The influences of SLCO1B1 and ABCB1 genotypes on the pharmacokinetics of simvastatin, in relation to CYP3A4 inhibition

Drug interactions in cardiology: focus on statins and their combination with other lipid-lowering drugs

INTRODUCTION

Statins are the primary therapeutic approach for treating hypercholesterolemia in hyperlipidemic high cardiovascular-risk patients, as stated by the recent European and American guidelines. However, in some patients, statin treatment is not sufficient to achieve the recommended plasma LDL-C levels, and the addition of a second hypolipidemic drug becomes mandatory. Concomitant administration of multiple medications may increase the risk of adverse events, potentially leading to statin-associated muscle or liver symptoms and non-adherence or discontinuation of statin therapy, such as in women. The addition of a second hypolipidemic drug (such as ezetimibe, anti-PCSK9 monoclonal antibodies, bempedoic acid, and inclisiran) may lead to drug-drug interactions (DDIs). The evaluation of the different pharmacokinetic profiles may improve and personalize the treatment.

AREAS COVERED

We aimed to give an update on the potential DDIs between statins and other hypolipidemic drugs currently used to treat high-risk hyperlipidemic patients.

EXPERT OPINION

It is fundamental to understand the risk associated with DDIs to manage better the addition of a concomitant hyperlipidemic drug to a statin-treated patient. Many health agencies have published specific guidelines for assessing DDIs, but these mainly apply to in vitro studies. New predictive approaches are being proposed and may help evaluate and manage DDIs.

Genetic polymorphisms in ABCB1 are correlated with the increased risk of atorvastatin-induced muscle side effects: a cross-sectional study

Genetic factors are recognized as risk factors for statin-associated muscle symptoms (SAMS), which are the most common cause of statin intolerance. The aim of this study was to determine whether there is an association between polymorphisms 1236C > T, 2677G > T/A, and 3435C > T in the ABCB1 gene, encoding the efflux transporter of statins, and SAMS, as results on this topic are still controversial. A cross-sectional study was conducted on patients with or without SAMS using atorvastatin. The influence of non-genetic variables on SAMS was also evaluated. Our results show that patients with TT genotype in 1236C > T, 2677G > T/A, and 3435C > T polymorphisms had higher risk of developing SAMS, compared to wild type and heterozygous carriers together (OR 4.292 p = 0.0093, OR 5.897 p = 0.0023 and OR 3.547 p = 0.0122, respectively). Furthermore, TTT/TTT diplotype was also associated with a higher risk of SAMS, OR 9.234 (p = 0.0028). Only family history of cardiovascular disease was found to be a risk factor for SAMS, in addition to the known non-genetic variables. We believe that ABCB1 genotyping has great potential to be incorporated into clinical practice to identify high-risk patients in a timely manner.

Improving Management of Portal Hypertension: The Potential Benefit of Non-Etiological Therapies in Cirrhosis

Portal hypertension is the consequence of cirrhosis and results from increased sinusoidal vascular resistance and hepatic blood inflow. Etiological therapies represent the first intervention to prevent a significant increase in portal pressure due to chronic liver damage. However, other superimposed pathophysiological drivers may worsen liver disease, including inflammation, bacterial translocation, endothelial dysfunction, and hyperactivation of hemostasis. These mechanisms can be targeted by a specific class of drugs already used in clinical practice. Albumin, rifaximin, statins, aspirin, and anticoagulants have been tested in cirrhosis and were a topic of discussion in the last Baveno consensus as non-etiological therapies. Based on the pathogenesis of portal hypertension in cirrhosis, our review summarizes the main mechanisms targeted by these drugs as well as the clinical evidence that considers them a valid complementary option to manage patients with cirrhosis and portal hypertension.

