SLCO1B1 Genotype Research Articles

Relationship between SLCO1B1 polymorphisms and methotrexate intolerance in Mexican children with juvenile idiopathic arthritis.

The most frequent adverse events (AEs) of methotrexate (MTX) are gastrointestinal symptoms and hepatotoxicity, which can affect its adherence, leading to reduced effectiveness. The SLCO1B1 gene codes for a liver protein (OATP1B1) responsible for drug transportation. Genetic variations within the SLCO1B1 gene locus impact drug transport, leading to altered pharmacokinetic profiles, delayed MTX clearance, and increased risk of toxicity. This study aimed to determine the association between single nucleotide polymorphisms (SNPs) in the SLCO1B1 gene (rs4149056, rs2306283) with the development of AEs in patients with juvenile idiopathic arthritis (JIA) treated with MTX. We performed an observational retrospective study to analyze the relationship between SNPs in the SLCO1B1 gene and the development of AEs in pediatric patients treated with MTX for JIA. Thirty patients with JIA were included, 22 females (73.3%), with a median age of 11years (IQR 8.3-15). The most frequent JIA subtype was rheumatoid factor-positive polyarthritis (36.7%). Twenty patients (66.7%) reported AEs. The *1B haplotype was the most frequent in this group (53.3%) and conferred a higher risk of developing AEs (OR = 3.89, 95% CI = 1.23 -12.29, p = 0.03). Patients with the allele *1B may benefit from lower doses of MTX. SLCO1B1 genotyping is a promising technique to identify patients at higher risk of AEs during treatment with MTX, thus requiring dose optimization.

Publication bias in pharmacogenetics of statin-associated muscle symptoms, an umbrella review with a meta-epidemiological study

Background and aimsStatin-associated muscle symptoms (SAMS) are a major cause of treatment discontinuation. Clinical Pharmacogenetics Implementation Consortium (CPIC) recommend dose adjustment for statin treatment according to known SLCO1B1 genotype to reduce SAMS. We hypothesized that the association between SLCO1B1 genotype and SAMS is misestimated because of publication bias. MethodsWe searched published systematic reviews on the association between SLCO1B1 genotype and SAMS. To assessed publication bias, we used funnel plot visual inspection, Egger’s test, and the Bayes Factor (BFPublication-bias) from Robust Bayesian Meta-Analysis (RoBMA). We compared the odds ratios (ORUncorrected) from meta-analyses before and after correcting for publication bias using trim-and-fill (ORTrim&Fill) and RoBMA (ORRoBMA) methods. ResultsWe included 8 cohort and 11 case-control studies, totaling 62 OR of three SLCO1B1 genotypes and six statin drugs. In the primary analysis, the funnel plot was suggestive of publication bias, confirmed by Egger’s test (p=0.001) and RoBMA (BFPublication-bias=18). Correcting the estimate for publication bias resulted in loss of the association, from a significant ORUncorrected (1.31 95%CI [1.13–1.53]) to corrected ORs suggesting no difference: ORTrim&Fill (1.07 95%CI [0.89–1.30]) and ORRoBMA (1.02 95%CI [1.00–1.33]). This suggested that publication bias overestimated the association by 18% and 23%, respectively. Similar results were found for genotype rs4149056, simvastatin and atorvastatin. ConclusionsThe effect of the SLCO1B1 genotype on the risk of developing SAMS is overestimated in the published literature, especially rs4149056. This could lead prescribers to incorrectly decreasing statin doses or even avoiding statin use, leading to a loss of the potential cardiovascular benefit of statins.

Allelic frequencies of polymorphism c.521T>C (rs4149056) favor preemptive SLCO1B1 genotyping in Armenia.

