Pharmacogenomics最新文献

The development of polygenic risk scores (PRSs), which make use of genetic testing to assess an individual's risk of developing certain diseases or conditions based on collective genetic variant information, can be applied in drug development to enrich clinical trials or predict response to treatment. From querying documents submitted to the Food & Drug Administration, the landscape of use of PRSs across time shows increased use in guiding clinical trials. Of the clinical trial protocols submitted, most were in the therapeutic areas of neurology, radiology (imaging and diagnostic pharmaceuticals), psychiatry, and oncology. Use of PRSs in clinical trials is most frequent in early drug development (phase 1, phase 1/2, or phase 3) and generally supports secondary or exploratory analyses. Additionally, about half of the protocols developed novel PRSs, and the other half used preexisting PRSs. As researchers, regulators, and clinicians aim to understand the results and implications of PRSs in clinical trials, the continued use of PRSs, despite being less common, reinforces the need for further exploration.

Pharmacogenomics (PGx) is an evolving field that integrates genetic information into clinical decision-making to optimize drug therapy and minimize adverse drug reactions (ADRs). Its application in rare disease (RD) drug development is promising, given the genetic basis of many RDs and the need for precision medicine approaches. Despite significant advancements, challenges persist in developing effective therapies for RDs due to small patient populations, genetic heterogeneity, and limited surrogate biomarkers. The Orphan Drug Act in the U.S. has incentivized RD drug development. However, the traditional drug approval process is constrained by logistical and economic challenges, necessitating innovative PGx-driven strategies. Identifying genetic biomarkers in the early drug development stages can optimize dose selection, enhance therapeutic efficacy, and reduce ADRs. Case studies such as eliglustat for Gaucher disease and ivacaftor for cystic fibrosis demonstrate the efficacy of PGx-guided treatment strategies. Integrating PGx into global drug development requires the harmonization of regulatory policies and increased diversity in genetic research. Artificial intelligence (AI) tools further enhance genetic analysis, disease prediction, and clinical decision-making. Modernizing drug labeling with PGx information is critical to ensuring safe and effective druguse. Collectively, PGx offers transformative potential in RD therapeutics by facilitating personalized medicine approaches and addressing unmet medical needs.

Objective: We aimed to identify the impact of CYP2C19 polymorphism testing on clinical outcomes in patients who have undergone carotid artery stenting (CAS).

Methods: This was a single-center retrospective cohort study. CYP2C19 polymorphisms were identified based on the presence of two normal functional alleles in normal metabolizers (NMs), a normal functional allele and a nonfunctional allele in intermediate metabolizers and two nonfunctional alleles in poor metabolizers. Patients were recommended for the CYP2C19 polymorphism testing followed by the change in dual antithrombotic drugs (DAPT) at the discretion of the supervising physician. The primary clinical endpoint was stent thrombosis (ST). Logistic regression was used to evaluate the relative risk of clinical outcomes.

Results: A total of 273 patients were included. The relative risk of ST was not reduced in patients who underwent CYP2C19 polymorphism testing than in patients without this test (3.1% vs. 3.9%, OR = 0.914, 95% CI = 0.218-3.841). The ST in NMs and non-NMs was 3.4% and 2.9%, respectively, and showing no reduction in NMs (OR = 1.145, 95% CI = 0.162-8.105). Changing DAPT did not reduce the relative risk of ST compared with non-changing (2.3% vs. 3.2%, OR = 1.604, 95% CI = 0.024-107.033).

Conclusions: CYP2C19 polymorphism was not related to stent thrombosis in patients with CAS.

Introduction: Genetic variants can impact medication response. The study of genetic variants on medications is called pharmacogenomics (PGx). Understanding PGx results can be difficult as results are reported differently than other laboratory tests. Patients have reported a lack of understanding and satisfaction with PGx information.

Methods: Surveys were emailed to patients seen in the PGx clinic and patients who participated in an elective screening (Sanford Chip) at Sanford Health. Surveys were conducted to assess literacy, understanding and satisfaction of PGx testing. Survey responses were summarized using descriptive statistics.

