Genetic Variations In Drug Metabolizing Enzymes Research Articles

Pharmacogenomics: New Personalized Medicine Approach

Ineffective treatments and the management of adverse drug reactions are responsible for a large proportion of health resources. Drug response and toxicity are significantly influenced by genetic variations in drug-metabolizing enzymes, transporters, and targets. This information may be used to help in selecting the appropriate drug and dosage. One of the newest methods of precision medicine is pharmacogenomics, which adjusts drug selection and dosage based on a patient's genetic characteristics. Pharmacogenomics can reduce the chances of negative effects of the drugs and increase the chances of successful treatment, as the medicines are only for the targeted individuals. It can also revolutionize the healthcare industry to be more specific.
 The main reason that drugs and their dosages are designed based on individuals’ genetic characteristics is that they have a great influence on drug metabolism and its response. The main evidence is the link between dosage requirements and genetic differences in drug transporters such as p-glycoprotein (ABCB1) and OATP-C (SLC21A6), and drug-metabolizing enzymes such as cytochrome P450 (CYP) 2D6, CYP2C19, and CYP2C9. Polymorphisms in these enzymes can alter the drug metabolism phenotype. As these are all proteins translated from genetic information, any change or point mutation can increase or decrease the effectiveness of the enzymes. This can cause more adverse effects in some individuals. Every individual’s body function is unique, and its right to say that the dosage of drugs depends on that.
 Many pharmacogenomic tests and their outcomes have confirmed the potential to improve therapeutic activities. In the field of oncology, the use of this technique to establish a connection between drug metabolism and genetic biomarkers is more reliable, effective, and precise. PD-L1 expression analysis is an example of pharmacogenomics potential. Expression analysis helps physicians select which cancer patients are suitable for immunotherapy techniques. Survival rates for lung and other cancers have drastically increased by comparing the genetic factors and immunotherapies that trigger the immune response against tumors. Similarly, pharmacogenomics has promising potential in antiviral drugs. It targets the genes that influence such drugs to predict treatment success. Therefore, as our understanding of the genome becomes clearer, the potential of pharmacogenomics will also grow.
 Although this is a very promising technique and many scientific organizations are pursuing this approach, there has been very little success in implementing it in clinical trials. Targeted drugs are very costly, as they require the identification of candidate genes and drug responses to these genes. Acceptance is another matter to focus on as most people are not comfortable with these approaches; they prefer conventional approaches. Implementation, cost, acceptance, defining targeted genes, and defining drug responses are all barriers to pharmacogenomics right now. Physicians and patients must adapt to avoid the side effects of drugs by implementing a personalized medicine approach.
 Despite growing interest, the adoption of pharmacogenomics is still in its early stages of development. Attempts to fully apply pharmacogenomics to enhance health will likely take decades. It is important to find a balance between equality concerns across varied populations and increased healthcare efficiency to successfully implement pharmacogenomics. This can be done more effectively through coordinated studies and worldwide collaboration. This not only makes it possible to better utilize resources and knowledge but also sparks political interest in and support for the personalized medicine approach.

Novel approaches to characterize individual drug metabolism and advance precision medicine.

Interindividual variability in drug metabolism can significantly affect drug concentrations in the body and subsequent drug response. Understanding an individual's drug metabolism capacity is important for predicting drug exposure and developing precision medicine strategies. The goal of precision medicine is to individualize drug treatment for patients to maximize efficacy and minimize drug toxicity. While advances in pharmacogenomics have improved our understanding of how genetic variations in drug-metabolizing enzymes (DMEs) affect drug response, non-genetic factors are also known to influence drug metabolism phenotypes. This minireview will discuss approaches beyond pharmacogenetic testing to phenotype DMEs- particularly the cytochrome P450 enzymes- in clinical settings. Several phenotyping approaches have been proposed: traditional approaches include phenotyping with exogenous probe substrates and the use of endogenous biomarkers; newer approaches include evaluating circulating non-coding RNAs (ncRNAs) and liquid biopsy-derived markers relevant to DME expression and function. The goals of this minireview are to: 1) provide a high-level overview of traditional and novel approaches to phenotype individual drug metabolism capacity; 2) describe how these approaches are being applied or can be applied to pharmacokinetic studies; and 3) discuss perspectives on future opportunities to advance precision medicine in diverse populations. Significance Statement This minireview provides an overview of recent advances in approaches to characterize individual drug metabolism phenotypes in clinical settings. Herein, we highlight the integration of existing pharmacokinetic biomarkers with novel approaches; also discussed are current challenges and existing knowledge gaps. The article concludes with perspectives on the future deployment of a liquid biopsy-informed physiologically based pharmacokinetic (PBPK) strategy for patient characterization and precision dosing.