Statins represent an important pharmacological factor for the prevention of cardiovascular diseases but may also cause severe cases of myotoxicity. Numerous studies have described the association of the SLCO1B1 gene variant c.521C with statin-induced myopathy across different populations. This study aimed at evaluating the usefulness of preemptive SLCO1B1 genotyping in Armenia. A total of 202 Armenian patients referred to the Center of Medical Genetics and Primary Health Care in Yerevan for upper respiratory tract infection between January and May 2022 were included in this study. Genotyping for SLCO1B1 c.521T>C (rs4149056) was performed using a commercially available real-time PCR assay (RealFast™). In total, 3/202 (1.5 %) samples were C/C homozygotes and 52/202 (25.7 %) were T/C heterozygotes, associated with a high and increased risk for statin-induced myopathy, respectively. The SLCO1B1 c.521C allelic frequency was 14.4 %. The observed allele frequency of 14.4 % for thec.521C variant is slightly lower than frequencies reportedfrom Europe, but relatively high compared to Asianpopulations, suggesting that preemptive SLCO1B1 genotyping could be a useful approach for the reduction of statin-induced adverse effects in Armenia.

CYP2B6 and ABCB1 genotypes predict methadone plasma exposure among patients on maintenance therapy against opioid addictions in Tanzania.

Methadone maintenance therapy (MMT) exhibits significant variability in pharmacokinetics and clinical response, partly due to genetic variations. However, data from sub-Saharan African populations are lacking. We examined plasma methadone variability and pharmacogenetic influences among opioid-addicted Tanzanian patients. Patients attending MMT clinics (n = 119) in Tanzania were genotyped for common functional variants of the CYP3A4, CYP3A5, CYP2A6, CYP2B6, CYP2C19, CYP2D6, ABCB1, UGT2B7 and SLCO1B1 genotypes. Trough plasma concentrations of total methadone, S-methadone (S-MTD) and R-methadone (R-MTD), with their respective metabolites, 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP), were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The methadone-to-EDDP metabolic ratio (MMR) was used to categorize the phenotype. The proportions of MMR-predicted ultrarapid, extensive, intermediate and slow methadone metabolizer phenotypes were 2.5%, 58.2%, 23.7% and 15.6%, respectively. CYP2B6 genotype significantly correlated with S-methadone (P = .006), total methadone (P = .03), and dose-normalized methadone plasma concentrations (P = .001). Metabolic ratios of R-methadone (R-MTD/R-EDDP), S-methadone (S-MTD/S-EDDP), and total methadone (MMR) were significantly higher among patients homozygous for defective variants (*6 or *18) than heterozygous or CYP2B6*1/*1 genotypes (P < .001). The metabolic ratio for S-MTD and total methadone was significantly higher among ABCB1c.3435T/T than in the C/C genotype. No significant effect of CYP2D6, CYP2C19, CYP3A4, CYP3A5, CYP2A6, UGT2B7 and SLCO1B1 genotypes on S-methadone, R-methadone, or total methadone was observed. Approximately one in six opioid-addicted Tanzanian patients are methadone slow metabolizers, influenced by genetic factors. Both the CYP2B6 and ABCB1 genotypes are strong predictors of methadone metabolic capacity and plasma exposure. Further investigation is needed to determine their predictive value for methadone treatment outcomes and to develop genotype-based dosing algorithms for safe and effective therapy.

Genome-Wide Association Study of Atorvastatin Pharmacokinetics: Associations With SLCO1B1, UGT1A3, and LPP.

