Drug Metabolizing Enzymes And Transporters Research Articles

Ultrasensitive Quantification of Drug-metabolizing Enzymes and Transporters in Small Sample Volume by Microflow LC-MS/MS.

Protein abundance data of drug-metabolizing enzymes and transporters (DMETs) are broadly applicable to the characterization of in vitro and in vivo models, in vitro to in vivo extrapolation (IVIVE), and interindividual variability prediction. However, the emerging need of DMET quantification in small sample volumes such as organ-on a chip effluent, organoids, and biopsies requires ultrasensitive protein quantification methods. We present an ultrasensitive method that relies on an optimized sample preparation approach involving acetone precipitation coupled with a microflow-based liquid chromatography-tandem mass spectrometry (µLC-MS/MS) for the DMET quantification using limited sample volume or protein concentration, i.e., liver tissues (1-100 mg), hepatocyte counts (~4000 to 1 million cells), and microsomal protein concentration (0.01-1 mg/ml). The method was applied to quantify DMETs in differential tissue S9 fractions (liver, intestine, kidney, lung, and heart) and cryopreserved human intestinal mucosa (i.e., CHIM). The method successfully quantified >75% of the target DMETs in the trypsin digests of 1 mg tissue homogenate, 15,000 hepatocytes, and 0.06 mg/ml microsomal protein concentration. The precision of DMET quantification measured as the coefficient of variation across different tissue weights, cell counts, or microsomal protein concentration was within 30%. The method confirmed significant extrahepatic abundance of non-cytochrome P450 enzymes such as dihydropyridine dehydrogenase (DPYD), epoxide hydrolases (EPXs), arylacetamide deacetylase(AADAC), paraoxonases (PONs), and glutathione S-transferases (GSTs). The ultrasensitive method developed here is applicable to characterize emerging miniaturized in vitro models and small volume biopsies. In addition, the differential tissue abundance data of the understudied DMETs will be important for physiologically-based pharmacokinetic (PBPK) modeling of drugs.

Multiple microRNAs regulate tacrolimus metabolism through CYP3A5

The CYP3A5 gene polymorphism accounts for the majority of inter-individual variability in tacrolimus pharmacokinetics. We found that the basal expression of CYP3A5 in donor grafts also played a significant role in tacrolimus metabolism under the same genetic conditions after pediatric liver transplantation. Thus, we hypothesized that some potential epigenetic factors could affect CYP3A5 expression and contributed to the variability. We used a high-throughput functional screening for miRNAs to identify miRNAs that had the most abundant expression in normal human liver and could regulate tacrolimus metabolism in HepaRG cells and HepLPCs. Four of these miRNAs (miR-29a-3p, miR-99a-5p, miR-532-5p, and miR-26-5p) were selected for testing. We found that these miRNAs inhibited tacrolimus metabolism that was dependent on CYP3A5. Putative miRNAs targeting key drug-metabolizing enzymes and transporters (DMETs) were selected using an in silico prediction algorithm. Luciferase reporter assays and functional studies showed that miR-26b-5p inhibited tacrolimus metabolism by directly regulating CYP3A5, while miR-29a-5p, miR-99a-5p, and miR-532-5p targeted HNF4α, NR1I3, and NR1I2, respectively, in turn regulating the downstream expression of CYP3A5; the corresponding target gene siRNAs markedly abolished the effects caused by miRNA inhibitors. Also, the expression of miR-29a-3p, miR-99a-5p, miR-532-5p, and miR-26b-5p in donor grafts were negatively correlated with tacrolimus C/D following pediatric liver transplantation. Taken together, our findings identify these miRNAs as novel regulators of tacrolimus metabolism.

Utility of Quantitative Proteomics for Enhancing the Predictive Ability of Physiologically Based Pharmacokinetic Models Across Disease States.

