CYP2C19 Genetic Polymorphisms in Children and Young Adults with Cancer: A Cross-sectional Study

BACKGROUND:In epilepsy, in spite of the best possible medications and treatment protocols, approximately one-third of the patients do not respond adequately to anti-epileptic drugs. Such interindividual variations in drug response are believed to result from genetic variations in candidate genes belonging to multiple pathways.MATERIALS AND METHODS:In the present pharmacogenetic analysis, a total of 402 epilepsy patients were enrolled. Of them, 128 were diagnosed as multiple drug-resistant epilepsy and 274 patients were diagnosed as having drug-responsive epilepsy. We selected a total of 10 candidate gene polymorphisms belonging to three major classes, namely drug transporters, drug metabolizers and drug targets. These genetic polymorphism included CYP2C9 c.430C>T (*2 variant), CYP2C9 c.1075 A>C (*3 variant), ABCB1 c.3435C>T, ABCB1c.1236C>T, ABCB1c.2677G>T/A, SCN1A c.3184 A> G, SCN2A c.56G>A (p.R19K), GABRA1c.IVS11 + 15 A>G and GABRG2 c.588C>T. Genotyping was performed using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methods, and each genotype was confirmed via direct DNA sequencing. The relationship between various genetic polymorphisms and responsiveness was examined using binary logistic regression by SPSS statistical analysis software.RESULTS:CYP2C9 c.1075 A>C polymorphism showed a marginal significant difference between drug resistance and drug-responsive patients for the AC genotype (Odds ratio [OR] = 0.57, 95% confidence interval [CI] = 0.32–1.00; P = 0.05). In drug transporter, ABCB1c.2677G>T/A polymorphism, allele A was associated with drug-resistant phenotype in epilepsy patients (P = 0.03, OR = 0.31, 95% CI = 0.10-0.93). Similarly, the variant allele frequency of SCN2A c.56 G>A single nucleotide polymorphism was significantly higher in drug-resistant patients (P = 0.03; OR = 1.62, 95% CI = 1.03, 2.56). We also observed a significant difference at the genotype as well as allele frequencies of GABRA1c.IVS11 + 15 A > G polymorphism in drug-resistant patients for homozygous GG genotype (P = 0.03, OR = 1.84, 95% CI = 1.05–3.23) and G allele (P = 0.02, OR = 1.43, 95% CI = 1.05–1.95).CONCLUSIONS:Our results showed that pharmacogenetic variants have important roles in epilepsy at different levels. It may be noted that multi-factorial diseases like epilepsy are also regulated by various other factors that may also be considered in the future.

Interindividual variability in drug response depends on a number of genetic and environmental factors. The metabolic enzymes are well known for their contribution to this variability due to drug-drug interactions and genetic polymorphisms. The phase I drug metabolism is highly dependent upon the cytochrome P450 mono-oxygenases (CYP) and their genetic polymorphism leads to the variable internal drug exposures. The highly polymorphic CYP2C9, CYP2C19 and CYP2D6 isozymes are responsible for metabolizing a large portion of routinely prescribed drugs and contribute significantly to adverse drug reactions and therapeutic failures. In this review, two attractive and easily implementable approaches are highlighted to recommend drug doses ensuring similar internal exposures in the face of these polymorphisms. The first approach relies on subpopulation-based dose recommendations that consider the original population dose as an average of the doses recommended in genetically polymorphic subpopulations. By using bioequivalence principles and assuming linear gene-dose effect, dose recommendations can be made for different metabolic phenotypes. The second approach relates area under the curve to two characteristic parameters; the contribution ratio (CR), computes for the contribution of the metabolic enzyme and the fractional activity (FA), considers the impact of the genetic polymorphism. This approach provides valid and error free internal drug exposure predictions and can take into consideration genetic polymorphisms and drug interactions and/ or both simultaneously. Despite certain advantages and limitations, both approaches provide a good initial frame-work for devising models to predict internal exposure and individualize drug therapy, one of the promises from human genome project.