In a genome-wide association study of atorvastatin pharmacokinetics in 158 healthy volunteers, the SLCO1B1 c.521T>C (rs4149056) variant associated with increased area under the plasma concentration-time curve from time zero to infinity (AUC0-∞) of atorvastatin (P = 1.2 × 10-10), 2-hydroxy atorvastatin (P = 4.0 × 10-8), and 4-hydroxy atorvastatin (P = 2.9 × 10-8). An intronic LPP variant, rs1975991, associated with reduced atorvastatin lactone AUC0-∞ (P = 3.8 × 10-8). Three UGT1A variants linked with UGT1A3*2 associated with increased 2-hydroxy atorvastatin lactone AUC0-∞ (P = 3.9 × 10-8). Furthermore, a candidate gene analysis including 243 participants suggested that increased function SLCO1B1 variants and decreased activity CYP3A4 variants affect atorvastatin pharmacokinetics. Compared with individuals with normal function SLCO1B1 genotype, atorvastatin AUC0-∞ was 145% (90% confidence interval: 98-203%; P = 5.6 × 10-11) larger in individuals with poor function, 24% (9-41%; P = 0.0053) larger in those with decreased function, and 41% (16-59%; P = 0.016) smaller in those with highly increased function SLCO1B1 genotype. Individuals with intermediate metabolizer CYP3A4 genotype (CYP3A4*2 or CYP3A4*22 heterozygotes) had 33% (14-55%; P = 0.022) larger atorvastatin AUC0-∞ than those with normal metabolizer genotype. UGT1A3*2 heterozygotes had 16% (5-25%; P = 0.017) smaller and LPP rs1975991 homozygotes had 34% (22-44%; P = 4.8 × 10-5) smaller atorvastatin AUC0-∞ than noncarriers. These data demonstrate that genetic variation in SLCO1B1, UGT1A3, LPP, and CYP3A4 affects atorvastatin pharmacokinetics. This is the first study to suggest that LPP rs1975991 may reduce atorvastatin exposure. [Correction added on 6 April, after first online publication: An incomplete sentence ("= 0.017) smaller in heterozygotes for UGT1A3*2 and 34% (22%, 44%; P × 10-5) smaller in homozygotes for LPP noncarriers.") has been corrected in this version.].

Non-targeted metabolomics for the identification of plasma metabolites associated with organic anion transporting polypeptide 1B1 function.

Our aim was to evaluate biomarkers for organic anion transporting polypeptide 1B1 (OATP1B1) function using a hypothesis-free metabolomics approach. We analyzed fasting plasma samples from 356 healthy volunteers using non-targeted metabolite profiling by liquid chromatography high-resolution mass spectrometry. Based on SLCO1B1 genotypes, we stratified the volunteers to poor, decreased, normal, increased, and highly increased OATP1B1 function groups. Linear regression analysis, and random forest (RF) and gradient boosted decision tree (GBDT) regressors were used to investigate associations of plasma metabolite features with OATP1B1 function. Of the 9152 molecular features found, 39 associated with OATP1B1 function either in the linear regression analysis (p < 10-5) or the RF or GBDT regressors (Gini impurity decrease > 0.01). Linear regression analysis showed the strongest associations with two features identified as glycodeoxycholate 3-O-glucuronide (GDCA-3G; p = 1.2 × 10-20 for negative and p = 1.7 × 10-19 for positive electrospray ionization) and one identified as glycochenodeoxycholate 3-O-glucuronide (GCDCA-3G; p = 2.7 × 10-16). In both the RF and GBDT models, the GCDCA-3G feature showed the strongest association with OATP1B1 function, with Gini impurity decreases of 0.40 and 0.17. In RF, this was followed by one GDCA-3G feature, an unidentified feature with a molecular weight of 809.3521, and the second GDCA-3G feature. In GBDT, the second and third strongest associations were observed with the GDCA-3G features. Of the other associated features, we identified with confidence two representing lysophosphatidylethanolamine 22:5. In addition, one feature was putatively identified as pregnanolone sulfate and one as pregnenolone sulfate. These results confirm GCDCA-3G and GDCA-3G as robust OATP1B1 biomarkers in human plasma.

Variability in plasma rifampicin concentrations and role of SLCO1B1, ABCB1, AADAC2 and CES2 genotypes in Ethiopian patients with tuberculosis

