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Circulation Jun 2024
Topics: Humans; Cardiomyopathy, Hypertrophic; Pharmacogenetics; Aminobutyrates; Benzylamines; Uracil; Urea
PubMed: 38829931
DOI: 10.1161/CIRCULATIONAHA.123.066916 -
The Pharmacogenomics Journal Jun 2024The aim was to determine if opioid neuroimmunopharmacology pathway gene polymorphisms alter serum morphine, morphine-3-glucuronide and morphine-6-glucuronide...
The aim was to determine if opioid neuroimmunopharmacology pathway gene polymorphisms alter serum morphine, morphine-3-glucuronide and morphine-6-glucuronide concentration-response relationships in 506 cancer patients receiving controlled-release oral morphine. Morphine-3-glucuronide concentrations (standardised to 11 h post-dose) were higher in patients without pain control (median (interquartile range) 1.2 (0.7-2.3) versus 1.0 (0.5-1.9) μM, P = 0.006), whereas morphine concentrations were higher in patients with cognitive dysfunction (40 (20-81) versus 29 (14-60) nM, P = 0.02). TLR2 rs3804100 variant carriers had reduced odds (adjusted odds ratio (95% confidence interval) 0.42 (0.22-0.82), P = 0.01) of opioid adverse events. IL2 rs2069762 G/G (0.20 (0.06-0.52)), BDNF rs6265 A/A (0.15 (0.02-0.63)) and IL6R rs8192284 carrier (0.55 (0.34-0.90)) genotypes had decreased, and IL6 rs10499563 C/C increased (3.3 (1.2-9.3)), odds of sickness response (P ≤ 0.02). The study has limitations in heterogeneity in doses, sampling times and diagnoses but still suggests that pharmacokinetics and immune genetics co-contribute to morphine pain control and adverse effects in cancer patients.
Topics: Humans; Morphine; Male; Female; Cancer Pain; Middle Aged; Analgesics, Opioid; Delayed-Action Preparations; Aged; Pharmacogenetics; Polymorphism, Single Nucleotide; Morphine Derivatives; Adult; Pharmacogenomic Variants; Toll-Like Receptor 2
PubMed: 38824169
DOI: 10.1038/s41397-024-00339-w -
The Journal of International Medical... May 2024Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently used in clinical microbiology laboratories. This study aimed to...
OBJECTIVE
Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently used in clinical microbiology laboratories. This study aimed to determine whether dual-polarity time-of-flight mass spectrometry (DP-TOF MS) could be applied to clinical nucleotide detection.
METHODS
This prospective study included 40 healthy individuals and 110 patients diagnosed with cardiovascular diseases. We used DP-TOF MS and Sanger sequencing to evaluate 17 loci across 11 genes associated with cardiovascular drug responses. In addition, we used DP-TOF MS to test 998 retrospectively collected clinical DNA samples with known results.
RESULTS
A, T, and G nucleotide detection by DP-TOF MS and Sanger sequencing revealed 100% concordance, whereas the C nucleotide concordance was 99.86%. Genotyping based on the results of the two methods showed 99.96% concordance. Regarding clinical applications, DP-TOF MS yielded a 99.91% concordance rate for known loci. The minimum detection limit for DNA was 0.4 ng; the inter-assay and intra-assay precision rates were both 100%. Anti-interference analysis showed that aerosol contamination greater than 10 copies/µL in the laboratory environment could influence the results of DP-TOF MS.
CONCLUSIONS
The DP-TOF MS platform displayed good detection performance, as demonstrated by its 99.96% concordance rate with Sanger sequencing. Thus, it may be applied to clinical nucleotide detection.
