Atorvastatin-related rhabdomyolysis and acute renal failure in a genetically predisposed patient with potential drug-drug interaction

Drug-Drug-Gene Interactions in Cardiovascular Medicine

Cardiovascular disease remains a leading cause of both morbidity and mortality worldwide. It is widely accepted that both concomitant medications (drug-drug interactions, DDIs) and genomic factors (drug-gene interactions, DGIs) can influence cardiovascular drug-related efficacy and safety outcomes. Although thousands of DDI and DGI (aka pharmacogenomic) studies have been published to date, the literature on drug-drug-gene interactions (DDGIs, cumulative effects of DDIs and DGIs) remains scarce. Moreover, multimorbidity is common in cardiovascular disease patients and is often associated with polypharmacy, which increases the likelihood of clinically relevant drug-related interactions. These, in turn, can lead to reduced drug efficacy, medication-related harm (adverse drug reactions, longer hospitalizations, mortality) and increased healthcare costs. To examine the extent to which DDGIs and other interactions influence efficacy and safety outcomes in the field of cardiovascular medicine, we review current evidence in the field. We describe the different categories of DDIs and DGIs before illustrating how these two interact to produce DDGIs and other complex interactions. We provide examples of studies that have reported the prevalence of clinically relevant interactions and the most implicated cardiovascular medicines before outlining the challenges associated with dealing with these interactions in clinical practice. Finally, we provide recommendations on how to manage the challenges including but not limited to expanding the scope of drug information compendia, interaction databases and clinical implementation guidelines (to include clinically relevant DDGIs and other complex interactions) and work towards their harmonization; better use of electronic decision support tools; using big data and novel computational techniques; using clinically relevant endpoints, preemptive genotyping; ensuring ethnic diversity; and upskilling of clinicians in pharmacogenomics and personalized medicine.

Optimising Seniors' Metabolism of Medications and Avoiding Adverse Drug Events Using Data on How Metabolism by Their P450 Enzymes Varies with Ancestry and Drug-Drug and Drug-Drug-Gene Interactions

Many individuals ≥65 have multiple illnesses and polypharmacy. Primary care physicians prescribe >70% of their medications and renew specialists’ prescriptions. Seventy-five percent of all medications are metabolised by P450 cytochrome enzymes. This article provides unique detailed tables how to avoid adverse drug events and optimise prescribing based on two key databases. DrugBank is a detailed database of 13,000 medications and both the P450 and other complex pathways that metabolise them. The Flockhart Tables are detailed lists of the P450 enzymes and also include all the medications which inhibit or induce metabolism by P450 cytochrome enzymes, which can result in undertreatment, overtreatment, or potentially toxic levels. Humans have used medications for a few decades and these enzymes have not been subject to evolutionary pressure. Thus, there is enormous variation in enzymatic functioning and by ancestry. Differences for ancestry groups in genetic metabolism based on a worldwide meta-analysis are discussed and this article provides advice how to prescribe for individuals of different ancestry. Prescribing advice from two key organisations, the Dutch Pharmacogenetics Working Group and the Clinical Pharmacogenetics Implementation Consortium is summarised. Currently, detailed pharmacogenomic advice is only available in some specialist clinics in major hospitals. However, this article provides detailed pharmacogenomic advice for primary care and other physicians and also physicians working in rural and remote areas worldwide. Physicians could quickly search the tables for the medications they intend to prescribe.

Investigating the clinical factors and comedications associated with circulating levels of atorvastatin and its major metabolites in secondary prevention

