Effect of cytochrome P450 3A5 genotype on atorvastatin pharmacokinetics and its interaction with clarithromycin

Pharmacokinetic and pharmacodynamic drug-drug interaction of Nomilin with atorvastatin in hyperlipidemic mice

Atorvastatin (Atv) is widely used to lower cholesterol levels and treat hyperlipidemia in clinical application. Nomilin (Nom) is a kind of limonoids, which is found and isolated from the citrus herbs of Rutaceae family, which are widely used as patent medicines, functional foods, and nutritional supplements in many countries. In previous studies, Nom has the effect of anti-obesity and curing other metabolic diseases. Nevertheless, in recent years, the drug-drug interaction (DDI) caused by the administration of drugs with synergistic effects have raised worldwide concerns. To investigate the DDI of Nom and Atv in vivo, the pharmacokinetic studies were performed with using C57BL/6 mice. The plasma concentrations of Nom and Atv were measured after oral administration of different drug combinations by a simple and sensitive UHPLC-MS/MS method. The experimental mice were randomly divided into five groups, including control group, model group, administered Nom individually group, administered Atv individually group and co-administered of Nom and Atv group. The lipid levels including total cholesterol (TC), triglycerides (TG), high density lipoproteins-cholesterol (HDL-C), low density lipoproteins-cholesterol (LDL-C) were measured for pharmacodynamic study. The hepatic microsomal Cytochrome P450 (CYP1A2, CYP2E1 and CYP3A11) activities were probed using cocktail assay. The gene and protein expressions of CYP3A11 were detected via qPCR and Western blot method. The results shown that the area under the plasma concentration-time curve (AUC) of Atv in administered Atv individually group was 69.30 ± 15.45 ng/mL × h, while that of combined Nom with Atv group was 42.37 ± 10.15 ng/mL × h (p＜0.05). The degree of reduction in lipid levels of mice treated with co-administration of Atv and Nom was less than that of mice treated with Atv alone. In addition, Nom could cause an increased hepatic microsomal CYP3A11 activity significantly, and induce the gene levels and protein expressions of CYP3A11 elevated in mice livers. In conclusion, Nom could up-regulate CYP3A11 activity, thereby impacting on the pharmacokinetic profile and pharmacodynamic effect of Atv. The findings provide more insight for the use risk of these two drugs to treat hyperlipidemia diseases.

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.

CYP3A5*3 and SLCO1B1 c.521T>C Polymorphisms Influence the Pharmacokinetics of Atorvastatin and 2-Hydroxy Atorvastatin

There is a large variability in individual responses to atorvastatin administration. This study assessed the pharmacogenetic effects of solute carrier organic anion transporter family member 1B1 (SLCO1B1, c.388A>G and c.521T>C) and cytochrome P450 3A5 (CYP3A5, CYP3A5*3) genetic polymorphisms on the pharmacokinetics of atorvastatin and its active metabolite, 2-hydroxy (2-OH) atorvastatin, in 46 individuals who were administered a clinically used single oral dosage of 80 mg. The Cmax and AUC of atorvastatin in CYP3A5*3/*3 carriers were 2.6- and 2.8-fold higher, respectively, than those in CYP3A5*1/*1 carriers, and similar results were observed for 2-OH atorvastatin pharmacokinetics. SLCO1B1 c.521T>C also increased the AUC of atorvastatin and 2-OH atorvastatin. The AUC ratio of atorvastatin and 2-OH atorvastatin were not affected by SLCO1B1 c.388A>G or c.521T>C, whereas CYP3A5*3 reduced the AUC ratio. In an analysis evaluating the simultaneous effect of the SLCO1B1 c.521T>C and CYP3A5*3 polymorphisms, SLCO1B1 c.521TT/CYP3A5*1/*1 carriers showed lower Cmax and AUC values for atorvastatin and 2-OH atorvastatin than in individuals with the SLCO1B1 c.521T>C and/or CYP3A5*3 genotypes. Among the participants with the SLCO1B1 c.521TT genotype, the CYP3A5*3 carriers had a higher systemic exposure to atorvastatin and 2-OH atorvastatin than the CYP3A5*1/*1 carriers. Thus, SLCO1B1 c.521T>C and CYP3A5*3 polymorphisms affect the pharmacokinetics of atorvastatin and 2-OH atorvastatin.