Statins and Hemostasis: Therapeutic Potential Based on Clinical Evidence

Hemostasis preserves blood fluidity and prevents its loss after vessel injury. The maintenance of blood fluidity requires a delicate balance between pro-coagulant and fibrinolytic status. Endothelial cells (ECs) in the inner face of blood vessels maintain hemostasis through balancing anti-thrombotic and pro-fibrinolytic activities. Dyslipidemias are linked to hemostatic alterations. Thus, it is necessary a better understanding of the underlying mechanisms linking hemostasis with dyslipidemia. Statins are drugs that decrease cholesterol levels in the blood and are the gold standard for treating hyperlipidemias. Statins can be classified into natural and synthetic molecules, approved for the treatment of hypercholesterolemia. The classical mechanism of action of statins is by competitive inhibition of a key enzyme in the synthesis pathway of cholesterol, the HMG-CoA reductase. Statins are frequently administrated by oral ingestion and its interaction with other drugs and food supplements is associated with altered bioavailability. In this review we deeply discuss the actions of statins beyond the control of dyslipidemias, focusing on the actions in thrombotic modulation, vascular and cardiovascular-related diseases, metabolic diseases including metabolic syndrome, diabetes, hyperlipidemia, and hypertension, and chronic diseases such as cancer, chronic obstructive pulmonary disease, and chronic kidney disease. Furthermore, we were prompted to delved deeper in the molecular mechanisms by means statins regulate coagulation acting on liver, platelets, and endothelium. Clinical evidence show that statins are effective regulators of dyslipidemia with a high impact in hemostasis regulation and its deleterious consequences. However, studies are required to elucidate its underlying molecular mechanism and improving their therapeutical actions.

Pharmacogenomics of lipid-lowering agents: the impact on efficacy and safety

Hyperlipidemia is a significant risk factor for cardiovascular disease morbidity and mortality. The lipid-lowering drugs are considered the cornerstone of primary and secondary prevention of atherosclerotic cardiovascular disease. Unfortunately, the lack of efficacy and associated adverse effects, ranging from mild-to-moderate to potentially life-threatening, lead to therapy discontinuation. Numerous reports support the role of gene polymorphisms in drugs' pharmacokinetic parameters and their associated adverse reactions. Therefore, this study aims to understand the pharmacogenomics of lipid-lowering drugs and the impact of genetic variants of key genes on the drugs' efficacy and toxicity. Indeed, genetically guided lipid-lowering therapy enhances overall safety, improves drug adherence and achieves long-term therapy.

Does the choice of applied physiologically‐based pharmacokinetics platform matter? A case study on simvastatin disposition and drug–drug interaction

Physiologically‐based pharmacokinetic (PBPK) models have an important role in drug discovery/development and decision making in regulatory submissions. This is facilitated by predefined PBPK platforms with user‐friendly graphical interface, such as Simcyp and PK‐Sim. However, evaluations of platform differences and the potential implications for disposition‐related applications are still lacking. The aim of this study was to assess how PBPK model development, input parameters, and model output are affected by the selection of PBPK platform. This is exemplified via the establishment of simvastatin PBPK models (workflow, final models, and output) in PK‐Sim and Simcyp as representatives of established whole‐body PBPK platforms. The major finding was that the choice of PBPK platform influenced the model development strategy and the final model input parameters, however, the predictive performance of the simvastatin models was still comparable between the platforms. The main differences between the structure and implementation of Simcyp and PK‐Sim were found in the absorption and distribution models. Both platforms predicted equally well the observed simvastatin (lactone and acid) pharmacokinetics (20–80 mg), BCRP and OATP1B1 drug–gene interactions (DGIs), and drug–drug interactions (DDIs) when co‐administered with CYP3A4 and OATP1B1 inhibitors/inducers. This study illustrates that in‐depth knowledge of established PBPK platforms is needed to enable an assessment of the consequences of PBPK platform selection. Specifically, this work provides insights on software differences and potential implications when bridging PBPK knowledge between Simcyp and PK‐Sim users. Finally, it provides a simvastatin model implemented in both platforms for risk assessment of metabolism‐ and transporter‐mediated DGIs and DDIs.

Monitoring Simvastatin Adherence in Patients With Coronary Heart Disease: A Proof-of-Concept Study Based on Pharmacokinetic Measurements in Blood Plasma

BACKGROUND

Poor statin adherence remains a public health concern associated with adverse outcomes. We evaluated the use of pharmacokinetic measurements to monitor adherence to simvastatin in patients with coronary heart disease (CHD).