Topics: Humans; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Female; Male; Prospective Studies; Cardiovascular Diseases; Middle Aged; Adult; Aged; Sequence Analysis, DNA; DNA; Retrospective Studies; Case-Control Studies; Polymorphism, Single Nucleotide
PubMed: 38819085
DOI: 10.1177/03000605241255568 -
Clinical and Translational Science Jun 2024Pharmacogenetic (PGx)-informed medication prescription is a cutting-edge genomic application in contemporary medicine, offering the potential to overcome the... (Review)
Review
Pharmacogenetic (PGx)-informed medication prescription is a cutting-edge genomic application in contemporary medicine, offering the potential to overcome the conventional "trial-and-error" approach in drug prescription. The ability to use an individual's genetic profile to predict drug responses allows for personalized drug and dosage selection, thereby enhancing the safety and efficacy of treatments. However, despite significant scientific and clinical advancements in PGx, its integration into routine healthcare practices remains limited. To address this gap, the Qatar Genome Program (QGP) has embarked on an ambitious initiative known as QPGx-CARES (Qatar Pharmacogenetics Clinical Applications and Research Enhancement Strategies), which aims to set a roadmap for optimizing PGx research and clinical implementation on a national scale. The goal of QPGx-CARES initiative is to integrate PGx testing into clinical settings with the aim of improving patient health outcomes. In 2022, QGP initiated several implementation projects in various clinical settings. These projects aimed to evaluate the clinical utility of PGx testing, gather valuable insights into the effective dissemination of PGx data to healthcare professionals and patients, and identify the gaps and the challenges for wider adoption. QPGx-CARES strategy aimed to integrate evidence-based PGx findings into clinical practice, focusing on implementing PGx testing for cardiovascular medications, supported by robust scientific evidence. The current initiative sets a precedent for the nationwide implementation of precision medicine across diverse clinical domains.
Topics: Humans; Qatar; Pharmacogenetics; Precision Medicine; Pharmacogenomic Testing
PubMed: 38818903
DOI: 10.1111/cts.13800 -
World Journal of Hepatology May 2024Hepatitis C virus (HCV)/human immunodeficiency virus (HIV) co-infection still involves 2.3 million patients worldwide of the estimated 37.7 million living with HIV,...
Hepatitis C virus (HCV)/human immunodeficiency virus (HIV) co-infection still involves 2.3 million patients worldwide of the estimated 37.7 million living with HIV, according to World Health Organization. People living with HIV (PLWH) are six times greater affected by HCV, compared to HIV negative ones; the greater prevalence is encountered among people who inject drugs and men who have sex with men: the risk of HCV transmission through sexual contact in this setting can be increased by HIV infection. These patients experience a high rate of chronic hepatitis, which if left untreated progresses to end-stage liver disease and hepatocellular carcinoma (HCC) HIV infection increases the risk of mother to child vertical transmission of HCV. No vaccination against both infections is still available. There is an interplay between HIV and HCV infections. Treatment of HCV is nowadays based on direct acting antivirals (DAAs), HCV treatment plays a key role in limiting the progression of liver disease and reducing the risk of HCC development in mono- and coinfected individuals, especially when used at an early stage of fibrosis, reducing liver disease mortality and morbidity. Since the sustained virological response at week 12 rates were observed in PLWH after HCV eradication, the AASLD has revised its simplified HCV treatment algorithm to also include individuals living with HIV. HCV eradication can determine dyslipidemia, since HCV promotes changes in serum lipid profiles and may influence lipid metabolism. In addition to these apparent detrimental effects on the lipid profile, the efficacy of DAA in HCV/HIV patients needs to be considered in light of its effects on glucose metabolism mediated by improvements in liver function. The aim of the present editorial is to describe the advancement in HCV treatment among PLWH.
PubMed: 38818300
DOI: 10.4254/wjh.v16.i5.661 -
NPJ Systems Biology and Applications May 2024Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding...
Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding strength, affect enhancer's chromatin activity or interaction, and eventually change expression level of downstream gene. Here, we propose a computational framework, PERD, to Predict the Enhancers Responsive to Drug. A machine learning model was trained to predict the genome-wide chromatin accessibility from transcriptome data using the paired expression and chromatin accessibility data collected from ENCODE and ROADMAP. Then the model was applied to the perturbed gene expression data from Connectivity Map (CMAP) and Cancer Drug-induced gene expression Signature DataBase (CDS-DB) and identify drug responsive enhancers with significantly altered chromatin accessibility. Furthermore, the drug responsive enhancers were related to the pharmacogenomics genome-wide association studies (PGx GWAS). Stepping on the traditional drug-associated gene signatures, PERD holds the promise to enhance the causality of drug perturbation by providing candidate regulatory element of those drug associated genes.
Topics: Chromatin; Humans; Machine Learning; Genome-Wide Association Study; Enhancer Elements, Genetic; Computational Biology; Transcriptome; Transcription Factors; Gene Expression Profiling; Pharmacogenetics
PubMed: 38816426
DOI: 10.1038/s41540-024-00388-8 -
Frontiers in Pharmacology 2024Chronic pain is a major socioeconomic burden in the Mediterranean region. However, we noticed an under-representation of these populations in the pharmacogenetics of...