Synergistic polymorphic interactions of phase II metabolizing genes and their association toward lung cancer susceptibility in North Indians

ABSTRACT Lung cancer is a multifactorial carcinoma with diverse heterogeneity. Genetic variations in drug-metabolizing enzymes may lead to defective detoxification and clearance of carcinogenic compounds. The high-order gene-gene interaction has been carried out between different genotypes of Phase II detoxification genes (NQO1, SULT1A1, NAT2, and EPHX1). Our results depict the genetic combination of SULT1A1 R 213 H with NAT2 × 5B L 161 L, SULT1A1 R 213 H with NAT2 × 5C K 268 R, EPHX1 H 139 R and NAT2 × 5B L 161 L exhibit a protective effect towards lung cancer risk. Further, the triple combinations of NQO1 P 187 S, EPHX1 Y 113 H, and EPHX1 H 139 R; NQO1 P 187 S, EPHX1 Y 113 H, and NAT2 × 6 R 197 Q; NQO1 P 187 S, EPHX1 Y 113 H, and NAT2 × 7 G 286 E; SULT1A1 R 213 H, EPHX1 H 139 R, and NAT2 × 7 G 286 E suggested a two-fold increased risk of lung cancer for subjects. Genetic polymorphisms of phase II detoxifying genes (NAT2, NQO1, EPHX1, SULT1A1) are prognostic markers for lung cancer.

Effect of Genetic Variations in Drug-Metabolizing Enzymes and Drug Transporters on the Pharmacokinetics of Rifamycins: A Systematic Review.

BackgroundRifamycins are a novel class of antibiotics clinically approved for tuberculosis chemotherapy. They are characterized by high inter-individual variation in pharmacokinetics. This systematic review aims to present the contribution of genetic variations in drug-metabolizing enzymes and transporter proteins to the inter-individual variation of rifamycin pharmacokinetics.MethodWe followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines. The search for relevant studies was done through PubMed, Embase, Web of Science, and Scopus databases. Studies reporting single nucleotide polymorphism in drug transporters and metabolizing enzymes’ influence on rifamycin pharmacokinetics were solely included. Two reviewers independently performed data extraction.ResultsThe search identified 117 articles of which 15 fulfilled the eligibility criteria and were included in the final data synthesis. The single nucleotides polymorphism in the drug transporters SLCO1B1 rs4149032, rs2306283, rs11045819, and ABCB1 rs1045642 for rifampicin, drug metabolizing enzyme AADAC rs1803155 for rifapentine and CES2 c.-22263A>G (g.738A>G) for rifampicin partly contributes to the variability of pharmacokinetic parameters in tuberculosis patients.ConclusionThe pharmacokinetics of rifamycins is influenced by genetic variation of drug-metabolizing enzymes and transporters. Controlled clinical studies are, however, required to establish these relationships.

A Population Pharmacokinetic-Pharmacogenetic Model of Lamotrigine in Chinese Children With Epilepsy.

The pharmacokinetics of lamotrigine (LTG) is complex and varies significantly among individuals, especially among children. Therefore, this study aimed to establish a population pharmacokinetic (PPK) model of LTG in Chinese children with epilepsy and to comprehensively evaluate the effects of genetic variations in drug-metabolizing enzymes, transporters, and a transcriptional regulator on LTG pharmacokinetics. Three hundred eighty-five steady-state plasma concentrations were obtained from 179 children (age 10.72 ± 3.05 years and body weight 46.23 ± 17.77 kg) with epilepsy during therapeutic drug monitoring. These patients were divided into the PPK-model group (n = 121) and the PPK-validation group (n = 58) and were genotyped for UGT1A4, UGT2B7, ABCB1, ABCG2, SLC22A1, and HNF4α. PPK analysis was performed by nonlinear mixed effects modeling. In the final model, apparent clearance (CL/F) of LTG was estimated to be 1.48 L/h; 500 mg valproic acid, oxcarbazepine, and UGT2B7-161TT genotype changed the CL/F by -46.2, +31.1, and -21.8%, respectively. Body weight was also identified as a significant covariate affecting LTG CL/F. A PPK-pharmacogenetic model of LTG in Chinese children with epilepsy was successfully established with nonlinear mixed effects modeling. Genotyping for UGT2B7-161C>T may be useful in titrating the optimal LTG dose.