Aims The lipid-lowering drug, atorvastatin (ATV), is 1 of the most commonly prescribed medications worldwide. The aim of this study was to comprehensively investigate and characterise the clinical factors and comedications associated with circulating levels of ATV and its metabolites in secondary prevention clinical practice. Methods The plasma concentrations of ATV, 2-hydroxy (2-OH) ATV, ATV lactone (ATV L) and 2-OH ATV L were determined in patients 1 month after hospitalisation for a non-ST elevation acute coronary syndrome. Factors were identified using all subsets multivariable regression and model averaging with the Bayesian information criterion. Exploratory genotype-stratified analyses were conducted using ABCG2 rs2231142 (Q141K) and CYP2C19 metaboliser status to further investigate novel associations. Results A total of 571 patients were included; 534 and 37 were taking ATV 80 mg and 40 mg daily, respectively. Clinical factors associated with ATV and/or its metabolite levels included age, sex, body mass index and CYP3A inhibiting comedications. Smoking was newly associated with increased ATV lactonisation and reduced hydroxylation. Proton pump inhibitors (PPIs) and loop diuretics were newly associated with modestly increased levels of ATV (14% and 38%, respectively) and its metabolites. An interaction between PPIs and CYP2C19 metaboliser status on exposure to specific ATV analytes (e.g. interaction P = .0071 for 2-OH ATV L) was observed. Overall model R² values were 0.14-0.24.ConclusionMultiple factors were associated with circulating ATV and metabolite levels, including novel associations with smoking and drug-drug(-gene) interactions involving PPIs and loop diuretics. Further investigations are needed to identify additional factors that influence ATV exposure.

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.

Complex Drug-Drug-Gene-Disease Interactions Involving Cytochromes P450: Systematic Review of Published Case Reports and Clinical Perspectives

Drug pharmacokinetics (PK) is influenced by multiple intrinsic and extrinsic factors, among which concomitant medications are responsible for drug-drug interactions (DDIs) that may have a clinical relevance, resulting in adverse drug reactions or reduced efficacy. The addition of intrinsic factors affecting cytochromes P450 (CYPs) activity and/or expression, such as genetic polymorphisms and diseases, may potentiate the impact and clinical relevance of DDIs. In addition, greater variability in drug levels and exposures has been observed when such intrinsic factors are present in addition to concomitant medications perpetrating DDIs. This variability results in poor predictability of DDIs and potentially dramatic clinical consequences. The present review illustrates the issue of complex DDIs using systematically searched published case reports of DDIs involving genetic polymorphisms, renal impairment, cirrhosis, and/or inflammation. Current knowledge on the impact of each of these factors on drug exposure and DDIs is summarized and future perspectives for the management of such complex DDIs in clinical practice are discussed, including the use of advanced Computerized Physician Order Entry (CPOE) systems, the development of model-based dose optimization strategies, and the education of healthcare professionals with respect to personalized medicine.

High-Intensity Atorvastatin-Induced Rhabdomyolysis in an Elderly Patient With NSTEMI: A Case Report and Review of the Literature

A 91-year-old male was admitted to the hospital for worsening muscle weakness, muscle pain, and unexplained soreness for the past 10 days. Four months prior to his admission, the patient had experienced a myocardial infarction and was initiated on atorvastatin 80 mg daily. Although the provider had instructed the patient to decrease the atorvastatin dose to 40 mg daily 3 months prior to admission, the patient did not adhere to the lower dose regimen until 10 days prior to hospitalization. Upon admission, the patient presented with muscle weakness and pain, a serum creatinine phosphokinase of 18 723 U/L, and a serum creatinine of 1.6 mg/dL. The atorvastatin dose was held and the patient was treated with intravenous fluids. The 2013 American College of Cardiology and American Heart Association Blood Cholesterol Practice Guidelines recommend the use of moderate-intensity statins in patients older than 75 years to prevent myopathy. However, in clinical practice, aggressive statin therapy is often prescribed for significant coronary disease. Prescribing high-intensity statins for patients with advanced age, such as this case, may increase the risk of rhabdomyolysis and other complications. This case report suggests that providers should avoid or be cautious with initiating high-intensity atorvastatin in elderly patients over 75 years to minimize the risk of rhabdomyolysis.

Pharmacogenetics of drug-drug interaction and drug-drug-gene interaction: a systematic review on CYP2C9, CYP2C19 and CYP2D6

Currently, most guidelines on drug-drug interaction (DDI) neither consider the potential effect of genetic polymorphism in the strength of the interaction nor do they account for the complex interaction caused by the combination of DDI and drug-gene interaction (DGI) where there are multiple biotransformation pathways, which is referred to as drug-drug-gene interaction (DDGI). In this systematic review, we report the impact of pharmacogenetics on DDI and DDGI in which three major drug-metabolizing enzymes - CYP2C9, CYP2C19 and CYP2D6 - are central. We observed that several DDI and DDGI are highly gene-dependent, leading to a different magnitude of interaction. Precision drug therapy should take pharmacogenetics into account when drug interactions in clinical practice are expected.