The aim of the study was to establish the frequencies of CYP2C9*1, *2, *3 and CYP2C19*1, *2 and *3 in the south Indian population and to compare them with the inter-racial distribution of the CYP2C9 and CYP2C19 genetic polymorphisms. Genotyping analyses of CYP2C9 and CYP2C19 were conducted in unrelated, healthy volunteers from the three south Indian states of Andhra Pradesh, Karnataka and Kerala, by the polymerase chain reaction-restriction fragment-length polymorphism (PCR-RFLP). The allele frequencies of the populations of these three states were then pooled with our previous genotyping data of Tamilians (also in south India), to arrive at the distribution of CYP2C9 and CYP2C19 alleles in the south Indian population. Frequencies of CYP2C9 and CYP2C19 alleles and genotypes among various populations were compared using the two-tailed Fisher's exact test. The frequencies of CYP2C9*1, *2 and *3 in the south Indian population were 0.88 (95% CI 0.85-0.91), 0.04 (95% CI 0.02-0.06) and 0.08 (95% CI 0.06-0.11), respectively. The frequencies of CYP2C9 genotypes *1/*1, *1/*2, *1/*3, *2/*2, *2/*3 and *3/*3 were 0.78 (95% CI 0.74-0.82), 0.05 (95% CI 0.03-0.07), 0.15 (95% CI 0.12-0.18), 0.01 (95% CI 0.0-0.02), 0.01 (95% CI 0.0-0.02) and 0.0, respectively. CYP2C19*1, *2 and *3 frequencies were 0.64 (95% CI 0.60-0.68), 0.35 (95% CI 0.31-0.39) and 0.01 (95% CI 0.0-0.03), respectively. As a result of a significant heterogeneity, the data on CYP2C19 genotype frequencies were not pooled. The frequency of CYP2C9*2 mutant alleles in south Indians was higher than in Chinese and Caucasians, while CYP2C9*3 was similar to Caucasians. CYP2C19*2 was higher than in other major populations reported so far. The relatively high CYP2C19 poor-metabolizer genotype frequency of 12.6% indicates that over 28 million south Indians are poor metabolizers of CYP2C19 substrates.

Atypical antipsychotic agents such as aripiprazole, clozapine, olanzapine, quetiapine and ziprasidone offer many advantages over conventional neuroleptics. These agents reduce negative symptoms of schizophrenia, are effective in treatment refractory cases, and have a markedly lower incidence of extrapyramidal symptoms and tardive dyskinesia. However, there is considerable patient-to-patient variability in therapeutic dose requirements of atypical antipsychotics and the propensity for side effects. Hence, the initial excitement since the introduction of atypical antipsychotics in late 1980s is now shifting towards a focus on individualization of pharmacotherapy and elucidation of the mechanistic basis of interindividual variability in drug response with use of pharmacokinetic and pharmacodynamic biomarkers. Pharmacogenomics, introduced in late 1990s, is the study of variability in drug response using information from the entire genome of a given individual patient. Both pharmacogenomics and conventional therapeutic drug monitoring (TDM) share the similar goal of improving pharmacotherapy through better explanation of individual variability in drug response. Hence, pharmacogenomic biomarkers offer a unique opportunity to complement and expand the scope of traditional TDM in clinical psychopharmacology. Importantly, pharmacogenomics enables the investigation of factors distal to drug exposure in the plasma compartment (e.g. drug targets at the biophase), thereby providing a more complete portrayal of sources of variability in psychotropic drug response. We discuss (1). the definitions for biomarkers and surrogate endpoints in the context of pharmacogenomics, (2). genetic variations in isozyme-specific atypical antipsychotic metabolism in vivo, (3). selected examples of pharmacogenomic variability in pertinent drug targets and, (4). the anticipated roadmap from implementation of pharmacogenomics to changes in healthcare and therapeutic policy. In addition, a conceptual framework that outlines the theoretical advantages of pharmacogenomics-guided TDM is presented using recent clinical applications as precedence.

Marked Interindividual Variability in the Response to Selective Inhibitors of Cyclooxygenase-2

Inter-individual variability in drug response is well known. Genetic polymorphism in genes encoding drug-metabolizing enzymes results in variation in drug metabolism and in turn drug response. The cytochrome P450 enzymes (CYP) play a central role in the metabolism of many therapeutic agents. CYP2C19 gene polymorphism is widely studied in Caucasians, African, and Oriental populations; however, far less is known about other ethnic groups such as Indians. Indian population is an inter-mixture of the Aryan, Dravidian, Kolarain, and the Mongoloid races. CYP2C19 gene polymorphism is reported in North Indian and South Indian populations yet not much is known about Maharashtrian population of Australoid-Europoid origin residing in Western India. This is the first report on CYP2C19 allele and genotype frequencies in Maharashtrian population. In this study, genotypes of major allelic variants of CYP2C19 gene in 139 unrelated healthy Maharashtrian subjects was determined and their frequencies were compared with previously studied Indian and other populations. Meta-analysis revealed that the study population is distinct from Caucasians, Africans and some of the Asian populations and significant heterogeneity exists among Indian subpopulations.