Disease states such as liver cirrhosis and chronic kidney disease can lead to altered pharmacokinetics (PK) of drugs by influencing drug absorption, blood flow to organs, plasma protein binding, apparent volume of distribution, and drug-metabolizing enzyme and transporter (DMET) abundance. Narrow therapeutic index drugs are particularly vulnerable to undesired pharmacodynamics (PD) because of the changes in drug PK in disease states. However, systematic clinical evaluation of disease effect on drug PK and PD is not always possible because of the complexity or the cost of clinical studies. Physiologically based PK (PBPK) modeling is emerging as an alternate method to extrapolate drug PK from the healthy population to disease states. These models require information on the effect of disease condition on the activity or tissue abundance of DMET proteins. Although immunoquantification-based abundance data were available in the literature for a limited number of DMET proteins, the emergence of mass spectrometry-based quantitative proteomics as a sensitive, robust, and high-throughput tool has allowed a rapid increase in data availability on tissue DMET abundance in healthy versus disease states, especially in liver tissue. Here, we summarize these data including the available immunoquantification or mRNA levels of DMET proteins (healthy vs disease states) in extrahepatic tissue and discuss the potential applications of DMET abundance data in enhancing the capability of PBPK modeling in predicting drug disposition across disease states. Successful examples of PBPK modeling that integrate differences in DMET proteins between healthy and disease states are discussed.

Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS)-Based Proteomics of Drug-Metabolizing Enzymes and Transporters.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics is a powerful tool for identifying and quantifying proteins in biological samples, outperforming conventional antibody-based methods in many aspects. LC-MS/MS-based proteomics studies have revealed the protein abundances of many drug-metabolizing enzymes and transporters (DMETs) in tissues relevant to drug metabolism and disposition. Previous studies have consistently demonstrated marked interindividual variability in DMET protein expression, suggesting that varied DMET function is an important contributing factor for interindividual variability in pharmacokinetics (PK) and pharmacodynamics (PD) of medications. Moreover, differential DMET expression profiles were observed across different species and in vitro models. Therefore, caution must be exercised when extrapolating animal and in vitro DMET proteomics findings to humans. In recent years, DMET proteomics has been increasingly utilized for the development of physiologically based pharmacokinetic models, and DMET proteins have also been proposed as biomarkers for prediction of the PK and PD of the corresponding substrate drugs. In sum, despite the existence of many challenges in the analytical technology and data analysis methods of LC-MS/MS-based proteomics, DMET proteomics holds great potential to advance our understanding of PK behavior at the individual level and to optimize treatment regimens via the DMET protein biomarker-guided precision pharmacotherapy.

Impact of Microbiome on Hepatic Metabolizing Enzymes and Transporters in Mice during Pregnancy.

The microbiome and pregnancy are known to alter drug disposition, yet the interplay of the two physiologic factors on the expression and/or activity of drug metabolizing enzymes and transporters (DMETs) is unknown. This study investigated the effects of microbiome on host hepatic DMETs in mice during pregnancy by comparing four groups of conventional (CV) and germ-free (GF) female mice and pregnancy status, namely, CV nonpregnant, GF non-pregnant, CV pregnant, and GF pregnant mice. Transcriptomic and targeted proteomics of hepatic DMETs were profiled by using multiomics. Plasma bile acid and steroid hormone levels were quantified by liquid chromatography tandem mass spectrometry. CYP3A activities were measured by mouse liver microsome incubations. The trend of pregnancy-induced changes in the expression or activity of hepatic DMETs in CV and GF mice was similar; however, the magnitude of change was noticeably different. For certain DMETs, pregnancy status had paradoxical effects on mRNA and protein expression in both CV and GF mice. For instance, the mRNA levels of Cyp3a11, the murine homolog of human CYP3A4, were decreased by 1.7-fold and 3.3-fold by pregnancy in CV and GF mice, respectively; however, the protein levels of CYP3A11 were increased similarly ∼twofold by pregnancy in both CV and GF mice. Microsome incubations revealed a marked induction of CYP3A activity by pregnancy that was 10-fold greater in CV mice than that in GF mice. This is the first study to show that the microbiome can alter the expression and/or activity of hepatic DMETs in pregnancy. SIGNIFICANCE STATEMENT: We demonstrated for the first time that microbiome and pregnancy can interplay to alter the expression and/or activity of hepatic drug metabolizing enzymes and transporters. Though the trend of pregnancy-induced changes in the expression or activity of hepatic drug metabolizing enzymes and transporters in conventional and germ-free mice was similar, the magnitude of change was noticeably different.