METHODS

Eighteen patients with CHD taking an evening dose of simvastatin 20 mg (n = 7), 40 mg (n = 5), or 80 mg (n = 6) were examined at steady-state pharmacokinetics. Ten patients were instructed to interrupt simvastatin dosing and return for blood sampling for the subsequent 3 days. Dose-normalized plasma concentrations of simvastatin lactone and simvastatin acid and the sum of the 2 were evaluated to discriminate between adherent dosing and dose omission. Bioanalytical quantification was performed using liquid chromatography-tandem mass spectrometry.

RESULTS

A simvastatin acid cutoff of 1.0 × 10 -2 nmol -1 ·L -1 ·mg -1 identified 100% of those omitting 2 doses and 60% of those omitting a single dose. Simvastatin acid showed superior ability to discriminate dose omission, as well as the best agreement between samples handled at ambient and cool temperatures (median deviation 3.5%; interquartile range -2.5% to 13%). The cutoff for a morning dose schedule, with a similar ability to discriminate, was estimated at 2.0 × 10 -3 nmol -1 ·L -1 ·mg -1 .

CONCLUSIONS

The present method discriminated between adherence and reduced adherence to simvastatin therapy in patients with CHD. Sample handling is feasible for routine practice, and the assessment of adherence can be performed by direct measurement of simvastatin acid in a blood sample, according to defined cutoff values. Further studies validating the cutoff value and utility for clinical application are encouraged.

Genomewide Association Study of Simvastatin Pharmacokinetics

We investigated genetic determinants of single-dose simvastatin pharmacokinetics in a prospective study of 170 subjects and a retrospective cohort of 59 healthy volunteers. In a microarray-based genomewide association study with the prospective data, the SLCO1B1 c.521T>C (p.Val174Ala, rs4149056) single nucleotide variation showed the strongest, genomewide significant association with the area under the plasma simvastatin acid concentration-time curve (AUC; P = 6.0 × 10^-10 ). Meta-analysis with the retrospective cohort strengthened the association (P = 1.6 × 10^-17 ). In a stepwise linear regression candidate gene analysis among all 229 participants, SLCO1B1 c.521T>C (P = 1.9 × 10^-13 ) and CYP3A4 c.664T>C (p.Ser222Pro, rs55785340, CYP3A4*2, P = 0.023) were associated with increased simvastatin acid AUC. Moreover, the SLCO1B1 c.463C>A (p.Pro155Thr, rs11045819, P = 7.2 × 10^-6 ) and c.1929A>C (p.Leu643Phe, rs34671512, P = 5.3 × 10^-4 ) variants associated with decreased simvastatin acid AUC. Based on these results and the literature, we classified the volunteers into genotype-predicted OATP1B1 and CYP3A4 phenotype groups. Compared with the normal OATP1B1 function group, simvastatin acid AUC was 273% larger in the poor (90% confidence interval (CI), 137%, 488%; P = 3.1 × 10^-6 ), 40% larger in the decreased (90% CI, 8%, 83%; P = 0.036), and 67% smaller in the highly increased function group (90% CI, 46%, 80%; P = 2.4 × 10^-4 ). Intermediate CYP3A4 metabolizers (i.e., heterozygous carriers of either CYP3A4*2 or CYP3A4*22 (rs35599367)), had 87% (90% CI, 39%, 152%, P = 6.4 × 10^-4 ) larger simvastatin acid AUC than normal metabolizers. These data suggest that in addition to no function SLCO1B1 variants, increased function SLCO1B1 variants and reduced function CYP3A4 variants may affect the pharmacokinetics, efficacy, and safety of simvastatin. Care is warranted if simvastatin is prescribed to patients carrying decreased function SLCO1B1 or CYP3A4 alleles.

Severe adverse cutaneous reactions induced by gefitinib combined with antihypertensive and antihyperlipidemic drugs in lung cancer: a case report

