Correction to: A pharmacogenomic study on the pharmacokinetics of tacrolimus in healthy subjects using the DMETTM Plus platform

AdMaPlace March 18, 2025

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Correction to: A pharmacogenomic study on the pharmacokinetics of tacrolimus in healthy subjects using the DMETTM Plus platform

Introduction

Although rs776746 T > C (also known as CYP3A5*3), which is a nonfunctioning allele of the CYP3A5 gene, is associated with decreased tacrolimus metabolism⁴, the role of other genes, including the ABCB1, CYP2C19, POR, UGT1A8, NOD2, and PPARA, in the pharmacokinetics of tacrolimus was either inconsistent or insignificant³.

Table 1 Generic variants associated with tacrolimus AUC_last based on the FDR‐adjusted multiple testing analysis. The four most significant associations are shown here.

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Table 2 Genetic variants having a coefficient greater than zero in the LASSO models for tacrolimus AUC_last and C_max.

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2. There was an error in the SUBJECTS and METHODS section, subsection “Determination of plasma concentrations of tacrolimus”, subsection “Pharmacokinetic analysis”, where “plasma concentration” should be “whole blood concentration”. A correction has been made, as well as their corresponding entries in Figure 1 and the caption on Table 3.

SUBJECTS AND METHODS

Determination of tacrolimus concentrations in whole blood

Whole-blood concentrations of tacrolimus were determined using a previously published LC/MS/MS method¹⁸ with some modifications. The blood sample preparation involved a liquid/liquid extraction with methyl tert-butyl ether.

Pharmacokinetic analysis

Tacrolimus concentrations from the reference formulation were used for the pharmacokinetic analysis in the present study. The maximum whole-blood concentration (C_max) of tacrolimus was determined directly from the observed whole-blood concentration data.

Table 3 P-values from a general linear model of the pharmacokinetic parameters for tacrolimus, where the CYP3A5 (rs776746) and NR1I2 (rs3814055) genotypes and their interaction term were the independent variables.

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Figure 1. Mean concentration-time profiles of tacrolimus (a) by different CYP3A5 and NR1I2 genotypes (n = 42) and (b) by two different combined CYP3A5 and NR1I2 genotypes (n = 9) where the genotypes represented the highest (CYP3A5 *3/*3 and NR1I2 T/T) and the lowest (CYP3A5 *1/*1 and NR1I2 C/C) exposure to tacrolimus. The error bars represent the standard deviations.

3. There was an error in the RESULTS section, subsection “Genetic effects of CYP3A5 and NR1I2 on tacrolimus pharmacokinetics”, where “CYP2A5*3/*3” should be “CYP3A5*3/*3”.

Results

Genetic effects of CYP3A5 and NR1I2 on tacrolimus pharmacokinetics

The greater the number of nonfunctioning *3 alleles in the CYP3A5 gene, the greater the mean exposure to tacrolimus (Figure 1a). Consequently, the geometric mean AUC_last and C_max of tacrolimus was 2.78 (95% CI: 1.66–4.66) and 1.64 (95% CI: 1.04–2.60) times greater, respectively, in the CYP3A5*3/*3 homozygote than in the *1/*1 wild-type (P < 0.05; Figure 1a).

4. There was a typo in the DISCUSSION section. The corrected sentence appears below.

Discussion

Recently, a clinical trial in 32 kidney transplant patients showed that subjects with the rs3814055 C/C genotype had 1.2 and 1.5 times greater clearance of tacrolimus than the rs3814055 T carriers, C/T and T/T genotypes, respectively,²⁷ which supports the findings in our study.

Categories: Medical research, Therapeutics

Tags: Medical research, Therapeutics

Plasma concentrations of venetoclax and Pharmacogenetics correlated with drug efficacy in treatment naive leukemia patients: a retrospective study

Venetoclax (VEN) was the only Bcl-2 inhibitor approved yet and showed large differences in clinical efficacy. The aim of the study was to explore the relationships between the plasma concentration and efficacy of VEN, and identify potential influencing factors. A retrospective cohort study was conducted and a total of 76 trough (C_0h) and 91 6 h post-dose plasma concentration (C_6h) blood concentrations of VEN were collected in 54 patients. C_6h/D concentration of VEN was found to be significantly correlated with treatment efficacy (p = 0.006) in leukemia patients with good or intermediate prognosis stratification. A ROC curve was then established and the cut-off value was calculated as 0.2868 μg/ml (AUC = 0.7097, p = 0.1081). Besides, patients co-administered with triazoles or carrying CYP3A5 rs776746 AA/AG genotypes were prone to induce higher VEN plasma concentration regardless of whether VEN dosage was reduced or not. Through LASSO-logistic regression and nomogram analysis, chemotherapy regimens and neutrophil percentages were identified as the critical elements that may predict drug response. Above all, in addition to identify prognostic stratification, AML patients taken with VEN were suggested to test plasma concentration routinely so as to achieve desired efficacy, especially when co-administered with triazoles or carried with CYP3A5 rs776746 AA/AG.