Assessment of the clinical utility of pharmacogenetic guidance in a comprehensive medication management service.

Pharmacists are poised to be the health care professionals best suited to provide medication-related consults and services based on a patient's genetics. Despite its potential benefits, the implementation of pharmacogenetic (PGx) testing into primary clinical settings has been slow among medically underserved populations. To our knowledge, this is the first time that PGx-driven recommendations have been incorporated into a Comprehensive Medication Management (CMM) service in a Hispanic population. The aim of this study is to evaluate the clinical utility of adding PGx guidance into pharmacist-driven CMM. This is a pre- and post-interventional design study. Patients were recruited from a psychologist's clinic. A total of 24 patients had a face-to-face interview with a pharmacist to complete a CMM, Personal Medication Record, and Medication-Related Action Plan (MAP) blind to PGx findings. Collected buccal DNA samples were genotyped using drug-metabolizing enzymes and transporters (DMET) Plus Array. The pharmacist generated new MAPs for each patient based on PGx results. Genetic variants that could potentially affect the safety and effectiveness of at least one drug in the pharmacotherapy were identified in 96% of patients, for whom the pharmacist changed the initial recommendations. Polymorphisms in genes encoding for isoenzymes CYP2D6, CYP2C19, and CYP2C9 were identified in 83%, 52%, and 41% of patients, respectively. Pharmacists performing CMM identified 22 additional medication problems after PGx determinations. Moreover, they agreed with the clinical utility of PGx in the studied sample based on perceived value of adding PGx to traditional CMM and its utility in the decision-making process of pharmacists. The study confirmed the critical role to be played by pharmacists in facilitating the clinical usage of relevant genetic information to optimize drug therapy decisions as well as their involvement on many levels of these multidisciplinary implementation efforts, including championing and leading PGx-guided CMM services.

Genetic and clinical prediction models for the efficacy and hepatotoxicity of methotrexate in patients with rheumatoid arthritis: a multicenter cohort study.

The objective of the study is to develop genetic and clinical prediction models for the efficacy and hepatotoxicity of methotrexate (MTX) in patients with rheumatoid arthritis (RA). Among RA patients treated with MTX, 1966 polymorphisms of 246 enzymes/transporters relevant to pharmacokinetics and pharmacodynamics were measured by the Drug Metabolism Enzymes and Transporters (DMET) microarray and direct sequencing, and clinical variables at baseline were collected. For efficacy, response criteria of the European League Against Rheumatism were used to classify patients as responders or non-responders. Hepatotoxicity was defined as elevations of aspartate aminotransferase or alanine aminotransferase ≥1.5 times the reference range upper limit. Among 166 patients, a genetic prediction model for efficacy using seven polymorphisms showed the area under the receiver operating characteristic curve (AUC) was 0.822, with 74.3% sensitivity and 76.8% specificity. A combined genetic and clinical model indicated the AUC was 0.844, with 81.5% sensitivity and 76.9% specificity. By incorporating clinical variables into the genetic model, the overall category-free net reclassification improvement (NRI) was 0.663 (P < 0.0001) and the overall integrated discrimination improvement (IDI) was 0.083 (P = 0.0009). For hepatotoxicity, a genetic prediction model using seven polymorphisms showed the AUC was 0.783 with 70.0% sensitivity and 80.0% specificity, while the combined model indicated the AUC was 0.906 with 85.1% sensitivity and 87.8% specificity (overall category-free NRI: 1.002, P < 0.0001; overall IDI: 0.254, P < 0.0001). Our genetic and clinical models demonstrated moderate diagnostic accuracy for MTX efficacy and high accuracy for hepatotoxicity. These findings should, however, be validated and interpreted with a caution until external validation.