Results: There were 121 responses that were initially collected. A total of 100 responses were included in the analysis. The median response amongst all individuals was 9 out of a possible 13 points on the PGx literacy assessment. PGx clinic patients had increased satisfaction compared to Sanford Chip patients for being able to understand results (p < 0.05), that PGx test provided information to improve my care plan (p < 0.05), that they feel confident that my medication will be effective for me based on my genetics (p < 0.05), were satisfied with communication of results (p < 0.001) and overall experience (p < 0.01).

Discussion: Implementation of a PGx clinic improves patient experience, confidence, and satisfaction.

Aim: To systematically assess clinical studies involving patients undergoing drug therapy, comparing different genotypes to assess the relationship with changes in QT intervals, with no limitations on study design, setting, population, dosing regimens, or duration.

Methods: This systematic review followed PRISMA guidelines and a pre-registered protocol. Clinical human studies on PGx markers of diQTP were identified, assessed using standardized tools, and categorized by design. Gene associations were classified as pharmacokinetic or pharmacodynamic. Identified genes underwent pathway enrichment analyses. Drugs were classified by third-level Anatomical Therapeutic Chemical (ATC) codes. Descriptive statistics were computed by study category and drug classes.

Results: Of 4,493 reports, 84 studies were included, identifying 213 unique variants across 42 drug classes, of which 10% were replicated. KCNE1-Asp85Asn was the most consistent variant. Most findings (82%) were derived from candidate gene studies, suggesting bias toward known markers. The diQTP-associated genes were mainly linked to "cardiac conduction" and "muscle contraction" pathways (false discovery rate = 4.71 × 10^-14). We also found an overlap between diQTP-associated genes and congenital long QT syndrome genes.

Conclusion: Key genes, drugs, and pathways were identified, but few consistent PGx markers emerged. Extensive, unbiased studies with diverse populations are crucial to advancing the field.

Registration: A protocol was pre-registered at PROSPERO under registration number CRD42022296097.

Data deposition: Data sets generated by this review are available at figshare: DOI: 10.6084/m9.figshare.27959616.

Aim: Providers can use combinatorial pharmacogenomic panels to aid psychiatric medication prescribing. Results are typically documented in static documents which list the genotype and predicted phenotype (interpretation). However, genotype-to-phenotype translations can differ between laboratories and change as scientific consensuses evolves. Here, we describe the implications of reinterpreting phenotype after combinatorial psychiatric pharmacogenomic testing in a real-world setting.

Patients and methods: 143 patients underwent testing from 2014 to 2021. Reported genotypes and phenotypes were compared to 2024 Clinical Pharmacogenetics Implementation Consortium definitions. Chi-square tests and logistic regression were used to examine the differences in phenotype frequencies before and after reinterpretation and examine the association with time since testing.

Results: Eighty-one patients (57%) required at least one updated interpretation. CYP2C19 interpretations changed for 44/143 patients (31%), followed by CYP2D6 (29%), CYP2B6 (3%), and CYP2C9 (1%). Reinterpretation reduced the number of CYP2D6 ultrarapid and poor metabolizers (p = 0.005), which has implications for antidepressant prescribing. Likelihood of a patient having a reinterpreted phenotype was not associated with time since reporting (p = 0.71).

Conclusions: Reported phenotypes from combinatorial PGx testing often do not align with current standardized definitions, even from tests performed recently. Health systems should establish procedures to standardize and periodically update pharmacogenomic interpretations.

Aim: To assess patient perspectives following evaluation in a multidisciplinary pharmacogenomics clinic run by a clinical pharmacist, genetic counselor, and physician.

Methods: A survey was distributed to 187 adults seen in the Brigham and Women's Hospital Pharmacogenomics Clinic. Participants who completed the survey were invited to complete a semi-structured interview. Interview subjects were selected based on order of responses, scheduling availability, and range of participant experiences with testing and the clinic process. Surveys were analyzed with descriptive statistics, and interview transcripts were analyzed with thematic analysis.