AdMaPlace March 18, 2025

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Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.

AdMaPlace March 17, 2025

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Discovery of ancestry-specific variants associated with clopidogrel response among Caribbean Hispanics

High on-treatment platelet reactivity (HTPR) with clopidogrel predicts ischemic events in adults with coronary artery disease, and while HTPR varies by ethnicity, no genome-wide association study (GWAS) of clopidogrel response has been conducted in Caribbean Hispanics. This study aimed to identify genetic predictors of HTPR in a cohort of 511 Puerto Rican cardiovascular patients treated with clopidogrel, stratified by P2Y12 reaction units (PRU) into responders and non-responders (HTPR). Local ancestry inference (LAI) and traditional GWAS identified variants in the CYP2C19 region associated with HTPR, primarily in individuals with European ancestry. Three variants (OSBPL10 rs1376606, DERL3 rs5030613, RGS6 rs9323567) showed suggestive significance, and a variant in UNC5C was linked to increased HTPR risk. These findings highlight the unique genetic landscape of Caribbean Hispanics and challenge the significance of CYP2C19*2 in predicting clopidogrel response in patients with high non-European ancestry. Further studies are needed to replicate these results in other diverse cohorts.

AdMaPlace March 16, 2025

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Atlas of imprinted and allele-specific DNA methylation in the human body

Allele-specific DNA methylation reflects genetic variation and parentally-inherited changes, and is involved in gene regulation and pathologies. Yet, our knowledge of this phenomenon is largely limited to blood. Here we present a comprehensive atlas of allele-specific DNA methylation using deep whole-genome sequencing across 39 normal human cell types. We identified 325k regions, covering 6% of the genome and 11% of CpGs, that show a bimodal distribution of methylated and unmethylated molecules. In 34k of these regions, genetic variations at individual alleles segregate with methylation patterns, validating allele-specific methylation. We also identified 460 regions showing parental allele-specific methylation, the majority of which are novel, as well as 78 regions associated with known imprinted genes. Surprisingly, sequence-dependent and parental allele-dependent methylation is often restricted to specific cell types, revealing unappreciated variation of allele-specific methylation across the human body. Finally, we validate tissue-specific, maternal allele-specific methylation of CHD7, offering a potential mechanism for the paternal bias in the inheritance mode of CHARGE syndrome associated with this gene. The atlas provides a resource for studying allele-specific methylation and regulatory mechanisms underlying imprinted expression in specific human cell types.

AdMaPlace March 12, 2025

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Iron homeostasis and ferroptosis in muscle diseases and disorders: mechanisms and therapeutic prospects

The muscular system plays a critical role in the human body by governing skeletal movement, cardiovascular function, and the activities of digestive organs. Additionally, muscle tissues serve an endocrine function by secreting myogenic cytokines, thereby regulating metabolism throughout the entire body. Maintaining muscle function requires iron homeostasis. Recent studies suggest that disruptions in iron metabolism and ferroptosis, a form of iron-dependent cell death, are essential contributors to the progression of a wide range of muscle diseases and disorders, including sarcopenia, cardiomyopathy, and amyotrophic lateral sclerosis. Thus, a comprehensive overview of the mechanisms regulating iron metabolism and ferroptosis in these conditions is crucial for identifying potential therapeutic targets and developing new strategies for disease treatment and/or prevention. This review aims to summarize recent advances in understanding the molecular mechanisms underlying ferroptosis in the context of muscle injury, as well as associated muscle diseases and disorders. Moreover, we discuss potential targets within the ferroptosis pathway and possible strategies for managing muscle disorders. Finally, we shed new light on current limitations and future prospects for therapeutic interventions targeting ferroptosis.

AdMaPlace March 9, 2025

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Introduction

SUBJECTS AND METHODS

Determination of tacrolimus concentrations in whole blood

Pharmacokinetic analysis

Results

Genetic effects of CYP3A5 and NR1I2 on tacrolimus pharmacokinetics

Discussion

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