The incidence of lung cancer is increasing yearly worldwide, and targeted medicines are the main choice for lung cancer patients. However, there has been no relevant research about the analysis and adjustment of drug combinations for cancer patients with hypertension and hyperlipidemia until now. Here, we reported a case of medicine adjustment for a patient of lung cancer with hypertension and hyperlipidemia. The patient was diagnosed as right lung adenocarcinoma with lymph node metastasis and continued taking gefitinib tablets to maintain therapeutic efficacy after the end of chemotherapy. Severe paronychia and a high plasma concentration of gefitinib were noticed when the patient visited the hospital for reexamination. The clinical pharmacist found that the patient took nifedipine sustained-release tablets and simvastatin tablets simultaneously, and these medicines were all substrates of CYP3A4. The clinical pharmacist suggested replacing the medicines for hypertension and hyperlipidemia with valsartan capsules (Diovan) and rosuvastatin calcium tablets (Crestor), respectively. The adverse cutaneous reactions were greatly relieved, and the plasma concentration of gefitinib was decreased when another reexamination was performed. Therapeutic drug monitoring was an important method in our case and provided valuable information to develop individualized treatment strategies. For cancer patients suffering from other diseases such as hypertension and hyperlipidemia, it is necessary to pay special attention to the drug-drug interactions and metabolic pathways among drug combinations.

Baicalein inhibits the pharmacokinetics of simvastatin in rats via regulating the activity of CYP3A4

CONTEXT

Baicalein and simvastatin possess similar pharmacological activities and indications. The risk of their co-administration was unclear.

OBJECTIVE

The interaction between baicalein and simvastatin was investigated to provide reference and guidance for the clinical application of the combination of these two drugs.

MATERIALS AND METHODS

The pharmacokinetics of simvastatin was investigated in Sprague-Dawley rats (n = 6). The rats were pre-treated with 20 mg/kg baicalein for 10 days and then administrated with 40 mg/kg simvastatin. The single administration of simvastatin was set as the control group. The rat liver microsomes were employed to assess the metabolic stability and the effect of baicalein on the activity of CYP3A4.

RESULTS

Baicalein significantly increased the AUC_(0-t) (2018.58 ± 483.11 vs. 653.05 ± 160.10 μg/L × h) and C_max (173.69 ± 35.49 vs. 85.63 ± 13.28 μg/L) of simvastatin. The t_1/2 of simvastatin was prolonged by baicalein in vivo and in vitro. The metabolic stability of simvastatin was also improved by the co-administration of baicalein. Baicalein showed an inhibitory effect on the activity of CYP3A4 with the IC₅₀ value of 12.03 μM, which is responsible for the metabolism of simvastatin.

DISCUSSION AND CONCLUSION

The co-administration of baicalein and simvastatin may induce drug-drug interaction through inhibiting CYP3A4. The dose of baicalein and simvastatin should be adjusted when they are co-administrated.

MassARRAY multigene screening combined with LDL-C and sdLDL-C detection for more favorable outcomes in type 2 diabetes mellitus therapy

BACKGROUND

To determine the clinical value of multigene polymorphisms, LDL-C and sdLDL-C on T2DM therapy.

METHODS

In total, 352 T2DM patients before and after treatment and 48 healthy individuals were enrolled in this study. LDL-C and sdLDL-C were detected in 352 T2DM patients and 48 healthy individuals by Quantimetrix Lipoprint System. The 11 gene polymorphisms-HTR3B (rs2276307, A > G), APOE (rs7412, c.526C > T), APOE (rs429358, c.388 T > C), CYP2C9*3 (rs1057910, c.1075A > C), KIF6 (rs20455, c.2155 T > C), HMGCR (rs17238540, T > G), HMGCR (rs17244841, A > T), ABCB1 (rs2032582, A > C/T), HTR7 (rs1935349, C > T), SLCO1B1 (rs4149056, c.521 T > C), and CETP (rs708272, G > A)-were screened in these 352 T2DM patients by the Agena Bioscience MassARRAY system before therapy.

RESULTS

Genetic polymorphisms associated with T2DM and statin effects in pretreatment patients were detected, then results showed that all 11 genes had heterozygous mutation, and 7 genes had homozygous mutation in 352 T2DM patients, more specifically reflected that these gene polymorphisms were common in Chinese T2DM patients. LDL-C and sdLDL-C were detected before and after treatment, sdLDL mainly existed in T2DM patients, and T2DM patients had higher mean levels of sdLDL-C than healthy people. After pharmacotherapy, the coincidence rates of decreases in LDL-C and sdLDL-C levels were 88.35% (311/352) and 84.09% (296/352), consistent with patients in remission.

CONCLUSIONS

Gene polymorphisms related to pharmacotherapy were common in Chinese T2DM patients. And the expression of LDL-C and sdLDL-C was consistent with the T2DM disease course. Combined multigene screening before therapy and LDL-C and sdLDL-C detection before and after therapy could better assist T2DM treatment.

