ABCB1 p.Ala80Glu
| Predicted by SNAP2: | C: N (53%), D: D (71%), E: D (71%), F: D (59%), G: N (61%), H: D (66%), I: N (93%), K: D (66%), L: N (82%), M: D (59%), N: N (61%), P: D (75%), Q: N (57%), R: N (53%), S: N (87%), T: N (93%), V: N (97%), W: D (63%), Y: D (71%), |
| Predicted by PROVEAN: | C: N, D: N, E: N, F: N, G: N, H: N, I: N, K: N, L: N, M: N, N: N, P: N, Q: N, R: N, S: N, T: N, V: N, W: D, Y: N, |
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[hide] Genetic polymorphisms of ATP-binding cassette tran... Expert Opin Pharmacother. 2005 Nov;6(14):2455-73. Sakurai A, Tamura A, Onishi Y, Ishikawa T
Genetic polymorphisms of ATP-binding cassette transporters ABCB1 and ABCG2: therapeutic implications.
Expert Opin Pharmacother. 2005 Nov;6(14):2455-73., [PMID:16259577]
Abstract [show]
Pharmacogenomics, the study of the influence of genetic factors on drug action, is increasingly important for predicting pharmacokinetics profiles and/or adverse reactions to drugs. Drug transporters, as well as drug metabolism play pivotal roles in determining the pharmacokinetic profiles of drugs and their overall pharmacological effects. There is an increasing number of reports addressing genetic polymorphisms of drug transporters. However, information regarding the functional impact of genetic polymorphisms in drug transporter genes is still limited. Detailed functional analysis in vitro may provide clear insight into the biochemical and therapeutic significance of genetic polymorphisms. This review addresses functional aspects of the genetic polymorphisms of human ATP-binding cassette transporters, ABCB1 and ABCG2, which are critically involved in the pharmacokinetics of drugs.
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129 N21D M89T N44S H2N F103L E108K N183S G185V I261V S400N R492C A599T L662R R669C V801M A893S/T I829V I849M M986V A999T G1063A P1051A Q1107P W1108R I1145M S1141T V1251I T1256K COOH ATP-binding site ATP-binding site EXTRACELLULAR INTRACELLULAR A80E Figure 2.
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ABCB1 p.Ala80Glu 16259577:129:240
status: NEW[hide] Clinical pharmacogenetics and potential applicatio... Curr Drug Metab. 2008 Oct;9(8):738-84. Zhou SF, Di YM, Chan E, Du YM, Chow VD, Xue CC, Lai X, Wang JC, Li CG, Tian M, Duan W
Clinical pharmacogenetics and potential application in personalized medicine.
Curr Drug Metab. 2008 Oct;9(8):738-84., [PMID:18855611]
Abstract [show]
The current 'fixed-dosage strategy' approach to medicine, means there is much inter-individual variation in drug response. Pharmacogenetics is the study of how inter-individual variations in the DNA sequence of specific genes affect drug responses. This article will highlight current pharmacogenetic knowledge on important drug metabolizing enzymes, drug transporters and drug targets to understand interindividual variability in drug clearance and responses in clinical practice and potential use in personalized medicine. Polymorphisms in the cytochrome P450 (CYP) family may have had the most impact on the fate of pharmaceutical drugs. CYP2D6, CYP2C19 and CYP2C9 gene polymorphisms and gene duplications account for the most frequent variations in phase I metabolism of drugs since nearly 80% of drugs in use today are metabolised by these enzymes. Approximately 5% of Europeans and 1% of Asians lack CYP2D6 activity, and these individuals are known as poor metabolizers. CYP2C9 is another clinically significant drug metabolising enzyme that demonstrates genetic