A repository of protein abundance data of drug metabolizing enzymes and transporters for applications in physiologically based pharmacokinetic (PBPK) modelling and simulation

Population factors such as age, gender, ethnicity, genotype and disease state can cause inter-individual variability in pharmacokinetic (PK) profile of drugs. Primarily, this variability arises from differences in abundance of drug metabolizing enzymes and transporters (DMET) among individuals and/or groups. Hence, availability of compiled data on abundance of DMET proteins in different populations can be useful for developing physiologically based pharmacokinetic (PBPK) models. The latter are routinely employed for prediction of PK profiles and drug interactions during drug development and in case of special populations, where clinical studies either are not feasible or have ethical concerns. Therefore, the main aim of this work was to develop a repository of literature-reported DMET abundance data in various human tissues, which included compilation of information on sample size, technique(s) involved, and the demographic factors. The collation of literature reported data revealed high inter-laboratory variability in abundance of DMET proteins. We carried out unbiased meta-analysis to obtain weighted mean and percent coefficient of variation (%CV) values. The obtained %CV values were then integrated into a PBPK model to highlight the variability in drug PK in healthy adults, taking lamotrigine as a model drug. The validated PBPK model was extrapolated to predict PK of lamotrigine in paediatric and hepatic impaired populations. This study thus exemplifies importance of the DMET protein abundance database, and use of determined values of weighted mean and %CV after meta-analysis in PBPK modelling for the prediction of PK of drugs in healthy and special populations.

Deciphering DMET genetic data: comprehensive assessment of Northwestern Han, Tibetan, Uyghur populations and their comparison to eleven 1000 genome populations

We investigated the allele frequencies of drug absorption, distribution, metabolism and elimination (ADME)-related drug-metabolizing enzymes and transporters (DMET) genes in the Northwestern Han, Tibetan and Uyghur populations and compared the related genes in these three populations with those in eleven 1000 Genome populations. We examined 1936 single nucleotide polymorphisms of 225 DMET genes involved in ADME processes and found 732, 679 and 804 sites were polymorphic in Han, Tibetan and Uyghur. Tibetan differed from Han in only four sites (p < .05), whereas Uyghur differed from Han and Tibetan in 24 and 21 sites, respectively (p < .05). The distributions of 1058 genotyping data of 245 individuals from Han, Tibetan and Uyghur were compared with 1207 other individuals from the eleven 1000 Genomes populations. The top four populations in Han that exhibited the smallest pairwise Fst values were CHB, Tibetan, CHD and JPT; those in Tibetan were Han, CHB, Uyghur and CHD; and those in Uyghur were Han, Tibetan, GIH and CEU. MEGA results revealed that CHB, CHD, JPT, Han, Tibetan and Uyghur were grouped in cluster 1. GIH, MEX, CEU and TSI were grouped in cluster 2. MKK, ASW, LWK and YRI were grouped in cluster 3.

Utility of Integrated Analysis of Pharmacogenomics and Pharmacometabolomics in Early Phase Clinical Trial: A Case Study of a New Molecular Entity.