Background Rifampicin, a key drug against tuberculosis (TB), displays wide between-patient pharmacokinetics variability and concentration-dependent antimicrobial effect. We investigated variability in plasma rifampicin concentrations and the role of SLCO1B1, ABCB1, arylacetamide deacetylase (AADAC) and carboxylesterase 2 (CES-2) genotypes in Ethiopian patients with TB. Methods We enrolled adult patients with newly diagnosed TB (n = 119) who had received 2 weeks of rifampicin-based anti-TB therapy. Venous blood samples were obtained at three time points post-dose. Genotypes for SLCO1B1 (c.388A > G, c.521T > C), ABCB1 (c.3435C > T, c.4036A > G), AADACc.841G > A and CES-2 (c.269-965A > G) were determined. Rifampicin plasma concentration was quantified using LC-MS/MS. Predictors of rifampicin C max and AUC0–7 h were analysed. Results The median rifampicin C max and AUC0–7 were 6.76 µg/mL (IQR 5.37–8.48) and 17.05 µg·h/mL (IQR 13.87–22.26), respectively. Only 30.3% of patients achieved the therapeutic efficacy threshold (C max>8 µg/mL). The allele frequency for SLCO1B1*1B (c.388A > G), SLCO1B1*5 (c.521T > C), ABCB1 c.3435C > T, ABCB1c.4036A > G, AADAC c.841G > A and CES-2 c.269-965A > G were 2.2%, 20.2%, 24.4%, 14.6%, 86.1% and 30.6%, respectively. Sex, rifampicin dose and ABCB1c.4036A > G, genotypes were significant predictors of rifampicin C max and AUC0–7. AADACc.841G > A genotypes were significant predictors of rifampicin C max. There was no significant influence of SLCO1B1 (c.388A > G, c.521T > C), ABCB1c.3435C > T and CES-2 c.269-965A > G on rifampicin plasma exposure variability. Conclusions Subtherapeutic rifampicin plasma concentrations occurred in two-thirds of Ethiopian TB patients. Rifampicin exposure varied with sex, dose and genotypes. AADACc.841G/G and ABCB1c.4036A/A genotypes and male patients are at higher risk of lower rifampicin plasma exposure. The impact on TB treatment outcomes and whether high-dose rifampicin is required to improve therapeutic efficacy requires further investigation.

Effect of SLCO1B1 c.521T>C polymorphism on the lipid response to statins in people living with HIV on a boosted protease inhibitor-containing regimen.

We previously observed that some individuals on HIV boosted protease inhibitor-containing regimen do not achieve their lipid targets despite elevated statin concentrations. This study evaluated whether the common single polymorphism c.521T>C in SLCO1B1, associated with reduced statin uptake in the liver, could explain this observation. People living with HIV in the Swiss HIV Cohort Study were eligible if they were on a boosted protease inhibitor concomitantly with a statin for at least 6 months and if their SLCO1B1 genotype was available. Furthermore, their lipids had to be documented before and after the introduction of the statin. The statin efficacy was defined as % change in total cholesterol, low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol and triglycerides levels after statin initiation compared to pretreatment levels. Lipid response was adjusted for differences in potency and dose between statins. In total, 88 people living with HIV were included, of whom 58, 28 and 2 carried the SLCO1B1 TT, TC and CC genotypes, respectively. The change in lipid levels after statin initiation tended to be lower in carriers of the polymorphism although the difference was not statistically significant (TT vs. TC/CC: total cholesterol: -11.7 vs. -4.8%; low-density lipoprotein- cholesterol: -20.6 vs. -7.4%; high-density lipoprotein-cholesterol: 1.6 vs. 0%; triglycerides: -11.5 vs. -7.9%). In the multiple linear regression, change in total cholesterol was inversely correlated with the total cholesterol level prestatin treatment (coefficient -6.60, 95% confidence interval: -9.63 to -3.56, P < .001). The lipid-lowering effect of statins tended to be attenuated by SLCO1B1 polymorphism and progressively declined as total cholesterol under the boosted protease inhibitor treatment decreased.

SLCO1B1 gene-based clinical decision support reduces statin-associated muscle symptoms risk with simvastatin.

Background: SLCO1B1 variants are known to be a strong predictor of statin-associated muscle symptoms (SAMS) risk with simvastatin. Methods: The authors conducted a retrospective chart review on 20,341 patients who had SLCO1B1 genotyping to quantify the uptake of clinical decision support (CDS) for genetic variants known to impact SAMS risk. Results: A total of 182 patients had 417 CDS alerts generated, and 150 of these patients (82.4%) received pharmacotherapy that did not increase risks for SAMS. Providers were more likely to cancel simvastatin orders in response to CDS alerts if genotyping had been done prior to the first simvastatin prescription than after (94.1% vs 28.5%, respectively; p<0.001). Conclusion: CDS significantly reduces simvastatin prescribing at doses associated with SAMS.