variants. Studies into CYP2C9 polymorphism have highlighted the importance of the CYP2C9*2 and CYP2C9*3 alleles. Extensive polymorphism also occurs in a majority of Phase II drug metabolizing enzymes. One of the most important polymorphisms is thiopurine S-methyl transferases (TPMT) that catalyzes the S-methylation of thiopurine drugs. With respect to drug transport polymorphism, the most extensively studied drug transporter is P-glycoprotein (P-gp/MDR1), but the current data on the clinical impact is limited. Polymorphisms in drug transporters may change drug's distribution, excretion and response. Recent advances in molecular research have revealed many of the genes that encode drug targets demonstrate genetic polymorphism. These variations, in many cases, have altered the targets sensitivity to the specific drug molecule and thus have a profound effect on drug efficacy and toxicity. For example, the beta (2)-adrenoreceptor, which is encoded by the ADRB2 gene, illustrates a clinically significant genetic variation in drug targets. The variable number tandem repeat polymorphisms in serotonin transporter (SERT/SLC6A4) gene are associated with response to antidepressants. The distribution of the common variant alleles of genes that encode drug metabolizing enzymes, drug transporters and drug targets has been found to vary among different populations. The promise of pharmacogenetics lies in its potential to identify the right drug at the right dose for the right individual. Drugs with a narrow therapeutic index are thought to benefit more from pharmacogenetic studies. For example, warfarin serves as a good practical example of how pharmacogenetics can be utilized prior to commencement of therapy in order to achieve maximum efficacy and minimum toxicity. As such, pharmacogenetics has the potential to achieve optimal quality use of medicines, and to improve the efficacy and safety of both prospective and licensed drugs.
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532 Nucleotide change rs number Amino acid change 49T>C rs28381804 F17L 61A>G rs61615398; rs9282564 N21D 131A>G rs1202183 N44S 178A>C rs41315618 I60L 239C>A rs9282565 A80E 266T>C Rs35810889 M89T 431T>C rs61607171 I144T 502G>A rs61122623 V168I 548A>G rs60419673 N183S 554G>T rs1128501 G185V 781A>G rs36008564 I261V 1199G>A rs2229109 S400N 1696G>A rs28381902 E566K 1777C>T rs28381914 R593C 1778G>A rs56107566 R593H 1795G>A rs2235036 A599T 1837G>T rs57001392 D613Y 1985T>G rs61762047 L662R 2005C>T rs35023033 R669C 2207A>T rs41316450 I736K 2398G>A rs41305517 D800N 2401G>A rs2235039 V801M 2485A>G rs2032581 I829V 2506A>G rs28381967 I836V 2547A>G rs36105130 I849M 2677T>A/G rs2032582 S893A/T 2975G>A rs56849127 S992N 3151C>G rs28401798 P1051A 3188G>C rs2707944 G1063A 3262G>A rs57521326 D1088N 3295A>G rs41309225 K1099E 3320A>C rs55852620 Q1107P 3322T>C rs35730308 W1108R 3410G>T rs41309228 S1137I 3421T>A rs2229107 S1141T 3502A>G rs59241388 K1168E 3669A>T rs41309231 E1223D 3751G>A rs28364274 V1251I 3767C>A r35721439 T1256K Data are from NCBI dbSNP (access date: 2 August 2008).
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ABCB1 p.Ala80Glu 18855611:532:163
status: NEW[hide] A novel polymorphism in ABCB1 gene, CYP2B6*6 and s... Br J Clin Pharmacol. 2009 Nov;68(5):690-9. Mukonzo JK, Roshammar D, Waako P, Andersson M, Fukasawa T, Milani L, Svensson JO, Ogwal-Okeng J, Gustafsson LL, Aklillu E
A novel polymorphism in ABCB1 gene, CYP2B6*6 and sex predict single-dose efavirenz population pharmacokinetics in Ugandans.