Current Evidence, Challenges, and Opportunities of Physiologically Based Pharmacokinetic Models of Atorvastatin for Decision Making

Atorvastatin (ATS) is the gold-standard treatment worldwide for the management of hypercholesterolemia and prevention of cardiovascular diseases associated with dyslipidemia. Physiologically based pharmacokinetic (PBPK) models have been positioned as a valuable tool for the characterization of complex pharmacokinetic (PK) processes and its extrapolation in special sub-groups of the population, leading to regulatory recognition. Several PBPK models of ATS have been published in the recent years, addressing different aspects of the PK properties of ATS. Therefore, the aims of this review are (i) to summarize the physicochemical and pharmacokinetic characteristics involved in the time-course of ATS, and (ii) to evaluate the major highlights and limitations of the PBPK models of ATS published so far. The PBPK models incorporate common elements related to the physicochemical aspects of ATS. However, there are important differences in relation to the analyte evaluated, the type and effect of transporters and metabolic enzymes, and the permeability value used. Additionally, this review identifies major processes (lactonization, P-gp contribution, ATS-Ca solubility, simultaneous management of multiple analytes, and experimental evidence in the target population), which would enhance the PBPK model prediction to serve as a valid tool for ATS dose optimization.

SLCO1B1 Phenotype and CYP3A5 Polymorphism Significantly Affect Atorvastatin Bioavailability

Atorvastatin, prescribed for the treatment of hypercholesterolemia, demonstrated overwhelming benefits in reducing cardiovascular morbidity and mortality. However, many patients discontinue therapy due to adverse reactions, especially myopathy. The Dutch Pharmacogenetics Working Group (DPWG) recommends an alternative agent to atorvastatin and simvastatin or a dose adjustment depending on other risk factors for statin-induced myopathy in SLCO1B1 rs4149056 CC or TC carriers. In contrast, the Clinical Pharmacogenetics Implementation Consortium (CPIC) published their guideline on simvastatin, but not on atorvastatin. In this work, we aimed to demonstrate the effect of SLCO1B1 phenotype and other variants (e.g., in CYP3A4/5, UGT enzymes or SLC transporters) on atorvastatin pharmacokinetics. For this purpose, a candidate-gene pharmacogenetic study was proposed. The study population comprised 156 healthy volunteers enrolled in atorvastatin bioequivalence clinical trials. The genotyping strategy comprised a total of 60 variants in 15 genes. Women showed higher exposure to atorvastatin compared to men (p = 0.001), however this difference disappeared after dose/weight (DW) correction. The most relevant pharmacogenetic differences were the following: AUC/DW and Cmax /DW based on (a) SLCO1B1 phenotype (p < 0.001 for both) and (b) CYP3A5*3 (p = 0.004 and 0.018, respectively). As secondary findings: SLC22A1 *2/*2 genotype was related to higher Cmax/DW (ANOVA p = 0.030) and SLC22A1 *1/*5 genotype was associated with higher Vd/F (ANOVA p = 0.032) compared to SLC22A1 *1/*1, respectively. Finally, UGT2B7 rs7439366 *1/*1 genotype was associated with higher tmax as compared with the *1/*3 genotype (ANOVA p = 0.024). Based on our results, we suggest that SLCO1B1 is the best predictor for atorvastatin pharmacokinetic variability and that prescription should be adjusted based on it. We suggest that the CPIC should include atorvastatin in their statin-SLCO1B1 guidelines. Interesting and novel results were observed based on CYP3A5 genotype, which should be confirmed with further studies.