In this report, we present a case study of how pharmacogenomics and pharmacometabolomics can be useful to characterize safety and pharmacokinetic profiles in early phase new drug development clinical trials. During conducting a first-in-human trial for a new molecular entity, we were able to determine the mechanism of dichotomized variability in plasma drug concentrations, which appeared closely related to adverse drug reactions (ADRs) through integrated omics analysis. The pharmacogenomics screening was performed from whole blood samples using the Affymetrix DMET (Drug-Metabolizing Enzymes and Transporters) Plus microarray, and confirmation of genetic variants was performed using real-time polymerase chain reaction. Metabolomics profiling was performed from plasma samples using liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. A GSTM1 null polymorphism was identified in pharmacogenomics test and the drug concentrations was higher in GSTM1 null subjects than GSTM1 functional subjects. The apparent drug clearance was 13-fold lower in GSTM1 null subjects than GSTM1 functional subjects (p < 0.001). By metabolomics analysis, we identified that the study drug was metabolized by cysteinylglycine conjugation in GSTM functional subjects but those not in GSTM1 null subjects. The incidence rate and the severity of ADRs were higher in the GSTM1 null subjects than the GSTM1 functional subjects. Through the integrated omics analysis, we could understand the mechanism of inter-individual variability in drug exposure and in adverse response. In conclusion, integrated multi-omics analysis can be useful for elucidating the various characteristics of new drug candidates in early phase clinical trials.

A Comprehensive Analysis of Gene Expression of Xenobiotic and Endogenous Metabolizing Enzymes and Transporters in Rat Multiple Organs

Drug metabolizing enzymes and transporters (DMETs) play crucial roles in drug absorption and disposition. Species differences in the interaction of compounds with DMETs may contribute to the accuracy of animal models in predicting human responses in clinical studies. Thus it is important to clarify the expression heterogeneity of DMETs between human and rat, that is commonly used as a model for evaluating drug efficacy and drug safety. We compared the expression patterns of DMETs based on a rat RNA-seq dataset and the human Genotype-Tissue Expression (GTEx) datasets. A relatively high correlation of expression of DMETs between rat and human was observed in most organs, while a lower correlation was detected in the liver and kidney; however, a greater number of genes were variably expressed in the latter two organs. We characterized the basal expression traits of DMETs in rat in terms of organ, sex, and developmental differences. Co-expressed modules across organs of DMETs were identified to include potential functionally- related genes. Interestingly, most of these modules showed liver- and/or kidney-specific expression. Moreover, we identified DMETs modules that were highly correlated to sex or developmental stages. Finally, we created networks containing sex and/or developmentally-related drugs and diseases with their related DMETs to display the clinical significance of sexually dimorphic and/or developmentally- specific DMET genes. Our study provides a deeper understanding of species differences in not only DMETs but specific susceptibility to adverse drug reactions (ADRs).

UGT1A1 polymorphisms associated with prolactin response in risperidone-treated children and adolescents with autism spectrum disorder.

The aim of this study was to investigate the association of drug-metabolizing enzyme and transporter (DMET) polymorphisms with the risperidone-induced prolactin response using an overlapping gene model between serum prolactin level and hyperprolactinemia in autism spectrum disorder (ASD) patients. Eighty-four ASD patients who were receiving risperidone for at least 1 month were recruited and then assigned to either the normal prolactin group or the hyperprolactinemia group based on their serum prolactin level. The genotype profile of 1936 (1931 single nucleotide polymorphisms (SNPs) and 5 copy number variation (CNVs) drug metabolism markers was obtained using the Affymetrix DMET Plus GeneChip microarray platform. Genotypes of SNPs used to test the accuracy of DMET genotype profiling were determined using TaqMan SNP Genotyping Assay kits. Eighty-four patients were selected for the allelic association study after microarray analyses (51 in the normal prolactin group, and 33 in the hyperprolactinemia group). An overlapping allelic association analysis of both analyses discovered five DMET SNPs with a suggestive association (P < 0.05) with risperidone-induced prolactin response. Three UGT1A1 SNPs (UGT1A1*80c.-364C > T, UGT1A1*93 c.-3156G > A, and UGT1A1 c.-2950A > G, showed a suggestive association with the risperidone-induced prolactin response and found to be in complete linkage disequilibrium (D' value of 1). In this DMET microarray platform, we found three UGT1A1 variants with suggestive evidences of association with the risperidone-induced prolactin response both measured by hyperprolactinemia and by prolactin level. However, due to the lack of validation studies confirmation and further exploration are needed in future pharmacogenomic studies.