Br J Clin Pharmacol. 2009 Nov;68(5):690-9., [PMID:19916993]
Abstract [show]
AIMS: Efavirenz exhibits pharmacokinetic variability causing varied clinical response. The aim was to develop an integrated population pharmacokinetic/pharmacogenetic model and investigate the impact of genetic variations, sex, demographic and biochemical variables on single-dose efavirenz pharmacokinetics among Ugandan subjects, using NONMEM. METHODS: Efavirenz plasma concentrations (n = 402) from 121 healthy subjects were quantified by high-performance liquid chromatography. Subjects were genotyped for 30 single nucleotide polymorphisms (SNPs), of which six were novel SNPs in CYP2B6, CYP3A5 and ABCB1. The efavirenz pharmacokinetics was described by a two-compartment model with zero- followed by first-order absorption. RESULTS: Apparent oral clearance (95% confidence interval) was 4 l h l(-1) (3.5, 4.5) in extensive metabolizers. In the final model, incorporating multiple covariates, statistical significance was found only for CYP2B6*6 and CYP2B6*11 on apparent oral clearance as well as ABCB1 (rs3842) on the relative bioavailability. Subjects homozygous for CYP2B6*6 (G516T, A785G) and *11 displayed 21 and 20% lower apparent oral clearance, respectively. Efavirenz relative bioavailability was 26% higher in subjects homozygous for ABCB1 (rs3842). The apparent peripheral volume of distribution was twofold higher in women compared with men. CONCLUSIONS: The model identified the four factors CYP2B6*6, CYP2B6*11, a novel variant allele in ABCB1 (rs3842) and sex as major predictors of efavirenz plasma exposure in a healthy Ugandan population after single-dose administration. Use of mixed-effects modelling allowed the analysis and integration of multiple pharmacogenetic and demographic covariates in a pharmacokinetic population model.
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No. Sentence Comment
119 785 A→G rs2279343 CYP2B6*4, *6, *7, *13, *16 *19, *20 K262R Reduced expression and activity 36.4 c.516 G→T rs3745274 CYP2B6*6, *7, *9, *13, *19, *20 Q172H Reduced expression and activity 35.6 c.136A→G rs35303484 CYP2B6*11 M46V Phenotypic null allele 13.6 c.983 T→C rs28399499 CYP2B6*16, *18 I328T Phenotypic null allele 10.4 c.64 C→T rs8192709 CYP2B6*2 R22C Phenotypic null allele 8.0 c.1282 C→T rs35010098 CYP2B6*21 P428T Phenotypic null allele 1.1 exon 8/-6 C→T rs35449271 New SNP Undetermined 32.0 296 G→A rs36060847 CYP2B6*12 G99E Reduced expression 3.6 1375 A→G rs3211369 CYP2B6*23 M459V Unknown 24.0 c.1172 T→A rs35979566 CYP2B6*15 I391N Reduced expression 7.7 CYP3A5 g.27289C→A rs28365083 CYP3A5*2 T398N Unknown 0 g.6986A→G rs776746 CYP3A5*3 Splicing defect Phenotypic null allele 18.2 g.14665A→G CYP3A5*4 Q200R Unknown 8.6 g.14690G→A CYP3A5*6 Splicing defect Phenotypic null allele 17.2 g.27131-27132insT rs241303343 CYP3A5*7 346 frame shift Phenotypic null allele 18.4 g.3699C→T rs28371764 CYP3A5*8 R28C Phenotypic null allele 0 g.19386G→A rs28383479 CYP3A5*9 A337T Decreased activity 11.4 ABCB1 c.1236 C→T rs1128503 Gly412Gly Phenotypic null allele 11.9 c.2677 G/A→T rs2032582 Ala/Thr893 Ser Phenotypic null allele 3.7 c.3435 T/C rs1045642 Ile1145Ile Phenotypic null allele 4.8 c.4036 A/G rs3842 New SNP 3' UTR Undetermined 16.8 c.1659 G→C rs2235012 Leu554Leu 1.1 exon 6/+139 C→T rs1202168 New SNP - Undetermined 18.6 exon 19/-88 T→C rs4728699 New SNP - Undetermined 7.7 c.781A→G rs36008564 Ile261Val 6.9 c.239C→A rs9282565 Ala80Glu 2.8 exon 12/+44 C→T rs20328588 New SNP Intron 13 Undetermined 5.1 c.1199G→A rs2229109 Ser400Asn 2.6 c.1795C→T rs2235036 Ala599Thr 7.0 exon 20/+24 G→A rs2235040 New SNP - Undetermined 4.6 *Position based on cDNA numbering (c.
