted in those patients with CYP2A6*1B variant (UM phenotype) and this suggests that this may due to accumulation of the active metabolite, dihydroartemisinin [ 35 ]. People with poor metabolizer phenotypes of CYP2A6 have higher concentrations of artesunate and lower concentrations of DHA. This may reduce the drug’s antimalarial activity since artesunate has higher intrinsic antimalarial activity compared to DHA. However, this has not been documented. Artemisinin The CYP450 isoform that mainly mediates the metabolism is artemisinin, a sesquiterpene lactone (Fig. 3 ), which is CYP2B6, with a contribution by CYP3A4 resulting in the formation of an active metabolite, dihydroartemisinin (DHA) [ 36 ]. Currently, more than 100 described SNPs of the CYP2B6 genes have been reported and the major CYP2B6 variants are CYP2B6*4, CYP2B6*6, CYP2B6*9, and CYP2B6*18 [ 36 ]. The CYP2B6*6 and CYP2CB6*9 are the most common allele and occur in up to over 50% of different populations. CYP2B6*4 which codes for Ultrarapid metabolizer phenotype is rare in Africans compared to Caucasians or Asians. On the other hand, CYP2B6*18 is more frequent in Africans and rare than in Caucasians and Asians (Table 1) [ 37 ]. While the UM phenotypes have reduced drug exposure, the PM variants are associated with increased plasma concentrations of artemisinin [ 29 , 36 ]. However, no studies have reported an association between the CYP2B6 gene variants and artemisinin efficacy. Artemether Artemether, a derivative of artemisinin (Fig. 3 ), undergoes rapid and extensive demethylated to the biologically active main metabolite DHA through CYP3A4/5 and CYP2B6. While the liver CYP3A4 is not important in the in vivo metabolism of artemether, this CYP450 isozyme plays a role in the presystemic metabolism of the drug [ 14 ]. To date, no studies have been published on artemether monotherapy pharmacogenetics. The few available reports concern Artemether-lumefantrine. The association of CYP2B6*6 genotype with artemether disposition was demonstrated in a decreased metabolism of artemether in CYP2B6*6 volunteers (PM) and the authors concluded this is unlikely to result in differences in artemether-lumefantrine efficacy and treatment outcomes [ 14 ]. In a study that assessed the effects of CYP2B6 and CYP3A4/5 polymorphisms on artemether disposition, no associations were found between artemether elimination and CYP2B6*6 , CYP3A4*1B and CYP3A5*3 alleles carriers [ 38 ]. Hence, CYP450 genetic variants may not influence artemether treatment outcome. Chloroquine The metabolism of Chloroquine, a 4-aminoquinoline (Fig. 3 ), is mediated mainly by CYP2C8 and to a lesser degree by CYP3A4 and CYP2D6 to an active metabolite (N-desethylchloroquine) in addition to other minor metabolites [ 39 ]. The frequencies of the CYP2C8 variants in different populations are depicted in Table 1 [ 37 ]. In malaria treatment/prophylaxis studies with chloroquine, a 2.5- to 5.6-fold interindividual variability in CQ concentrations has been reported and this may affect treatment outcome. A study has shown that in chloroquine/primaquine treated patients, there is a relationship between CYP2C8 allele variants and gametocytemia and parasitemia clearance rates. Wild-type individuals achieved a greater reduction of gametocytes than low-activity alleles of CYP2C8 (i.e., *2, *3, and *4) carriers. The results suggested that CYP2C8, CYP2C9 and CYP23A5 genetic variants may influence chloroquine pharmacokinetics thereby affecting treatment outcome [ 40 ]. Chlorproguanil and proguanil The biguanide derivatives, chlorproguanil and proguanil (Fig. 3 ) are bioactivated to chlorcycloguanil and cycloguanil, respectively, by CYP2C19 and, to a lesser extent, by CYP3A4 [ 15 ]. The frequency of the CYP2C19 alleles varies considerably among different ethnic populations (Table 1). The two variant alleles, CYP2C19*2 and CYP2C19*3, are largely associated with the PM phenotype while CYP2C19*17 allele results in increased CYP2C19 expression and activity. The CYP2C19*2 is the most common CYP2C19 variant among the various populations, while CYP2C19*3 allele frequencies in most populations is below 1% but more prevalent among Asians [ 28 , 37 ]. Although a study on malaria prophylaxis with proguanil in Tanzanians showed that treatment failure was associated with decreased metabolism of the drug in PM phenotypes, other studies demonstrated that the therapeutic efficacy of proguanil was comparable between the PM and EM patients even when the extent of metabolism differed significantly between the two groups [ 28 ]. Further studies are required to clearly understand the clinical significance of CYP2C19 polymorphism in the efficacy of proguanil/cycloguanil. Lumefantrine Lumefantrine which belongs to the group of arylamine alcohols like halofantrine (Fig. 3 ), is used together with artemether in artemisinin-based combination therapy (ACT). This highly lipophilic drug is primarily metabolized by CYP3A4/CYP3A5 to a metabolite, desbutyl-lumefantrine which has a significantly higher antimalarial activity than the parent drug, (IC 50 4–5-fold higher) [ 41 ]. Both CYP3A4 and 3A5 have overlapping substrate specificities. The CYP3A5 gene has three major alleles that have been well studied (CYP3A5*3, CYP3A5*6 and CYP3A5*7) and they are associated with slow metabolizer phenotypes. Their frequencies vary considerably among different ethnic populations (Table 1) [ 32 ]. The most studied variants of CYP3A4 that occur in high frequencies include, CYP3A4*1B, CYP3A4*1G, and CYP3A4*22 (Table 1). Several studies have investigated whether the CYP3A4 variants result in significant differences in blood levels of lumefantrine. These studies demonstrated that the major CYP3A4 and CYP3A5 gene variants showed no relationship with pharmacokinetic profiles of lumefantrine or treatment outcomes [ 38 , 43 ]. On the other hand, another study reported that CYP3A5*3 genetic variant was associated with only a high maximum plasma concentration of lumefantrine [ 44 ]. Thus, further investigations are required to clarify the association between CYP3A5*3 gene variants, lumefantrine pharmacokinetics and therapeutic efficacy. Mefloquine Mefloquine, a 4-quinoline derivative (Fig. 3 ), is metabolised by CYP3A4/CYP3A5 to two major pharmacologically inactive metabolites, carboxymefloquine and hydroxymefloquine [ 33 ]. The variants of CYP3A4 and CYP3A5 genes and the frequencies of occurrence in different ethnic populations are presented in Table 1. Pharmacogenetic studies of mefloquine are mostly on genetic polymorphisms of the major genes encoding drug efflux transporters - ABCB1, ABCG2, and ABCC1. While no significant association was found between the ABCG2 and ABCC1 gene polymorphisms and mefloquine treatment outcome, patients carrying the ABCB1 (TT) variant were found to have a three-times greater chance of successful treatment compared with other genotypes (CC and CT). Thus, predicting responses to artesunate-mefloquine treatment can be based on using ABCB1 polymorphisms as useful genetic markers [ 45 ]. Primaquine Primaquine, an 8-aminoquinoline (Fig. 3 ) has been an antimalarial drug of choice in the treatment of Plasmodium vivax and Plasmodium ovale hypnozoites, and for malaria prophylaxis. It is metabolized principally by CYP2D6 isoenzyme to active metabolites, the mainly 5-hydroxy derivative of primaquine, that are responsible for the pharmacological effect of primaquine [ 12 ]. CYP2D6 gene is highly polymorphic with over 90 known allelic variants, demonstrating significant inter-individual and inter-ethnic differences in its activity (Table 1) [ 37 , 46 ]. Some of these CYP2D6 variants with a prevalence of > 1% in different ethnic groups are shown in Table 1 Most of them are PM except CYP2D6*1xN and CYP2D6*2xN variants that are Ultra-rapid metabolizer phenotypes (Table 1), Recent studies have shown an association of polymorphisms related to CYP2D6 activity and treatment outcome with primaquine. PM phenotypes of CYP2D6 variants have been shown to lead to therapeutic failure with primaquine [ 46 ]. Piperaquine Piperaquine, a bisquinoline (Fig. 3 ), had been extensively used as a monotherapy but it is now commonly used as a partner drug with dihydroartemisinin in the ACT. Piperaquine is metabolized primarily by CYP3A4 and CYP3A5 into two major metabolites, piperaquine N -oxide and piperaquine N , N -dioxide, and both metabolites are biologically active contributing to the efficacy of piperaquine [ 47 ]. The variants of CYP3A4 and CYP3A5 genes and their occurrence frequencies are shown in Table 1. There is a paucity of studies on the pharmacogenetics of piperaquine. In studying the effect