Multimorbidity, polypharmacy, and drug-drug-gene interactions following a non-ST elevation acute coronary syndrome: analysis of a multicentre observational study

Longitudinal associations of polypharmacy and frailty with major cardiovascular events and mortality among more than half a million middle-aged participants of the UK Biobank

BACKGROUND

Studies of the associations of polypharmacy and frailty with adverse health outcomes in middle-aged adults are limited. Furthermore, a potentially stronger association of polypharmacy with adverse health outcomes in frail than in non-frail adults is of interest.

OBJECTIVE

To evaluate associations of frailty (assessed using a frailty index) and polypharmacy (defined as taking five or more drugs) with major cardiovascular events, cancer incidence, all-cause, cardiovascular disease-specific, and cancer-specific mortality.

METHODS

Cox proportional hazards regression models were used to analyze 501,548 participants of the UK Biobank cohort study aged 40-69 years who were followed up for an average of 12 years.

RESULTS

The prevalence of pre-frailty and frailty were 43.2 % and 2.3 %, respectively, and that of polypharmacy was 18.3 %. Although strongly associated with each other, frailty and polypharmacy were independently, statistically significantly associated with major cardiovascular events, cardiovascular disease-specific, and all-cause mortality. In addition, the hazard ratios of polypharmacy were stronger among (pre-)frail than non-frail study participants. No profound associations with cancer incidence and cancer mortality were observed. No sex and age differences were observed.

CONCLUSIONS

This large cohort study showed that polypharmacy and frailty are independent risk factors for major cardiovascular events, cardiovascular disease-specific and all-cause mortality in both middle-aged (40-64 years) and older people (≥ 65 years). In addition, the hazard ratios of polypharmacy were stronger among (pre-)frail than non-frail study participants. This underlines the need to avoid polypharmacy as far as possible not only in older but also in middle-aged subjects (40-64 years), especially if they are pre-frail or frail.

CYP3A genetic variation and taxane-induced peripheral neuropathy: a systematic review, meta-analysis, and candidate gene study

Background: Taxane-induced peripheral neuropathy (TIPN) is an important cause of premature treatment cessation and dose-limitation in cancer therapy. It also reduces quality of life and survivorship in affected patients. Genetic polymorphisms in the CYP3A family have been investigated but the findings have been inconsistent and contradictory. Methods: A systematic review identified 12 pharmacogenetic studies investigating genetic variation in CYP3A4*22 and CYP3A5*3 and TIPN. In our candidate gene study, 288 eligible participants (211 taxane participants receiving docetaxel or paclitaxel, and 77 control participants receiving oxaliplatin) were successfully genotyped for CYP3A4*22 and CYP3A5*3. Genotyping data was transformed into a combined CYP3A metaboliser phenotype: Poor metabolisers, intermediate metabolisers and extensive metabolisers. Individual genotypes and combined CYP3A metaboliser phenotypes were assessed in relation to neurotoxicity, including by meta-analysis where possible. Results: In the systematic review, no significant association was found between CYP3A5*3 and TIPN in seven studies, with one study reporting a protective association. For CYP3A4*22, one study has reported an association with TIPN, while four other studies failed to show an association. Evaluation of our patient cohort showed that paclitaxel was found to be more neurotoxic than docetaxel (p < 0.001). Diabetes was also significantly associated with the development of TIPN. The candidate gene analysis showed no significant association between either SNP (CYP3A5*3 and CYP3A4*22) and the development of TIPN overall, or severe TIPN. Meta-analysis showed no association between these two variants and TIPN. Transformed into combined CYP3A metaboliser phenotypes, 30 taxane recipients were poor metabolisers, 159 were intermediate metabolisers, and 22 were extensive metabolisers. No significant association was observed between metaboliser status and case-control status. Summary: We have shown that the risk of peripheral neuropathy during taxane chemotherapy is greater in patients who have diabetes. CYP3A genotype or phenotype was not identified as a risk factor in either the candidate gene analysis or the systematic review/meta-analysis, although we cannot exclude the possibility of a minor contribution, which would require a larger sample size.

The investigation of the complex population-drug-drug interaction between ritonavir-boosted lopinavir and chloroquine or ivermectin using physiologically-based pharmacokinetic modeling

OBJECTIVES

Therapy failure caused by complex population-drug-drug (PDDI) interactions including CYP3A4 can be predicted using mechanistic physiologically-based pharmacokinetic (PBPK) modeling. A synergy between ritonavir-boosted lopinavir (LPVr), ivermectin, and chloroquine was suggested to improve COVID-19 treatment. This work aimed to study the PDDI of the two CYP3A4 substrates (ivermectin and chloroquine) with LPVr in mild-to-moderate COVID-19 adults, geriatrics, and pregnancy populations.

