Search Results (165)

Aspirin has been the default backbone of antiplatelet therapy after percutaneous coronary intervention (PCI) for over two decades, anchored by landmark trials that established 12-month dual antiplatelet therapy (DAPT) as the standard of care. Three developments have prompted reassessment of this paradigm: the markedly lower thrombotic risk of contemporary drug-eluting stents, the greater potency and consistency of potent P2Y₁₂ inhibitors (ticagrelor, prasugrel), and increasing recognition that major bleeding independently worsens outcomes after PCI. Recent randomised trials have systematically tested aspirin withdrawal at varying time points. Immediate aspirin-free strategies (NEO-MINDSET, STOPDAPT-3) demonstrated an early signal of excess ischaemic events in the ACS component of enrolled populations, suggesting that aspirin remains important during the earliest post-PCI period in ACS. One-month strategies (T-PASS, ULTIMATE-DAPT, TARGET-FIRST) and three-month strategies (TWILIGHT, TICO, DUAL-ACS) showed that transition to P2Y₁₂ monotherapy after an initial DAPT period significantly reduces bleeding without increasing ischaemic events in selected populations. Beyond one year, long-term randomised trials including the HOST-EXAM 10-year follow-up (Lancet 2026) and the STOPDAPT-2 5-year landmark analysis (Circ Cardiovasc Interv 2026), together with study-level meta-analyses (PANTHER) and recent individual patient data meta-analyses, provide converging evidence that clopidogrel monotherapy outperforms aspirin for chronic secondary prevention without excess bleeding. The choice of P2Y₁₂ agent is critical: clopidogrel monotherapy in ACS during the first post-procedural year carries excess thrombotic risk owing to CYP2C19 pharmacogenomic variability, whereas ticagrelor and prasugrel provide more reliable protection. This review synthesises the mechanistic rationale, trial evidence across all time points, special clinical contexts (oral anticoagulation, coronary artery bypass grafting, high bleeding risk), guideline evolution, and methodological considerations, providing a practical framework for individualising post-PCI antiplatelet therapy. Full article

Pharmaceutical care provides the conceptual foundation for integrating pharmacogenetics into everyday pharmacy practice. Defined by Hepler and Strand as “the responsible provision of drug therapy for the purpose of achieving specific outcomes that improve a patient’s quality of life”, pharmaceutical care emphasizes a patient-centered approach in which the pharmacist collaborates with the patient, physician, and other healthcare professionals to design, implement, and monitor individualized therapeutic plans. In this context, pharmacogenetics can be regarded as an extension of pharmaceutical care: while the traditional model relies on monitoring patient outcomes and adherence, PGx adds a genetic dimension that allows treatment to be optimized from the very beginning. The pharmacist’s role therefore evolves from not only ensuring safe and effective use of medicines, but also interpreting genetic test results, supporting adherence to genetically guided therapy, and educating patients about the implications of their personal genetic profile. The introduction of pharmacogenetics testing as one of the potential services offered by community pharmacies is a promising proposition that may revolutionize the approach to drug therapy. Pharmacogenetics, a subset of pharmacogenomics, focuses on the study of DNA sequence variations that influence response to drugs. Thanks to advances in the field of genomics, it has become possible to study the genetic basis of variability in drug response. The identification of alleles responsible for the rapid or slow metabolism of xenobiotics has ushered in a new era in pharmacology. The aim of this interdisciplinary field, combining genetics and pharmacology, is to adapt treatment to a specific patient based on the analysis of their genome and gene polymorphism. Throughout the world, pharmacogenetics is gaining importance as a tool for personalizing medicine. In countries such as the United States, Canada, and the United Kingdom, programs integrating pharmacogenetics with healthcare are being developed. Clinical trials and the implementation of genetic tests into medical practice allow for better matching of medications and reducing the risk of side effects. Pharmacists will play a key role in integrating pharmacogenetics into healthcare. As specialists in the field of pharmacotherapy, they will support physicians in interpreting the results of genetic tests and adapting drug therapy to the individual needs of the patient. Additionally, pharmacists can educate patients and healthcare professionals about the benefits of pharmacogenetics and monitor the effects and safety of medications. Their involvement in the process of personalization of treatment may contribute to improving the effectiveness and safety of pharmacological therapies. Full article