of SNPs in Cytochrome P450 isoenzyme genes on the metabolism of Artemisinin-Based Combination therapies, genetic variants were found to have no significant effect on piperaquine elimination [ 38 ]. Pyronaridine Pyronaridine, benzonaphthyridine derivative (Fig. 3 ) , had previously been used in the treatment of malaria as a single agent, but it is currently used in combination with artesunate, representing a second-generation ACT. The metabolism of pyronaridine is mediated by multiple and varied pathways and studies with recombinant human CYP450 isoforms indicated that pyronaridine could be metabolised by CYP1A2, CYP2D6 and CYP3A4, generating nine metabolites [ 48 ]. The literature is deficient in studies on the investigation of a possible association of genes of CYP450 isoforms and pharmacokinetics or therapeutic efficacy of pyronaridine. Pyronaridine has a low metabolic turnover, thus pharmacogenetic studies of the drug might not be of significant value [ 48 ]. Pyronaridine is a P-glycoprotein substrate and may exhibit variable oral absorption based on its significant P-gp-mediated efflux [ 49 ]. It has been postulated that due to the importance of this transport protein in the processes of oral absorption for a range of susceptible drugs, the polymorphism of P-gp gene might be able to affect the oral absorption of pyronaridine Quinine Quinine is metabolised by CYP3A4/3A5 to its main metabolite, 3-hydroxyquinine, which contributes up to 10% of the antimalarial activity of the parent compound [ 25 ]. In vitro study on the effects of different CYP3A4 allelic variants on intrinsic clearance towards quinine revealed that most of the variants had significantly reduced activity while 2 variants showed increased activity and 2 other variants had no significant differences in quinine metabolism, compared with a wild-type allele, CYP3A4*1A [ 25 ]. These results suggest that if these in vitro findings are replicated in vivo , patients that are carriers of these alleles with reduced intrinsic clearance of quinine may potentially be CYP3A4 poor metabolizers and may therefore be more prone to adverse reactions of the quinine and also require lower doses of the drug to achieve therapeutic efficacy. Pharmacogenetic studies in Tanzanian and Ugandan populations demonstrated a significant influence of a few CYP3A5 variant alleles (CYP3A5*3,*4,*6, *7, and *9) on quinine metabolism [ 50 , 51 ]. Considering that quinine is an antimalarial drug with a narrow therapeutic window and concentration-dependent serious adverse reactions, these findings suggest that CYP3A4/5 allelic variants with PM phenotypes can be associated with adverse reactions of antimalarial therapy with quinine. Tafenoquine Tafenoquine, a new long half-life 8-aminoquinoline drug (a primaquine analogue) was approved for use in the chemoprophylaxis and treatment of malaria. Similar to primaquine, the metabolism of tafenoquine is principally mediated by CYP2D6 but clinical studies have not identified an association of CYP2D6 polymorphism with tafenoquine efficacy [ 52 ]. Thus, the efficacy did not appear to be reduced in poor or intermediate metabolizers of CYP2D6 Clinical Implication and Challenges of Pharmacogenomics Testing The integration of pharmacogenetic testing into clinical practice has been evolving over the years, Currently, the Clinical Pharmacogenetics Implementation Consortium (CPIC) and other related pharmacogenomics research organizations have provided specific pharmacogenetic guidelines on how to use pharmacogenetic information relating to some drugs. Pharmacogenetics/pharmacogenomics testing is required especially for some classes of drugs that are metabolized by one or more variant alleles of the Cytochrome P450 enzymes, and drugs with a narrow therapeutic window [ 53 ]. Interpretation of pharmacogenetics test results for pro-drugs is different from those of active parent drugs. For example, while poor metabolizers of active parent drugs have high plasma drug levels that may result in toxicity, the same phenotype taking a pro-drug will have a therapeutic failure because its metabolite which is the active moiety is not produced. A typical example is primaquine where CYP2D6 PM phenotypes have treatment failures. The corollary is that while ultrarapid metabolizers of active parent drugs may have reduced drug response due to rapid drug clearance, the same phenotype taking a pro-drug will manifest increased drug response following efficient generation of the metabolites which have therapeutic activity [ 54 ]. There are currently about 400 medicines with FDA-approved information on pharmacogenetic product labels but only three antimalarial drugs (chloroquine, primaquine and quinine) are currently contained in the list [ 55 ]. However, PharmGKB has Drug label annotations containing pharmacogenetic information for over 800 drugs which include most of the antimalarial drugs such as lumefantrine, artesunate, chloproguanil, dapsone, tefanoquine, artemether, proguanil, and mefloquine ( http://www.pgrn.org/pharmgkb.html ) [ 56 ]. Many challenges have been encountered in the implementation of pharmacogenetics in a clinical setting. Some of these challenges include: (a) Most healthcare providers do not have a clear understanding of the applicability of pharmacogenetic tests. Drug dosage adjustment based on non-genetic parameters like age, body weight, and hepatic and renal function tests is well understood and practiced. Some resources have addressed this lacuna and these include the Clinical Pharmacogenetics Implementation Consortium (CPIC; https://cpicpgx.org/ ) Other resources are: My Drug Genome ( https://www.mydruggenome.org ); IGNITE ( https://www.gmkb.org ); GTR ( https://www.ncbi.nlm.nih.gov/gtr ); eMERGE ( https://www.emerge-network.org ) and Coursera ( https://www.coursera.org/learn/personalizedmed ) [ 57 ]. (b) Even when the clinical utility of pharmacogenetics tests is appreciated, some clinicians can encounter difficulties in the interpretation of the pharmacogenetic test results to apply them to patient care. Again, there are several resources available for the relevant knowledge and necessary information [ 55 ]. (c) The cost of the genetic tests is another challenge as only a few genetic tests are paid for by health insurance companies. The additional financial burden on the patient by bearing the cost of the test constitutes an impediment. However, with proper education, the stakeholders would realize that the benefits of pharmacogenetic testing outweigh the cost of the test [ 58 , 59 ]. (d) Several technical issues are involved in SNP genotyping process before the results can be translated into clinical practice. These include a long duration of time that may be required in the experimental procedure for SNP validation, determination of the most applicable SNPs panels, investigating the correlation that exists between an SNP and enzyme activity, sorting out several low-risk polymorphisms, taking into account that the distribution and frequency of SNPs differ among different ethnic groups and this makes it problematic to apply the findings of one ethnic group to another group. Conclusion Inter-individual drug responses due to genetic factors (Pharmacogenomics) constitute a major factor that influences the efficacy and safety of drugs in general. Variability in the genes that encode for the major CYP450 isoforms that mediate the metabolism of antimalarial drugs, contribute significantly to inter-individual drug response. Most healthcare providers are more familiar with the application of non-genetic variables in dosage adjustment strategies but they do not have a clear understanding of the applicability of pharmacogenetic tests to improve therapeutic drug response. However, several resources are freely available to be used in bridging that knowledge gap. Therefore, the introduction of individual SNP genotyping into clinical settings has the potential of providing relevant information regarding dosage adjustment of antimalarial drugs toward achieving optimal drug response Abbreviations ACT artemisinin-based combination therapy CPIC Clinical Pharmacogenetics Implementation Consortium CQ chloroquine CYP450 cytochrome P450 DEAQ desethylamodiaquine DHA dihydroartemisinin EM Extensive Metabolizer IM Intermediate Metabolizer PGRN Pharmacogenomics Research Network PM Poor Metabolizer SNPs Single Nucleotide Polymorphisms ULR unstructured literature review UM Ultra-rapid Metabolizer WHO World Health Organization Declarations Ethics approval and consent to participate Not applicable Consent for publication All the authors read and approved the manuscript for publication Availability of data and materials Not applicable Competing interests The authors declare no relevant conflicts of interest or financial relationships. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions Conceptualization: COO; Data curation: CON; Formal analysis: JOS; Funding acquisition: COO, JOS and CON; Investigation: JOS; Methodology: COO; Project administration: CON; Resources: COO; Software: CON; Supervision: COO; Validation: JOS; Visualization: JOS; Original draft: CON; Review & editing: COO and JOS. Acknowledgements Not applicable References World Health Organization. World malaria report 2021. Geneva, 2021 Conrad MD, Rosenthal Antimalarial drug resistance in Africa: The calm before the storm? Inf Dis 2019;19(10):e338-e351. https://doi.org/10.1016/S1473-3099(19)30261-0 Ahmed S, Zhou Z, Zhou J. Pharmacogenomics of Drug Metabolizing Enzymes and Transporters: Relevance to Precision Medicine. Geno Proteo Bioinform . 2016;4:298-313. https://doi.org/10.1016/j.gpb.2016.03.008 Agarwal A, Ressler D, Snyder G. 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The latest report by the World Health Organization (WHO) indicates that there were about 241\u0026nbsp;million malaria cases and about 627,000 malaria deaths in 2020, with the WHO African Region accounting for about 95% of this malaria cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The emergence of antimalarial drug resistance challenges the control and treatment of malaria, necessitating regular monitoring of drug efficacy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The current mainstay and first-line treatment for the majority of cases of malaria is artemisinin-based combination therapy (ACT) in which a rapid-acting with short half-life artemisinin or its derivative is combined with a drug with a longer half-life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Another strategy aimed at enhancing the effectiveness of antimalarial treatment is the application of findings on Pharmacogenetics/pharmacogenomics of antimalarial drugs.\u003c/p\u003e \u003cp\u003ePharmacogenetics involves the study of how inter-individual differences in a single gene can affect an individual\u0026rsquo;s response to particular drugs, while the term pharmacogenomics is much broader and it involves investigating the entire genome to assess their effects on drug responses. This field of study aims at developing effective and safe medications with doses that are tailored to variations in a person\u0026rsquo;s genes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Plasma drug levels have been reported to vary as much as 1000-fold when the same drug dose is administered to different individuals with the same body weights, and this is due to variations in genes encoding cytochrome P450 (CYP450) and other Drug metabolizing enzymes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt has been established that there are more than 14\u0026nbsp;million Single Nucleotide Polymorphisms (SNPs) in the entire human genome, and these variations in the human genome occur approximately in every 300\u0026ndash;1000 nucleotides [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Most SNPs are attributable to independent single mutational events in the past [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Thus, where there are inter-individual variations in drug responses, the identification of a variant of the gene that mediates the variation would lead to optimization of the treatment efficacy and reduction of the adverse effect profiles of drugs in a given population [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, it is known that apart from genetic factors other factors can influence the outcome of drug therapy and these include environmental factors (exposure to some chemicals in the environment), physiological factors (age, sex, hepatic and renal functions, pregnancy), lifestyle factors (smoking, drinking alcohol, exercise) and concomitant drug use [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Pharmacogenomics is a part of personalized medicine or precision medicine that individualizes therapy by using SNPs and tailor-making medicines to each patient for effective therapy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. We have carried out numerous studies in our laboratories on pharmacokinetics and pharmacogenomics of different antimalarial drugs and diverse anti-infective agents to generate data for optimization of the drug efficacies [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn earlier review on the pharmacogenetics of antimalarial drugs by Kerb and co-workers was reported when, as acknowledged by the authors, pharmacogenetic research into antimalarial drugs was still in its infancy [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Within more than 12 years since the publication of this review article,\u003csup\u003e15\u003c/sup\u003e a deluge of publications have appeared in the literature on pharmacogenetic studies into antimalarial drugs. A later review article by Elewa and Wilby on pharmacogenetics of antimalarial drugs reported only on pharmacogenetic studies in malarial patients with associated clinical outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Thus, only a few studies (about 10) met the inclusion criteria and this limited the coverage of studies on the pharmacogenetics of diverse antimalarial drugs. We found it necessary to present an updated report on current perspectives on pharmacogenomics in general, and also provide up-to-date insights into polymorphisms within the genes encoding for drug-metabolizing enzymes which alter the pharmacokinetics of antimalarial drugs, with the potential to cause variations in antimalarial drug responses.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eReview Strategy and study selection\u003c/h2\u003e \u003cp\u003eThis review is designed to study the insights and current perspectives of pharmacogenomics of antimalarial drugs. It identifies the dominant roles of pharmacogenomics in antimalarial drugs. As a result, unstructured literature review (ULR) as a method is adopted in this study to summarize research findings transparently (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Studies show that ULR has been popularised in medical studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKey literature databases were selected based on the quality, extant content, and broad area of the discipline. These include Elsevier, IEEExplore, Pubmed, Scopus, Web of Science, Google Scholar, ProQuest, ScienceDirect, and BioMed Central. The keywords related to the study were identified and used for the study, purposedly to identify related works. These are \u0026ldquo;antimalarial drugs\u0026rdquo;, \u0026ldquo;personalized medicine\u0026rdquo;, \u0026ldquo;pharmacogenomics\u0026rdquo;, \u0026ldquo;pharmacogenetics\u0026rdquo;, \u0026ldquo;metabolism\u0026rdquo;, \u0026ldquo;cytochromes\u0026rdquo;, \u0026ldquo;amodiaquine\u0026rdquo;, \u0026ldquo;artesunate\u0026rdquo;, \u0026ldquo;artemisinin\u0026rdquo; \u0026ldquo;artemether\u0026rdquo;, \u0026ldquo;chloroquine\u0026rdquo;, \u0026ldquo;chlorproguanil\u0026rdquo;, \u0026ldquo;lumefantrine\u0026rdquo;, \u0026ldquo;mefloquine\u0026rdquo; \u0026ldquo;piperaquine\u0026rdquo;, \u0026ldquo;primaquine\u0026rdquo;, \u0026ldquo;proguanil\u0026rdquo;, \u0026ldquo;pyronaridine\u0026rdquo;, \u0026ldquo;quinine\u0026rdquo;, \u0026ldquo;tafenoquine\u0026rdquo;. These keywords were explicitly used to search in the selected database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData extraction\u003c/h2\u003e \u003cp\u003eTo identify eligible papers, 6 criteria were put in place:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe paper must be peer-reviewed.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMust be written in the English language.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe paper must be in the pharmacogenomics/pharmacogenetics discipline.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe paper is investigating the pharmacogenomics of antimalarial drugs\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe paper described the metabolism of antimalarial drugs.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe paper identified at least one cytochrome P450 metabolizing enzyme.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBefore accepting any of the papers for investigation, further screening was performed using these conditions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe Paper is not available for download.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe Paper\u0026rsquo;s findings are a repetition of an earlier reviewed work.