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BACKGROUND: Endocrine therapy is the most important treatment modality of breast cancer patients whose tumors express the estrogen receptor α (ERα). The androgen receptor (AR) is also expressed in the vast majority (80-90%) of ERα-positive tumors. AR-targeting drugs are not used in clinical practice, but have been evaluated in multiple trials and preclinical studies.
METHODS: We performed a genome-wide study to identify hormone/drug-induced single nucleotide polymorphism (SNP) genotype - dependent gene-expression, known as PGx-eQTL, mediated by either an AR agonist (dihydrotestosterone) or a partial antagonist (enzalutamide), utilizing a previously well characterized lymphoblastic cell line panel. The association of the identified SNPs-gene pairs with breast cancer phenotypes were then examined using three genome-wide association (GWAS) studies that we have published and other studies from the GWAS catalog.
RESULTS: We identified 13 DHT-mediated PGx-eQTL loci and 23 Enz-mediated PGx-eQTL loci that were associated with breast cancer outcomes post ER antagonist or aromatase inhibitors (AI) treatment, or with pharmacodynamic (PD) effects of AIs. An additional 30 loci were found to be associated with cancer risk and sex-hormone binding globulin levels. The top loci involved the genes IDH2 and TMEM9, the expression of which were suppressed by DHT in a PGx-eQTL SNP genotype-dependent manner. Both of these genes were overexpressed in breast cancer and were associated with a poorer prognosis. Therefore, suppression of these genes by AR agonists may benefit patients with minor allele genotypes for these SNPs.
CONCLUSIONS: We identified AR-related PGx-eQTL SNP-gene pairs that were associated with risks, outcomes and PD effects of endocrine therapy that may provide potential biomarkers for individualized treatment of breast cancer.

Thiopurines, an effective therapy for Crohn\'s disease (CD), often lead to adverse events (AEs). Gene polymorphisms affecting thiopurine metabolism may predict AEs. This retrospective study in CD patients (n = 114) with TPMT activity > 5 Units/Red Blood Cells analyzed TPMT (c.238 G > C, c.460 G > A, c.719 A > G), ITPA (c.94 C > A, IVS2 + 21 A > C), and NUDT15 (c.415 C > T) polymorphisms. All patients received azathioprine (median dose 2.2 mg/kg) with 41.2% experiencing AEs, mainly myelotoxicity (28.1%). No NUDT15 polymorphisms were found, 7% had TPMT, and 31.6% had ITPA polymorphisms. AEs led to therapy modifications in 41.2% of patients. Multivariate analysis identified advanced age (OR 1.046, p = 0.007) and ITPA IVS2 + 21 A > C (OR 3.622, p = 0.015) as independent predictors of AEs. IVS2 + 21 A > C was also associated with myelotoxicity (OR 2.863, p = 0.021). These findings suggest that ITPA IVS2 + 21 A > C polymorphism and advanced age predict AEs during thiopurine therapy for CD with intermediate-normal TPMT activity.

Objective: The effectiveness of antidepressant treatment for mood disorders is often limited by either a poor response or the emergence of adverse effects. These complications often necessitate multiple drug trials. This clinical challenge intensifies during pregnancy, when medications must be selected to improve the likelihood of response and optimize reproductive outcomes. We determined the distribution of common pharmacogenetic variants, metabolizer phenotypes, past medication responses, and side effects in childbearing-aged individuals seeking treatment in a tertiary care perinatal mental health clinic.
Methods: Sixty treatment-seeking women (based on sex at birth) with DSM-5- defined bipolar disorder (n = 28) or major depressive disorder (n = 32) provided DNA samples and completed psychiatric diagnostic and severity assessments between April 2014 and December 2017. Samples were genotyped for single-nucleotide variants in drug metabolizing enzyme genes of commonly prescribed antidepressants (cytochrome P450 [CYP] 1A2, 2B6, 2C9, 2C19, 2D6, 3A4, and 3A5), and the frequency of normative metabolizer status was compared to reference populations data from Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. The Antidepressant Treatment History Form was used to record historic medication trials and side effects.
Results: A significantly greater proportion of extensive metabolizers for CYP2B6 was observed in the study population when compared to CPIC population frequency databases in Caucasians (0.64 vs 0.43 [95% CI: 0.49-0.76]; P value = .006) and African Americans (0.71 vs 0.33 [95% CI: 0.29-0.96]; P value = .045). No significant association was found between metabolizer phenotype and the likelihood of a medication side effect.
Conclusion: Pharmacogenomic testing may have value for personalized prescribing in individuals capable of or considering pregnancy.

