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1.  PharmGKB summary - very important pharmacogene information for GSTT1 
Pharmacogenetics and Genomics  2012;22(8):646-651.
This PharmGKB summary briefly discusses the very important pharmacogene GSTT1 and its variants that can influence drug responses. A fully interactive version of this short review, with links to individual paper annotations and population descriptions can be found at
PMCID: PMC3395771  PMID: 22643671
2.  Identification of genetic variants and gene expression relationships associated with pharmacogenes in humans 
Pharmacogenetics and genomics  2008;18(6):545-549.
The very important pharmacogenes (VIPs) were selected by Pharmacogenetic Research Network (National Institutes of Health-PGRN) owing to their significant effects on drug treatment both at the pharmacokinetic and pharmacodynamic levels. Our objective was to identify single nucleotide polymorphisms (SNPs) that potentially affected the expression of these genes or potential SNP–gene interactions involved to improve our understanding of genetic effects on drug therapy.
Basic methods
Gene expression was evaluated in 176 International HapMap lymphoblastoid cell lines derived from CEU (CEPH, Utah residents with ancestry from northern and western Europe; n = 87) and YRI (Yoruba in Ibadan, Nigeria; n = 89) using Affymetrix GeneChip Human Exon 1.0 ST arrays (Affymetrix Laboratory, Affymetrix Inc., Santa Clara, California, USA) with interrogation of greater than 17000 human genes. Genome-wide association was performed between over two million publicly available HapMap SNPs and gene expression.
Main results
The expression of two PGRN-VIPs (GSTT1 and GSTM1) are significantly associated with SNPs within 2.5 Mb of the genes; whereas the expression of three and ten PGRN-VIPs are significantly associated with distant-acting SNPs in CEU and YRI, respectively. In addition, three and four PGRN-VIPs harbor SNPs that are distantly associated with other gene expressions in CEU and YRI, respectively.
Principal conclusion
Using this information, one may identify genetic variants that are significantly associated with the expression of any set of genes of interest; or evaluate potential gene–gene interaction through SNP expression relationships.
PMCID: PMC2567052  PMID: 18496134
exon array; expression quantitative trait loci; gene expression; glutathione-S-transferase; pharmacogenes
3.  Analysis of multiple compound–protein interactions reveals novel bioactive molecules 
The authors use machine learning of compound-protein interactions to explore drug polypharmacology and to efficiently identify bioactive ligands, including novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein coupled receptors and protein kinases.
We have demonstrated that machine learning of multiple compound–protein interactions is useful for efficient ligand screening and for assessing drug polypharmacology.This approach successfully identified novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein-coupled receptors and protein kinases.These bioactive compounds were not detected by existing computational ligand-screening methods in comparative studies.The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. Perturbations of biological systems by chemical probes provide broader applications not only for analysis of complex systems but also for intentional manipulations of these systems. Nevertheless, the lack of well-characterized chemical modulators has limited their use. Recently, chemical genomics has emerged as a promising area of research applicable to the exploration of novel bioactive molecules, and researchers are currently striving toward the identification of all possible ligands for all target protein families (Wang et al, 2009). Chemical genomics studies have shown that patterns of compound–protein interactions (CPIs) are too diverse to be understood as simple one-to-one events. There is an urgent need to develop appropriate data mining methods for characterizing and visualizing the full complexity of interactions between chemical space and biological systems. However, no existing screening approach has so far succeeded in identifying novel bioactive compounds using multiple interactions among compounds and target proteins.
High-throughput screening (HTS) and computational screening have greatly aided in the identification of early lead compounds for drug discovery. However, the large number of assays required for HTS to identify drugs that target multiple proteins render this process very costly and time-consuming. Therefore, interest in using in silico strategies for screening has increased. The most common computational approaches, ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS; Oprea and Matter, 2004; Muegge and Oloff, 2006; McInnes, 2007; Figure 1A), have been used for practical drug development. LBVS aims to identify molecules that are very similar to known active molecules and generally has difficulty identifying compounds with novel structural scaffolds that differ from reference molecules. The other popular strategy, SBVS, is constrained by the number of three-dimensional crystallographic structures available. To circumvent these limitations, we have shown that a new computational screening strategy, chemical genomics-based virtual screening (CGBVS), has the potential to identify novel, scaffold-hopping compounds and assess their polypharmacology by using a machine-learning method to recognize conserved molecular patterns in comprehensive CPI data sets.