Poster Number: NR 31 - Combinatorial Pharmacogenomics Reduces Polypharmacy and Medication Cost in Elderly Patients with Anxiety and Depression

Introduction: The frequency of adverse drug reactions (ADRs) and therapeutic failures are often increased in elderly patients. Individual genetic variations in drug-metabolizing enzymes and neurotransmitter transporters and receptors add to the heterogeneity of treatment response and safety. Selection of genetically concordant medications is not only important for proper efficacy and avoiding ADRs, but also affects polypharmacy, the number of prescriptions needed to treat psychiatric and associated comorbid conditions, and therefore also impacts healthcare costs.

Investigating the impact of missense mutations in hCES1 by in silico structure-based approaches.

Genetic variations in drug-metabolizing enzymes have been reported to influence pharmacokinetics, drug dosage, and other aspects that affect therapeutic outcomes. Most particularly, non-synonymous single-nucleotide polymorphisms (nsSNPs) resulting in amino acid changes disrupt potential functional sites responsible for protein activity, structure, or stability, which can account for individual susceptibility to disease and drug response. Investigating the impact of nsSNPs at a protein's structural level is a key step in understanding the relationship between genetic variants and the resulting phenotypic changes. For this purpose, in silico structure-based approaches have proven their relevance in providing an atomic-level description of the underlying mechanisms. The present review focuses on nsSNPs in human carboxylesterase 1 (hCES1), an enzyme involved in drug metabolism. We highlight how prioritization of functional nsSNPs through computational prediction techniques in combination with structure-based approaches, namely molecular docking and molecular dynamics simulations, is a powerful tool in providing insight into the underlying molecular mechanisms of nsSNPs phenotypic effects at microscopic level. Examples of in silico studies of carboxylesterases (CESs) are discussed, ranging from exploring the effect of mutations on enzyme activity to predicting the metabolism of new hCES1 substrates as well as to guiding rational design of CES-selective inhibitors.

A wide pharmacogenomic panel from a single oncology center in São Paulo: Data from 103 patients since 2005.

e22050 Background: Pharmacogenomics has recently emerged as a major tool in the clinical analysis of cancer treatment efficacy. Understanding how mutations alter drug metabolism is crucial for both prognosis and choosing the most likely beneficial drug for each individual patient. Methods: 103 patients' files were studied and the most common malignancies were breast's (43 patients) and colorectal's (19 pts). The median age was 55 years (IQR 46-65), 32 (31%) were men. We performed a retrospective analysis of the pharmacogenetic-assay results and clinical files, which were recorded between 2005 and 2013. The goal of the study was to correlate the mutagenic profile of cancer patients, by identifying mutations in genes responsible for drug metabolism and DNA repair. Results: All distributions were in Hardy Weinberg, except for UGT1A1. The UGT1A1 analysis revealed that 14/51 patients were at increased risk of neutropenia during treatment with irinotecan due to mutations in that gene. Analysis of mutations affecting fluoropyrimidine metabolism, which upregulate both TS and DPD2A genes, yielded the following results: TS-TYMS mutations in 40/73 pts; MTHFR (55/ 89), and DPD2A (1/74), which were associated with higher toxicity in those patients. As for genes encoding DNA repair proteins such as XRCC1 Arg194, XRCC1 Arg 399 , XRCC3, XRCC7, 52/54 patients had mutations in this gene family. In the study of COMT Val, responsible for the proliferative potential and the carcinogenic effect, mutations were found in 30/45 patients. At least one mutation was found in 48/61 patients in genes encoding DNA-repair and cell detoxification enzymes, such as GSTM1, GSTT1 and GSTP1. As for the CYP2D6 family (variants 3,4,5,6,10), responsible for Tamoxifen metabolism, 12/31 patients presented at least one mutation. Conclusions: Most patients showed genetic variations in drug-metabolizing enzymes. Pharmacogenomics has become a useful tool in the selection of the best drug for each individual patient, thereby decreasing the probability of poor clinical responses and allowing a better control of side effects.