CYP2C19 metabolizer phenotypes may affect the efficacy of statins on lowering small dense low-density lipoprotein cholesterol of patients with coronary artery disease.

Dyslipidemia is a major cause of arteriosclerotic cardiovascular disease (ASCVD), and low-density lipoprotein cholesterol (LDL-C) is the profile to be reduced to prevent disease progression. Small dense low-density lipoprotein cholesterol (sdLDL-C) has been proven to be a more effective biomarker than LDL-C for ASCVD primary and secondary prevention. CYP2C19 is an important drug metabolism gene. This study aimed to investigate the relationship between sdLDL-C and coronary artery disease (CAD) risk factors and explore the influence of CYP2C19 metabolizer phenotypes on the sdLDL-C lowering efficacy of statins. This study recruited 182 patients with CAD and 200 non-CAD controls. Baseline laboratory indices of fasting blood were detected, including blood lipids, glucose, and creatinine. In addition, LDL-C subfractions were separated and quantified. Gene polymorphisms of SLCO1B1 and CYP2C19 were detected in patients with CAD. The LDL-C subfractions levels of patients with CAD were followed up after statin drug treatment. Total cholesterol, LDL-C, LDLC-2, LDLC-3, LDLC-4, LDLC-5, LDLC-6, LDLC-7, and sdLDL-C levels of patients with CAD were significantly higher than those in non-CAD controls. Meanwhile, sdLDL-C (AUC = 0.838) and LDLC-4 (AUC = 0.835) performed outstandingly in distinguishing patients with CAD from controls. Based on CYP2C19 metabolizer phenotypes, 113 patients with CAD were divided into the extensive metabolizer (EM, n = 49), intermediate metabolizer (IM, n = 52), and poor metabolizer (PM, n = 12) groups. The patients with IM and PM metabolizer phenotypes had better sdLDL-C lowering efficacy after taking statin drugs than patients with EM phenotype (P = 0.0268, FDR = 0.0536). The SLCO1B1 genotype had no significant impact on the efficacy of statins (P = 0.1611, FDR = 0.1611). sdLDL-C and LDLC-4 outperformed other blood lipids such as LDL-C for CAD risk screening. CYP2C19 metabolizer phenotypes had the potential to predict the efficacy of statins in lowering sdLDL-C.

Should we test for SLCO1B1 genotype before prescribing statins?—a discussion of clinical trial results

Should we test for SLCO1B1 genotype before prescribing statins?—a discussion of clinical trial results

ABCG2 and SLCO1B1 gene polymorphisms in the Croatian population

Background Organic anion-transporting polypeptide 1B1 (OATP1B1) and the ATP-binding cassette subfamily G member 2, ABCG2, are important transporters involved in the transport of endogenous substrates and xenobiotics, including drugs. Genetic polymorphisms of these transporters have effect on transporter activity. There is significant interethnic variability in the frequency of allele variants. Aim To determined allele and genotype frequencies of ABCG2 and SLCO1B1 genes in Croatian populations of European descent. Subjects and methods A total of 905 subjects (482 women) were included. Genotyping for ABCG2 c.421C > A (rs2231142) and for SLCO1B1 c.521T > C (rs4149056), was performed by real-time polymerase chain reaction (PCR) using TaqMan® DME Genotyping Assays. Results For ABCG2 c.421C > A, the frequency of CC, CA and AA genotypes was 81.4%, 17.8% and 0.8% respectively. The frequency of variant ABCG2 421 A allele was 9.7%. For SLCO1B1 c.521T > C, the frequency of TT, TC and CC genotypes was 61.7%, 34.8% and 3.5% respectively. The frequency of variant SLCO1B1 521 C allele was 20.9%. Conclusion The frequency of the ABCG2 and SLCO1B1 allelic variants and genotypes in the Croatian population is in accordance with other European populations. Pharmacogenetic analysis can serve to individualise drug therapy and minimise the risk of developing adverse drug reactions.

