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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 -
Therapeutic Advances in Cardiovascular... 2024Atrial fibrillation (AF) accounts for 40% of all cardiac arrhythmias and is associated with a high risk of stroke and systemic thromboembolic complications. Dabigatran,... (Review)
Review
Atrial fibrillation (AF) accounts for 40% of all cardiac arrhythmias and is associated with a high risk of stroke and systemic thromboembolic complications. Dabigatran, rivaroxaban, apixaban, and edoxaban are direct oral anticoagulants (DOACs) that have been proven to prevent stroke in patients with non-valvular AF. This review summarizes the pharmacokinetics, pharmacodynamics, and drug interactions of DOACs, as well as new data from pharmacogenetic studies of these drugs. This review is aimed at analyzing the scientific literature on the gene polymorphisms involved in the metabolism of DOACs. We searched PubMed, Cochrane, Google Scholar, and CyberLeninka (Russian version) databases with keywords: 'dabigatran', 'apixaban', 'rivaroxaban', 'edoxaban', 'gene polymorphism', 'pharmacogenetics', '', '', '', '', and ''. The articles referred for this review include (1) full-text articles; (2) study design with meta-analysis, an observational study in patients taking DOAC; and (3) data on the single-nucleotide polymorphisms and kinetic parameters of DOACs (plasma concentration), or a particular clinical outcome, published in English and Russian languages during the last 10 years. The ages of the patients ranged from 18 to 75 years. Out of 114 reviewed works, 24 were found eligible. As per the available pharmacogenomic data, polymorphisms affecting DOACs are different. This may aid in developing individual approaches to optimize DOAC pharmacotherapy to reduce the risk of hemorrhagic complications. However, large-scale population studies are required to determine the dosage of the new oral anticoagulants based on genotyping. Information on the genetic effects is limited owing to the lack of large-scale studies. Uncovering the mechanisms of the genetic basis of sensitivity to DOACs helps in developing personalized therapy based on patient-specific genetic variants and improves the efficacy and safety of DOACs in the general population.
Topics: Humans; Atrial Fibrillation; Administration, Oral; Hemorrhage; Pharmacogenomic Variants; Risk Factors; Anticoagulants; Treatment Outcome; Stroke; Risk Assessment; Phenotype; Polymorphism, Single Nucleotide; Vitamin K; Drug Interactions
PubMed: 38801157
DOI: 10.1177/17539447241249886 -
Frontiers in Immunology 2024Desmoplastic melanoma (DM) is a rare subtype of melanoma characterized by high immunogenicity which makes it particularly suitable for immune checkpoint inhibitors...
BACKGROUND
Desmoplastic melanoma (DM) is a rare subtype of melanoma characterized by high immunogenicity which makes it particularly suitable for immune checkpoint inhibitors (ICIs) treatment.
CASE PRESENTATION
We report the case of a 53-year-old man with metastatic DM successfully treated with the combination of anti-CTLA-4 and anti-PD-1 antibodies, who developed serious immune-related adverse events (irAEs). The primary tumor was characterized by absent PD-L1 expression and no-brisk lymphocytes infiltration. NGS showed absence of BRAF mutation, a high tumor mutational burden, and an UV-induced DNA damage signature. Metastatic lesions regressed rapidly after few cycles of ICIs until complete response, however the patient developed serious irAEs including hypothyroidism, adrenal deficiency, and acute interstitial nephritis which led to the definitive suspension of treatment. Currently, the patient has normal renal functionality and no disease relapse after 26 months from starting immunotherapy, and after 9 months from its definitive suspension.
CONCLUSION
Efficacy and toxicity are two sides of the same coin of high sensitivity to ICIs in DM. For this reason, these patients should be closely monitored during ICIs therapy to promptly identify serious side effects and to correctly manage them.
Topics: Humans; Male; Melanoma; Middle Aged; Immune Checkpoint Inhibitors; Immunotherapy; Skin Neoplasms; CTLA-4 Antigen; Treatment Outcome; Programmed Cell Death 1 Receptor
PubMed: 38799429
DOI: 10.3389/fimmu.2024.1369531 -
BMC Psychiatry May 2024Tailoring antidepressant drugs (AD) to patients' genetic drug-metabolism profile is promising. However, literature regarding associations of ADs' treatment effect and/or...
BACKGROUND
Tailoring antidepressant drugs (AD) to patients' genetic drug-metabolism profile is promising. However, literature regarding associations of ADs' treatment effect and/or side effects with drug metabolizing genes CYP2D6 and CYP2C19 has yielded inconsistent results. Therefore, our aim was to longitudinally investigate associations between CYP2D6 (poor, intermediate, and normal) and CYP2C19 (poor, intermediate, normal, and ultrarapid) metabolizer-status, and switching/discontinuing of ADs. Next, we investigated whether the number of perceived side effects differed between metabolizer statuses.
METHODS
Data came from the multi-site naturalistic longitudinal cohort Netherlands Study of Depression and Anxiety (NESDA). We selected depression- and/or anxiety patients, who used AD at some point in the course of the 9 years follow-up period (n = 928). Medication use was followed to assess patterns of AD switching/discontinuation over time. CYP2D6 and CYP2C19 alleles were derived using genome-wide data of the NESDA samples and haplotype data from the PharmGKB database. Logistic regression analyses were conducted to investigate the association of metabolizer status with switching/discontinuing ADs. Mann-Whitney U-tests were conducted to compare the number of patient-perceived side effects between metabolizer statuses.
RESULTS
No significant associations were observed of CYP metabolizer status with switching/discontinuing ADs, nor with the number of perceived side effects.
CONCLUSIONS
We found no evidence for associations between CYP metabolizer statuses and switching/discontinuing AD, nor with side effects of ADs, suggesting that metabolizer status only plays a limited role in switching/discontinuing ADs. Additional studies with larger numbers of PM and UM patients are needed to further determine the potential added value of pharmacogenetics to guide pharmacotherapy.
Topics: Humans; Cytochrome P-450 CYP2D6; Cytochrome P-450 CYP2C19; Male; Antidepressive Agents; Female; Middle Aged; Adult; Longitudinal Studies; Netherlands; Anxiety Disorders; Depressive Disorder
PubMed: 38797832
DOI: 10.1186/s12888-024-05764-6 -
Pharmaceuticals (Basel, Switzerland) Apr 2024Polypharmacy is a global healthcare concern, especially among the elderly, leading to drug interactions and adverse reactions, which are significant causes of death in...
Polypharmacy is a global healthcare concern, especially among the elderly, leading to drug interactions and adverse reactions, which are significant causes of death in developed nations. However, the integration of pharmacogenetics can help mitigate these risks. In this study, the data from 483 patients, primarily elderly and polymedicated, were analyzed using Eugenomic's personalized prescription software, g-Nomic. The most prescribed drug classes included antihypertensives, platelet aggregation inhibitors, cholesterol-lowering drugs, and gastroprotective medications. Drug-lifestyle interactions primarily involved inhibitions but also included inductions. Interactions were analyzed considering gender. Significant genetic variants identified in the study encompassed , and . To prevent adverse reactions and enhance medication effectiveness, it is strongly recommended to consider pharmacogenetics testing. This approach shows great promise in optimizing medication regimens and ultimately improving patient outcomes.
PubMed: 38794134
DOI: 10.3390/ph17050565