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAn extended journal paper from a conference is preferred to the conference paper.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAfter performing the search query, each paper\u0026rsquo;s abstract and keywords were manually sieved to exclude papers not related to the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results And Discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eEvolution of Pharmacogenetics and Genes of Importance\u003c/h2\u003e\n\u003cp\u003eThe concept of pharmacogenetics/pharmacogenomics has been evolving along with remarkable strides made in the completion of the map of human genome sequence by the International Human Genome Sequencing Consortium [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Following the completion of the human genome project in the 2000s, numerous researchers have gone into studying the impact of genetic variation, especially SNPs, on drug response. The current status of pharmacogenetics development can be seen at PharmGKB website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pgrn.org/pharmgkb.html\u003c/span\u003e\u003c/span\u003e developed by the US-based Pharmacogenomics Research Network (PGRN) that provides general information on individual polymorphisms and the impact of pharmacogenetics on response to specific drugs [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. PharmGKB collates drug dosing guidelines based on variations in pharmacogenetics which are published by the Clinical Pharmacogenetics Implementation Consortium (CPIC), and other Pharmacogenetics Working Groups in different countries. With the availability of more validated scientific reports and data, drug regulatory agencies of several countries have listed hundreds of drugs requiring the determination of genomic biomarkers for optimization of drug efficacy and safety through dosage adjustment [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eCytochrome P450s (CYP450s), a superfamily of haemoproteins, play a very important role in phase I drug metabolism as they are known to metabolize about 80 to 90% of clinically used drugs [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. The CYP450 isoforms that are very important in human drug metabolism include CYP1A2, CYP2A6, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), and are each encoded by different genes [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Alterations in enzyme function such as increased or decreased activity result from mutations in a CYP gene that encodes for the enzyme. A mutant allele that occurs at a frequency of not less than one percent in a population is recognized as a pharmacogenetic polymorphism. Generally, polymorphisms can be identified in a population through genetic studies (identifying the mutant allele) and/or through determining altered enzyme function (ie phenotype studies) [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. In terms of the extent of variations in drug metabolism in different ethnic groups and the number of drugs that are metabolized by each CYP450, it has been shown that the most important polymorphic CYPs are 1A2, 2C9, 2C19, and 2D6 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, widespread polymorphisms have also been observed in other CYP genes, such as CYP1A1, 2A6, 2C8, 3A4, and 3A5 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Pharmacogenetic studies have shown that individuals can be classified into one of four general metabolizer types - Poor Metabolizer (PM), Intermediate Metabolizer (IM), Extensive Metabolizer (EM) and Ultra-rapid Metabolizer (UM). In the poor metabolizer group, the enzyme activity is abolished because they have a gene variant in which there are two nonfunctional alleles or the entire gene is deleted such that adverse drug effects at standard doses may be experienced due to drug accumulation. An intermediate metabolizer phenotype has decreased enzyme activity and is usually found in individuals carrying one nonfunctional allele and another allele with reduced function. The extensive metabolizer (now called normal metabolizer) phenotype is characterized by normal enzyme activity because one or two alleles have normal function, while the Ultra-rapid Metabolizer has increased enzyme activity as they carry more than one extra functional gene [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. The variant alleles of CYP genes are distributed differently among different ethnic populations [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe enzymes involved in Phase II biotransformation also have isoforms that exhibit genetic polymorphisms which can influence the outcome of drug therapy. Generally, there are fewer instances in which drug clearance is influenced by variations in the genes that encode for the phase II enzymes because phase I metabolic reactions are usually the rate-limiting steps in the overall drug pharmacokinetic process [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIt is pertinent to note that genetic variations resulting in inter-individual drug responses are not only due to the presence of variants of genes encoding drug-metabolizing enzymes, genetic variation in drug transporters and drug targets (e.g., receptors) can also have a great effect on drug efficacy, with several examples already identified [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eImpact of Pharmacogenetics on Antimalarial Treatment Efficacy\u003c/h2\u003e\n\u003cp\u003ePre-emptive genotyping of actionable genetic variants could be a good tool to optimize pharmacotherapy in patients [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. The impact of genetic variants on antimalarial drugs and their clinical implications are outlined below and summarized in Table\u0026nbsp;1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAntimalarial drugs and associated CYP450 Enzyme variants with Phenotype frequencies in different ethnic populations\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eAntimalarial Drug/Enzyme Variants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePhenotype\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003ePhenotype\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Frequency\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;in Africans (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003ePhenotype\u003c/p\u003e\n\u003cp\u003eFrequency\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;in Caucasians (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003ePhenotype Frequency in Asians (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"249\"\u003e\n\u003cp\u003eClinical Implication\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" width=\"459\"\u003e\n\u003cp\u003e\u003cstrong\u003eAmodiaquine, Chloroquine\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"249\"\u003e\n\u003cp\u003e\u003cstrong\u003eAmodiaquine:\u003c/strong\u003e Treatment outcomes with amodiaquine do not to vary with the CYP2C8 genetic variants. There is increased risk of amodiaquine related ADR in PM\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChloroquine\u003c/strong\u003e: Wild-type CYP2C8 individuals achieved greater reduction of gametocytes than PM\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2C8*2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e11 -22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e\u0026lt;1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2C8*3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0 \u0026ndash; 2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e\u0026lt;0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" width=\"459\"\u003e\n\u003cp\u003e\u003cstrong\u003eArtesunate\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"7\" width=\"249\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIncreased adverse effect due to accumulation of active metabolite in UM\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePossible reduction in antimalarial activity in PM\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2A6*1B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003eUM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e11 - 18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e28 - 35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e26 - 