Pharmacogenetic variability and the probability of site of action target attainment during tuberculosis meningitis treatment: A physiologically based pharmacokinetic modeling and simulations study

Objective and methodsOur objective was to investigate the role of patient pharmacogenetic variability in determining site of action target attainment during tuberculous meningitis (TBM) treatment. Rifampin and isoniazid PBPK model that included SLCO1B1 and NAT2 effects on exposures respectively were obtained from literature, modified, and validated using available cerebrospinal-fluid (CSF) concentrations. Population simulations of isoniazid and rifampin concentrations in brain interstitial fluid and probability of target attainment according to genotypes and M. tuberculosis MIC levels, under standard and intensified dosing, were conducted. ResultsThe rifampin and isoniazid model predicted steady-state drug concentration within brain interstitial fluid matched with the observed CSF concentrations. At MIC level of 0.25 mg/L, 57% and 23% of the patients with wild type and heterozygous SLCO1B1 genotype respectively attained the target in CNS with rifampin standard dosing, improving to 98% and 91% respectively with 35 mg/kg dosing. At MIC level of 0.25 mg/L, 33% of fast acetylators attained the target in CNS with isoniazid standard dosing, improving to 90% with 7.5 mg/kg dosing. ConclusionIn this study, the combined effects of pharmacogenetic and M. tuberculosis MIC variability were potent determinants of target attainment in CNS. The potential for genotype-guided dosing during TBM treatment should be further explored in prospective clinical studies.

Effect of SLCO1B1 genotypes on statin intolerance and response to statins

Abstract Background Statins are a cornerstone therapy for management of hypercholesterolaemia in primary and secondary prevention of cardiovascular disease, however there is individual variation regarding efficacy of therapy and adverse side-effects experienced. Purpose Polymorphisms of the SLCO1B1 gene have shown correlation with the risk of statin side-effects and efficacy of treatment, however in real-world clinical practice, their utility remains to be established. Methods We carried out retrospective multiple linear regression analysis using robust standard errors of 129 individuals undergoing genetic testing for the SLCO1B1 polymorphisms rs4149056 and rs2306283 to investigate for an association to statin side effects and efficacy respectively. In addition, logistic regressions which account for the binary nature of the outcomes were performed. The rs4149056 gene has 3 outcomes: T/T (considered least associated with statin intolerance), C/C (considered most associated with statin intolerance) and T/C (considered intermediately associated with statin intolerance). The rs2306283 gene has 3 outcomes: A/A (considered least associated with statin response), G/G (considered most associated with statin response) and A/G (considered intermediately associated with statin response). Results According to multiple regression and logistic regression analyses SLCO1B1 rs4149056 genotypes (C/C and T/C relative to the base type T/T) do not predict the incidence of myalgia, myopathy, creatine kinase rise, liver function derangement or gastrointestinal side effects at 5% significance. SLCO1B1 rs2306283 (G/G and A/G relative to the base type A/A) do not predict LDL response to statins (p-values 0.33 and 0.52 respectively). Conclusions The presence of rs4149056 C/C and T/C genotypes was not associated with an increased risk of statin side-effects. Similarly rs2306283 genotypes did not correlate with the efficacy of statin treatment. These genotype predictors were primarily based on Simvastatin – an earlier generation statin, whereas the majority of the patients in this cohort were treated with more contemporary and widely used statins such as Atorvastatin and Rosuvastatin. We therefore conclude that these genotypic markers may have limited application in modern day clinical practice and that substantial investment of limited resources towards them may not be justified. Funding Acknowledgement Type of funding sources: None.

Effect of SLCO1B1 genotypes on statin intolerance and response

Effect of SLCO1B1 genotypes on statin intolerance and response

A comprehensive pharmacogenomic study indicates roles for SLCO1B1, ABCG2 and SLCO2B1 in rosuvastatin pharmacokinetics.