Humanized Transgenic Mouse Models for Drug Metabolism and Pharmacokinetic Research

Extrapolation of the metabolic, pharmacokinetic and toxicological data obtained from animals to humans is not always straightforward, given the remarkable species difference in drug metabolism that is due in large part to the differences in drug-metabolizing enzymes between animals and humans. Furthermore, genetic variations in drug-metabolizing enzymes may significantly alter pharmacokinetics, drug efficacy and safety. Thus, humanized transgenic mouse lines, in which the human drug-metabolizing enzymes are expressed in mouse tissues in the presence or absence of mouse orthologues, have been developed to address such challenges. These humanized transgenic mice are valuable animal models in understanding the significance of specific human drug-metabolizing enzymes in drug clearance and pharmacokinetics, as well as in predicting potential drug-drug interactions and chemical toxicity in humans. This review, therefore, aims to summarize the development and application of some humanized transgenic mouse models expressing human drug-metabolizing enzymes. The limitations of these genetically modified mouse models are also discussed.

Opening the Gate for Genomics Data into Clinical Research: A Use Case in Managing Patients‘ DNA Samples from the Bench to Drug Development

The use of human genetic polymorphism data in drug development is not a recent event. Typically, the detection of patients' genetic variations in drug-metabolizing enzymes has become common practice in clinical laboratories. What is new is the scale and diversity of genomics data that has entered into the drug research and development decision-making process. At least three concurrent events contribute to this paradigm shift: first the growing body of evidence that establishes that interindividual variation in both therapeutic response and adverse events are attributable to a genetic component; second the technological progress that enables the consistent and reproducible detection of human genomic quantities; third the expectation that the productivity of new drug development will be increased by identifying which patients would benefit from candidate therapies early in the clinical process. This influx of human genomics data into clinical laboratories requires some logistical adjustment in terms of data management. The major specifications of an information solution system intended for a clinical genomic laboratory are its compliance with regulatory procedures, regarding the handling of human genetic data and its subsequent integration into an existing clinical data management system from the hosting institution. The purpose of this article is to inform the community of the challenges in setting up a center for genomics data that ensures accurate, traceable and integrated data for laboratory management. This is by no means the only way to accomplish the same goal, and is simply presented as one way that Pfizer chose to solve these issues.

Gene Polymorphisms and Pharmacogenetics in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a systemic, chronic and inflammatory disease of unknown etiology with genetic predisposition. The advent of new biological agents, as well as the more traditional disease-modifying antirheumatic drugs, has resulted in highly efficient therapies for reducing the symptoms and signs of RA; however, not all patients show the same level of response in disease progression to these therapies. These variations suggest that RA patients may have different genetic regulatory mechanisms. The extensive polymorphisms revealed in non-coding gene-regulatory regions in the immune system, as well as genetic variations in drug-metabolizing enzymes, suggest that this type of variation is of functional and evolutionary importance and may provide clues for developing new therapeutic strategies. Pharmacogenetics is a rapidly advancing area of research that holds the promise that therapies will soon be tailored to an individual patient’s genetic profile.

ABCB1 (P-Glycoprotein/MDR1) Gene G2677T/A Sequence Variation (Polymorphism): Lack of Association with Side Effects and Therapeutic Response in Depressed Inpatients Treated with Amitriptyline

Amitriptyline belongs to the class of tricyclic antidepressants (TCAs), which have been a cornerstone of antidepressive therapy for more than 4 decades. Despite being replaced by newer drugs in the United States, TCAs, and amitriptyline in particular, are still widely used in Europe and many parts of the world, where prescriptions for TCAs far outnumber those for newer, more expensive drugs. Recently, we reported significant correlations between sequence variations in the genes that encode the metabolizing enzymes cytochrome P450 2D6 (CYP2D6) and 2C19 (CYP2C19), serum amitriptyline/nortriptyline concentrations, and adverse drug reactions in a population of 50 depressed inpatients (1)(2). However, genetic variations in drug-metabolizing enzymes do not sufficiently explain the wide interindividual variations commonly observed in drug response and clinical outcome. Obviously, other genetic and environmental factors must be considered as well. The influence of sequence variations in the gene ATP-binding cassette, subfamily B (MDR/TAP), member 1 ( ABCB1 ) on the disposition …