The CGBVS strategy used in this study was made up of five steps: CPI data collection, descriptor calculation, representation of interaction vectors, predictive model construction using training data sets, and predictions from test data (Figure 1A). Importantly, step 1, the construction of a data set of chemical structures and protein sequences for known CPIs, did not require the three-dimensional protein structures needed for SBVS. In step 2, compound structures and protein sequences were converted into numerical descriptors. These descriptors were used to construct chemical or biological spaces in which decreasing distance between vectors corresponded to increasing similarity of compound structures or protein sequences. In step 3, we represented multiple CPI patterns by concatenating these chemical and protein descriptors. Using these interaction vectors, we could quantify the similarity of molecular interactions for compound–protein pairs, despite the fact that the ligand and protein similarity maps differed substantially. In step 4, concatenated vectors for CPI pairs (positive samples) and non-interacting pairs (negative samples) were input into an established machine-learning method. In the final step, the classifier constructed using training sets was applied to test data.
To evaluate the predictive value of CGBVS, we first compared its performance with that of LBVS by fivefold cross-validation. CGBVS performed with considerably higher accuracy (91.9%) than did LBVS (84.4%; Figure 1B). We next compared CGBVS and SBVS in a retrospective virtual screening based on the human β2-adrenergic receptor (ADRB2). Figure 1C shows that CGBVS provided higher hit rates than did SBVS. These results suggest that CGBVS is more successful than conventional approaches for prediction of CPIs.
We then evaluated the ability of the CGBVS method to predict the polypharmacology of ADRB2 by attempting to identify novel ADRB2 ligands from a group of G-protein-coupled receptor (GPCR) ligands. We ranked the prediction scores for the interactions of 826 reported GPCR ligands with ADRB2 and then analyzed the 50 highest-ranked compounds in greater detail. Of 21 commercially available compounds, 11 showed ADRB2-binding activity and were not previously reported to be ADRB2 ligands. These compounds included ligands not only for aminergic receptors but also for neuropeptide Y-type 1 receptors (NPY1R), which have low protein homology to ADRB2. Most ligands we identified were not detected by LBVS and SBVS, which suggests that only CGBVS could identify this unexpected cross-reaction for a ligand developed as a target to a peptidergic receptor.
The true value of CGBVS in drug discovery must be tested by assessing whether this method can identify scaffold-hopping lead compounds from a set of compounds that is structurally more diverse. To assess this ability, we analyzed 11 500 commercially available compounds to predict compounds likely to bind to two GPCRs and two protein kinases. Functional assays revealed that nine ADRB2 ligands, three NPY1R ligands, five epidermal growth factor receptor (EGFR) inhibitors, and two cyclin-dependent kinase 2 (CDK2) inhibitors were concentrated in the top-ranked compounds (hit rate=30, 15, 25, and 10%, respectively). We also evaluated the extent of scaffold hopping achieved in the identification of these novel ligands. One ADRB2 ligand, two NPY1R ligands, and one CDK2 inhibitor exhibited scaffold hopping (Figure 4), indicating that CGBVS can use this characteristic to rationally predict novel lead compounds, a crucial and very difficult step in drug discovery. This feature of CGBVS is critically different from existing predictive methods, such as LBVS, which depend on similarities between test and reference ligands, and focus on a single protein or highly homologous proteins. In particular, CGBVS is useful for targets with undefined ligands because this method can use CPIs with target proteins that exhibit lower levels of homology.
In summary, we have demonstrated that data mining of multiple CPIs is of great practical value for exploration of chemical space. As a predictive model, CGBVS could provide an important step in the discovery of such multi-target drugs by identifying the group of proteins targeted by a particular ligand, leading to innovation in pharmaceutical research.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
PMCID: PMC3094066  PMID: 21364574
chemical genomics; data mining; drug discovery; ligand screening; systems chemical biology
4.  PharmGKB summary: very important pharmacogene information for cytochrome P450, family 2, subfamily C, polypeptide 19 
Pharmacogenetics and Genomics  2012;22(2):159-165.
This PharmGKB summary briefly discusses the CYP2C19 gene and current understanding of its function, regulation, and pharmacogenomic relevance.
PMCID: PMC3349992  PMID: 22027650
antidepressants; clopidogrel; CYP2C19*17; CYP2C19*2; CYP2C19; proton pump inhibitors; rs4244285
5.  Pharmacogene regulatory elements: from discovery to applications 
Genome Medicine  2012;4(5):45.