The influence of recipient SLCO1B1 rs2291075 polymorphism on tacrolimus dose-corrected trough concentration in the early period after liver transplantation

Purpose

To explore the relationship between rs2291075 polymorphism in SLCO1B1 gene, which encodes an influx transmembrane protein transporter, and tacrolimus dose–corrected trough concentration (C/D, ng ml⁻¹ mg⁻¹ kg⁻¹) in the early period after liver transplantation.

Methods

CYP3A5 rs776746 and SLCO1B1 rs2291075 polymorphisms of 210 liver transplantation patients and their corresponding donor livers were assessed by PCR amplification and DNA sequencing. The influence of gene polymorphisms on C/D values of tacrolimus was analyzed. The early postoperative period after liver transplantation was divided into the convalescence phase (1–14 days) and stationary phase (15–28 days) according to the change of liver function and tacrolimus C/D values.

Results

The combined analysis of donor and recipient CYP3A5 rs776746 could distinguish the metabolic phenotype of tacrolimus into three groups: fast elimination (FE), intermediate elimination (IE), and slow elimination (SE), which was entitled the FIS classification system. Tacrolimus C/D ratios of recipient SLCO1B1 rs2291075 CT and TT carriers were very close and were significantly lower than those of recipient SLCO1B1 rs2291075 CC genotype carriers in convalescence phase (p = 0.0195) and in stationary phase (p = 0.0152). There were no statistically significant differences between tacrolimus C/D ratios of patients carried with SLCO1B1 rs2291075 CT, TT genotype donors, and those carried with SLCO1B1 rs2291075 CC genotype donors. A model consisting of tacrolimus daily dose, total bilirubin, FIS classification, and recipient SLCO1B1 rs2291075 could predict tacrolimus C/D ratios in the convalescence phase by multivariate analysis. However, recipient SLCO1B1 rs2291075 genotype failed to enter forecast model for C/D ratios in stationary phase. Recipient SLCO1B1 rs2291075 genotype had significant effect on tacrolimus C/D ratios in convalescence phase (p = 0.0300) and stationary phase (p = 0.0400) in subgroup, which excluded the interference come from donor and recipient CYP3A5 rs776746.

Conclusion

SLCO1B1 rs2291075 could be a novel genetic locus associated with tacrolimus metabolism. The combined analysis of donor and recipient CYP3A5 rs776746, recipient SLCO1B1 rs2291075 genotypes, could be helpful to guide the personalized administration of tacrolimus in early period after liver transplantation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00228-020-03058-w.

Multi-factorial pharmacokinetic interactions: unraveling complexities in precision drug therapy

Introduction: Precision drug therapy requires accounting for pertinent factors in pharmacokinetic (PK) inter-individual variability (i.e., pharmacogenetics, diseases, polypharmacy, and natural product use) that can cause sub-therapeutic or adverse effects. Although each of these individual factors can alter victim drug PK, multi-factorial interactions can cause additive, synergistic, or opposing effects. Determining the magnitude and direction of these complex multi-factorial effects requires understanding the rate-limiting redundant and/or sequential PK processes for each drug.Areas covered: Perturbations in drug-metabolizing enzymes and/or transporters are integral to single- and multi-factorial PK interactions. Examples of single factor PK interactions presented include gene-drug (pharmacogenetic), disease-drug, drug-drug, and natural product-drug interactions. Examples of multi-factorial PK interactions presented include drug-gene-drug, natural product-gene-drug, gene-gene-drug, disease-natural product-drug, and disease-gene-drug interactions. Clear interpretation of multi-factorial interactions can be complicated by study design, complexity in victim drug PK, and incomplete mechanistic understanding of victim drug PK.Expert opinion: Incorporation of complex multi-factorial PK interactions into precision drug therapy requires advances in clinical decision tools, intentional PK study designs, drug-metabolizing enzyme and transporter fractional contribution determinations, systems and computational approaches (e.g., physiologically-based pharmacokinetic modeling), and PK phenotyping of progressive diseases.