METHODS

The PDDI of LPVr with ivermectin or chloroquine was investigated. Pearson's correlations between plasma, saliva, and lung interstitial fluid (ISF) levels were evaluated. Target site (lung epithelial lining fluid [ELF]) levels of ivermectin and chloroquine were estimated.

RESULTS

Upon LPVr coadministration, while the chloroquine plasma levels were reduced by 30, 40, and 20%, the ivermectin plasma levels were increased by a minimum of 425, 234, and 453% in adults, geriatrics, and pregnancy populations, respectively. The established correlation equations can be useful in therapeutic drug monitoring (TDM) and dosing regimen optimization.

CONCLUSIONS

Neither chloroquine nor ivermectin reached therapeutic ELF levels in the presence of LPVr despite reaching toxic ivermectin plasma levels. PBPK modeling, guided with TDM in saliva, can be advantageous to evaluate the probability of reaching therapeutic ELF levels in the presence of PDDI, especially in home-treated patients.

Pharmacogenomics: current status and future perspectives

Inter-individual variability in drug response, be it efficacy or safety, is common and likely to become an increasing problem globally given the growing elderly population requiring treatment. Reasons for this inter-individual variability include genomic factors, an area of study called pharmacogenomics. With genotyping technologies now widely available and decreasing in cost, implementing pharmacogenomics into clinical practice - widely regarded as one of the initial steps in mainstreaming genomic medicine - is currently a focus in many countries worldwide. However, major challenges of implementation lie at the point of delivery into health-care systems, including the modification of current clinical pathways coupled with a massive knowledge gap in pharmacogenomics in the health-care workforce. Pharmacogenomics can also be used in a broader sense for drug discovery and development, with increasing evidence suggesting that genomically defined targets have an increased success rate during clinical development.

The role of CYP2C9*2, CYP2C9*3 and VKORC1-1639 variants on the susceptibility of upper gastrointestinal bleeding: A full case-control study

Purpose: To investigate whether interindividual variability in the CYP2C9 (*2 and *3 alleles) and VKORC1 (rs9923231) genes is associated with increased risk of upper gastrointestinal bleeding (UGIB) in users of non-steroidal anti-inflammatory drugs (NSAIDs) or low-dose aspirin (LDA). Methods: A full case-control study including 200 cases of patients diagnosed with UGIB and 706 controls was conducted in a Brazilian hospital complex. To perform an analysis of NSAIDs dose-effect, the defined daily dose (DDD) for NSAIDs was calculated in the 7-day etiologic window preceding the data index. Three categories of DDD, considering the genotypes of the genetic variants, were established: non-users of NSAIDs (DDD = 0), DDD ≤0.5, and DDD >0.5. Genetic variants and LDA or NSAIDs use synergism was estimated through Synergism Index (SI) and Relative Excess Risk Due To Interaction (RERI). Results: For DDDs of NSAIDs upward of 0.50, a risk of UGIB was identified in carriers of the *3 allele (OR: 15,650, 95% CI: 1.41-174.10) and in carriers of the variant homozygous genotype (TT) of rs9923231 (OR: 38,850, 95% CI: 2.70-556.00). In LDA users, the risk of UGIB was observed to be similar between carriers of the wild type homozygous genotype and carriers of the variant alleles for the CYP2C9 and VKORC1 genes. No synergism was identified. Conclusion: Our findings suggest an increased risk of UGIB in carriers of the variant allele of rs9923231 and in carriers of the *3 allele associated with doses of NSAIDs greater than 0.5. Hence, the assessment of these variants might reduce the incidence of NSAIDs-related UGIB and contribute to the safety of the NSAIDs user.

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.

Multimorbidity and combined interventions for patients with coronary heart disease in Chinese population: Latent class analysis of a multi-center study

BACKGROUND AND AIMS

This study aimed to explore the profile of multimorbidity phenotype clusters and their discrepancy in mortality and the efficiency of combined interventions on blood pressure, glucose and lipid in each cluster.

METHODS

Fine and Gray competing risk regression models and Kaplan-Meier curves were used to assess the association between multimorbidity and mortality and rehospitalization. Fine and Gray competing risk regression models and subgroup analyses were used to estimate the relations between combined interventions and mortality.