Regulatory elements play an important role in the variability of individual responses to drug treatment. This has been established through studies on three classes of elements that regulate RNA and protein abundance: promoters, enhancers and microRNAs. Each of these elements, and genetic variants within them, are being characterized at an exponential pace by next-generation sequencing (NGS) technologies. In this review, we outline examples of how each class of element affects drug response via regulation of drug targets, transporters and enzymes. We also discuss the impact of NGS technologies such as chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA sequencing (RNA-Seq), and the ramifications of new techniques such as high-throughput chromosome capture (Hi-C), chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and massively parallel reporter assays (MPRA). NGS approaches are generating data faster than they can be analyzed, and new methods will be required to prioritize laboratory results before they are ready for the clinic. However, there is no doubt that these approaches will bring about a systems-level understanding of the interplay between genetic variants and drug response. An understanding of the importance of regulatory variants in pharmacogenomics will facilitate the identification of responders versus non-responders, the prevention of adverse effects and the optimization of therapies for individual patients.
PMCID: PMC3506911  PMID: 22630332
ChIP-Seq; enhancers; miRNA; next-generation sequencing; pharmacogenomics; promoters; RNA-Seq
Identifying genetic variants that affect drug response or play a role in disease is an important task for clinicians and researchers. Before individual variants can be explored efficiently for effect on drug response or disease relationships, specific candidate genes must be identified. While many methods rank candidate genes through the use of sequence features and network topology, only a few exploit the information contained in the biomedical literature. In this work, we train and test a classifier on known pharmacogenes from PharmGKB and present a classifier that predicts pharmacogenes on a genome-wide scale using only Gene Ontology annotations and simple features mined from the biomedical literature. Performance of F=0.86, AUC=0.860 is achieved. The top 10 predicted genes are analyzed. Additionally, a set of enriched pharmacogenic Gene Ontology concepts is produced.
PMCID: PMC3910248  PMID: 24297559
7.  An environmental epigenetic study of ADRB2 5'-UTR methylation and childhood asthma severity 
Beta-2 adrenergic receptor (ADRB2) is the primary target of both short- and long-acting beta-agonist asthma medications. ADRB2 5'-UTR methylation changes in blood have the potential to act as a surrogate biomarker of responsiveness to beta-agonist treatment and childhood asthma severity.
To study the association between ADRB2 5'-UTR methylation, NO2 exposure and childhood asthma severity.
We compared ADRB2 5'-UTR methylation levels in blood between 60 children with mild asthma and 122 children with severe asthma using methylation-specific PCR. We also investigated potential joint effects between NO2 exposure and ADRB2 5'-UTR methylation.
We found a significant association between intermediate (OR: 4.11, 95% CI: 1.58–10.73) and high levels (OR: 7.63, 95% CI: 3.02–19.26) of ADRB2 methylation and severe childhood asthma. In addition, we found a significant association between indoor exposure to NO2, an air pollutant and known asthmogen, and severe asthma among children exhibiting high ADRB2 methylation (OR: 4.59, 95% CI: 1.03–20.55) but no association among children exhibiting low levels of ADRB2 methylation (OR: 0.35, 95% CI: 0.01–14.13).
Conclusions and Clinical Relevance
These findings support the potential use of ADRB2 5'-UTR methylation as a biomarker of both asthma severity and risk for NO2-associated asthma exacerbations in children, and present the first evidence of an epigenetic link between an important environmental exposure and childhood asthma severity.
PMCID: PMC3673701  PMID: 22862293
ADRB2; methylation; asthma severity; epigenetic; NO2
8.  Genetic variation in the beta-2 adrenergic receptor (ADRB2) predicts functional gastrointestinal diagnoses and poorer health-related quality of life 
The beta-2 adrenergic receptor (ADRB2) is an important target for epinephrine, a neurotransmitter in pain signalling. ADRB2 haplotypes affect receptor expression and ligand response, and have been linked to painful non-GI disorders.
To assess whether ADRB2 polymorphisms (rs1042713, rs1042714) are risk alleles for functional GI (FGID) and extraintestinal functional (EIFD) diagnoses, and whether ADRB2 predicts GI symptoms and health-related quality of life (HRQOL).
Of 398 subjects (49.6 ± 2.9 years, 68.0% female), 170 (42.5%) met Rome III criteria for ≥1 FGID [IBS (n = 139, 34.9%); functional dyspepsia (FD, n = 136, 34.1%), functional chest pain (FCP, n = 25, 6.2%)], while 228 were healthy controls. FGID subjects reported on bowel symptom severity and burden (10-cm VAS), frequency (days/last 2 weeks), EIFD, psychiatric diagnoses and HRQOL (SF 36). Multivariable models determined the contribution of ADRB2 polymorphisms to HRQOL, and mediational analyses assessed functional diagnoses as potential intermediates.
rs1042714 minor G alleles were associated with FGID diagnoses (OR 1.8; 95% CI 1.2–2.7; P = 0.009), particularly FD (OR 2.1, 95% CI 1.3–3.3), with trends towards IBS (P = 0.19) and FCP (P = 0.06) diagnoses. Within IBS, G allele carriers had more severe bowel symptoms (P = 0.025), and symptomatic days (P = 0.009). G allele carriers had greater numbers of EIFD (1.0 ± 0.1 vs. 0.4 ± 0.07, P < 0.001) and poorer HRQOL. The effect of ADRB2 on HRQOL was partially mediated by FGID, EIFD and psychiatric diagnoses.