The aim was to comprehensively investigate the effects of genetic variability on the pharmacokinetics of rosuvastatin. We conducted a genome-wide association study and candidate gene analyses of single dose rosuvastatin pharmacokinetics in a prospective study (n =159) and a cohort of previously published studies (n =88). In a genome-wide association meta-analysis of the prospective study and the cohort of previously published studies, the SLCO1B1 c.521 T > C (rs4149056) single nucleotide variation (SNV) associated with increased area under the plasma concentration-time curve (AUC) and peak plasma concentration of rosuvastatin (P= 1.8×10-12 and P= 3.2×10-15 ). The candidate gene analysis suggested that the ABCG2 c.421C > A (rs2231142) SNV associates with increased rosuvastatin AUC (P= .0079), while the SLCO1B1 c.388A > G (rs2306283) and SLCO2B1 c.1457C > T (rs2306168) SNVs associate with decreased rosuvastatin AUC (P= .0041 and P= .0076). Based on SLCO1B1 genotypes, we stratified the participants into poor, decreased, normal, increased and highly increased organic anion transporting polypeptide (OATP) 1B1 function groups. The OATP1B1 poor function phenotype associated with 2.1-fold (90% confidence interval 1.6-2.8, P =4.69×10-5 ) increased AUC of rosuvastatin, whereas the OATP1B1 highly increased function phenotype associated with a 44% (16-62%; P= .019) decreased rosuvastatin AUC. The ABCG2 c.421A/A genotype associated with 2.2-fold (1.5-3.0; P =2.6×10-4 ) increased AUC of rosuvastatin. The SLCO2B1 c.1457C/T genotype associated with 28% decreased rosuvastatin AUC (11-42%; P =.01). These data suggest roles for SLCO1B1, ABCG2 and SLCO2B1 in rosuvastatin pharmacokinetics. Poor SLCO1B1 or ABCG2 function genotypes may increase the risk of rosuvastatin-induced myotoxicity. Reduced doses of rosuvastatin are advisable for patients with these genotypes.

Development of pharmacogenomic algorithm to optimize nateglinide dose for the treatment of type 2 diabetes mellitus.

Nateglinide is a meglitinide used for the treatment of type 2 diabetes mellitus. Individual studies demonstrated the association of CYP2C9, SLCO1B1, and MTNR1B variants with the safety and efficacy of nateglinide. The current study aimed to develop a pharmacogenomic algorithm to optimize nateglinide therapy. Multiple linear regression (MLR) and classification and regression tree (CART) were used to develop a pharmacogenomic algorithm for nateglinide dosing based on the published nateglinide pharmacokinetic data on the area under the curve data (AUC) and Cmax (n = 143). CYP2C9 metabolizer phenotype, SLCO1B1, MTNR1B genotypes, and CYP2C9 inhibitor usage were used as the input variables. The results and associations were further confirmed by meta-analysis and in silico studies. The MLR models of AUC and Cmax explain 87.4% and 59% variability in nateglinide pharmacokinetics. The Bland and Altman analysis of the nateglinide dose predicted by these two MLR models showed a bias of ± 26.28mg/meal. The CART algorithm was proposed based on these findings. This model is further justified by the meta-analysis showing increased AUCs in CYP2C9 intermediate metabolizers and SLCOB1 TC and CC genotypes compared to the wild genotypes. The increased AUC in SLCO1B1 mutants is due to decreased binding affinity of nateglinide to the mutant affecting the influx of nateglinide into hepatocytes. MTNR1B rs10830963 G-allele-mediated poor response to nateglinide is attributed to increased transcriptional factor binding causing decreased insulin secretion. CYP2C9, SLCO1B1, and MTNR1B genotyping help in optimizing nateglinide therapy based on this algorithm and ensuring safety and efficacy.

Effects of Soy Isoflavones and Green Tea Extract on Simvastatin Pharmacokinetics and Influence of the SLCO1B1 521T > C Polymorphism.