Personalized medicine in lipid-modifying therapy

The choice of lipid-modifying treatment is largely based on the absolute level of cardiovascular risk and baseline lipid profile. Statins are the first-line treatment for most patients requiring reduction of low-density-lipoprotein cholesterol (LDL-C) and ezetimibe and proprotein convertase subtilisin/kexin type 9 inhibitors can be added to reach LDL-C targets. Statins have some adverse effects that are somewhat predictable based on phenotypic and genetic factors. Fibrates or omega-3 fatty acids can be added if triglyceride levels remain elevated. The RNA-targeted therapeutics in development offer the possibility of selective liver targeting for specific lipoproteins such as lipoprotein(a) and long-term reduction of LDL-C with infrequent administration of a small-interfering RNA may help to overcome the problem of adherence to therapy.

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.

Statin-Related Myotoxicity: A Comprehensive Review of Pharmacokinetic, Pharmacogenomic and Muscle Components

Statins are a cornerstone in the pharmacological prevention of cardiovascular disease. Although generally well tolerated, a small subset of patients experience statin-related myotoxicity (SRM). SRM is heterogeneous in presentation; phenotypes include the relatively more common myalgias, infrequent myopathies, and rare rhabdomyolysis. Very rarely, statins induce an anti-HMGCR positive immune-mediated necrotizing myopathy. Diagnosing SRM in clinical practice can be challenging, particularly for mild SRM that is frequently due to alternative aetiologies and the nocebo effect. Nevertheless, SRM can directly harm patients and lead to statin discontinuation/non-adherence, which increases the risk of cardiovascular events. Several factors increase systemic statin exposure and predispose to SRM, including advanced age, concomitant medications, and the nonsynonymous variant, rs4149056, in SLCO1B1, which encodes the hepatic sinusoidal transporter, OATP1B1. Increased exposure of skeletal muscle to statins increases the risk of mitochondrial dysfunction, calcium signalling disruption, reduced prenylation, atrogin-1 mediated atrophy and pro-apoptotic signalling. Rare variants in several metabolic myopathy genes including CACNA1S, CPT2, LPIN1, PYGM and RYR1 increase myopathy/rhabdomyolysis risk following statin exposure. The immune system is implicated in both conventional statin intolerance/myotoxicity via LILRB5 rs12975366, and a strong association exists between HLA-DRB1*11:01 and anti-HMGCR positive myopathy. Epigenetic factors (miR-499-5p, miR-145) have also been implicated in statin myotoxicity. SRM remains a challenge to the safe and effective use of statins, although consensus strategies to manage SRM have been proposed. Further research is required, including stringent phenotyping of mild SRM through N-of-1 trials coupled to systems pharmacology omics- approaches to identify novel risk factors and provide mechanistic insight.

Interaction potential between clarithromycin and individual statins-A systematic review

The high prevalence of statin and clarithromycin utilization creates potential for overlapping use. The objectives of this MiniReview were to investigate the evidence base for drug-drug interactions between clarithromycin and currently marketed statins and to present management strategies for these drug combinations. We conducted a systematic literature review following PRISMA guidelines with English language studies retrieved from PubMed and EMBASE (from inception through March 2019). We included 29 articles (16 case reports, 5 observational, 5 clinical pharmacokinetic and 3 in vitro studies). Based on mechanistic/clinical studies involving clarithromycin or the related macrolide erythromycin (both strong inhibitors of CYP3A4 and of hepatic statin uptake transporters OATP1B1 and OATP1B3), clarithromycin is expected to substantially increase systemic exposure to simvastatin and lovastatin (>5-fold increase in area under the plasma concentration-time curve (AUC)), moderately increase AUCs of atorvastatin and pitavastatin (2- to 4-fold AUC increase) and slightly increase pravastatin exposure (≈2-fold AUC increase) while having little effect on fluvastatin or rosuvastatin. The 16 cases of statin-clarithromycin adverse drug reactions (rhabdomyolysis (n = 14) or less severe clinical myopathy) involved a CYP3A4-metabolized statin (simvastatin, lovastatin or atorvastatin). In line, a cohort study found concurrent use of clarithromycin and CYP3A4-metabolized statins to be associated with a doubled risk of hospitalization with rhabdomyolysis or other statin-related adverse events as compared with azithromycin-statin co-administration. If clarithromycin is necessary, we recommend (a) avoiding co-administration with simvastatin, lovastatin or atorvastatin; (b) withholding or dose-reducing pitavastatin; (c) continuing pravastatin therapy with caution, limiting pravastatin dose to 40 mg daily; and (d) continuing fluvastatin or rosuvastatin with caution.