BACKGROUND: Cytochrome P450 2C19 (CYP2C19) intermediate and poor metabolizer patients exhibit diminished clopidogrel clinical effectiveness after percutaneous coronary intervention (PCI). However, outcome studies to date have lacked racial diversity. Thus, the impact of CYP2C19 genotype on cardiovascular outcomes in patients treated with clopidogrel who identify as Black or African American remains unclear.
RESULTS: Adults among 5 institutions who self-identified as Black or African American, underwent PCI and clinical CYP2C19 genotyping, and were treated with clopidogrel were included. Data were abstracted from health records. Major atherothrombotic (composite of death, myocardial infarction, ischemic stroke, stent thrombosis, or revascularization for unstable angina) and bleeding event rates within 1 year after PCI were compared across CYP2C19 metabolizer groups using multivariable Cox regression adjusted for potential confounders and baseline variables meeting a threshold of P<0.10. The population included 567 Black patients treated with clopidogrel (median age, 62 years; 46% women; 70% with an acute coronary syndrome indication for PCI). Major atherothrombotic events rates were significantly higher among clopidogrel-treated intermediate and poor metabolizers (24 of 125 [19.2%]) versus patients treated with clopidogrel without a no function allele (43 of 442 [9.7%]; 35.1 versus 15.9 events per 100 person-years; adjusted hazard ratio, 2.00 [95% CI, 1.20-3.33], P=0.008). Bleeding event rates were low overall (23 of 567 [4.1%]) and did not differ among the metabolizer groups.
CONCLUSIONS: Black patients with CYP2C19 intermediate and poor metabolizer phenotypes who are treated with clopidogrel exhibit increased risk of adverse cardiovascular outcomes after PCI in a real-world clinical setting. Bleeding outcomes should be interpreted cautiously. Prospective studies are needed to determine whether genotype-guided use of prasugrel or ticagrelor in intermediate and poor metabolizers improves outcomes in Black patients undergoing PCI.

Specific selective serotonin reuptake inhibitors (SSRIs) metabolism is strongly influenced by two pharmacogenes, CYP2D6 and CYP2C19. However, the effectiveness of prospectively using pharmacogenetic variants to select or dose SSRIs for depression is uncertain in routine clinical practice. The objective of this prospective, multicenter, pragmatic randomized controlled trial is to determine the effectiveness of genotype-guided selection and dosing of antidepressants on control of depression in participants who are 8 years or older with ≥3 months of depressive symptoms who require new or revised therapy. Those randomized to the intervention arm undergo pharmacogenetic testing at baseline and receive a pharmacy consult and/or automated clinical decision support intervention based on an actionable phenotype, while those randomized to the control arm have pharmacogenetic testing at the end of 6-months. In both groups, depression and drug tolerability outcomes are assessed at baseline, 1 month, 3 months (primary), and 6 months. The primary end point is defined by change in Patient-Reported Outcomes Measurement Information System (PROMIS) Depression score assessed at 3 months versus baseline. Secondary end points include change inpatient health questionnaire (PHQ-8) measure of depression severity, remission rates defined by PROMIS score < 16, medication adherence, and medication side effects. The primary analysis will compare the PROMIS score difference between trial arms among those with an actionable CYP2D6 or CYP2C19 genetic result or a CYP2D6 drug-drug interaction. The trial has completed accrual of 1461 participants, of which 562 were found to have an actionable phenotype to date, and follow-up will be complete in April of 2024.

Computational methods analyze genomic data to identify genetic variants linked to drug responses, thereby guiding personalized medicine. This study analyzed 942 whole-genome sequences from the Electricity Generating Authority of Thailand (EGAT) cohort to establish a population-specific pharmacogenomic database (TPGxD-1) in the Thai population. Sentieon (version 201808.08) implemented the GATK best workflow practice for variant calling. We then annotated variant call format (VCF) files using Golden Helix VarSeq 2.5.0 and employed Stargazer v2.0.2 for star allele analysis. The analysis of 63 very important pharmacogenes (VIPGx) reveals 85,566 variants, including 13,532 novel discoveries. Notably, we identified 464 known PGx variants and 275 clinically relevant novel variants. The phenotypic prediction of 15 VIPGx demonstrated a varied metabolic profile for the Thai population. Genes like CYP2C9 (9%), CYP3A5 (45.2%), CYP2B6 (9.4%), NUDT15 (15%), CYP2D6 (47%) and CYP2C19 (43%) showed a high number of intermediate metabolizers; CYP3A5 (41%), and CYP2C19 (9.9%) showed more poor metabolizers. CYP1A2 (52.7%) and CYP2B6 (7.6%) were found to have a higher number of ultra-metabolizers. The functional prediction of the remaining 10 VIPGx genes reveals a high frequency of decreased functional alleles in SULT1A1 (12%), NAT2 (84%), and G6PD (12%). SLCO1B1 reports 20% poor functional alleles, while PTGIS (42%), SLCO1B1 (4%), and TPMT (5.96%) showed increased functional alleles. This study discovered new variants and alleles in the 63 VIPGx genes among the Thai population, offering insights into advancing clinical pharmacogenomics (PGx). However, further validation is needed using other computational and genotyping methods.

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.

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 (R2 = 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.

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\', \'ABCB1\', \'CES1\', \'SULT1A\', \'ABCG2\', and \'CYP3A4\'. 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.
Gene polymorphism as a cause of hemorrhagic complications in patients with non-valvular atrial fibrillation treated with oral vitamin K-independent anticoagulantsAtrial 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.