Pharmacogenomics最新文献

Aim: The study aimed to examine the association of two selected candidate SNPs rs2242480 (CYP3A4) and rs1045642 (ABCB1) with metabolic ratio of plasma norfentanyl to fentanyl concentrations in patients undergoing major breast surgeries.

Methods: The retrospective cross-sectional study was done in 257 female patients. DNA extraction, genotyping of selected SNPs, and drug levels measurement were employed.

Results: A total of 257 female patients were recruited with no loss to follow up. There was no significant association between the two mentioned SNPs and the metabolic ratio (p value > 0.05). As an exploratory analysis, there was a moderately significant negative correlation between metabolic ratio and pupillary constriction to fentanyl (r = -0.27; p < 0.001). There was also a weak but significant positive correlation between metabolic ratio and time for first analgesia in the postoperative period (r = 0.17; p = 0.01).

Conclusion: There was no significant association with the selected candidate SNPs in CYP3A4 and ABCB1 genes and metabolic ratio of norfentanyl to fentanyl in South Indian patients undergoing major breast surgery.

Aim: Valproic acid (VPA) is a classic broad-spectrum antiepileptic drug, with significant pharmacokinetic variability. Genetic polymorphisms contribute to this variability, influencing both VPA trough serum concentration (VPA concentration) and VPA-induced liver injury. Our study aims to investigate the association between polymorphisms of uridine diphosphate glucuronyl transferase (UGT) 1A6, UGT2B7 and VPA concentration and screen for potential genetic loci affecting VPA-induced liver injury.Methods: This study included epilepsy patients treated with VPA. PCR-RFLP method was used to determine the genotypes of UGT1A6 and UGT2B7. Chemiluminescent microparticle immunoassay was used to measure VPA concentration. Multiple linear regression and logistic regression were employed to analyze factors influencing VPA concentration and VPA-induced liver injury, respectively.Results: The correlation between UGT polymorphism and VPA concentration was analyzed in 133 samples. For VPA-induced liver injury, 105 patients were analyzed, with 29 in the liver injury group and 76 in the control group. Our finding showed patients with the UGT1A6-T19G variant had significantly lower VPA concentrations compared with wild-type patients and UGT1A6-T19G, A541G, A552C and UGT2B7-C802T, G211T, A268G polymorphisms showed no impact on VPA-induced liver injury.Conclusion: This study demonstrated UGT1A6-T19G polymorphisms affected the VPA concentration, providing a theoretical basis for the individualized clinical use of VPA.

This paper presents a methodology for automatically extracting insights from PubMed articles using a Natural Language Processing (NLP) framework. Our approach, leveraging advanced NLP techniques and Named Entity Recognition (NER), is crucial for advancing pharmacogenomics and other scientific fields that benefit from streamlined access to literature through automated services like RESTful APIs.Building a new NLP model presents several challenges. First, it is essential to have a thorough understanding of the field in order to define relevant entities. Second, the construction of a diverse and consistent set of examples is crucial. Finally, the effective utilization of pre-established models is of paramount importance, as demonstrated in this work.Our model, validated via ten-fold cross-validation, achieved over 70% recall and precision for all entities in the training set. We provide a reproducible pipeline for the scientific community and propose a structured approach for qualitative analysis and clustering of results. This methodology refines literature reviews, optimizes knowledge extraction, and supports broader application across diverse research domains. An online platform could further extend these benefits to researchers, educators, and practitioners.

Background: Our objective was to explore the pharmacogenetic impact of three known functional variants in drug target genes and determine whether they can explain the inter-individual variation in therapeutic response.

Methods: In a post hoc analysis of data from randomized controlled clinical trials of chiglitazar, we genotyped 481 Chinese patients with T2DM and investigated the association of variants in PPAR genes with the therapeutic outcome separated by dose using linear regression.

Results: rs1800234, a gain-of-function variant of PPARA, had a dose-dependent pharmacogenetic impact on the therapeutic response to chiglitazar. The C allele was significantly associated with reduced therapeutic response in the 48 mg group, while no significant association was observed in the 32 mg group. In addition, in patients without the C allele, patients treated with 48 mg chiglitazar had a better therapeutic response than those treated with 32 mg chiglitazar. To the contrary, in patients with the C allele, patients treated with 48 mg chiglitazar had a worse therapeutic response than those treated with 32 mg of chiglitazar.

Conclusion: The PPARA variant rs1800234 had a dose-dependent pharmacogenetic impact on the therapeutic response to chiglitazar. It could help explain the absence of a dose effect of chiglitazar and serve as a potential biomarker for the chosen dose of chiglitazar in the future. In addition, our study provided important reference for the design and clinical application of multi-target drugs.

Acute generalized exanthematous pustulosis (AGEP) is a rare drug reaction characterized by numerous pustules on an erythematous base. In some cases, hydroxychloroquine (HCQ) can cause AGEP. There is an association between HLA genes and AGEP according to pharmacogenomic studies. In this case report, we present the case of a 36-year-old female who developed HCQ-induced AGEP with HLA-typing. According to our findings, the patient had HLA-B 58:01, HLA-C 08:01, and HLA-A 02:06. A pharmacoeconomic perspective of HLA genotyping before drug prescription is shown in this result.