Prediction of pharmacokinetic drug-drug interactions causing atorvastatin-induced rhabdomyolysis using physiologically based pharmacokinetic modelling

Atorvastatin and its lactone form metabolite are reported to be associated with statin-induced myopathy (SIM) such as myalgia and life-threatening rhabdomyolysis. Though the statin-induced rhabdomyolysis is not common during statin therapy, its incidence will significantly increase due to pharmacokinetic drug-drug interactions (DDIs) with inhibitor drugs which inhibit atorvastatin's and its lactone's metabolism and hepatic uptake. Thus, the quantitative analysis of DDIs of atorvastatin and its lactone with cytochrome P450 3A4 (CYP3A4) and organic anion-transporting polypeptide (OATP) inhibitors is of great importance. This study aimed to predict pharmacokinetic DDIs possibly causing atorvastatin-induced rhabdomyolysis using Physiologically Based Pharmacokinetic (PBPK) Modelling. Firstly, we refined the PBPK models of atorvastatin and atorvastatin lactone for predicting the DDIs with CYP3A4 and OATP inhibitors. Thereafter, we predicted the exposure changes of atorvastatin and atorvastatin lactone originating from the case reports of atorvastatin-induced rhabdomyolysis using the refined models. The simulation results show that pharmacokinetic DDIs of atorvastatin and its lactone with fluconazole, palbociclib diltiazem and cyclosporine are significant. Consequently, clinicians should be aware of necessary dose adjustment of atorvastatin being used with these four inhibitor drugs.

Physiologically-Based Pharmacokinetic Modeling of Atorvastatin Incorporating Delayed Gastric Emptying and Acid-to-Lactone Conversion

The drug-drug interaction profile of atorvastatin confirms that disposition is determined by cytochrome P450 (CYP) 3A4 and organic anion transporting polypeptides (OATPs). Drugs that affect gastric emptying, including dulaglutide, also affect atorvastatin pharmacokinetics (PK). Atorvastatin is a carboxylic acid that exists in equilibrium with a lactone form in vivo. The purpose of this work was to assess gastric acid-mediated lactone equilibration of atorvastatin and incorporate this into a physiologically-based PK (PBPK) model to describe atorvastatin acid, lactone, and their major metabolites. In vitro acid-to-lactone conversion was assessed in simulated gastric fluid and included in the model. The PBPK model was verified with in vivo data including CYP3A4 and OATP inhibition studies. Altering the gastric acid-lactone equilibrium reproduced the change in atorvastatin PK observed with dulaglutide. The model emphasizes the need to include gastric acid-lactone conversion and all major atorvastatin-related species for the prediction of atorvastatin PK.