RESULTS

Three distinct multimorbidity clusters were observed: Class 1 named severe class, Class 2 termed moderate class, and Class 3 named mild class. Competing risk regression models revealed that patients in Class 1 have the greatest burden of mortality and rehospitalization compared to Class 3 after confounder adjustment, with HRs 1.43 (95% CI 1.30-1.56, P < 0.001) and 2.97 (95% CI 2.74-3.21, P < 0.001), respectively. The patients in Class 2 have moderate risk of mortality and rehospitalization compared to Class 3 after confounder adjustment, with HRs 1.41 (95% CI 1.30-1.52, P < 0.001) and 2.39 (95% CI 2.23-2.56, P < 0.001), respectively. Furthermore, we found that combined interventions on blood pressure, glucose and lipid simultaneously could further benefit on survival compared to each individual intervention or two in combine.

CONCLUSIONS

This study found that multimorbidity among patients with CHD was common and increased the risks of death and rehospitalization. Three multimorbidity clusters that were significantly associated with death and rehospitalization were identified. Simultaneous intervention on blood pressure, glucose and lipid level may further benefit CHD patient in survival.

Pharmacogenomics and circadian rhythms as mediators of cardiovascular drug-drug interactions

This article summarizes the current literature and documents new evidence concerning drug-drug interactions (DDI) stemming from pharmacogenomic and circadian rhythm determinants of therapies used to treat common cardiovascular diseases (CVD), such as atherosclerosis and hypertension. Patients with CVD often have more than one pathophysiologic condition, namely metabolic syndromes, hypertension, hyperlipidemia, and hyperglycemia, among others, which necessitate polytherapeutic or polypharmaceutic management. Interactions between drugs, drugs and food/food supplements, or drugs and genetic/epigenetic factors may have adverse impacts on the cardiovascular and other systems of the body. The mechanisms underlying cardiovascular DDI may involve the formation of a complex pharmacointeractome, including the absorption, distribution, metabolism, and elimination of drugs, which affect their respective bioavailability, efficacy, and/or harmful metabolites. The pharmacointeractome of cardiovascular drugs is likely operated with endogenous rhythms controlled by circadian clock genes. Basic and clinical investigations have improved the knowledge and understanding of cardiovascular pharmacogenomics and pharmacointeractomes, and additionally they have presented new evidence that the staging of deterministic circadian rhythms, according to the dosing time of drugs, e.g., upon awakening vs. at bedtime, cannot only differentially impact their pharmacokinetics and pharmacodynamics but also mediate agonistic/synergetic or antagonistic DDI. To properly manage CVD patients and avoid DDI, it is important that clinicians have sufficient knowledge of their multiple risk factors, i.e., age, gender, and life style elements (like diet, smoking, psychological stress, and alcohol consumption), and comorbidities, such as diabetes, hypertension, dyslipidemia, and depression, and the potential interactions between genetic or epigenetic background of their prescribed therapeutics.

Pharmacogenomics of NSAID-Induced Upper Gastrointestinal Toxicity

Non-steroidal anti-inflammatory drugs (NSAIDs) are a group of drugs which are widely used globally for the treatment of pain and inflammation, and in the case of aspirin, for secondary prevention of cardiovascular disease. Chronic non-steroidal anti-inflammatory drug use is associated with potentially serious upper gastrointestinal adverse drug reactions (ADRs) including peptic ulcer disease and gastrointestinal bleeding. A few clinical and genetic predisposing factors have been identified; however, genetic data are contradictory. Further research is needed to identify clinically relevant genetic and non-genetic markers predisposing to NSAID-induced peptic ulceration.

The Influence of Pharmacogenetics on the Clinical Relevance of Pharmacokinetic Drug-Drug Interactions: Drug-Gene, Drug-Gene-Gene and Drug-Drug-Gene Interactions

Drug interactions are a well-known cause of adverse drug events, and drug interaction databases can help the clinician to recognize and avoid such interactions and their adverse events. However, not every interaction leads to an adverse drug event. This is because the clinical relevance of drug-drug interactions also depends on the genetic profile of the patient. If inhibitors or inducers of drug metabolising enzymes (e.g., CYP and UGT) are added to the drug therapy, phenoconcversion can occur. This leads to a genetic phenotype that mismatches the observable phenotype. Drug-drug-gene and drug-gene-gene interactions influence the toxicity and/or ineffectivness of the drug therapy. To date, there have been limited published studies on the impact of genetic variations on drug-drug interactions. This review discusses the current evidence of drug-drug-gene interactions, as well as drug-gene-gene interactions. Phenoconversion is explained, the and methods to calculate the phenotypes are described. Clinical recommendations are given regarding the integratation of the PGx results in the assessment of the relevance of drug interactions in the future.