Drug Class Combination–Associated Acute Kidney Injury

Objective: To evaluate the quality of available evidence of drug class combinations and their association with the development of acute kidney injury (AKI). Data Sources: A search of MEDLINE and Embase databases was completed using the following terms: “risk factor AND (acute kidney injury or acute kidney failure) AND (drug or medication).” Study Selection and Data Extraction: Inclusion criteria were the following: English language, full-text availability, and at least 1 drug-combination. Each citation was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria. The literature was evaluated using the quality of evidence component of GRADE. No standardized definition of AKI was applied throughout.. Data Synthesis: Out of 2139 total citations, 151 were assessed for full-text review, with 121 citations (6%) meeting inclusion criteria, producing76 unique drug class combinations. Overall, 56 combinations (73.7%) were considered very low quality; 12 (15.8%) were considered low quality. There were 8 (10.5%) of moderate quality, and no combination was considered high quality. 58 (76%) combinations that had a single citation,with a mean of 1.6 citations per drug class combination. The combination of nonsteroidal anti-inflammatory drugs (NSAIDs) and diuretics was reported in 10 citations, the largest number of citations. Conclusions: Our study demonstrates a lack of well-designed studies addressing drug class combination–associated AKI. The combination of NSAIDs and diuretics with or without additional renin-angiotensin aldosterone agents had the strongest level of evidence. Despite limitations, the information included in this review may result in additional scrutiny about combining certain individual nephrotoxic drugs.

Increased dosage of cyclosporine induces myopathy with increased seru creatine kinase in an elderly patient on chronic statin therapy

WHAT IS KNOWN AND OBJECTIVE

The concomitant administration of atorvastatin and cyclosporine has been shown to increase the serum concentration of 3-hydroxy-3-methylglutaryl coenzyme A, which may be associated with the elevation of creatine kinase and an increased risk of myopathy. Our objective is to report on a case of statin-induced myopathy associated with concomitant use of cyclosporine and other contributing factors.

CASE SUMMARY

An 88-year-old Chinese male patient with comorbidities received polypharmacy treatment, including atorvastatin and cyclosporine. After the dosage of cyclosporine was increased to 300 mg every day for 8 months, the patient developed body pain and leg weakness, with a serum creatine kinase increase and evidence on magnetic resonance imaging of muscle oedema.

WHAT IS NEW AND CONCLUSION

Cyclosporine is a moderate inhibitor of the cytochrome P450 CYP3A4 isoenzyme, which is known to increase the serum level of atorvastatin. We hypothesized that the pharmacological and pharmacokinetic properties of atorvastatin-induced myopathy are the result of its interaction with high dosage of cyclosporine.

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.

Diagnosis, prevention, and management of statin adverse effects and intolerance: Canadian Working Group Consensus update

The Proceedings of a Canadian Working Group Consensus Conference, first published in 2011, provided a summary of statin-associated adverse effects and intolerance and management suggestions. In this update, new clinical studies identified since then that provide further insight into effects on muscle, cognition, cataracts, diabetes, kidney disease, and cancer are discussed. Of these, the arenas of greatest controversy pertain to purported effects on cognition and the emergence of diabetes during long-term therapy. Regarding cognition, the available evidence is not strongly supportive of a major adverse effect of statins. In contrast, the linkage between statin therapy and incident diabetes is more firm. However, this risk is more strongly associated with traditional risk factors for new-onset diabetes than with statin itself and any possible negative effect of new-onset diabetes during statin treatment is far outweighed by the cardiovascular risk reduction benefits. Additional studies are also discussed, which support the principle that systematic statin rechallenge, and lower or intermittent statin dosing strategies are the main methods for dealing with suspected statin intolerance at this time.