The effect of CYP1A2 gene polymorphism on the metabolism of theophylline

This aim of the study was to investigate the effect of CYP1A2 gene polymorphism on the metabolism of theophylline in minority and Han nationality. A total of 50 cases of Han (Han group) and 50 minority nationalities (ethnic groups) treated with theophylline were selected for the study. The genotype and allele frequencies of the two groups of CYP1A2 gene, G-3113A and G-3860A, were compared to determine the rate of theophylline clearance. The results showed that there was no significant difference in the concentration of the homeostasis and the rate of the theophylline removal rate (P>0.05). There was no significant difference in the genotype and allele frequencies of the CYP1A2 gene, G-3113A and G-3860A apolymorphic site. This study employed a logarithm to determine theophylline clearance in order to correlate it with the normal distribution. The results showed that the theophylline clearance of the two groups of CYP1A2 G-3113A gene loci A allele carriers (AA+GA genotype) was significantly lower than that of the G allele carriers (GG genotype), and a significant difference between the groups was identified (P<0.05). There was no significant difference in the theophylline clearance rates in the two groups for the CYP1A2 gene, G-3860A apolymorphic site (P>0.05). Compared to the GG genotype of the CYP1A2 gene, the G-3113A site AA and GA genotype patients had a low clearance rate in the theophylline, whereas there was no correlation between teh genotypes of the CYP1A2 gene, G-3860A and the rate of theophylline clearance.

Construction and verification of CYP3A5 gene polymorphisms using a Saccharomyces cerevisiae expression system to predict drug metabolism

The present study evaluated the ability of a Saccharomyces cerevisiae expression system to predict the pharmacokinetic (PK) activity of a calcium channel blocker in patients with distinct cytochrome P450 3A5 (CYP3A5) polymorphisms. The blood pressure lowering activity of amlodipine in 57 hypertensive patients with CYP3A5*1/*1, CYP3A5*1/*3, CYP3A5*4 and CYP3A5*6 polymorphisms was evaluated by the current study. Subsequently, a Saccharomyces cerevisiae expression system for CYP3A5 gene polymorphisms was constructed to examine the PK activity of CYP3A5*1/*1, CYP3A5*4 and CYP3A5*6 polymorphisms. This system was used to predict the PK of amlodipine and was compared with the in vivo data from different gene polymorphism groups. The blood pressure lowering activity of amlodipine in hypertensive patients varied among CYP3A5 polymorphisms. The in vivo results demonstrated that CYP3A5*6 exhibited the highest metabolic rate, followed by CYP3A5*1/*1, CYP3A5*4 and CYP3A5*1/*3. The difference between CYP3A5*6 and CYP3A5*1/*1 was not statistically significant (P=0.5). In accordance with in vivo data, CYP3A5*1/*1 exhibited the highest in vitro metabolic rate, followed by CYP3A5*6 and CYP3A5*4. With the exception of the comparison between CYP3A5*6 and CYP3A5*1/*1, polymorphisms exhibited statistically significant differences compared with CYP3A5*1/*1 (P<0.05). The Saccharomyces cerevisiae expression system may be a cost effective and potentially useful tool for assessing the PK activity of drugs that are metabolized by CYP3A5.

Results of a Doravirine-Atorvastatin Drug-Drug Interaction Study

Doravirine is a novel, highly potent, nonnucleoside reverse transcriptase inhibitor that is administered once daily and that is in development for the treatment of HIV-1 infection. In vitro and clinical data suggest that doravirine is unlikely to cause significant drug-drug interactions via major drug-metabolizing enzymes or transporters. As a common HIV-1 infection comorbidity, hypercholesterolemia is often treated with statins, including the commonly prescribed atorvastatin. Atorvastatin is subject to drug-drug interactions with cytochrome P450 3A4 (CYP3A4) inhibitors. Increased exposure due to CYP3A4 inhibition may lead to serious adverse events (AEs), including rhabdomyolysis. Furthermore, atorvastatin is a substrate for breast cancer resistance protein (BCRP), of which doravirine may be a weak inhibitor; this may increase atorvastatin exposure. The potential of doravirine to affect atorvastatin pharmacokinetics was investigated in a two-period, fixed-sequence study in healthy individuals. In period 1, a single dose of atorvastatin at 20 mg was administered followed by a 72-h washout. In period 2, doravirine at 100 mg was administered once daily for 8 days, with a single dose of atorvastatin at 20 mg concomitantly being administered on day 5. Sixteen subjects were enrolled, and 14 completed the trial; 2 discontinued due to AEs unrelated to the treatment. The atorvastatin area under the curve from time zero to infinity was similar with and without doravirine (geometric mean ratio [GMR] for doravirine-atorvastatin/atorvastatin, 0.98; 90% confidence interval [CI], 0.90 to 1.06), while the maximum concentration decreased by 33% (GMR for doravirine-atorvastatin/atorvastatin, 0.67; 90% CI, 0.52 to 0.85). These changes were deemed not to be clinically meaningful. Both of the study drugs were generally well tolerated. Doravirine had no clinically relevant effect on atorvastatin pharmacokinetics in healthy subjects, providing support for the coadministration of doravirine and atorvastatin.