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ABCB1 p.Ala80Glu 19916993:119:1695
status: NEW[hide] Genetic variations in ABCB1 and CYP3A5 as well as ... Ther Drug Monit. 2010 Jun;32(3):346-52. Mukonzo JK, Waako P, Ogwal-Okeng J, Gustafsson LL, Aklillu E
Genetic variations in ABCB1 and CYP3A5 as well as sex influence quinine disposition among Ugandans.
Ther Drug Monit. 2010 Jun;32(3):346-52., [PMID:20357698]
Abstract [show]
Quinine is one of the most effective antimalarial drugs, although its clinical use is limited as a result of its narrow safety margin. Quinine is a substrate of the polymorphic p-glycoprotein and CYP3A4/3A5. This study aimed to examine the effects of genetic variations in ABCB1 and CYP3A5 genes, sex, demographic, and biochemical variables (serum albumin, creatinine, alanine aminotransferase and albumin) on quinine disposition among Ugandans. Quinine (600 mg) was orally administered to 140 healthy volunteers. Quinine and its metabolite 3-hydroxyquinine concentrations were determined from 16-hour postdose plasma by high-performance liquid chromatography. CYP3A5 activity was measured using quinine/3-hydroxyquinine ratio (metabolic ratio). Genotyping for a total of 20 single nucleotide polymorphisms in ABCB1 (n = 13) and CYP3A5 (n = 7) was done using Taqman and minisequencing on microarray. There were 20.5- and 13-fold variations in body weight-adjusted plasma quinine concentrations (mean +/- standard deviation, 5.26 +/- 2.5 mumol/L; range, 0.88-18.10 mumol/L) and quinine-to-3-hydroxyquinine metabolic ratio (mean +/- standard deviation, 7.68 +/- 3.3 mumol/L; range, 1.66-22.3 mumol/L), respectively. Weight-adjusted plasma quinine concentration was significantly influenced by sex and ABCB1 haplotype. There was a significant sex difference in quinine metabolic ratio, women being faster metabolizers than men (P = 0.01). CYP3A5 genotype/haplotype significantly (P = 0.03) influenced quinine disposition with a clear CYP3A5*1 gene dose effect. The result confirms that quinine disposition is influenced mainly by sex as well as by ABCB1 and CYP3A5 genotypes. Despite being fast metabolizers, women display higher quinine bioavailability than men in Uganda. This may have clinical significance in determining an individual's susceptibility to quinine-associated adverse reactions such as cinchonism.
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79 T rs2032582 Ala/Thr893 Ser Altered activity 3.7 c.3435 C/T rs1045642 Ile1145Ile Altered activity 4.8 c.4036 A/G rs3842 New SNP 3` UTR Undetermined 16.8 c.1659 G.C rs2235012 Leu554Leu Detected only in blacks 1.1 Exon 6/ +139 C.T rs1202168 New SNP - Undetermined 18.6 Exon 19/-88 T.C rs4728699 - Undetermined 7.7 c.781A.G rs36008564 Ile261Val 6.9 c.239C.A rs9282565 Ala80Glu 2.8 Exon 12/+44 C.T rs20328588 New SNP Intron 13 Undetermined 5.1 c.1199G.A rs2229109 Ser400Asn 2.6 c.1795C.T rs2235036 Ala599Thr 7.0 Exon 20/+24 G.A rs2235040 New SNP - Undetermined 4.6 CYP3A5 g.27289C.A rs28365083 CYP3A5*2 T398N Unknown 0 g.6986A.G rs776746 CYP3A5*3 Splicing defect Phenotypic null allele 18.2 g.14665A.G CYP3A5*4 Q200R Unknown 6.7 g.14690G.A CYP3A5*6 Splicing defect Phenotypic null allele 17.2 g.27131-27132insT rs241303343 CYP3A5*7 346 frame shift Phenotypic null allele 11.4 g.3699C.T rs28371764 CYP3A5*8 R28C Phenotypic null allele 0 g.19386G.A rs28383479 CYP3A5*9 A337T Decreased activity 9.6 *Position based on cDNA numbering (c.