57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2A6*2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e1-5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2A6*4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.5-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.1-4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e5-24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2A6*7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e2-13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2A6*9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e6-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e5-8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e16-22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYPA6*10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.4-4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" width=\"459\"\u003e\n\u003cp\u003e\u003cstrong\u003eArtemisinin, Artemether\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"5\" width=\"249\"\u003e\n\u003cp\u003e\u003cstrong\u003eArtemisinin\u003c/strong\u003e: No documented association of CYP2B6 variants with drug efficacy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArtemether:\u003c/strong\u003e \u003cem\u003eCYP2B6\u003c/em\u003e and\u0026nbsp;\u003cem\u003eCYP3A4/5\u003c/em\u003e\u0026nbsp;polymorphisms have no obvious effects on artemether treatment outcome\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2B6*4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003eUM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e4 - 6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e3 - 40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2B6*6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e25 \u0026ndash; 50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e15 \u0026ndash; 25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e12 - 19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2B6*9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e20 - 50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e2 \u0026ndash; 29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e2 - 47\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2B6*18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e2 - 8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" width=\"459\"\u003e\n\u003cp\u003e\u003cstrong\u003eProguanil, Chlorproguanil\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" width=\"249\"\u003e\n\u003cp\u003eTreatment failure may or may not be associated with decreased formation of cycloguanil in PM phenotypes. Further studies are required to clarify the clinical significance of \u003cem\u003eCYP2C19 \u003c/em\u003epolymorphism\u0026nbsp; in Proguanil efficacy\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2C19*2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e15 \u0026ndash; 25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e29 \u0026ndash; 35\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2C19*3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e\u0026lt;1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e2 - 9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2C19*17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003eUM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"708\"\u003e\n\u003cp\u003e\u003cstrong\u003eMefloquine, Artemether, Lumefantrine, Piperaquine, Quinine\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP3A5*3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e12 \u0026ndash; 40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e82 - 95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e40 \u0026ndash; 80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"6\" width=\"249\"\u003e\n\u003cp\u003e\u003cstrong\u003eMefloquine:\u003c/strong\u003e ABCB1 (TT) variant were found to have a three-times greater chance of successful treatment outcome compared with other genotypes (CC and CT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLumefantrine\u003c/strong\u003e: Most studies demonstrated that \u003cem\u003eCYP3A4 and CYP3A5 \u003c/em\u003egene variants showed no relationship with pharmacokinetic profiles of lumefantrine or treatment outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePiperaquine:\u003c/strong\u003e CYP450 genetic variants were found to have no significant effect piperaquine elimination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuinine\u003c/strong\u003e: There is a significant influence of few CYP3A5 variant alleles (CYP3A5*3,*4,*6, *7, and *9) on quinine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP3A5*6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e7-17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP3A5*7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP3A4*1B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003eEM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e2.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP3A4*1G\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003eEM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e8.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP3A4*22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e2.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" width=\"459\"\u003e\n\u003cp\u003e\u003cstrong\u003ePrimaquine, Tafenoquine\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"9\" width=\"249\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimaquine\u003c/strong\u003e: PM phenotypes of CYP2D6 variants have been shown to lead to therapeutic failure with primaquine.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTafenoquine\u003c/strong\u003e: No association of CYP2D6 polymorphism with tafenoquine efficacy has been observed\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2D6*1xN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003eUM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0 -1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0 \u0026ndash; 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0 \u0026ndash; 3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2D6*2xN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003eUM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e1-2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0-1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2D6*2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e14-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e20-25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e13-29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2D6*4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e3-6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e10-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e1-12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2D6*5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e4-5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e1-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e1-4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2D6*10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e3-7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e2-5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e1-10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2D6*17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e17-20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0-2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eCYP2D6*41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e6-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e8-17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e2-13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eAmodiaquine\u003c/h2\u003e\n\u003cp\u003eAmodiaquine, a 4-aminoquinoline (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), is rapidly metabolized by CYP2C8 to N-desethylamodiaquine (DEAQ) which is 3-times less active than the parent drug but has a slower rate of elimination [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. The CYP2C8 gene has primarily two major alleles, CYP2C8*2 and CYP2C*\u003cem\u003e3\u003c/em\u003e, and both are associated with slow metabolizer phenotype, with \u003cem\u003e*3\u003c/em\u003e identified to result in significantly impaired metabolism. The prevalence of CYP2C8 gene varies in different ethnic populations as shown in Table\u0026nbsp;1 [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. A recent study has shown that \u003cem\u003eCYP2C8*2\u003c/em\u003e and *3 frequencies among Eritreans are intermediate between the values documented for Caucasians and Africans [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. Studies have been undertaken to determine whether the efficacy of amodiaquine is affected by CYP2C8 polymorphisms. It has been shown that treatment outcomes with amodiaquine do not vary with the CYP2C8 genetic variants, but there was an increased risk of non-serious adverse events in CYP2C8*2 or CYP2C8*3 