Physiologically based pharmacokinetic modeling of disposition and drug-drug interactions for atorvastatin and its metabolites

Atorvastatin is the most commonly used of all statins to lower cholesterol. Atorvastatin is extensively metabolized in both gut and liver to produce several active metabolites. The purpose of the present study is to develop a physiologically based pharmacokinetic (PBPK) model for atorvastatin and its two primary metabolites, 2-hydroxy-atorvastatin acid and atorvastatin lactone, using in vitro and in vivo data. The model was used to predict the pharmacokinetic profiles and drug-drug interaction (DDI) effect for atorvastatin and its metabolites in different DDI scenarios. The predictive performance of the model was assessed by comparing predicted results to observed data after coadministration of atorvastatin with different medications such as itraconazole, clarithromycin, cimetidine, rifampin and phenytoin. This population based PBPK model was able to describe the concentration-time profiles of atorvastatin and its two metabolites reasonably well in the absence or presence of those drugs at different dose regimens. The predicted maximum concentration (Cmax), area under the concentration-time curve (AUC) values and between-phase ratios were in good agreement with clinically observed data. The model has also revealed the importance of different metabolic pathways on the disposition of atorvastatin metabolites. This PBPK model can be utilized to assess the safety and efficacy of atorvastatin in the clinic. This study demonstrated the feasibility of applying PBPK approach to predict the DDI potential of drugs undergoing complex metabolism.

Impact of CYP3A5 Gene Polymorphism on Efficacy of Simvastatin

Background: One of the promises of human genetics is individualized therapy. Therefore, we evaluated the impact of CYP3A5 gene polymorphism on the effectiveness of simvastatin (a HMG-CoA reductase inhibitor). Methods: Patients (n = 191) with hypercholesterolemia were treated with simvastatin for at least 6 months and were genotyped for the CYP3A5 polymorphism. Results: The frequency of CYP3A5 polymorphism was 0.5% for WT (wild-type), 15.6% for HT (heterozygous, expressors) and 83.9% for HM (homozygous, non-expressors). Differences in lipid profile before and after dose-response of simvastatin treatment were described as % difference {[(variable after-variable before)/variable before]*100}. There was a trend towards the decrease of low density lipoprotein cholesterol (LDL-C) in HT individuals who had a -35.2% reduction with a dose of 20 mg simvastatin and HM individuals who had a slightly higher decrease (-37.5%) despite the lower dose of simvastatin (10 mg, p = 0.07). Furthermore, HT genotype individuals had significantly higher than expected (6-8%) LDL-C % difference between 20 and 40 mg of simvastatin (-35.2 vs -49.2%, p = 0.037). In individuals with HM genotype a significant LDL-C % difference was found between 10 and 40 mg of simvastatin (-37.5 vs -48.4%, p = 0.023). Conclusion: The individuals with HM polymorphism display a trend towards higher LDL-C reductions compared with HT polymorphism. Within the same genotype, differences between doses were also observed. These findings need to be confirmed in larger studies.