Pharmacogenetics of Statin-Induced Myotoxicity

Statins, a class of lipid-lowering medications, have been a keystone treatment in cardiovascular health. However, adverse effects associated with statin use impact patient adherence, leading to statin discontinuation. Statin-induced myotoxicity (SIM) is one of the most common adverse effects, prevalent across all ages, genders, and ethnicities. Although certain demographic cohorts carry a higher risk, the impaired quality of life attributed to SIM is significant. The pathogenesis of SIM remains to be fully elucidated, but it is clear that SIM is multifactorial. These factors include drug-drug interactions, renal or liver dysfunction, and genetics. Genetic-inferred risk for SIM was first reported by a landmark genome-wide association study, which reported a higher risk of SIM with a polymorphism in the SLCO1B1 gene. Since then, research associating genetic factors with SIM has expanded widely and has become one of the foci in the field of pharmacogenomics. This review provides an update on the genetic risk factors associated with SIM.

An updated review of pharmacokinetic drug interactions and pharmacogenetics of statins

INTRODUCTION

Hydroxymethylglutaryl-coenzyme A reductase inhibitors (statins) lower cholesterol synthesis in patients with hypercholesterolemia. Increased statin exposure is an important risk factor for skeletal muscle toxicity. Potent inhibitors of cytochrome P450 (CYP) 3A4 significantly increase plasma concentrations of the active forms of simvastatin, lovastatin, and atorvastatin. Fluvastatin is metabolized by CYP2C9, whereas pravastatin, rosuvastatin, and pitavastatin are unaffected by inhibition by either CYP. Statins also have different affinities for membrane transporters involved in processes such as intestinal absorption, hepatic absorption, biliary excretion, and renal excretion.

AREAS COVERED

In this review, the pharmacokinetic aspects of drug-drug interactions with statins and genetic polymorphisms of CYPs and drug transporters involved in the pharmacokinetics of statins are discussed.

EXPERT OPINION

Understanding the mechanisms underlying statin interactions can help minimize drug interactions and reduce the adverse side effects caused by statins. Since recent studies have shown the involvement of drug transporters such as OATP and BCRP as well as CYPs in statin pharmacokinetics, further clinical studies focusing on the drug transporters are necessary. The establishment of biomarkers based on novel mechanisms, such as the leakage of microRNAs into the peripheral blood associated with the muscle toxicity, is important for the early detection of statin side effects.

Association between SLCO1B1 polymorphism distribution frequency and blood lipid level in Chinese adults

BACKGROUND

The variation of serum lipid levels can be part-related to certain genes. One such gene, SLCO1B1, encodes a transporter that may have a role in lipid metabolism. We hypothesised that differences in certain SLCO1B1 genotypes are related to levels of serum lipids.

MATERIALS AND METHODS

We recruited 636 subjects who were genotyped for SLCO1B1 variants *1a, *1b, *5 and *15. Routine liver function tests, renal function tests and routine lipid indices were measured by standard techniques.

RESULTS

The most frequent genotypes were *1b/*1b (29.3%), *1b/*15 (27.5%), *1a/*1b (21.1%), *1a/*15 and *1b/*5 (10.2%) and *1a/*1a (8.5%). There were significant differences in levels of triglycerides and HDL in the four SLCO1B1 genotypes *1a/*1a, *1b/*1b, *1a/*1b and *1b/*15 (all p < 0.05).

CONCLUSION

The genotypes *1a/*1a and *1a/*1b indicate a high risk of cardiovascular disease, while the *1b/*1b group may have a relatively low risk. SLCO1B1 may be involved in the metabolism of triglycerides and HDL. We have provided a tool for identifying potentially high-risk groups that could be helpful for early diagnosis and prevention, individualized drug therapy and even gene therapy.

The atorvastatin metabolic phenotype shift is influenced by interaction of drug-transporter polymorphisms in Mexican population: results of a randomized trial