BACKGROUND
Chronic pain is a major socioeconomic burden in the Mediterranean region. However, we noticed an under-representation of these populations in the pharmacogenetics of pain management studies. In this context, we aimed 1) to decipher the pharmacogenetic variant landscape among Mediterranean populations compared to worldwide populations in order to identify therapeutic biomarkers for personalized pain management and 2) to better understand the biological process of pain management through investigation of pharmacogenes pathways.
MATERIALS AND METHODS
We collected genes and variants implicated in pain response using the Prisma guidelines from literature and PharmGK database. Next, we extracted these genes from genotyping data of 829 individuals. Then, we determined the variant distribution among the studied populations using multivariate (MDS) and admixture analysis with R and STRUCTURE software. We conducted a Chi2 test to compare the interethnic frequencies of the identified variants. We used SNPinfo web server, miRdSNP database to identify miRNA-binding sites. In addition, we investigated the functions of the identified genes and variants using pathway enrichment analysis and annotation tools. Finally, we performed docking analysis to assess the impact of variations on drug interactions.
RESULTS
We identified 63 variants implicated in pain management. MDS analysis revealed that Mediterranean populations are genetically similar to Mexican populations and divergent from other populations. STRUCTURE analysis showed that Mediterranean populations are mainly composed of European ancestry. We highlighted differences in the minor allele frequencies of three variants (rs633, rs4680, and rs165728) located in the gene. Moreover, variant annotation revealed ten variants with potential miRNA-binding sites. Finally, protein structure and docking analysis revealed that two missense variants (rs4680 and rs6267) induced a decrease in COMT protein activity and affinity for dopamine.
CONCLUSION
Our findings revealed that Mediterranean populations diverge from other ethnic groups. Furthermore, we emphasize the importance of pain-related pathways and miRNAs to better implement these markers as predictors of analgesic responses in the Mediterranean region.
PubMed: 38813106
DOI: 10.3389/fphar.2024.1380613 -
Clinics and Practice Apr 2024(1) Background: The aim of this study was to analyze the impact of pharmacogenetic-guided antidepressant therapy on the 12-month evolution of the intensity of depressive...
(1) Background: The aim of this study was to analyze the impact of pharmacogenetic-guided antidepressant therapy on the 12-month evolution of the intensity of depressive symptoms in patients with recurrent depressive disorder (RDD) in comparison to a control group of depressive subjects who were treated conventionally. (2) Methods: This prospective longitudinal study was conducted between 2019 and 2022, and the patients were evaluated by employing the Hamilton Depression Rating Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A) and the Clinical Global Impressions Scale: Severity and Improvement. We followed them up at 1, 3, 6, and 12 months. (3) Results: Of the 76 patients with RDD, 37 were tested genetically (Group A) and 39 were not (Group B). Although the patients from Group A had statistically significantly more severe MDD at baseline than those from Group B ( < 0.001), by adjusting their therapy according to the genetic testing, they had a progressive and more substantial reduction in the severity of RDD symptoms [F = 74.334; η = 0.674; < 0.001], indicating a substantial association with the results provided by the genetic testing (67.4%). (4) Conclusions: In patients with RDD and a poor response to antidepressant therapy, pharmacogenetic testing allows for treatment adjustment, resulting in a constant and superior reduction in the intensity of depression and anxiety symptoms.
PubMed: 38804388
DOI: 10.3390/clinpract14030056 -
The Pharmacogenomics Journal May 2024Lack of efficacy or adverse drug response are common phenomena in pharmacological therapy causing considerable morbidity and mortality. It is estimated that 20-30% of...