Pharmacogenetics of Statin-Induced Myopathy: A Focused Review of the Clinical Translation of Pharmacokinetic Genetic Variants

Statins are the most commonly prescribed drugs in the United States and are extremely effective in reducing major cardiovascular events in the millions of Americans with hyperlipidemia. However, many patients (up to 25%) cannot tolerate or discontinue statin therapy due to statin-induced myopathy (SIM). Patients will continue to experience SIM at unacceptably high rates or experience unnecessary cardiovascular events (as a result of discontinuing or decreasing their statin therapy) until strategies for predicting or mitigating SIM are identified. A promising strategy for predicting or mitigating SIM is pharmacogenetic testing, particularly of pharmacokinetic genetic variants as SIM is related to statin exposure. Data is emerging on the association between pharmacokinetic genetic variants and SIM. A current, critical evaluation of the literature on pharmacokinetic genetic variants and SIM for potential translation to clinical practice is lacking. This review focuses specifically on pharmacokinetic genetic variants and their association with SIM clinical outcomes. We also discuss future directions, specific to the research on pharmacokinetic genetic variants, which could speed the translation into clinical practice. For simvastatin, we did not find sufficient evidence to support the clinical translation of pharmacokinetic genetic variants other than SLCO1B1. However, SLCO1B1 may also be clinically relevant for pravastatin- and pitavastatin-induced myopathy, but additional studies assessing SIM clinical outcome are needed. CYP2D6*4 may be clinically relevant for atorvastatin-induced myopathy, but mechanistic studies are needed. Future research efforts need to incorporate statin-specific analyses, multi-variant analyses, and a standard definition of SIM. As the use of statins is extremely common and SIM continues to occur in a significant number of patients, future research investments in pharmacokinetic genetic variants have the potential to make a profound impact on public health.

The pharmacogenomics of statins

The statin class of cholesterol-lowering drugs have been used for decades to successfully lower plasma cholesterol concentrations and cardiovascular risk. Adverse effects of statins are generally considered mild, but increase with age of patients and polypharmacy. One aspect of statin therapy that is still difficult for prescribers to predict is the individual's response to statin therapy. Recent advances in the field of pharmacogenomics have indicated variants of candidate genes that affect statin efficacy and safety. In this review, a number of candidates that affect statin pharmacokinetics and pharmacodynamics are discussed. Some of these candidates, in particular those involved in import and efflux of statins, have now been linked to increased risk of side effects. Furthermore, pharmacogenomic studies continue to reveal new players that are involved in the fine-tuning of the complex regulation of cholesterol homeostasis and response to statins.

Statin pharmacogenomics: pursuing biomarkers for predicting clinical outcomes

Indicated for treating hyperlipidemias and for the prevention of cardiovascular disease (CVD), statins rank among the most commonly prescribed drug classes. While statins are considered to be highly effective in preventing atherosclerotic events, a substantial portion of treated patients still progress to overt CVD. Genetic factors are thought to contribute substantially to treatment outcome. Several candidate genes have been associated with statin dose requirements and treatment outcomes, but a clinically relevant pharmacogenomics test to guide statin therapy has not yet emerged. Here we define basic pharmacogenomics terminology, present strong candidate genes (CETP, HMGCR, SLCO1B1, ABCB1, and CYP3A4/5), and discuss the challenges in developing much-needed statin pharmacogenomics biomarkers for predicting treatment outcomes.

CYP3A4/5 combined genotype analysis for predicting statin dose requirement for optimal lipid control

BACKGROUND

Statins are indicated for prevention of atherosclerotic cardiovascular disease. Metabolism of certain statins involves the cytochrome P450 3A (CYP3A) enzymes, and CYP3A4*22 significantly influences the dose needed for achieving optimal lipid control for atorvastatin, simvastatin, and lovastatin. CYP3A4/5 combined genotype approaches have proved useful in some studies involving CYP3A substrates. We intend to compare a combined genotype analysis to our previously reported single gene CYP3A4 analysis.

METHODS

A total of 235 patients receiving stable statin doses were genotyped and grouped by CYP3A4/5 status.