ADRB2 minor alleles at rs1042714 predict FGID and EIFD, and may influence bowel symptom severity and HRQOL. These findings provide indirect evidence of sympathetic nervous system role in FGID pathophysiology.
PMCID: PMC4017784  PMID: 23786226
9.  Possible association of β2- and β3-adrenergic receptor gene polymorphisms with susceptibility to breast cancer 
Breast Cancer Research : BCR  2001;3(4):264-269.
The involvement of β2-adrenergic receptor (ADRB2) and β3-adrenergic receptor (ADRB3) in both adipocyte lipolysis and thermogenic activity suggests that polymorphisms in the encoding genes might be linked with interindividual variation in obesity, an important risk factor for postmenopausal breast cancer. In order to examine the hypothesis that genetic variations in ADRB2 and ADRB3 represent interindividual susceptibility factors for obesity and breast cancer, we conducted a hospital-based, case-control study in the Aichi Cancer Center, Japan.
A self-administered questionnaire was given to 200 breast cancer patients and 182 control individuals, and pertinent information on lifestyle, family history and reproduction was collected. ADRB2 and ADRB3 genotypes were determined by polymerase chain reaction (PCR) restriction fragment length polymorphism assessment.
Twenty-five (12.4%) breast cancer patients and 32 (17.6%) control individuals were found to bear a glutamic acid (Glu) allele for the ADRB2 gene (odds ratio [OR] 0.67, 95% confidence interval [CI] 0.38-1.18), and 60 (30.0%) breast cancer patients and 61 (33.5%) control individuals were found to bear an Arg allele for the ADRB3 gene (OR 0.85, 95% CI 0.55-1.31). A significantly lower risk was observed in those who carried the Glu ADRB2 allele and who reported first childbirth when they were younger than 25 years (OR 0.35; 95% CI 0.13-0.99).
A potential association may exist between risk of breast cancer and polymorphisms in the ADRB2 and ADRB3 genes; further studies in larger samples and/or in different ethnic groups are warranted to investigate this potential association.
PMCID: PMC34110  PMID: 11434877
β2-adrenergic receptor gene; β3-adrenergic receptor gene; breast cancer risk; polymorphisms; reproductive history
10.  PharmGKB very important pharmacogene: SLCO1B1 
Pharmacogenetics and Genomics  2010;20(3):211-216.
PMCID: PMC3086841  PMID: 19952871
OATP2; organic anion transporter; pharmacogenomics; pharmacokinetics; SLCO1B1; statins
11.  PharmGKB summary: very important pharmacogene information for angiotensin-converting enzyme 
Pharmacogenetics and Genomics  2010;20(2):143-146.
PMCID: PMC3098760  PMID: 19898265
ACE:insertion/deletion; angiotensin-converting enzyme inhibitors; antihypertensives; pharmacogenomic; PharmGKB
12.  Very important pharmacogene summary: thiopurine S-methyltransferase 
Pharmacogenetics and Genomics  2010;20(6):401-405.
PMCID: PMC3086840  PMID: 20154640
acute lymphoblastic leukemia; pharmacogenetics; pharmacogenomics; polymorphism; thiopurine drugs
13.  PharmGKB summary: very important pharmacogene information for CYP2B6 
Pharmacogenetics and Genomics  2010;20(8):520-523.
PMCID: PMC3086041  PMID: 20648701
bupropion; cytochrome P450; CYP2B6; efavirenz; pharmacogene
14.  PharmGKB: very important pharmacogene – HMGCR 
Pharmacogenetics and Genomics  2011;21(2):98-101.
PMCID: PMC3098759  PMID: 20084049
HMGCR; 3-hydroxy-3-methylglutaryl coenzyme A reductase; PharmGKB; pravastatin; statin
15.  Very important pharmacogene summary: ABCB1 (MDR1, P-glycoprotein) 
Pharmacogenetics and Genomics  2011;21(3):152-161.