Background/Objectives: Pharmacogenomics (PGx) enables personalized therapy by identifying genetic variants that influence drug response. Despite the advantages of next-generation sequencing (NGS), few clinically validated, guideline-aligned panels comprehensively detect common, rare, and structurally complex pharmacogenetic variants. Methods: We developed and analytically validated Action PharmaKitDx, a targeted NGS panel covering 335 pharmacogenes, including all priority genes recommended by CPIC, DPWG, and CPNDS. Performance was assessed using Coriell HapMap and GeT-RM reference materials across multiple library preparation workflows and Illumina platforms. Clinical feasibility was evaluated in 41 patient samples from diverse specialties. Results were compared with established reference methods, including PCR-based assays, STR analysis, Sanger sequencing, and whole-exome sequencing. Results: Analytical validation: More than 99% of target bases achieved ≥30× coverage. Analytical accuracy, sensitivity, specificity, and positive predictive value exceeded 99.3%, with repeatability and reproducibility >99.7%. Concordance with GeT-RM haplotypes reached 98% after star-allele harmonization. The panel accurately detected complex variants, including CYP2D6 copy-number changes and hybrid alleles. Clinical validation: Full concordance with prior genotyping was observed in clinical samples. Beyond the initial testing indication, each sample harbored a mean of six actionable variants (range 2–10). Thirty-six rare (minor allele frequency <1%) potentially actionable variants were additionally identified. Conclusions: Action PharmaKitDx demonstrates high analytical performance and broad clinical applicability, supporting its implementation as a scalable solution for comprehensive pharmacogenetic testing and precision prescribing. Full article

Traditional one size fits all pharmacotherapy often yields suboptimal clinical outcomes, preventable adverse drug reactions (ADRs), and significant drug waste, imposing substantial economic and ecological burdens on healthcare systems. This review evaluates the transformative potential of pharmacogenomics (PGx) testing, particularly cytochrome P450 (CYP) gene variants, as a foundation for an ecosystem-centric accountability framework for green pharmacy and links human metabolic variability to specific environmental outcomes. Personalized CYP profiling is shown to minimize the environmental release of unused drugs and potentially ecotoxic metabolites into aquatic ecosystems, in contrast to standard uniform drug use approaches. The limitations of ethnicity-based dosing models, which rely on population genetic variation, are examined in the context of increasing global genetic admixture. It is argued that individual genetic profiling, conceptualized as a PGx-Green Passport, provides a reliable safety standard that accounts for individual differences, thereby enhancing efficiency and well-being in a globalized society. By integrating clinical data, including real-world evidence on hospital utilization, with sustainability frameworks, this review demonstrates that PGx-guided therapy is not only a tool for clinical efficiency but also a fundamental requirement for systematically achieving environmentally sustainable healthcare. Full article

(1) Background: This follow-up retrospective analysis used electronic medical record (EMR) data from a health system to identify patients and medications prescribed in accordance with Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. (2) Methods: This analysis included EMR data from a clinical research data warehouse encompassing 928,291 patients seen at an academic medical center between 2020 and 2024. The study evaluated 75 commercially available medications linked to 52 evidence-based CPIC pharmacogenomic (PGx) guidelines. (3) Results: Of the 928,291 patient encounters, 709,673 medication orders were recorded, with 416,621 patients (44.8%) prescribed at least 1 of the 75 CPIC-associated medications. This compares with 845,518 patients who had an encounter in 2015–2019 with 590,526 medication orders, and 335,849 (56.9%) patients had medication orders represented by CPIC-associated medications. One to three CPIC-associated medications accounted for 76.6% of patients in 2020–2024 compared to 75.6% in 2015–2019. (4) Conclusions: The findings demonstrate that the proportion of patients prescribed a CPIC-actionable medication remained just under half of those evaluated within a single institution’s EMR. About three-quarters of patients over the ten-year period had between one to three CPIC-associated medications identified, and the top five classes of medications remained the same in the two periods. This understanding of patient volume may help organizations as they begin to assess the implementation of PGx services. Full article