Genetic Influences on Smoking

Smoking is a complex behavior influenced by environ-mental and genetic factors. A number of twin studies demonstrate genetic influences on multiple aspects of smoking including initiation, dependence, continuation, amount consumed, and cessation. It is likely that this genetic risk involves a complex interaction between multiple genes in different biologic pathways. These may include genes involved in the general aspect of dependence (eg, genes within the dopaminergic system) and those more specific to nicotine dependence (eg, genes encoding nicotinic acetylcholine receptors and nicotine-metabolizing enzymes). Genome-wide linkage analyses and gene association studies provide approaches for identifying these genes. This review provides a brief highlight of data from some of the candidate gene association studies and discusses some of the discrepancies found in the literature. In addition, the authors have reviewed some recent studies examining how genetic variations in drug-metabolizing enzymes and drug targets can influence the response to treatment with drugs such as bupropion and nicotine.These latter studies provide the potential for maximizing treatment efficacy by individualizing the type, dosage, and duration of the treatment based on an individual's genotype/phenotype. Significant advances have been made in understanding the genetic contribution to smoking behaviors. These and future studies will provide potential directions for novel prevention and treatment strategies.

Pharmacogenetics of therapies in rheumatoid arthritis

Rheumatoid arthritis (RA) is an inflammatory, aggressive arthritis causing irreversible joint destruction and damage when left untreated. Disease-modifying antirheumatic drugs (DMARDs) are the mainstay of treatment for RA and ameliorate not only the clinical signs and symptoms but also joint damage associated with the disease. In recent years, biological therapies have been introduced for the treatment of RA, and the effectiveness of these agents in slowing the clinical and radiographic progression in RA has been established beyond question. However, there is significant variability in the response of patients with RA to these therapies. Moreover, the biological therapies are expensive, totaling several thousand dollars in yearly patient costs. Pharmacogenomics, the study of genetic variations in drug-metabolizing enzymes and their translation to differential responses to drugs, is a nascent but rapidly evolving field. The application of pharmacogenomics to therapies used in RA, particularly the new expensive biological agents, holds great promise for tailoring therapy with these agents based on a patient's genetics. Published literature on the pharmacogenetics of commonly used DMARDs and the emerging body of literature on the pharmacogenetics of the new biological therapies in RA are the focus of this review. As evident from the contents of this review, pharmacogenomics is an exciting field which is progressing productively and rapidly. Pharmacogenomic approaches offer powerful tools to optimize drug therapy in individual patients.

Simultaneous genotyping of seven single-nucleotide polymorphisms in the MDR1 gene by single-tube multiplex minisequencing.

Responses to different drugs can vary widely among different individuals as a result of genetic variations in drug-metabolizing enzymes, transporters, receptors, and/or other cofactors. The multidrug resistance 1 (MDR1) transporter, a well-characterized member of the ATP-binding cassette superfamily, was shown to efflux a wide variety of structurally and functionally unrelated drugs, including anticancer, antiarrhythmic, antidepressant, antipsychotic, and antiviral agents. The pharmacogenetics of the MDR1 multidrug transporter have recently received much scientific attention. Several single-nucleotide polymorphisms (SNPs) have been identified in the MDR1 gene; some occur only in specific ethnic groups, whereas others occur in all ethnic groups but at significantly different allele frequencies among the different races [see Ref. (1) and references therein]. Nonetheless, the functional significance of these SNPs remains unclear. Various functional associations, some paradoxical, have been observed between the synonymous SNP (exon 26 3435C→T) and MDR1 protein expression and plasma drug concentrations (2)(3)(4)(5), drug-induced side effects (6), and drug response (7). The SNP exon 26 3435T allele has been associated with lower MDR1 expression in the duodenum (2), leukocytes (5), and placental tissues (8), leading to lower rhodamine efflux (5) and increased plasma digoxin concentrations (2). In addition, early-onset Parkinson patients have higher frequency of the SNP exon 26 3435T allele compared with late-onset patients or unaffected controls (9). However, although this same allele has been associated with lower MDR1 expression in peripheral blood mononuclear cells and better response to HIV-1 drugs, it has also been associated with lower plasma concentrations of nelfinavir (7). Additionally, the SNP exon 26 3435T allele has been associated with an increased risk of nortriptyline-induced postural hypertension, although blood concentrations of nortriptyline in these individuals were not significantly different from those in individuals carrying the C allele (6). Furthermore, no association has been demonstrated between the …