RESULTS

The number and demographic composition of the patients categorized into the combined genotype groups were consistent with those reported for other cohorts. Dose requirement was significantly associated with the ordered combined-genotype grouping; median daily doses were nearly 40% greater for CYP3A4/5 intermediate metabolizers compared with poor metabolizers, and median daily doses were nearly double for extensive metabolizers compared with poor metabolizers. The combined-genotype approach, however, did not improve the genotype-dosage correlation p-values when compared with the previously-reported analysis; values changed from 0.129 to 0.166, 0.036 to 0.185, and 0.014 to 0.044 for atorvastatin, simvastatin, and the combined statin analysis, respectively.

CONCLUSIONS

The previously-reported single-gene approach was superior for predicting statin dose requirement in this cohort.

Diabetes mellitus reduces the clearance of atorvastatin lactone: results of a population pharmacokinetic analysis in renal transplant recipients and in vitro studies using human liver microsomes

BACKGROUND AND OBJECTIVE

Patients with diabetes mellitus might be at a higher risk of HMG-CoA reductase inhibitor (statin)-induced myotoxicity, possibly because of reduced clearance of the statin lactone. The present study was designed to investigate the effect of diabetes on the biotransformation of atorvastatin acid, both in vivo in nondiabetic and diabetic renal transplant recipients, and in vitro in human liver samples from nondiabetic and diabetic donors.

SUBJECTS AND METHODS

A total of 312 plasma concentrations of atorvastatin acid and atorvastatin lactone, from 20 nondiabetic and 32 diabetic renal transplant recipients, were included in the analysis. Nonlinear mixed-effects modelling was employed to determine the population pharmacokinetic estimates for atorvastatin acid and atorvastatin lactone. In addition, the biotransformation of these compounds was studied using human liver microsomal fractions obtained from 12 nondiabetic and 12 diabetic donors.

RESULTS

In diabetic patients, the plasma concentration of atorvastatin lactone was significantly higher than that of atorvastatin acid throughout the 24-hour sampling period. The optimal population pharmacokinetic model for atorvastatin acid and atorvastatin lactone consisted of a two- and one-compartment model, respectively, with interconversion between atorvastatin acid and atorvastatin lactone. Parent drug was absorbed orally with a population estimate first-order absorption rate constant of 0.457 h(-1). The population estimates of apparent oral clearance (CL/F) of atorvastatin acid to atorvastatin lactone, intercompartmental clearance (Q/F), apparent central compartment volume of distribution after oral administration (V(1)/F) and apparent peripheral compartment volume of distribution after oral administration (V(2)/F) for atorvastatin acid were 231 L/h, 315 L/h, 325 L and 4910 L, respectively. The population estimates of apparent total clearance of atorvastatin lactone (CL(M)/F), apparent intercompartmental clearance of atorvastatin lactone (Q(M)/F) and apparent volume of distribution of atorvastatin lactone after oral administration (V(M)/F) were 85.4 L/h, 166 L/h and 249 L, respectively. The final covariate model indicated that the liver enzyme lactate dehydrogenase was related to CL/F and alanine aminotransferase (ALT) was related to Q/F. Importantly, diabetic patients have 3.56 times lower CL(M)/F than nondiabetic patients, indicating significantly lower clearance of atorvastatin lactone in these patients. Moreover, in a multivariate population pharmacokinetics model, diabetes status was the only significant covariate predicting the values of the CL(M)/F. Correspondingly, the concentration of atorvastatin acid remaining in the microsomal incubation was not significantly different between nondiabetic and diabetic liver samples, whereas the concentration of atorvastatin lactone was significantly higher in the samples from diabetic donors. In vitro studies, using recombinant enzymes, revealed that cytochrome P450 (CYP) 3A4 is the major CYP enzyme responsible for the biotransformation of atorvastatin lactone.

CONCLUSIONS

These studies provide compelling evidence that the clearance of atorvastatin lactone is significantly reduced by diabetes, which leads to an increased concentration of this metabolite. This finding can be clinically valuable for diabetic transplant recipients who have additional co-morbidities and are on multiple medications.