Results: Forty-two survey responses were received; 13 participants were interviewed. Quantitative data demonstrated high satisfaction with the multidisciplinary clinic model and belief that pharmacogenomic testing has value. Qualitative analysis identified four themes: 1) Self-Advocacy as a Patient Responsibility in the Utilization of Pharmacogenomic Results, 2) High Satisfaction with Multidisciplinary Pharmacogenomics Clinic Model and Team, 3) Utility of Pharmacogenomics, and 4) Desire for Pharmacogenomics Resources.

Conclusion: Patients value the care provided by a multidisciplinary pharmacogenomics clinic team, but they need to advocate for the use of their results with other healthcare professionals.

Receptor tyrosine kinase pathways are frequently deregulated in cancer. Inhibiting these pathways with small molecule inhibitors or monoclonal antibodies has become a crucial addition to the therapeutic armamentarium in oncology. Since the introduction of drugs that target receptor tyrosine kinase pathways, it has become evident that not all patients respond to treatment. Therefore, biomarkers to predict response and benefit of drugs targeting tyrosine kinases have been sought. Monoclonal antibodies targeting the Epidermal Growth Factor Receptor (EGFR), one of the four receptors of the EGFR family were among the first targeted therapies used in solid tumors. Two drugs of this class, cetuximab and panitumumab, have been used in patients with metastatic colorectal cancer initially without any biomarker requirement. Soon, it became clear that responses were mostly observed in patients without mutations in KRAS oncogene. Currently, additional mutations of the pathway, including non-exon 2 mutations in KRAS, mutations in the homologous GTPase NRAS, in kinase BRAF and PIK3CA and other pathway proteins, have been added in the evaluation for responsiveness prediction to cetuximab and panitumumab. In this review, the predictive biomarker landscape for anti-EGFR monoclonal antibody inhibitors in metastatic colorectal cancers with no extended RAS and BRAF mutations will be examined.

Aim: To explore the effect of CYP2D6*10 (100C > T) and ADRB1 1165 G > C polymorphisms on heart rate of post-PCI (percutaneous coronary intervention) patients treated with metoprolol succinate sustained-release tablets. Methods: A total of 756 inpatients with metoprolol succinate sustained-release tablets were selected and the genotypes of CYP2D6*10 and ADRB1 1165G > C were detected in 319 patients using gene chip detection. The target heart rate was defined as a resting heart rate < 70 beats/min. Clinical data were collected. Results: A total of 319 inpatients were enrolled in the study. The mutant allele frequencies of CYP2D6 and ADRB1 were 57.21 and 69.44%, respectively. Whatever the dose of metoprolol, the heart rates were lower in patients with homozygous mutation of CYP2D6 than those with heterozygous mutation and wild-type (p < 0.05). Nevertheless, this effect was not seen between different genotypes of ADRB1. Logistic regression analysis showed that the dose of metoprolol and the genotypes of CYP2D6 were predictors of heart rate <70 beats/min in these patients. Further multivariate analysis indicated that patients with homozygous mutation had better control of heart rates compared with those with wild-type and heterozygous mutation of CYP2D6*10 genotypes (all: p < 0.001). Conclusion:CYP2D6*10 polymorphisms were associated with the heart rate of post-PCI patients treated with metoprolol succinate sustained-release tablets.

This study shows that the distribution of CYP3A5 alleles (*1, *3, *6 and *7) and genotype-predicted CYP3A5 phenotypes vary significantly across Latin American cohorts (Brazilians and the One Thousand Genomes Admixed American superpopulation), as well as among subcohorts comprising individuals with the highest proportions of Native, European or sub-Saharan African ancestry. Differences in biogeographical ancestry across the study groups are the likely explanation for these results. The differential distribution of CYP3A5 phenotypes has major pharmacogenomic implications, affecting the proportion of individuals carrying high risk CYP3A5 phenotypes for the immunosuppressant tacrolimus and the number of patients that would need to be genotyped to prevent acute rejection in kidney transplant recipients under tacrolimus treatment.