allele carriers compared to the wild-type [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eArtesunate\u003c/h2\u003e\n\u003cp\u003eArtesunate, a sesquiterpene lactone derivative (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), is metabolized to an active dihydroartemisinin (DHA) primarily by CYP2A6 [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. The CYP2A6 gene is reported to be highly polymorphic, with over 35 different CYP2A6 alleles described and the majority of them have been shown to alter CYP2A6 enzyme activity. The most common of these CYP2A6 variant alleles include CYP2A6*1B, CYP2A6*2, CYP2A6*4, CYP2A6*7, CYP2A6*9 and CYP2A6*10 [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. Across ancestral groups, a wide variation in the frequency of CYP2A6 alleles is observed, as summarized in Table\u0026nbsp;1 [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. A study assessing the influence of CYP2A6 gene on the incidence of adverse effects in healthy Malaysian volunteers receiving single doses of artesunate with amodiaquine showed that significantly more adverse effects were noted in those patients with CYP2A6*1B variant (UM phenotype) and this suggests that this may due to accumulation of the active metabolite, dihydroartemisinin [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. People with poor metabolizer phenotypes of CYP2A6 have higher concentrations of artesunate and lower concentrations of DHA. This may reduce the drug\u0026rsquo;s antimalarial activity since artesunate has higher intrinsic antimalarial activity compared to DHA. However, this has not been documented.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eArtemisinin\u003c/h2\u003e\n\u003cp\u003eThe CYP450 isoform that mainly mediates the metabolism is artemisinin, a sesquiterpene lactone (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), which is CYP2B6, with a contribution by CYP3A4 resulting in the formation of an active metabolite, dihydroartemisinin (DHA) [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Currently, more than 100 described SNPs of the CYP2B6 genes have been reported and the major CYP2B6 variants are CYP2B6*4, CYP2B6*6, CYP2B6*9, and CYP2B6*18 [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. The CYP2B6*6 and CYP2CB6*9 are the most common allele and occur in up to over 50% of different populations. CYP2B6*4 which codes for Ultrarapid metabolizer phenotype is rare in Africans compared to Caucasians or Asians. On the other hand, CYP2B6*18 is more frequent in Africans and rare than in Caucasians and Asians (Table\u0026nbsp;1) [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. While the UM phenotypes have reduced drug exposure, the PM variants are associated with increased plasma concentrations of artemisinin [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, no studies have reported an association between the CYP2B6 gene variants and artemisinin efficacy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eArtemether\u003c/h2\u003e\n\u003cp\u003eArtemether, a derivative of artemisinin (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), undergoes rapid and extensive demethylated to the biologically active main metabolite DHA through CYP3A4/5 and CYP2B6. While the liver CYP3A4 is not important in the \u003cem\u003ein vivo\u003c/em\u003e metabolism of artemether, this CYP450 isozyme plays a role in the presystemic metabolism of the drug [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTo date, no studies have been published on artemether monotherapy pharmacogenetics. The few available reports concern Artemether-lumefantrine. The association of CYP2B6*6 genotype with artemether disposition was demonstrated in a decreased metabolism of artemether in CYP2B6*6 volunteers (PM) and the authors concluded this is unlikely to result in differences in artemether-lumefantrine efficacy and treatment outcomes [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn a study that assessed the effects of CYP2B6 and CYP3A4/5 polymorphisms on artemether disposition, no associations were found between artemether elimination and \u003cem\u003eCYP2B6*6\u003c/em\u003e, CYP3A4*1B and CYP3A5*3 alleles carriers [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. Hence, CYP450 genetic variants may not influence artemether treatment outcome.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eChloroquine\u003c/h2\u003e\n\u003cp\u003eThe metabolism of Chloroquine, a 4-aminoquinoline (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), is mediated mainly by CYP2C8 and to a lesser degree by CYP3A4 and CYP2D6 to an active metabolite (N-desethylchloroquine) in addition to other minor metabolites [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. The frequencies of the CYP2C8 variants in different populations are depicted in Table\u0026nbsp;1 [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. In malaria treatment/prophylaxis studies with chloroquine, a 2.5- to 5.6-fold interindividual variability in CQ concentrations has been reported and this may affect treatment outcome. A study has shown that in chloroquine/primaquine treated patients, there is a relationship between CYP2C8 allele variants and gametocytemia and parasitemia clearance rates. Wild-type individuals achieved a greater reduction of gametocytes than low-activity alleles of CYP2C8 (i.e., *2, *3, and *4) carriers. The results suggested that CYP2C8, CYP2C9 and CYP23A5 genetic variants may influence chloroquine pharmacokinetics thereby affecting treatment outcome [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eChlorproguanil and proguanil\u003c/h2\u003e\n\u003cp\u003eThe biguanide derivatives, chlorproguanil and proguanil (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) are bioactivated to chlorcycloguanil and cycloguanil, respectively, by CYP2C19 and, to a lesser extent, by CYP3A4 [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. The frequency of the CYP2C19 alleles varies considerably among different ethnic populations (Table\u0026nbsp;1). The two variant alleles, CYP2C19*2 and CYP2C19*3, are largely associated with the PM phenotype while CYP2C19*17 allele results in increased CYP2C19 expression and activity. The CYP2C19*2 is the most common CYP2C19 variant among the various populations, while CYP2C19*3 allele frequencies in most populations is below 1% but more prevalent among Asians [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Although a study on malaria prophylaxis with proguanil in Tanzanians showed that treatment failure was associated with decreased metabolism of the drug in PM phenotypes, other studies demonstrated that the therapeutic efficacy of proguanil was comparable between the PM and EM patients even when the extent of metabolism differed significantly between the two groups [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. Further studies are required to clearly understand the clinical significance of CYP2C19 polymorphism in the efficacy of proguanil/cycloguanil.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eLumefantrine\u003c/h2\u003e\n\u003cp\u003eLumefantrine which belongs to the group of arylamine alcohols like halofantrine (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), is used together with artemether in artemisinin-based combination therapy (ACT). This highly lipophilic drug is primarily metabolized by CYP3A4/CYP3A5 to a metabolite, desbutyl-lumefantrine which has a significantly higher antimalarial activity than the parent drug, (IC\u003csub\u003e50\u003c/sub\u003e 4\u0026ndash;5-fold higher) [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. Both CYP3A4 and 3A5 have overlapping substrate specificities. The CYP3A5 gene has three major alleles that have been well studied (CYP3A5*3, CYP3A5*6 and CYP3A5*7) and they are associated with slow metabolizer phenotypes. Their frequencies vary considerably among different ethnic populations (Table\u0026nbsp;1) [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. The most studied variants of CYP3A4 that occur in high frequencies include, CYP3A4*1B, CYP3A4*1G, and CYP3A4*22 (Table\u0026nbsp;1). Several studies have investigated whether the CYP3A4 variants result in significant differences in blood levels of lumefantrine. These studies demonstrated that the major CYP3A4 and CYP3A5 gene variants showed no relationship with pharmacokinetic profiles of lumefantrine or treatment outcomes [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. On the other hand, another study reported that CYP3A5*3 genetic variant was associated with only a high maximum plasma concentration of lumefantrine [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. Thus, further investigations are required to clarify the association between CYP3A5*3 gene variants, lumefantrine pharmacokinetics and therapeutic efficacy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eMefloquine\u003c/h2\u003e\n\u003cp\u003eMefloquine, a 4-quinoline derivative (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), is metabolised by CYP3A4/CYP3A5 to two major pharmacologically inactive metabolites, carboxymefloquine and hydroxymefloquine [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. The variants of CYP3A4 and CYP3A5 genes and the frequencies of occurrence in different ethnic populations are presented in Table\u0026nbsp;1. Pharmacogenetic studies of mefloquine are mostly on genetic polymorphisms of the major genes encoding drug efflux transporters - ABCB1, ABCG2, and ABCC1. While no significant association was found between the ABCG2 and ABCC1 gene polymorphisms and mefloquine treatment outcome, patients carrying the ABCB1 (TT) variant were found to have a three-times greater chance of successful treatment compared with other genotypes (CC and CT). Thus, predicting responses to artesunate-mefloquine treatment can be based on using ABCB1 polymorphisms as useful genetic markers [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003ePrimaquine\u003c/h2\u003e\n\u003cp\u003ePrimaquine, an 8-aminoquinoline (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) has been an antimalarial drug of choice in the treatment of \u003cem\u003ePlasmodium vivax\u003c/em\u003e and \u003cem\u003ePlasmodium ovale\u003c/em\u003e hypnozoites, and for malaria prophylaxis. It is metabolized principally by CYP2D6 isoenzyme to active metabolites, the mainly 5-hydroxy derivative of primaquine, that are responsible for the pharmacological effect of primaquine [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. CYP2D6 gene is highly polymorphic with over 90 known allelic variants, demonstrating significant inter-individual and inter-ethnic differences in its activity (Table\u0026nbsp;1) [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. Some of these CYP2D6 variants with a prevalence of \u0026gt;\u0026thinsp;1% in different ethnic groups are shown in Table\u0026nbsp;1 Most of them are PM except CYP2D6*1xN and CYP2D6*2xN variants that are Ultra-rapid metabolizer phenotypes (Table\u0026nbsp;1), Recent studies have shown an association of polymorphisms related to CYP2D6 activity and treatment outcome with primaquine. PM phenotypes of CYP2D6 variants have been shown to lead to therapeutic failure with primaquine [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003ePiperaquine\u003c/h2\u003e\n\u003cp\u003ePiperaquine, a bisquinoline (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), had been extensively used as a monotherapy but it is now commonly used as a partner drug with dihydroartemisinin in the ACT. Piperaquine is metabolized primarily by CYP3A4 and CYP3A5 into two major metabolites, piperaquine \u003cem\u003eN\u003c/em\u003e-oxide and piperaquine \u003cem\u003eN\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e-dioxide, and both metabolites are biologically active contributing to the efficacy of piperaquine [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]. The variants of CYP3A4 and CYP3A5 genes and their occurrence frequencies are shown in Table\u0026nbsp;1. There is a paucity of studies on the pharmacogenetics of piperaquine. In studying the effect of SNPs in Cytochrome P450 isoenzyme genes on the metabolism of Artemisinin-Based Combination therapies, genetic variants were found to have no significant effect on piperaquine elimination [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003ePyronaridine\u003c/h2\u003e\n\u003cp\u003ePyronaridine, benzonaphthyridine derivative (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e, had previously been used in the treatment of malaria as a single agent, but it is currently used in combination with artesunate, representing a second-generation ACT. The metabolism of pyronaridine is mediated by multiple and varied pathways and studies with recombinant human CYP450 isoforms indicated that pyronaridine could be metabolised by CYP1A2, CYP2D6 and CYP3A4, generating nine metabolites [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. The literature is deficient in studies on the investigation of a possible association of genes of CYP450 isoforms and pharmacokinetics or therapeutic efficacy of pyronaridine. Pyronaridine has a low metabolic turnover, thus pharmacogenetic studies of the drug might not be of significant value [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. Pyronaridine is a P-glycoprotein substrate and may exhibit variable oral absorption based on its significant P-gp-mediated efflux [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. It has been postulated that due to the importance of this transport protein in the processes of oral absorption for a range of susceptible drugs, the polymorphism of P-gp gene might be able to affect the oral absorption of pyronaridine\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003eQuinine\u003c/h2\u003e\n\u003cp\u003eQuinine is metabolised by CYP3A4/3A5 to its main metabolite, 3-hydroxyquinine, which contributes up to 10% of the antimalarial activity of the parent compound [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. In vitro study on the effects of different CYP3A4 allelic variants on intrinsic clearance towards quinine revealed that most of the variants had significantly reduced activity while 2 variants showed increased activity and 2 other variants had no significant differences in quinine metabolism, compared with a wild-type allele, CYP3A4*1A [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. These results suggest that if these \u003cem\u003ein vitro\u003c/em\u003e findings are replicated \u003cem\u003ein vivo\u003c/em\u003e, patients that are carriers of these alleles with reduced intrinsic clearance of quinine may potentially be CYP3A4 poor metabolizers and may therefore be more prone to adverse reactions of the quinine and also require lower doses of the drug to achieve therapeutic efficacy. Pharmacogenetic studies in Tanzanian and Ugandan populations demonstrated a significant influence of a few CYP3A5 variant alleles (CYP3A5*3,*4,*6, *7, and *9) on quinine metabolism [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. Considering that quinine is an antimalarial drug with a narrow therapeutic window and concentration-dependent serious adverse reactions, these findings suggest that CYP3A4/5 allelic variants with PM phenotypes can be associated with adverse reactions of antimalarial therapy with quinine.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003eTafenoquine\u003c/h2\u003e\n\u003cp\u003eTafenoquine, a new long half-life 8-aminoquinoline drug (a primaquine analogue) was approved for use in the chemoprophylaxis and treatment of malaria. Similar to primaquine, the metabolism of tafenoquine is principally mediated by CYP2D6 but clinical studies have not identified an association of CYP2D6 polymorphism with tafenoquine efficacy [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]. Thus, the efficacy did not appear to be reduced in poor or intermediate metabolizers of CYP2D6\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003eClinical Implication and Challenges of Pharmacogenomics Testing\u003c/h2\u003e\n\u003cp\u003eThe integration of pharmacogenetic testing into clinical practice has been evolving over the years, Currently, the Clinical Pharmacogenetics Implementation Consortium (CPIC) and other related pharmacogenomics research organizations have provided specific pharmacogenetic guidelines on how to use pharmacogenetic information relating to some drugs. Pharmacogenetics/pharmacogenomics testing is required especially for some classes of drugs that are metabolized by one or more variant alleles of the Cytochrome P450 enzymes, and drugs with a narrow therapeutic window [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. Interpretation of pharmacogenetics test results for pro-drugs is different from those of active parent drugs. For example, while poor metabolizers of active parent drugs have high plasma drug levels that may result in toxicity, the same phenotype taking a pro-drug will have a therapeutic failure because its metabolite which is the active moiety is not produced. A typical example is primaquine where CYP2D6 PM phenotypes have treatment failures. The corollary is that while ultrarapid metabolizers of active parent drugs may have reduced drug response due to rapid drug clearance, the