Pharmacogenetic relevant polymorphisms of CYP2C9, CYP2C19, CYP2D6, and CYP3A5 in Bhutanese population

Genetic variation of CYP2C9 gene and its correlation with cardiovascular disease risk factors

BACKGROUND

The major enzyme that is responsible for Sulfonylureas (SUs) metabolism is hepatic cytochrome P-450 2C9 (CYP2C9). It is encoded by the polymorphic gene CYP2C9, which has many allelic variants, among those the CYP2C9*2 and CYP2C9*3 are the most common and clinically significant allelic variations. People with diabetes mellitus type 2 (T2DM) are more likely to develop cardiovascular disease (CVD), and their risk of dying from it is more than two times higher than that of people without the condition. The purpose of this study was to evaluate the association of genetic variations in the CYP2C9 gene with cardiovascular risk factors by investigating CYP2C9*1, *2, *3, *5, *11, and *13 allelic variants.

METHODS AND RESULTS

A total of 226 participants were enrolled in the current case-control study. Allele-specific amplification- PCR (ASA-PCR) was used to determine the allele of different variations and the results were confirmed by sequencing. The findings of this study showed the presence of the CYP2C9*2 allele in the T2DM group does not differ from its percentage in the control group. Also, CYP2C9*3 allele frequencies identified by Hardy-Weinberg equilibrium (HWE) analysis law were not significant, p = 0.6593 and 0.5828 in T2DM and control groups. There is no statistically significant difference between the control and diabetes groups involving the distribution of CYP2C9 alleles and CYP2C9*5, *11, and *13 polymorphisms were absent in the Iraqi population. No carrier for the CYP2C9*3 homozygous state was found in both groups.

CONCLUSIONS

According to these results T2DM patients with the CYP2C9*2 and *3 variants have an increased risk of developing hypertension.

Differences in the Proportion of CYP2C19 Loss-of-Function Between Cerebral Infarction and Coronary Artery Disease Patients

Background

Cytochrome P450 2C19 (CYP2C19) genotypes and metabolic phenotypes (extensive metabolizer (EM), intermediate metabolizer (IM), and poor metabolizer (PM)) are related to the metabolism of therapeutic drugs for cardiovascular and cerebrovascular diseases. This study aimed to investigate the differences of CYP2C19 gene polymorphism distribution between coronary artery disease (CAD) patients and cerebral infarction (CI) patients.

Methods

We identified 413 CI patients, 509 CAD patients, and 241 CI+CAD patients from 2016 to 2020 and studied genotypes of CYP2C19 rs4986893 (636G>A) and rs4244285 (681G>A) polymorphisms using PCR-gene chip detection method. Differences in CYP2C19 genotypes and metabolic phenotypes between the groups were compared. To analyze the efficacy of CYP2C19 metabolic phenotypes in discriminating between cerebral infarction and coronary artery disease, multiple logistic regression analysis was conducted after adjusting for gender, age, smoking history, drinking history, hypertension, and diabetes.

Results

There were significant differences in the distribution of CYP2C19 genotypes and metabolic phenotypes between CI and CAD patients. The results of multivariate logistic regression (adjusted for sex, age, smoking, drinking, hypertension, and diabetes) indicated that CYP2C19 IM phenotype (IM vs EM: OR 1.443, 95% CI: 1.086-1.918, P=0.011) and CYP2C19 IM+PM phenotype (IM or PM vs EM: OR 1.440, 95% CI: 1.100-1.885, P=0.008) may be indicators of CI from CAD.

Conclusion

CYP2C19 EM metabolic phenotype was dominant in CAD patients, and CYP2C19 IM metabolic phenotype was dominant in CI patients. After adjusting for other confounding factors, patients with the CYP2C19 IM metabolic phenotype were more likely to develop CI than CAD.

CYP2C19 loss-of-function is associated with increased risk of hypertension in a Hakka population: a case-control study

BACKGROUND

Genetic factors have a certain proportion in the risk factors of hypertension. The purpose was to investigate the relationship of cytochrome P450 2C19 (CYP2C19) polymorphisms with hypertension in Hakka population.

METHODS

The study included 1,872 hypertensive patients and 1,110 controls. The genotypes of CYP2C19 rs4244285 and rs4986893 of all individuals were detected and analyzed.

