Association of CYP3A5 Gene Polymorphisms and Amlodipine-Induced Peripheral Edema in Chinese Han Patients with Essential Hypertension

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection

Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient's characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient's ICS response in the next year based on the patient's characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.

Very important pharmacogenetic variants landscape and potential clinical relevance in the Zhuang population from Yunnan province

The gradual evolution of pharmacogenomics has shed light on the genetic basis for inter-individual drug response variations across diverse populations. This study aimed to identify pharmacogenomic variants that differ in Zhuang population compared with other populations and investigate their potential clinical relevance in gene-drug and genotypic-phenotypic associations. A total of 48 variants from 24 genes were genotyped in 200 Zhuang subjects using the Agena MassARRAY platform. The allele frequencies and genotype distribution data of 26 populations were obtained from the 1000 Genomes Project, followed by a comparison and statistical analysis. After Bonferroni correction, significant differences in genotype frequencies were observed of CYP3A5 (rs776746), ACE (rs4291), KCNH2 (rs1805123), and CYP2D6 (rs1065852) between the Zhuang population and the other 26 populations. It was also found that the Chinese Dai in Xishuangbanna, China, Han Chinese in Beijing, China, and Southern Han Chinese, China showed least deviation from the Zhuang population. The Esan in Nigeria, Gambian in Western Division, The Gambia, and Yoruba in Ibadan, Nigeria exhibited the largest differences. This was also proved by structural analysis, Fst analysis and phylogenetic tree. Furthermore, these differential variants may be associated with the pharmacological efficacy and toxicity of Captopril, Amlodipine, Lisinopril, metoclopramide, and alpha-hydroxymetoprolol in the Zhuang population. Our study has filled the gap of pharmacogenomic information in the Zhuang population and has provided a theoretical framework for the secure administration of drugs in the Zhuang population.

Genetic and phenotypic frequency distribution of ACE, ADRB1, AGTR1, CYP2C9*3, CYP2D6*10, CYP3A5*3, NPPA and factors associated with hypertension in Chinese Han hypertensive patients

We analyzed the polymorphisms of 7 antihypertensive drugs-related genes and the factors associated with hypertension in hypertensive patients of Han ethnicity in Qingyang, China. A total of 354 hypertensive patients of Han ethnicity were enrolled from Qingyang, China. The ACE (I/D), ADRB1 (1165G > C), AGTR1 (1166A > C), CYP2C9*3, CYP2D6*10, CYP3A5*3 and NPPA (T2238C) polymorphisms were assessed. Clinical data of patients was also obtained. The influencing factors of hypertension were evaluated. The genotype frequencies of ACE, ADRB1, AGTR1, CYP2C9, CYP3A5 and NPPA loci were in Hardy-Weinberg equilibrium, with mutation frequencies of 39.27%, 74.29%, 6.21%, 4.80%, 72.46% and 0.71%, respectively. CYP2D6 locus was not in Hardy-Weinberg equilibrium. There was no statistical difference in allele frequencies between different genders (P > .05). There was significant difference in the frequencies of ACE (I/D) and NPPA (T2238C) loci among different regions of China (P < .05). Gender, ACE (I/D) and ADRB1 (1165G > C) gene polymorphism, smoking, homocysteine and HDL levels were associated hypertension. The mutation frequencies of ADRB1 (1165G > C) and CYP3A5*3 were high in hypertensive patients of Han ethnicity in Qingyang, suggesting these patients may be more sensitive to beta-blockers and calcium ion antagonists. Meanwhile, hypertension was associated with gender, ACE (I/D) and ADRB1 (1165G > C) gene polymorphisms, smoking, homocysteine and HDL levels.

Adverse Drug Reactions of Antihypertensives and CYP3A5*3 Polymorphism Among Chronic Kidney Disease Patients

Chronic kidney disease (CKD) patients may be more susceptible to adverse drug reactions (ADRs), given their complex medication regimen and altered physiological state driven by a decline in kidney function. This study aimed to describe the relationship between CYP3A5*3 polymorphism and the ADR of antihypertensive drugs in CKD patients. This retrospective, multi-center, observational cohort study was performed among adult CKD patients with a follow-up period of up to 3 years. ADRs were detected through medical records. CYP3A5*3 genotyping was performed using the direct sequencing method. From the 200 patients recruited in this study, 33 (16.5%) were found to have ADRs related to antihypertensive drugs, with 40 ADRs reported. The most frequent ADR recorded was hyperkalemia (n = 8, 20.0%), followed by bradycardia, hypotension, and dizziness, with 6 cases (15.0%) each. The most common suspected agents were angiotensin II receptor blockers (n = 11, 27.5%), followed by angiotensin-converting enzyme inhibitors (n = 9, 22.5%). The CYP3A5*3 polymorphism was not found to be associated with antihypertensive-related ADR across the genetic models tested, despite adjustment for other possible factors through multiple logistic regression (p > 0.05). After adjusting for possible confounding factors, the factors associated with antihypertensive-related ADR were anemia (adjusted odds ratio [aOR] 5.438, 95% confidence interval [CI]: 2.002, 14.288) and poor medication adherence (aOR 3.512, 95% CI: 1.470, 8.388). In conclusion, the CYP3A5*3 polymorphism was not found to be associated with ADRs related to antihypertensives in CKD patients, which requires further verification by larger studies.

Beta adrenergic antagonists and antianginal drugs

Beta adrenergic antagonists and antianginal drugs are used with the aim to ultimately decrease mortality and enable patients to lead an improved quality of life by avoidance of anginal episodes. Each class of medications used for this purpose have a variety of actual or potential side effects associated with their use. Side effects and drug interactions involving these medications are discussed in the following chapter. A special review is included that examines available evidence in the context of the current COVID-19 pandemic. Evidence presented should be used in the context of the patient populations described and may aid clinical decision making through avoidance or identification of actual or potential side effects. This review includes literature published from November 2019 to January 2021.