Genetic polymorphism of CYP2C19 in Chinese ethnic populations

Statins are among the most prescribed drugs worldwide to reduce the risk of cardiovascular events. Interindividual variability in drug response is a major clinical problem and is of concern during drug development. Statins, such as atorvastatin, are taken orally and access to their site of action in the liver is greatly facilitated by both intestinal and hepatic transporters. To examine the impact of polymorphisms of the multidrug resistance 1(MDR1) and solute carrier organic anion transporter 1B1 (SLCO1B1) genes on the therapeutic response to atorvastatin as well as the presence of gender-gene interaction. Serum lipid levels were determined at baseline and 4weeks following 40mg/day atorvastatin treatment in 50 Egyptian hypercholesterolemic patients (27 males and 23 females). Identification of MDR1 C3435T and SLCO1B1 A388G gene polymorphisms was performed using a polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Treatment with atorvastatin resulted in a mean reduction of total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), and triglyceride (TG) of 8.7%, 9.2%, and 4.1%, respectively, and a mean increase of high density lipoprotein cholesterol (HDL-C) of 1%. Baseline and post-treatment HDL-C levels were statistically significantly higher in the MDR 1 TT homozygotes when compared with the CC wild type. The percentage change in TC, LDL-C, TG, and HDL-C did not show any statistically significant difference when compared among the different MDR 1 C3435T or SLCO1B1 A388G genotypes. The SLCO1B1 GG homozygotes showed a decrease in TG, whereas there was an increase in TG following atorvastatin treatment in AA and AG carriers in females; however, males did not show any statistically significant difference. There was no statistically significant association between either the coronary artery disease (CAD) risk factors (family history of CAD, hypertension, diabetes mellitus, smoking) or concomitant medications with the percentage change in different lipid parameters. MDR1 C3435T was associated with baseline and post-treatment HDL-C variation. SLCO1B1 A388G showed gender-related effects on TG change following atorvastatin treatment. None of the comorbidities or the concomitant medications influenced the percentage change of lipid parameters following atorvastatin treatment. The results of this study may lead to an improved understanding of the genetic determinants of lipid response to atorvastatin treatment.

Corticosteroids, such as prednisone or dexamethasone, constitute integral components of antineoplastic regimens for Acute Lymphoblastic Leukemia (ALL) therapy, albeit accompanied by significant adverse effects. The multifactorial nature of interindividual variability in drug response, encompassing genetic polymorphisms, underscores the complexity of pharmacotherapy outcomes. However, pharmacogenetic investigations hitherto have predominantly focused on cohorts of European and North American descent, thus limiting the generalizability of findings to populations with minimal representation. Indigenous populations in Brazil, particularly those inhabiting the Amazon region, exhibit a distinctive genetic heritage, predominantly characterized by Native American ancestry. These populations frequently manifest suboptimal therapeutic responses and elevated mortality rates following ALL treatment. Therefore, delineating the molecular signatures of genes implicated in the corticosteroid pathway within these indigenous cohorts assumes paramount importance. This study identified novel variants within genes associated with the glucocorticoid pathway in indigenous Amazonian populations and conducted comparative analyses of variant frequencies across diverse global populations. The findings underscore the genetic uniqueness of indigenous groups and highlight the potential impact of genetic factors on adverse responses to ALL treatment. Precision medicine approaches tailored to the genetic peculiarities of indigenous populations emerge as imperative strategies for optimizing therapeutic efficacy and mitigating treatment-related toxicities in these communities.

Introduction: Arterial thrombosis is considered a multifactorial disease, resulting from the interaction of genetic and acquired risk factors. Objectives: The aim of this study was to investigate the presence of the polymorphism in inhibitor of plasminogen activator type 1 (PAI-1) and apolipoprotein E (ApoE) genes and its interactions with PAI-1 levels and lipids and apolipoprotein profiles, respectively, as well as the frequencies of these polymorphisms and their association with thrombosis. Methods: Ninety-seven patients [48 with arterial ischemic stroke (IS) and 49 with peripheral arterial disease (PAD)], treated at the hematology medical service were included in this study. Polymorphisms were also investigated in 201 control subjects. Polymorphisms were investigated by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Results: For the PAI-1 polymorphism, there were 54.2% heterozygous (HT) genotypes and 12.5% homozygous (HM) genotypes in the patients' group, and 52.7% HT genotypes and 21.3% HM genotypes in the controls. For the ApoE polymorphism, there were 56.3% (e3e3), 6.3% (e4e4), 8.3% (e2e3), 4.2% (e2e4) and 24.9% (e3e4) in the patients, and 61.2% (e3e3), 4.5% (e4e4), 8% (e2e3), 4.5% (e2e4) and 21.8% (e3e4) in the controls. Conclusion: No significant difference was observed by comparing patients and controls. In this study, no association was found between the presence of the evaluated polymorphisms and the occurrence of thrombotic events.