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ABCB1 p.Ala80Glu 20357698:79:364
status: NEW[hide] Genetic association analysis of transporters ident... Pharmacogenet Genomics. 2012 Jun;22(6):447-65. Grover S, Gourie-Devi M, Bala K, Sharma S, Kukreti R
Genetic association analysis of transporters identifies ABCC2 loci for seizure control in women with epilepsy on first-line antiepileptic drugs.
Pharmacogenet Genomics. 2012 Jun;22(6):447-65., [PMID:22565165]
Abstract [show]
OBJECTIVE: The ATP-binding cassette (ABC) superfamily of transporters is known to efflux antiepileptic drugs (AEDs) primarily in the brain, gastrointestinal tract, liver, and kidneys. In addition, they are also known to be involved in estrogen disposition and may modulate seizure susceptibility and drug response. The objective of the present study was to investigate the role of genetic variants from ABC transporters in seizure control in epilepsy patients treated with monotherapy of first-line AEDs for 12 months. METHODS: On the basis of gene coverage and functional significance, a total of 98 single nucleotide polymorphisms from ABCB1, ABCC1, and ABCC2 were genotyped in 400 patients from North India. Of these, 216 patients were eligible for therapeutic assessment. Genetic variants were compared between the 'no-seizures' and the 'recurrent-seizures' groups. Bonferroni corrections for multiple comparisons and adjustment for covariates were performed before assessment of associations. RESULTS: Functionally relevant promoter polymorphisms from ABCC2: c.-1549G>A and c.-1019A>G either considered alone or in haplotype and diplotype combinations were observed for a significant association with seizure control in women (odds ratio>3.5, P<10, power>95%). Further, low protein-expressing CGT and TGT (c.-24C>T, c.1249G>A, c.3972C>T) haplotypes were always observed to be present in combination with the AG (c.-1549G>A, c.-1019A>G) haplotype that was over-represented in women with 'no seizures'. CONCLUSION: The distribution of the associated variants supports the involvement of ABCC2 in controlling seizures in women possibly by lowering of its expression. The biological basis of this finding could be an altered interaction of ABCC2 with AEDs and estrogens. These results necessitate replication in a larger pool of patients.
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87 - 330-21247T > C Intron 1 0.005 6 rs4148731 chr7:87239329 c.-330 - 8935C > T Intron 1 0.000 7 rs9282564 chr7:87229440 c.61A > G Exon 3 (Asn21Asp) 0.000 8 rs9282565 chr7:87214875 c.239C > A Exon 5 (Ala80Glu) 0.000 9 rs28381826 chr7:87214531 c.286 + 297G > A Intron 5 0.000 10 rs1989830 chr7:87205663 c.287 - 6124C > T Intron 5 0.135 11 rs2520464 chr7:87201086 c.287 - 1547A > G Intron 5 0.409 12 rs2235023 chr7:87190452 c.827+ 127G > A Intron 9 0.000 13 rs10276036 chr7:87180198 c.1000 - 44C > T Intron 10 0.401 14 rs2229109 chr7:87179809 c.1199G > A Exon 12 (Ser400Asn) 0.000 15 rs1128503 chr7:87179601 c.1236T > C Exon 13 (Gly412Gly) 0.390 16 rs2235036 chr7:87175271 c.1795G > A Exon 16 (Ala599Thr) 0.000 17 rs2235039 chr7:87165854 c.2401G > A Exon 21 (Val801Met) 0.000 18 rs2235040 chr7:87165750 c.2481 + 24G > A Intron 21 0.155 19 rs2032581 chr7:87160810 