Parallel extraction of association rules from genomics data

High-throughput experimental platforms like microarrays produce massive amounts of omics data for each analyzed sample. As an example, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform can discover Single Nucleotide Polymorphisms (SNPs) from 225 human genes involved in absorption, distribution, metabolism, and excretion (ADME) of drugs, enabling large pharmacogenomics studies. Moreover, the application of such platforms to large populations of subjects is further increasing the size of experimental datasets produced in clinical studies. Thus, the production of big omics datasets is a first reason to use parallel computing in bioinformatics. Such omics datasets are usually analyzed with classical statistical analysis and, more recently, by using data mining methods that can extract knowledge hidden in the data, e.g. by highlighting multiple associations among features of the data. However, the use of standard off-the-shelf data mining algorithms to large omic datasets, especially when considering association rule mining, poses two main issues: (i) huge requests of central memory that may prevent the execution of data mining software on personal/desktop computers; and (ii) very long response time, that may increase the time requested for completing extensive pharmacogenomics studies. To overcome the limits of standard association rule mining algorithms when applied to omics datasets, we propose PARES (Parallel Association Rules Extractor from SNPs), a novel parallel algorithm for the efficient extraction of association rules from omics datasets. PARES is implemented as a multi-thread version of an optimized version of the Frequent Pattern Growth (FP-Growth) algorithm. Moreover, it includes a customized SNPs datasets preprocessing strategy based on a Fisher’s Test Filter to discard the trivial transactions from the input dataset, reducing the search space from which to build many independent FP-Trees. The experimental results show that PARES has a good speedup and a high memory management efficiency, with respect to several association rule mining algorithms implemented in main off-the-shelf data mining platforms.

Drug metabolizing enzyme and transporter genes associated with plasma risperidone level in Thai autism spectrum disorder

BackgroundThe associations between genetic variants of drug metabolizing enzyme and transporter (DMET) genes and steady-state plasma concentrations of risperidone, 9-hydroxyrisperidone, total active-moiety, and metabolic ratio remain unclear.ObjectiveThe objective of the present study was to present the results of the association between genetic variants of DMET gene and steady-state plasma concentration risperidone and its metabolite using Affymetrix DMET Plus genotyping microarray.MethodsSubjects eligible for this study included male and female adolescents with ASD diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria and being treated with risperidone for at least 4 weeks prior to the blood sample collection. Blood samples were drawn prior to the next dose of risperidone intake to determine the steady-state plasma trough concentrations of risperidone and 9-hydroxyrisperidone. Genotyping profile was obtained using the microarray. Steady-state plasma risperidone and 9-hydroxyrisperidone were measured using liquid chromatography/tandem mass spectrometry (LC-MS/MS) assay.ResultsThe polymorphisms of UGT2B4, CYP2D6 were highly associated with metabolic ratio. Of all the DMET analysis, ABCB11 (3084A &gt; G, 420A &gt; G, 368G &gt; A, and 236G &gt; A) and ADH7 (690G &gt; A and –5360G &gt; A) were found to be associated with plasma concentrations of risperidone (P &lt; 0.01). In addition, 6 genetic variations among the SLC transporter family were associated with the plasma concentration of 9-hydroxyrisperidone.DiscussionsThis study provides a pharmacogenomic approach to investigate further among the DMET genetic variants which influence plasma concentration of risperidone. The treatment of ASD should be based on genetic factors making the challenge of psychopharmacological treatment more efficacious with lesser adverse events.Disclosure of interestThe author has not supplied his/her declaration of competing interest.

A novel ABCC6 haplotype is associated with azathioprine drug response in myasthenia gravis.