PMCID: PMC3098758  PMID: 20216335
ABC transporter; drug permeability; multidrug resistance; pharmacogenomics; PharmGKB
16.  PharmGKB summary: very important pharmacogene information for PTGS2 
Pharmacogenetics and Genomics  2011;21(9):607-613.
PMCID: PMC3141084  PMID: 21063235
cyclooxygenase-2; coxibs; non-steroidal anti-inflammatory drugs; pharmacogenomics; PTGS2; rs20417; rs5275; rs689466
17.  PharmGKB summary: very important pharmacogene information for CYP1A2 
Pharmacogenetics and Genomics  2012;22(1):73-77.
PMCID: PMC3346273  PMID: 21989077
CYP1A2; caffeine; pharmacogene; pharmGKB
18.  PharmGKB summary: very important pharmacogene information for G6PD 
Pharmacogenetics and Genomics  2012;22(3):219-228.
PMCID: PMC3382019  PMID: 22237549
drug-induced oxidative stress; glucose-6-phosphate dehydrogenase deficiency; hemolytic anemia; pharmacodynamics; pharmacokinetics; polymorphic variants
19.  PharmGKB summary: very important pharmacogene information for CYP3A5 
Pharmacogenetics and genomics  2012;22(7):555-558.
PMCID: PMC3738061  PMID: 22407409
CYP3A5; CYP3A5*2; CYP3A5*3; CYP3A5*6; CYP3A5*7; pharmacogenomics; rs10264272; rs28365083; rs76293380; rs776746
20.  PharmGKB Summary - Very Important Pharmacogene Information for Cytochrome P-450, Family 2, Subfamily A, polypeptide 6 (CYP2A6) 
Pharmacogenetics and genomics  2012;22(9):695-708.
PMCID: PMC3413746  PMID: 22547082
CYP2A6; inter-individual variation; pharmacokinetics; genetic polymorphisms; drug metabolism; drug efficacy
21.  Very important pharmacogene summary for VDR 
Pharmacogenetics and genomics  2012;22(10):758-763.
PMCID: PMC3678550  PMID: 22588316
drug response; genetic variants; pharmacogenomics; vitamin D receptor
22.  PharmGKB Summary: Very Important Pharmacogene Information for Epidermal Growth Factor Receptor (EGFR) 
Pharmacogenetics and genomics  2013;23(11):636-642.
PMCID: PMC3966564  PMID: 23962910
Epidermal growth factor receptor (EGFR); tyrosine kinase inhibitor; erlotinib; gefitinib; pharmacogenomics
23.  Let’s make data on essential pharmacogenes available for every patient everywhere: The Medicine Safety Code initiative 
Pharmacogenomics  2013;14(13):1529-1531.
PMCID: PMC4028543  PMID: 24088121
pharmacogenetics; health care systems; biomedical informatics; clinical decision support systems
24.  Screening for 392 polymorphisms in 141 pharmacogenes 
Biomedical Reports  2014;2(4):463-476.
Pharmacogenomics is the study of the association between inter-individual genetic differences and drug responses. Researches in pharmacogenomics have been performed in compliance with the use of several genotyping technologies. In this study, a total of 392 single-nucleotide polymorphisms (SNPs) located in 141 pharmacogenes, including 21 phase I, 13 phase II, 18 transporter and 5 modifier genes, were selected and genotyped in 150 subjects using the GoldenGate assay or the SNaPshot technique. These variants were in Hardy-Weinberg equilibrium (HWE) (P>0.05), except for 22 SNPs. Genotyping of the 392 SNPs revealed that the minor allele frequencies of 47 SNPs were <0.05, 105 SNPs were monomorphic and 22 variants were not in HWE. Also, based on previous studies, we predicted the association between the polymorphisms of certain pharmacogenes, such as cytochrome P450 2D6, cytochrome P450 2C9, vitamin K epoxide reductase complex, subunit 1, cytochrome P450 2C19, human leukocyte antigen, class I, B and thiopurine S-methyltransferase, and drug efficacy. In conclusion, our study demonstrated the allele distribution of SNPs in 141 pharmacogenes as determined by high-throughput screening. Our results may be helpful in developing personalized medicines by using pharmacogene polymorphisms.
PMCID: PMC4051470  PMID: 24944790
gene screening; pharmacogene; single-nucleotide polymorphism
25.  PharmGKB summary: very important pharmacogene information for UGT1A1 
Pharmacogenetics and genomics  2014;24(3):177-183.
PMCID: PMC4091838  PMID: 24492252
atazanavir; Crigler–Najjar syndrome; Gilbert’s syndrome; indinavir; irinotecan; pharmacogenetics; UGT1A1

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