The interindividual variability in drug response is a major issue in clinical practice and in drug development. Sulfoconjugation is an important Phase II reaction catalyzed by cytosolic sulfotransferases (SULTs), playing a major role in homeostatic functions, xenobiotic detoxification, and carcinogen bioactivation. SULT display wide interindividual variability, explained only partially by genetic variation, suggesting that other non-genetic, epigenetic, and environmental influences could be major determinants of variability in SULT activity. This review focuses on the factors known to influence SULT variability in expression and activity and the available evidence regarding the impact of SULT variability on drug response.

The epigenetic regulation of expression of genes involved in the absorption, distribution, metabolism, and excretion (ADME) of drugs contributes to interindividual variability in drug response. Epigenetic mechanisms include DNA methylation, histone modifications, and miRNAs. This review systematically outlines the influence of DNA methylation on ADME gene expression and highlights the consequences for interindividual variability in drug response or drug-induced toxicity and the implications for personalized medicine.

The use of pharmacogenetic guidelines in personalizing treatments has shown the potential to reduce interindividual variability in drug response by enabling genotype-matched dosing and drug selection. However, other important factors, such as patient gender, may interact strongly with pharmacogenetics in determining the individual profile of toxicity and efficacy but are still rarely considered when planning pharmacological treatment. The literature indicates that males and females respond differently to drugs, with women being at higher risk for toxicity and having different plasma exposure to drugs at standard doses. Recent studies have shown that pharmacogenetic variants may have different predictive value in different sexes, as in the case of treatment with opioids, angiotensin-converting enzyme inhibitors, or proton pump inhibitors. Of particular interest is the case of treatment with fluoropyrimidines for cancer. A significant increase in toxicity has been described in female patients, with a more pronounced effect of specific DPYD and TYMS polymorphisms also noted. This manuscript reviews the major findings in the field of sex-specific pharmacogenomics. SIGNIFICANCE STATEMENT: Interindividual variability in drug response is an emerging issue in pharmacology. The genetic profile of patients, as well as their gender, may play a role in the identification of patients more exposed to the risk of adverse drug reactions or poor efficacy. This article reviews the current state of research on the interaction between gender and pharmacogenetics in addressing interindividual variability.

Biological variation is ubiquitous in nature. Despite highly standardized breeding and husbandry under controlled environmental conditions, phenotypic diversity exists in laboratory mice and rats just as it does in humans. The resulting inter-individual variability affects various characteristics of animal disease models, including the responsiveness to drugs. Thus, the common practice of averaging data within an experimental group can lead to misinterpretations in neuroscience and other research fields. In this commentary, the impact of inter-individual variation in drug responsiveness is illustrated by examples from the testing of antiseizure medications in rodent temporal lobe epilepsy models. Individual mice and rats rendered epileptic by treatment according to standardized protocols fall into groups that either do or do not respond to antiseizure medications, thus mimicking the clinical situation in patients with epilepsy. Population responses are not normally distributed, and divergent responding is concealed in averages subjected to parametric statistical tests. Genetic, epigenetic, and environmental factors are believed to contribute to inter-individual variation in drug response but the specific molecular and physiological causes are not well understood. Being aware of inter-individual variability in rodents allows an improved interpretation of both behavioral phenotypes and drug effects in a pharmacological experiment.

Epigenomics is a rapidly growing field. New developments in epigenetics, such as the recently described modified cytosine variants (e.g., 5-hydroxymethylcytosine, 5hmC) and an arsenal of novel noncoding forms of RNA, can be applied in the area of drug pharmacokinetics and pharmacodynamics. Epigenetic aberrations can affect drug treatment by modulating the expressions of key genes involved in the metabolism and distribution of drugs as well as drug targets, thereby contributing to interindividual variation in drug response. These epigenetic alterations, along with the epigenetic profiles of circulating nucleic acids, have great potential to be used as biomarkers for personalized therapy, particularly in the treatment of cancer. In this review we present an update of pharmacoepigenetics with respect to epigenetic regulation of ADME genes (genes related to drug absorption, distribution, metabolism, and excretion) and drug targets, and we illustrate how this information can be used for predicting interindividual variations in drug response.