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing various scientific and clinical disciplines including pharmacogenomics (PGx) by enabling the analysis of complex datasets and the development of predictive models. The integration of AI and ML with PGx has the potential to provide more precise, data-driven insights into new drug targets, drug efficacy, drug selection, and risk of adverse events. While significant effort to develop and validate these tools remain, ongoing advancements in AI technologies, coupled with improvements in data quality and depth is anticipated to drive the transition of these tools into clinical practice and delivery of individualized treatments and improved patient outcomes. The successful development and integration of AI-assisted PGx tools will require careful consideration of ethical, legal, and social issues (ELSI) in research and clinical practice. This paper explores the intersection of PGx with AI, highlighting current research and potential clinical applications, and ELSI including privacy, oversight, patient and provider knowledge and acceptance, and the impact on patient-provider relationship and new roles.

Objective: This study aimed to estimate the clinical utility of performing multi-gene pharmacogenetic testing on patients undergoing gynecologic surgery/procedure by evaluating the prescribing rate of Clinical Pharmacogenetics Implementation Consortium (CPIC) level A medications and frequency of drug-gene interactions (DGIs).

Methods: The electronic health record was queried for 76 current procedural terminology codes to identify gynecologic surgeries/procedures that occurred between 1 January 2015 to 31 December 2020 in patients with at least one of 152 international classification of disease codes. Prescription data for CPIC level A medications was extracted. Those enrolled in the Penn Medicine Biobank were assessed for DGIs.

Results: The cohort consisted of 7798 female patients and 682 were in the biobank. Up to 6 years following their surgery or procedure, 80% were ordered ≥1 CPIC level A medication. Over half (54%) of these medications were ordered within 3 days after their surgery or procedure. The most common CPIC level A medications ordered were ibuprofen (57%) and ondansetron (42%). Overall, 7% of the cohort had ≥1 known or predicted DGI with medications they were prescribed.

Conclusion: Multi-gene pharmacogenetic testing may be beneficial to gynecologic surgery/procedure patients by assisting clinicians with prescribing postoperative analgesics and future medications.

Aim: Pharmacogenomics is emerging in South Asia, including Sri Lanka, with potential to optimize drug therapy and reduce adverse effects. This review evaluates the state of pharmacogenomics research in Sri Lanka, emphasizing population-specific factors to guide future advancements.Materials & methods: A literature search was performed across PubMed/Web-of-Science/SciVerse-Scopus/Embase, and Sri Lanka Journals Online, along with searches for relevant theses in local health repositories/university databases. Studies were categorized into clinical correlational, descriptive or novel assay development studies.Results: Eleven published articles and eight theses were included. One study examined somatic variants (KRAS gene), while all others focused on germline variants. There were two clinical correlational studies: tamoxifen adverse effects and CYP2D6 variants and FTO gene rs9939609 variants and weight gain caused by second-generation antipsychotics. Eight descriptive studies evaluated prevalence of CYP2D6 variants, HLA-B*15:02 allele, KRAS gene mutations and variants related to statin, warfarin and anticancer drug metabolism. Additionally, nine studies developed, validated and tested novel assays for detecting key pharmacogenomically important variants.Conclusion: While pharmacogenomics research in Sri Lanka has made strides, more clinical studies and broader genomic research are needed. Overcoming challenges related to funding, public awareness and regional collaboration is essential to advance personalized medicine and improve therapeutic outcomes in Sri Lanka and South Asia.

Determining variant TPMT alleles to predict patient response to thiopurine therapy represents one of the first successful implementations of pharmacogenomics in clinical practice. However, despite the TPMT-adjusted thiopurine dosing, some TPMT wild-type patients still exhibit toxicity at standard doses. Over the past decade, the pharmacogene NUDT15 has emerged as a significant co-modulator of thiopurine therapy. Initially, NUDT15 was considered important predominantly in Asian populations, but recent studies have highlighted its relevance in European populations as well.To evaluate the pharmacogenetic significance of NUDT15 in the Slovenian population, we sequenced extended regions of exon 1 and exon 3 in 109 healthy individuals and 37 patients with acute lymphoblastic leukemia.We identified eight variants, including one with established clinical significance (allele *3) and one extremely rare variant (Chr13 at 48045861; GRCh38, NC_000013.11). The frequencies of most previously described variants in both the general population and in the ALL cohort were consistent with those reported in other European populations, except for rs45465203, which was less frequent in the Slovenian population. None of the variants, except for NUDT15*3, were associated with cumulative thiopurine doses in ALL patients. However, these variants warrant further investigation in larger ALL cohorts.

This study aims to use machine learning model to predict laboratory aspirin resistance (AR) in Chinese stroke patients by incorporating patient characteristics and single nucleotide polymorphisms of GP1BA and LTC4S. 2405 patients were analyzed to measure the Mutation frequency of GP1BA rs6065 and LTC4S rs730012. 112 patients with first-stroke arteriostenosis were prospectively enrolled to establish machine learning model. GP1BA rs6065 mutation frequency is 5.26% and LTC4S rs730012 is 14.78%. GP1BA rs6065 CT patients have more sensitivity to aspirin than CC genotype. Simple linear regression identified significant associations with age, smoking, HDL and GP1BA rs6065. Random forest (RF) and extreme gradient boosting (XGBoost) demonstrated predictive capabilities for AR. Findings suggest pre-identifying GP1BA rs6065 could optimize aspirin treatment, enabling personalized care and future research avenues.