c.2485A > G Exon 22 (Ile829Val) 0.000 20 rs2032582 chr7:87160618 c.2677T/A > G Exon 22 (Ser/Thr893Ala) 0.318 21 rs7779562 chr7:87144816 c.3085 -72G > C Intron 25 0.043 22 rs2707944 chr7:87144641 c.3188C > G Exon 26 (Ala1063Gly) 0.000 23 rs2229107 chr7:87138659 c.3421A > T Exon 27 (Thr1141Ser) 0.000 24 rs1045642 chr7:87138645 c.3435T > C Exon 27 (Ile1145Ile) m Expression and activity [28] m mRNA expression [29] Altered substrate specificity [30] 0.375 25 rs2235048 chr7:87138511 c.3489 + 80C > T Intron 27 0.381 26 rs17064 chr7:87133470 c.3932A > T 30 UTR 0.000 ABCC1 1 rs504348 chr16:16043174 rs50438C > G Near gene region k Promoter activity [31] 0.135 2 rs215106 chr16:16047542 c.48 + 3886A > G Intron 1 0.210 3 rs215049 chr16:16070768 c.48 + 27112G > C Intron 1 0.245 4 rs246220 chr16:16082128 c.49 - 19545C > G Intron 1 0.118 5 rs119774 chr16:16086833 c.49 - 14840G > A Intron 1 0.089 6 rs246217 chr16:16090354 c.49 - 11319C > A Intron 1 0.118 7 rs2014800 chr16:16099966 c.49 - 1707C > T Intron 1 0.398 8 rs41494447 chr16:16101842 c.218C > T Exon 2 (Thr73Ile) 0.000 9 rs4781712 chr16:16103232 c.226 - 401A > G Intron 2 0.355 10 rs246240 chr16:16119024 c.616 -7942A > G Intron 5 0.114 11 rs924135 chr16:16123459 c.616 - 3507A > T Intron 5 0.412 12 rs903880 chr16:16130514 c.809 + 54C > A Intron 7 0.147 13 rs8187852 chr16:16139709 c.1057G > A Exon 9 (Met353Val) 0.000 14 rs35587 chr16:16139714 c.1062T > C Exon 9 (Asn354Asn) 0.182 15 rs35592 chr16:16141823 c.1219 - 176T > C Intron 9 0.172 16 rs60782127 chr16:16142079 c.1299G > T Exon 10 (Arg433Ser) k Transport of leukotriene C4 and estrone sulfate [32] 0.008 17 rs3765129 chr16:16149901 c.1474 - 48C > T Intron 11 0.032 18 rs35597 chr16:16158034 c.1678 - 3979G > A Intron 12 0.320 19 rs35621 chr16:16168608 c.1913 - 1575C > T Intron 14 0.103 20 rs45511401 chr16:16173232 c.2012G > T Exon 16 (Gly671Val) 0.024 21 rs4148356 chr16:16177275 c.2168G > A Exon 17 (Arg723Gln) 0.000 22 rs3851713 chr16:16184873 c.2644 + 428A > T Intron 19 0.340 23 rs2239995 chr16:16192565 c.2645 - 3919G > A Intron 19 0.324 24 rs11864374 chr16:16201885 c.2871 + 1155G > A Intron 21 0.338 25 rs35529209 chr16:16205325 c.2965G > A Exon 22 (Thr989Ala) k Transport of estradiol 17b-glucuronide [32] 0.000 26 rs3887893 chr16:16205501 c.3079 + 62G > A Intron 22 0.448 27 rs13337489 chr16:16208683 c.3140G > C Exon 23 (Ser1047Cys) 0.000 28 rs2299670 chr16:16220858 c.3819 + 1090A > G Intron 26 0.399 29 rs8057331 chr16:16230411 c.4202C > T Exon 29 (Thr1401Met) 0.000 30 rs212090 chr16:16236004 c.5462T > A 30 UTR 0.357 31 rs212093 chr16:16237754 rs212093G > A Near gene region 0.429 32 rs4148382 chr16:16238494 rs4148382G > A Near gene region 0.034 ABCC2 1 g.-1774G > delG chr10:101535688 g.-1774G > delG Near gene region k Promoter activity [33] 0.000 2 rs1885301 chr10:101541053 c.-1549G > A Near gene region k Promoter activity [haplotype containing (- 1549A)-(- 24T)] [33] k Clearance of irinotecan (ABCC2*2 containing the G allele) [34] 0.379 450 Pharmacogenetics and Genomics 2012, Vol 22 No 6 Table 2 (continued) N dbSNP ida Positionb Allelesc Gene location (effect) Function MAF 3 rs2804402 chr10:101541583 c.
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ABCB1 p.Ala80Glu 22565165:87:197
status: NEW