We investigated the association of single nucleotide polymorphisms (SNPs) in drug-metabolizing enzymes and transporters (DMETs) with the response to azathioprine (AZA) in patients affected by myasthenia gravis (MG) to determine possible genotype-phenotype correlations. Genomic DNA from 180 AZA-treated MG patients was screened through the Affymetrix DMET platform, which characterizes 1931 SNPs in 225 genes. The significant SNPs, identified to be involved in AZA response, were subsequently validated by allelic discrimination and direct sequencing. SNP analysis was carried out using the SNPassoc R package and the haploblocks were determined using haploview software. We studied 127 patients in the discovery phase and 53 patients in the validation phase. We showed that two SNPs (rs8058694 and rs8058696) found in ATP-binding cassette subfamily C member 6, a subfamily member of ATP-binding cassette genes, constituted a new haplotype associated with AZA response in MG patients in the discovery cohort (P=0.011; odds ratio: 0.40; 95% confidence interval: 0.20-0.83) and in the combined cohort (P=0.04; odds ratio: 1.58). These findings highlight the role that the ATP-binding cassette subfamily C member 6 haplotype may play in AZA drug response. In view of the significant effects and AZA intolerance, these novel SNPs should be taken into consideration in pharmacogenetic profiling for AZA.

Epigenome-Wide Association (DNA Methylation) Study of Sex Differences in Normal Human Kidney

Studies have identified epigenetic sex differences in several human tissues and have implicated epigenetic factors in the regulation of tissue-specific expression. Studies have also shown that women and men respond differently to various drugs, thereby influencing the pharmacokinetics, pharmacodynamics, adverse reactions, efficacy, and safety of a drug. Using Illumina Human Methylation450 BeadChip kit, we investigated the influence of sex on DNA methylation patterns in normal human kidneys (16 females and 15 males). We then related the methylome to mRNA expression levels in kidney structure/function and Drug Metabolizing Enzyme and Transporter (DMET) genes (32 females and 59 males). Our findings indicate that 429 methylated sites on autosomal chromosomes had significant sex-specific differences in the normal human kidney. Methylated sites in/near regions associated with DMET genes or with genes involved in renal structure/function and disease were identified for subsequent analysis. Validation of 2 DMETs genes (POR and ABCA3) and 2 renal structure/function/disease genes (LAMA5 and PLAT) exhibited significant sex-specific differences in mRNA expression. Our results highlight sitespecific sexual dimorphisms (epigenetic-based) in normal human kidney. Importantly, we provide a reference methylome for normal human kidney, which may be utilized to improve our understanding of renal disease and assessing the overall safety and effectiveness of a drug in the kidney.

Pharmacogenomic Study Reveals New Variants of Drug Metabolizing Enzyme and Transporter Genes Associated with Steady-State Plasma Concentrations of Risperidone and 9-Hydroxyrisperidone in Thai Autism Spectrum Disorder Patients.

The present study sought to investigate the genetic variants in drug metabolizing enzyme and transporter (DMET) genes associated with steady-state plasma concentrations of risperidone among Thai autism spectrum disorder (ASD) patients. ASD patients taking risperidone for at least 1 month were enrolled for this pharmacogenomic study. Genotyping profile was obtained using Affymetrix DMET Plus array interrogating 1931 variants in 231 genes. Steady-state plasma risperidone and 9-hydroxyrisperidone were measured using liquid chromatography/tandem mass spectrometry assay. The final analysis included 483 markers for 167 genes. Six variants, ABCB11 (c.3084A > G, c.∗420A > G, c.∗368G > A, and c.∗236G > A) and ADH7 (c.690G > A and c.-5360G > A), were found to be associated with plasma concentrations of risperidone. 9-Hydroxyrisperidone and the total active-moiety levels were associated with six gene variants, SCLO1B1 (c.-11187G > A and c.521T > C), SLCO1B3 (c.334G > T, c.699A > G, and c.1557G > A), and SLC7A5 c.∗438C > G. Polymorphisms in UGT2B4 c.∗448A > G and CYP2D6 (c.1661G > C, c.4180G > C, and c.-2178G > A) showed considerable but not significant associations with metabolic ratio. This pharmacogenomic study identifies new genetic variants of DMET genes in monitoring risperidone therapy.

OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes.