Atorvastatin (ATV) is a blood cholesterol-lowering drug used to prevent cardiovascular events, the leading cause of death worldwide. As pharmacokinetics, metabolism and response vary among individuals, we wanted to determine the most reliable metabolic ATV phenotypes and identify novel and preponderant genetic markers that affect ATV plasma levels. A controlled, randomized, crossover, single-blind, three-treatment, three-period, and six-sequence clinical study of ATV (single 80-mg oral dose) was conducted among 60 healthy Mexican men. ATV plasma levels were measured using high-performance liquid chromatography mass spectrometry. Genotyping was performed by real-time PCR with TaqMan probes. Four ATV metabolizer phenotypes were found: slow, intermediate, normal and fast. Six gene polymorphisms, SLCO1B1-rs4149056, ABCB1-rs1045642, CYP2D6-rs1135840, CYP2B6-rs3745274, NAT2-rs1208, and COMT- rs4680, had a significant effect on ATV pharmacokinetics (P < 0.05). The polymorphisms in SLCO1B1 and ABCB1 seemed to have a greater effect and were especially important for the shift from an intermediate to a normal metabolizer. This is the first study that demonstrates how the interaction of genetic variants affect metabolic phenotyping and improves understanding of how SLCO1B1 and ABCB1 variants that affect statin metabolism may partially explain the variability in drug response. Notwithstanding, the influence of other genetic and non-genetic factors is not ruled out.

Drug-drug-gene interactions and adverse drug reactions

The economic and health burden caused by adverse drug reactions has increased dramatically in the last few years. This is likely to be mediated by increasing polypharmacy, which increases the likelihood for drug–drug interactions. Tools utilized by healthcare practitioners to flag potential adverse drug reactions secondary to drug–drug interactions ignore individual genetic variation, which has the potential to markedly alter the severity of these interactions. To date there have been limited published studies on impact of genetic variation on drug–drug interactions. In this review, we establish a detailed classification for pharmacokinetic drug–drug–gene interactions, and give examples from the literature that support this approach. The increasing availability of real-world drug outcome data linked to genetic bioresources is likely to enable the discovery of previously unrecognized, clinically important drug–drug–gene interactions.

Use of pharmacogenetic data to guide individualized opioid prescribing after surgery

BACKGROUND

Despite the current strategies aimed at avoiding opioid overprescription by implementing institutional guidelines, the use of opioids after surgical procedures remains highly variable. It is well known that opioids are activated by the cytochrome p450 CYP2D6 enzyme to exert pharmacologic effect. Individual variation in CYP2D6 activity affects drug metabolism, and genotyping can be performed to predict an individual's ability to metabolize CYP2D6 substrates. We postulate that the pharmacogenomic identification of patients with different opioid metabolism capacity may allow for the individualization of postsurgical opioid prescription.

METHODS

This study was generated by the unison of data from 2 prior initiatives taking place at our Institution. In the first study, patients undergoing 1 of 25 elective surgical procedures were prospectively identified as part of a quality initiative and surveyed by phone 21 to 35 days after hospital discharge to complete a 29-question survey regarding opioid utilization and pain experience. Additional chart abstraction was conducted to obtain prescribing data and pain scores during the hospitalization. The second study was the Mayo Clinic Right Drug, Right Dose, Right Time study protocol, in which 5 pharmacogenes, including CYP2D6, were genotyped for 1,000 Mayo Clinic Biobank participants. The goal of this study was to implement preemptive pharmacogenomics in an academic health care setting and to generate data for further pharmacogenomic research. Patients were classified by their predicted CYP2D6 activity based on their CYP2D6 genotype.

RESULTS

Of the 2,486 patients with prospective opioid utilization data, 21 had pharmacogenetic data available and were included in the analysis. These patients were classified according to their activity as opioid metabolizers, with 10 patients (48%) classified as intermediate, 4 patients (19%) as intermediate to normal, and 7 patients (33%) as normal or extensive. Compared with the intermediate to normal and intermediate phenotypes, normal or extensive patients had the highest percentages of preoperative opioid naivety and recorded pain scores throughout the surgical experience. The percentage of unused opioids for intermediate, intermediate to normal, and normal or extensive categories was 79%, 63%, and 46%, respectively. Moreover, of the 14 patients declaring the highest level of satisfaction for their pain control after discharge, 60% belonged to intermediate, 100% to intermediate to normal, and 57% to the normal or extensive group.

CONCLUSION

This study outlines a possible correlation between genetically controlled metabolism and opioid requirements after surgery. In this setting, an increased CYP2D6 enzymatic activity was associated to a greater opioid consumption, lesser amount of unused opioids, and a lower satisfaction level from opioid prescription.