ObjectivesGreen tea and soy products are extensively consumed by many people and they may influence the activity of drug metabolizing enzymes and drug transporters to result in drug interactions. This study was performed to evaluate the effect of green tea and soy isoflavone extracts on the pharmacokinetics of simvastatin in healthy subjects and to clarify the role of polymorphisms in the SLCO1B1 drug transporter in this effect.MethodsThis was an open-label, three-phase randomized crossover pharmacokinetic study. A single dose of simvastatin 20 mg was taken on three occasions (without herbs, with green tea, and with soy isoflavones) by healthy male Chinese subjects. The green tea and soy isoflavone extracts were given at a dose containing EGCG 800 mg once daily or soy isoflavones about 80 mg once daily for 14 days before simvastatin dosing with at least 4-weeks washout period between phases.ResultsAll the 18 subjects completed the study. Intake of soy isoflavones was associated with reduced systemic exposure to simvastatin acid [geometric mean (% coefficient of variation) AUC0–24h from 16.1 (44.2) h⋅μg/L to 12.1 (54.6) h⋅μg/L, P < 0.05) but not the lactone. Further analysis showed that the interaction between simvastatin and the soy isoflavones only resulted in a significant reduction of AUC in subjects with the SLCO1B1 521TT genotype and not in those with the 521C variant allele. There was no overall effect of the green tea extract on simvastatin pharmacokinetics but the group with the SLCO1B1 521TT genotype showed reduced AUC values for simvastatin acid.ConclusionThis study showed repeated administration of soy isoflavones reduced the systemic bioavailability of simvastatin in healthy volunteers that was dependent on the SLCO1B1 genotype which suggested that soy isoflavones-simvastatin interaction is impacted by genotype-related function of this liver uptake transporter.

The Clinical Pharmacogenetics Implementation Consortium Guideline for SLCO1B1, ABCG2, and CYP2C9 genotypes and Statin-Associated Musculoskeletal Symptoms.

Statins reduce cholesterol, prevent cardiovascular disease, and are among the most commonly prescribed medications in the world. Statin-associated musculoskeletal symptoms (SAMS) impact statin adherence and ultimately can impede the long-term effectiveness of statin therapy. There are several identified pharmacogenetic variants that impact statin disposition and adverse events during statin therapy. SLCO1B1 encodes a transporter (SLCO1B1; alternative names include OATP1B1 or OATP-C) that facilitates the hepatic uptake of all statins. ABCG2 encodes an efflux transporter (BCRP) that modulates the absorption and disposition of rosuvastatin. CYP2C9 encodes a phase I drug metabolizing enzyme responsible for the oxidation of some statins. Genetic variation in each of these genes alters systemic exposure to statins (i.e., simvastatin, rosuvastatin, pravastatin, pitavastatin, atorvastatin, fluvastatin, lovastatin), which can increase the risk for SAMS. We summarize the literature supporting these associations and provide therapeutic recommendations for statins based on SLCO1B1, ABCG2, and CYP2C9 genotype with the goal of improving the overall safety, adherence, and effectiveness of statin therapy. This document replaces the 2012 and 2014 Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for SLCO1B1 and simvastatin-induced myopathy.

SLCO1B1*5 Allele Is Associated With Atorvastatin Discontinuation and Adverse Muscle Symptoms in the Context of Routine Care.

The SLCO1B1 genotype is known to influence patient adherence to statin therapy, in part by increasing the risk for statin‐associated musculoskeletal symptoms (SAMSs). The SLCO1B1*5 allele has previously been associated with simvastatin discontinuation and SAMSs. Prior analyses of the relationship between SLCO1B1*5 and atorvastatin muscle side effects have been inconclusive due to insufficient power. We now quantify the impact of SLCO1B1*5 on atorvastatin discontinuation and SAMSs in a large observational cohort using electronic medical record data from a single health care system. In our study cohort (n = 1,627 patients exposed to atorvastatin during the course of routine clinical care), 56% (n = 912 of 1,627 patients) discontinued atorvastatin and 18% (n = 303 of 1,627 patients) developed SAMSs. A univariate model revealed that SLCO1B1*5 increased the likelihood that patients would stop atorvastatin during routine care (odds ratio 1.2; 95% confidence interval (CI), 1.1–1.5; P = 0.04). A multivariate Cox proportional hazards model further demonstrated that this same variant was associated with time to atorvastatin discontinuation (hazard ratio 1.2; 95% CI, 1.1–1.4; P = 0.004). Additional time‐to‐event analyses also revealed that SCLO1B1*5 was associated with SAMSs (hazard ratio 1.4; 95% CI, 1.1–1.7; P = 0.02). Atorvastatin discontinuation was associated with SAMSs (odds ratio 1.67; P = 0.0001) in our cohort.