Background: The identification of biomarkers for the estimation of cancer patients’ survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion) gene variants of a patient and to correlate them with drug-dependent adverse events. Therefore, the analysis of survival distribution of patients starting from their profile obtained using DMET data may reveal important information to clinicians about possible correlations among drug response, survival rate, and gene variants. Methods: In order to provide support to this analysis we developed OSAnalyzer, a software tool able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients and evaluate their association with ADME gene variants. Results: The tool is able to perform an automatic analysis of DMET data enriched with survival events. Moreover, results are ranked according to statistical significance obtained by comparing the area under the curves that is computed by using the log-rank test, allowing a quick and easy analysis and visualization of high-throughput data. Conclusions: Finally, we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patients.

A comparison of DMET Plus microarray and genome-wide technologies by assessing population substructure.

The capacity of the Affymetrix drug metabolism enzymes and transporters (DMET) Plus pharmacogenomics genotyping chip to estimate population substructure and cryptic relatedness was evaluated. The results were compared with estimates using genome-wide HapMap data for the same individuals. For 301 unrelated individuals, spanning three continental populations and one admixed population, genotypic data were collected using the Affymetrix DMET Plus microarray. Genome-wide data on these individuals were obtained from HapMap release 3. Population substructure was assessed using Eigenstrat and ADMIXTURE software for both platforms. Cryptic relatedness was explored by inbreeding coefficient estimation. Nonparametric tests were used to determine correlations of the analytical results of the two genotyping platforms. Principal components analysis identified population substructure for both datasets, with 15.8 and 16.6% of the total variance explained in the first two principal components for DMET Plus and HapMap data, respectively. ADMIXTURE results correctly identified four subpopulations within each dataset. Nonparametric rank correlations indicated significant associations between analyses with an average ρ=0.7272 (P<10) across the three continental populations and ρ=0.4888 for the admixed population. Concordance correlation coefficients (average ρc=0.9693 across all four subpopulations) strongly indicate concordance between ADMIXTURE results. Inbreeding coefficients were slightly inflated (16 individuals>0.15) using DMET Plus data and no cryptic relatedness was indicated using HapMap data. The inflated inbreeding estimation could be because of the limited number of markers provided by DMET as a random sample of 1832 markers from HapMap also yielded inflated estimates of cryptic relatedness (39 individuals>0.15). Furthermore, use of single nucleotide polymorphisms located in genes involved in metabolism and transport may have different allele frequencies in subpopulations than single nucleotide polymorphisms sampled from the whole genome. The DMET Plus pharmacogenomics genotyping chip is effective in quantifying population substructure across the three continental populations and inferring the presence of an admixed population. On the basis of our results, these microarrays offer sufficient depth for covariate adjustment of population substructure in genomic association studies.

Clinically relevant genetic variants of drug-metabolizing enzyme and transporter genes detected in Thai children and adolescents with autism spectrum disorder.

Single-nucleotide polymorphisms (SNPs) among drug-metabolizing enzymes and transporters (DMETs) influence the pharmacokinetic profile of drugs and exhibit intra- and interethnic variations in drug response in terms of efficacy and safety profile. The main objective of this study was to assess the frequency of allelic variants of drug absorption, distribution, metabolism, and elimination-related genes in Thai children and adolescents with autism spectrum disorder. Blood samples were drawn from 119 patients, and DNA was extracted. Genotyping was performed using the DMET Plus microarray platform. The allele frequencies of the DMET markers were generated using the DMET Console software. Thereafter, the genetic variations of significant DMET genes were assessed. The frequencies of SNPs across the genes coding for DMETs were determined. After filtering the SNPs, 489 of the 1,931 SNPs passed quality control. Many clinically relevant SNPs, including CYP2C19*2, CYP2D6*10, CYP3A5*3, and SLCO1B1*5, were found to have frequencies similar to those in the Chinese population. These data are important for further research to investigate the interpatient variability in pharmacokinetics and pharmacodynamics of drugs in clinical practice.