same phenotype taking a pro-drug will manifest increased drug response following efficient generation of the metabolites which have therapeutic activity [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. There are currently about 400 medicines with FDA-approved information on pharmacogenetic product labels but only three antimalarial drugs (chloroquine, primaquine and quinine) are currently contained in the list [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]. However, PharmGKB has Drug label annotations containing pharmacogenetic information for over 800 drugs which include most of the antimalarial drugs such as lumefantrine, artesunate, chloproguanil, dapsone, tefanoquine, artemether, proguanil, and mefloquine (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.pgrn.org/pharmgkb.html\u003c/span\u003e\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eMany challenges have been encountered in the implementation of pharmacogenetics in a clinical setting. Some of these challenges include: (a) Most healthcare providers do not have a clear understanding of the applicability of pharmacogenetic tests. Drug dosage adjustment based on non-genetic parameters like age, body weight, and hepatic and renal function tests is well understood and practiced. Some resources have addressed this lacuna and these include the Clinical Pharmacogenetics Implementation Consortium (CPIC; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cpicpgx.org/\u003c/span\u003e\u003c/span\u003e) Other resources are: My Drug Genome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mydruggenome.org\u003c/span\u003e\u003c/span\u003e); IGNITE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gmkb.org\u003c/span\u003e\u003c/span\u003e); GTR (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gtr\u003c/span\u003e\u003c/span\u003e); eMERGE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.emerge-network.org\u003c/span\u003e\u003c/span\u003e) and Coursera (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.coursera.org/learn/personalizedmed\u003c/span\u003e\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e(b) Even when the clinical utility of pharmacogenetics tests is appreciated, some clinicians can encounter difficulties in the interpretation of the pharmacogenetic test results to apply them to patient care. Again, there are several resources available for the relevant knowledge and necessary information [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e(c) The cost of the genetic tests is another challenge as only a few genetic tests are paid for by health insurance companies. The additional financial burden on the patient by bearing the cost of the test constitutes an impediment. However, with proper education, the stakeholders would realize that the benefits of pharmacogenetic testing outweigh the cost of the test [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e(d) Several technical issues are involved in SNP genotyping process before the results can be translated into clinical practice. These include a long duration of time that may be required in the experimental procedure for SNP validation, determination of the most applicable SNPs panels, investigating the correlation that exists between an SNP and enzyme activity, sorting out several low-risk polymorphisms, taking into account that the distribution and frequency of SNPs differ among different ethnic groups and this makes it problematic to apply the findings of one ethnic group to another group.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eInter-individual drug responses due to genetic factors (Pharmacogenomics) constitute a major factor that influences the efficacy and safety of drugs in general. Variability in the genes that encode for the major CYP450 isoforms that mediate the metabolism of antimalarial drugs, contribute significantly to inter-individual drug response. Most healthcare providers are more familiar with the application of non-genetic variables in dosage adjustment strategies but they do not have a clear understanding of the applicability of pharmacogenetic tests to improve therapeutic drug response. However, several resources are freely available to be used in bridging that knowledge gap. Therefore, the introduction of individual SNP genotyping into clinical settings has the potential of providing relevant information regarding dosage adjustment of antimalarial drugs toward achieving optimal drug response\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;artemisinin-based combination therapy\u003c/p\u003e\n\u003cp\u003eCPIC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Clinical Pharmacogenetics Implementation Consortium\u003c/p\u003e\n\u003cp\u003eCQ\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;chloroquine\u003c/p\u003e\n\u003cp\u003eCYP450\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;cytochrome P450\u003c/p\u003e\n\u003cp\u003eDEAQ\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;desethylamodiaquine\u003c/p\u003e\n\u003cp\u003eDHA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;dihydroartemisinin\u003c/p\u003e\n\u003cp\u003eEM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Extensive Metabolizer\u003c/p\u003e\n\u003cp\u003eIM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Intermediate Metabolizer\u003c/p\u003e\n\u003cp\u003ePGRN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Pharmacogenomics Research Network\u003c/p\u003e\n\u003cp\u003ePM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Poor Metabolizer\u003c/p\u003e\n\u003cp\u003eSNPs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Single Nucleotide Polymorphisms\u003c/p\u003e\n\u003cp\u003eULR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;unstructured literature review\u003c/p\u003e\n\u003cp\u003eUM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ultra-rapid Metabolizer\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors read and approved the manuscript for publication\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no relevant conflicts of interest or financial relationships.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: COO; Data curation: CON; Formal analysis: JOS; Funding acquisition: COO, JOS and CON; Investigation: JOS; Methodology: COO; Project administration: CON; Resources: COO; Software: CON; Supervision: COO; Validation: JOS; Visualization: JOS; Original draft: CON; Review \u0026amp; editing: COO and JOS.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. 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Genetic and non-genetic factors are known to influence the pharmacokinetics and/or pharmacodynamics of drugs including antimalarial drugs resulting in variability in drug responses. This article aimed to update perspectives on pharmacogenomics and also provide an updated appraisal of genetic variability in drug-metabolizing enzymes which alter the disposition of antimalarial drugs causing variations in treatment outcomes. Important literature databases such as Elsevier, IEEExplore, Pubmed, Scopus, Web of Science, Google Scholar, ProQuest, ScienceDirect, and BioMed Central were selected based on the quality, extant content, and broad area of the discipline. The specific keywords related to the study were identified and used for the study purposedly to identify related works. Advances in genetic research have facilitated the identification of Single Nucleotide Polymorphisms (SNPs) that alter the activity of drug-metabolizing enzymes that metabolize most antimalarial drugs. There is an association between isoforms of CYP450 gene variants and the efficacy of some antimalarial drugs, and this can be applied to the optimization of malarial therapy. Although identification of cytochrome P450 (CYP450) gene variants can be used for personalization of malaria treatment, several challenges are encountered in this process but some resources provide education and guidelines on how to use the pharmacogenetic results of specific drugs.\u003c/p\u003e","manuscriptTitle":"Insights and Current Perspectives on Pharmacogenomics of Antimalarial Drugs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-08 16:50:19","doi":"10.21203/rs.3.rs-2030964/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":"90973aad-4db3-468d-96dd-0b625c8c9e46","owner":[],"postedDate":"September 8th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-02-21T16:14:26+00:00","versionOfRecord":[],"versionCreatedAt":"2022-09-08 16:50:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2030964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2030964","identity":"rs-2030964","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}