The clinical significance of statins-macrolides interaction: comprehensive review of in vivo studies, case reports, and population studies

The objectives of this article were to review the mechanism and clinical significance of statins-macrolides interaction, determine which combination has the highest risk for the interaction, and identify key patients' risk factors for the interaction in relation to the development of muscle toxicity. A literature review was conducted in PubMed and Embase (1946 to December 2018) using combined terms: statins - as group and individual agents, macrolides - as group and individual agents, drug interaction, muscle toxicity, rhabdomyolysis, CYP3A4 inhibitors, and OAT1B inhibitors, with forward and backward citation tracking. Relevant English language in vivo studies in healthy volunteers, case reports, and population studies were included. The interaction between statins and macrolides depends on the type of statin and macrolide used. The mechanism of the interaction is due to macrolides' inhibition of CYP3A4 isoenzyme and OAT1B transporter causing increased exposure to statins. The correlation of this increased statin's exposure to the development of muscle toxicity could not be established, unless the patient had other risk factors such as advanced age, cardiovascular diseases, renal impairment, diabetes, and the concomitant use of other CYP3A4 inhibitors. Simvastatin, lovastatin, and to lesser extent atorvastatin are the statins most affected by this interaction. Rosuvastatin, fluvastatin, and pravastatin are not significantly affected by this interaction. Telithromycin, clarithromycin, and erythromycin are the most "offending" macrolides, while azithromycin appears to be safe to use with statins. This review presented a clear description of the clinical significance of this interaction in real practice. Also, it provided health care professionals with clear suggestions and recommendations on how to overcome this interaction. In conclusion, understanding the different characteristics of each statin and macrolide, as well as key patients' risk factors, will enable health care providers to utilize both groups effectively without compromising patient safety.

Statins: Old drugs as new therapy for liver diseases?

In addition to lowering cholesterol levels, statins have pleiotropic effects, particularly anti-inflammatory, antiangiogenic, and antifibrotic, that may be beneficial in some chronic inflammatory conditions. Statins have only recently been investigated as a potential treatment option in chronic liver diseases because of concerns related to their safety in patients with impaired liver function. A number of experimental studies in animal models of liver diseases have shown that statins decrease hepatic inflammation, fibrogenesis and portal pressure. In addition, retrospective cohort studies in large populations of patients with cirrhosis and pre-cirrhotic conditions have shown that treatment with statins, with the purpose of decreasing high cholesterol levels, was associated with a reduced risk of disease progression, hepatic decompensation, hepatocellular carcinoma development, and death. These beneficial effects persisted after adjustment for disease severity and other potential confounders. Finally, a few randomised controlled trials have shown that treatment with simvastatin decreases portal pressure (two studies) and mortality (one study). Statin treatment was generally well tolerated but a few patients developed severe side effects, particularly rhabdomyolysis. Despite these promising beneficial effects, further randomised controlled trials in large series of patients with hard clinical endpoints should be performed before statins can be recommended for use in clinical practice.

Physiologically Based Pharmacokinetic Modeling of Fimasartan, Amlodipine, and Hydrochlorothiazide for the Investigation of Drug-Drug Interaction Potentials

PURPOSE

To build a physiologically based pharmacokinetic (PBPK) model for fimasartan, amlodipine, and hydrochlorothiazide, and to investigate the drug-drug interaction (DDI) potentials.

METHODS

The PBPK model of each drug was developed using Simcyp software (Version 15.0), based on the information obtained from literature sources and in vitro studies. The predictive performance of the model was assessed by comparing the predicted PK profiles and parameters with the observed data collected from healthy subjects after multiple oral doses of fimasartan, amlodipine, and hydrochlorothiazide. The DDI potentials after co-administration of three drugs were simulated using the final model.

RESULTS

The predicted-to-observed ratios of all the pharmacokinetic parameters met the acceptance criterion. The PBPK model predicted no significant DDI when fimasartan was co-administered with amlodipine or hydrochlorothiazide, which is consistent with the observed clinical data. In the simulation of DDI at steady-state after co-administration of three drugs, the model predicted that fimasartan exposure would be increased by ~24.5%, while no changes were expected for the exposures of amlodipine and hydrochlorothiazide.

CONCLUSIONS

The developed PBPK model adequately predicted the pharmacokinetics of fimasartan, amlodipine, and hydrochlorothiazide, suggesting that the model can be used to further investigate the DDI potential of each drug.