Lack of efficacy or adverse drug response are common phenomena in pharmacological therapy causing considerable morbidity and mortality. It is estimated that 20-30% of this variability in drug response stems from variations in genes encoding drug targets or factors involved in drug disposition. Leveraging such pharmacogenomic information for the preemptive identification of patients who would benefit from dose adjustments or alternative medications thus constitutes an important frontier of precision medicine. Computational methods can be used to predict the functional effects of variant of unknown significance. However, their performance on pharmacogenomic variant data has been lackluster. To overcome this limitation, we previously developed an ensemble classifier, termed APF, specifically designed for pharmacogenomic variant prediction. Here, we aimed to further improve predictions by leveraging recent key advances in the prediction of protein folding based on deep neural networks. Benchmarking of 28 variant effect predictors on 530 pharmacogenetic missense variants revealed that structural predictions using AlphaMissense were most specific, whereas APF exhibited the most balanced performance. We then developed a new tool, APF2, by optimizing algorithm parametrization of the top performing algorithms for pharmacogenomic variations and aggregating their predictions into a unified ensemble score. Importantly, APF2 provides quantitative variant effect estimates that correlate well with experimental results (R = 0.91, p = 0.003) and predicts the functional impact of pharmacogenomic variants with higher accuracy than previous methods, particularly for clinically relevant variations with actionable pharmacogenomic guidelines. We furthermore demonstrate better performance (92% accuracy) on an independent test set of 146 variants across 61 pharmacogenes not used for model training or validation. Application of APF2 to population-scale sequencing data from over 800,000 individuals revealed drastic ethnogeographic differences with important implications for pharmacotherapy. We thus think that APF2 holds the potential to improve the translation of genetic information into pharmacogenetic recommendations, thereby facilitating the use of Next-Generation Sequencing data for stratified medicine.
Topics: Humans; Pharmacogenetics; Pharmacogenomic Variants; Precision Medicine; Algorithms; Computational Biology
PubMed: 38802404
DOI: 10.1038/s41397-024-00338-x -
Cellular and Molecular Neurobiology May 2024Considering the variability in individual responses to opioids and the growing concerns about opioid addiction, prescribing opioids for postoperative pain management...
Considering the variability in individual responses to opioids and the growing concerns about opioid addiction, prescribing opioids for postoperative pain management after spine surgery presents significant challenges. Therefore, this study undertook a novel pharmacogenomics-based in silico investigation of FDA-approved opioid medications. The DrugBank database was employed to identify all FDA-approved opioids. Subsequently, the PharmGKB database was utilized to filter through all variant annotations associated with the relevant genes. In addition, the dpSNP ( https://www.ncbi.nlm.nih.gov/snp/ ), a publicly accessible repository, was used. Additional analyses were conducted using STRING-MODEL (version 12), Cytoscape (version 3.10.1), miRTargetLink.2, and NetworkAnalyst (version 3). The study identified 125 target genes of FDA-approved opioids, encompassing 7019 variant annotations. Of these, 3088 annotations were significant and pertained to 78 genes. During variant annotation assessments (VAA), 672 variants remained after filtration. Further in-depth filtration based on variant functions yielded 302 final filtered variants across 56 genes. The Monoamine GPCRs pathway emerged as the most significant signaling pathway. Protein-protein interaction (PPI) analysis revealed a fully connected network comprising 55 genes. Gene-miRNA Interaction (GMI) analysis of these 55 candidate genes identified miR-16-5p as a pivotal miRNA in this network. Protein-Drug Interaction (PDI) assessment showed that multiple drugs, including Ibuprofen, Nicotine, Tramadol, Haloperidol, Ketamine, L-Glutamic Acid, Caffeine, Citalopram, and Naloxone, had more than one interaction. Furthermore, Protein-Chemical Interaction (PCI) analysis highlighted that ABCB1, BCL2, CYP1A2, KCNH2, PTGS2, and DRD2 were key targets of the proposed chemicals. Notably, 10 chemicals, including carbamylhydrazine, tetrahydropalmatine, Terazosin, beta-methylcholine, rubimaillin, and quinelorane, demonstrated dual interactions with the aforementioned target genes. This comprehensive review offers multiple strong, evidence-based in silico findings regarding opioid prescribing in spine pain management, introducing 55 potential genes. The insights from this report can be applied in exome analysis as a pharmacogenomics (PGx) panel for pain susceptibility, facilitating individualized opioid prescribing through genotyping of related variants. The article also points out that African Americans represent an important group that displays a high catabolism of opioids and suggest the need for a personalized therapeutic approach based on genetic information.
Topics: Humans; Pain, Postoperative; Precision Medicine; Analgesics, Opioid; Pharmacogenetics; Pain Management; Computer Simulation; Spine
PubMed: 38801645
DOI: 10.1007/s10571-024-01466-5