RESULTS

The genotype and allele distributions of CYP2C19 rs4244285 were significantly different between hypertension group and control group. The CYP2C19 *1/*1 genotype was the most predominant among the subjects (40.8%), followed by the CYP2C19 *1/*2 genotype (40.5%). The percentage of CYP2C19*1, *2, and *3 allele was 64.2%, 30.8%, and 5.0%, respectively. The proportion of intermediate metabolizers (IM) (49.3% vs. 42.9%), poor metabolizers (PM) (14.3% vs. 8.9%) (P < 0.001), and CYP2C19*2 allele (33.8% vs. 25.7%, P < 0.001) in hypertension group was significantly higher than that in control group. Multivariate logistic regression (adjusted for gender, age, smoking, and drinking) indicated that CYP2C19 *1/*2, *1/*3, and *2/*2 genotypes may increase susceptibility to hypertension. And the CYP2C19 IM genotype (IM vs. EM: OR 1.514, 95% CI: 1.291-1.775, P < 0.001), PM genotype (PM vs. EM: OR 2.120, 95% CI: 1.638-2.743, P < 0.001), IM + PM genotypes (IM + PM vs. EM: OR 1.617, 95% CI: 1.390-1.882, P < 0.001) may increase risk of hypertension.

CONCLUSIONS

CYP2C19 loss-of-function (IM, PM genotypes) is independent risk factor for hypertension susceptibility. Specifically, the risk genotypes include CYP2C19 *1/*2, *1/*3, and *2/*2.

Published population pharmacokinetic models of valproic acid in adult patients: a systematic review and external validation in a Chinese sample of inpatients with bipolar disorder

BACKGROUND

This study reviewed all published valproic acid (VPA) population pharmacokinetic (PPK) models in adult patients and assessed them using external validation methods to determine predictive performance.

METHODS

Thirteen published PPK models (labeled with letters A to M) not restricted to children were identified in PubMed, Embase, and Web of Science databases. They were evaluated in a sample totaling 411 serum concentrations from 146 adult inpatients diagnosed with bipolar disorder in a Chinese hospital. Serum concentrations of VPA were analyzed by validated ultra-performance liquid chromatography-tandem mass spectrometry. Performance was assessed by 4 tests (prediction-based diagnostics, visual predictive checks, normalized prediction distribution error, and Bayesian forecasting).

RESULTS

Models K and L, developed in large samples of Chinese and Thai patients, showed good performance in our Chinese dataset. Models H and J demonstrated good performance in Tests 2 and 3 of the 4 tests, respectively. Another 7 models exhibited intermediate performance. The models with the worst performance, F and M, could not be improved by Bayesian forecasting.

CONCLUSION

In our validation study the most important factors contributing to good performance were absence of children, Asian ethnicity, one-compartment models and inclusion of body weight and VPA dose in previously published models.

Text Mining Protocol to Retrieve Significant Drug-Gene Interactions from PubMed Abstracts

Genes and proteins form the basis of all cellular processes and ensure a smooth functioning of the human system. The diseases caused in humans can be either genetic in nature or may be caused due to external factors. Genetic diseases are mainly the result of any anomaly in gene/protein structure or function. This disruption interferes with the normal expression of cellular components. Against external factors, even though the immunogenicity of every individual protects them to a certain extent from infections, they are still susceptible to other disease-causing agents. Understanding the biological pathway/entities that could be targeted by specific drugs is an essential component of drug discovery. The traditional drug target discovery process is time-consuming and practically not feasible. A computational approach could provide speed and efficiency to the method. With the presence of vast biomedical literature, text mining also seems to be an obvious choice which could efficiently aid with other computational methods in identifying drug-gene targets. These could aid in initial stages of reviewing the disease components or can even aid parallel in extracting drug-disease-gene/protein relationships from literature. The present chapter aims at finding drug-gene interactions and how the information could be explored for drug interaction.

Population pharmacokinetics of valproic acid in adult Chinese patients with bipolar disorder

PURPOSE

To develop and validate a population pharmacokinetic (PPK) model of valproic acid (VPA) in adult Chinese patients with bipolar disorder, and provide guidance for individualized therapy in this population.

METHODS

A total of 1104 serum concentrations from 272 patients were collected in this study. The data analysis was performed using a nonlinear mixed-effects modeling approach. Covariates included demographic parameters, biological characteristics, and concomitant medications. Bootstrap validation (1000 runs), normalized prediction distribution error (NPDE), and external validation of 50 patients were employed to evaluate the final model.

RESULTS

A one-compartment model with first-order absorption and elimination was developed for VPA extended-release tablets. VPA clearance was significantly influenced by three variables: sex (12% higher in male patients), daily dose (increasing with the 0.13 exponent), and body weight (increasing with the 0.56 exponent). Typical values for the absorption rate constant (K_a), apparent clearance (CL/F), and apparent distribution volume (V/F) for a female patient weighing 70 kg administered VPA 1000 mg/day were 0.18 h^-1, 0.46 L/h, and 12.84 L, respectively. The results of model evaluation indicated a good stable and precise performance of the final model.

CONCLUSIONS

A qualified PPK model of VPA was developed in Chinese patients with bipolar disorder. This model could be used as a suitable tool for the personalization of VPA dosing for bipolar patients.