Background: Warfarin pharmacogenomics is critical due to its narrow therapeutic index and significant interpatient variability. While machine learning (ML) can predict anticoagulation control status (ACS), its “black-box” nature limits clinical translatability. Explainable Artificial Intelligence (XAI) addresses this by providing interpretable insights. This study applied ML and XAI to a warfarin pharmacogenomic dataset to predict poor ACS and explain model decisions. Methods: A post hoc analysis was conducted on a cross-sectional dataset of 232 patients receiving warfarin for ≥6 months. Data included age, gender, interacting drugs, SAMe-TT2R2 score, and genotypes for CYP2C9, VKORC1, and CYP4F2. Poor ACS was defined as time in therapeutic range (TTR) < 70%. The dataset was split into training (70%) and testing (30%) cohorts. Three models, Random Forest, XGBoost, and Logistic Regression, were developed and evaluated using AUC-ROC, sensitivity, and specificity. XAI techniques, including permutation importance and SHapley Additive exPlanations (SHAP), were employed for global and local interpretability. Results: Of 232 patients, 141 (60.8%) had poor ACS. XGBoost and Random Forest demonstrated comparable predictive accuracy (AUC-ROC: 0.67), outperforming Logistic Regression. Sensitivity was 0.83 and 0.79 for XGBoost and Random Forest, respectively. However, specificity was modest for both ensemble methods (Random Forest: 0.48; XGBoost: 0.41) and extremely low for Logistic Regression (0.04), indicating poor discrimination, particularly for identifying patients with adequate anticoagulation control. Globally, important predictors included the age, SAMe-TT2R2 score, CYP2C9 (*2/*2), female gender, and VKORC1 (C/T). XAI revealed predictions were primarily driven by VKORC1, CYP4F2, SAMe-TT2R2 scores, and drug interactions. Concordance between XAI predictions and actual ACS was 78% for adequate and 88.6% for poor ACS. SHAP analysis showed VKORC1 provided a stable risk signal (mean absolute SHAP: 1.44 ± 0.49 in concordant cases), while CYP2C9 was a high-variance, high-impact driver of discordance (mean SHAP: 3.44 ± 3.79 in discordant cases). Conclusions: ML models, particularly ensemble methods, show modest ability to predict poor warfarin control with limited ability to correctly identify patients with adequate control from our dataset. XAI transforms these models into interpretable tools, with SHAP analysis attributing predictions to specific genetic and clinical features. While predictive accuracy remains modest, this approach enhances transparency and provides a foundation for generating hypotheses that may ultimately support clinical decision-making in pharmacogenomic-guided warfarin therapy. Full article

Warfarin is one of the most used oral anticoagulants, even after the arrival of non-vitamin K oral anticoagulants. Warfarin has been implicated in approximately one-third of emergency hospitalizations for adverse drug events among older adults in national U.S. data. Warfarin dose has been shown to vary between patients with up to 10 times the standard dose. This variability is due to multiple factors such as age, gender, diet, body size, co-medications, and the genetic background of the patient, where the genetic background accounts for 50% of warfarin dose variability among Europeans. Sadly, these findings do not apply to Caribbean Hispanic populations such as Puerto Ricans due to them having an admixed genetic profile. In the field of pharmacogenomics (PGx), the utility of machine learning (ML) has been used to predict individual drug responses by analyzing complex genetic and clinical data, which helps personalize medicine by tailoring treatments to a patient’s genetic makeup. Inclusion of ethno-specific variants has demonstrated improvement on the application of ML to a specific population. This study compares eight ML methods to predict warfarin sensitivity in Puerto Rican Caribbean Hispanics. This study is a secondary analysis of genetic and clinical data from 217 Puerto Rican patients treated with warfarin for thromboembolic disorders. After quality control filtering and exclusion of participant records with incomplete genetic and clinical data, 146 participants are retained for analysis. Data are divided into 65% and 35% to be used as training and test sets. Model performance is determined by comparing the precision and accuracy metrics, computed through the corresponding confusion matrixes. A gradient boosting classifier (GDB) achieves the highest overall accuracy (0.7500) and weighted precision of (0.7642); however, sensitivity for detecting warfarin-sensitive patients remains low. Feature importance analysis suggests that rs202201137 could contribute to model predictions, although overall detection of warfarin-sensitive individuals remains limited. Full article

Background: Treating depression and anxiety in adolescents can be challenging due to interindividual variability in medication response. With current trial-and-error prescribing practices, adolescents may undergo multiple medication changes over months or years before an effective and tolerated drug and dose are identified. Pharmacogenomic (PGx) testing can identify interindividual differences in drug metabolism, and evidence supporting PGx-guided prescribing in adults with mental disorders is growing. However, its impact on pediatric psychotropic prescribing remains underexplored. Methods: This is a protocol for a parallel-arm, multicentre, randomized controlled trial. Canadian adolescents aged 12–17 years who are initiating or switching a selective serotonin reuptake inhibitor (SSRI) for depression and/or an anxiety disorder under physician care are eligible. A total of 452 participants will be randomized 1:1 to PGx-guided SSRI prescribing (experimental) or SSRI prescribing based on current practice guidelines (control). Participants, caregivers, prescribing clinicians, outcome assessors, and investigators will be blinded to treatment allocation. Dual primary outcomes are symptom remission at 12 weeks, measured with the Quick Inventory of Depressive Symptomatology–Adolescent (QIDS-A17-SR) and the Screen for Child Anxiety Related Disorders (SCARED). Secondary outcomes, assessed at 4, 8, and 12 weeks, include participant- and physician-rated changes in depressive and anxiety symptoms, role functioning, health-related quality of life, health care utilization, cost-effectiveness, side-effect burden, medication burden, and adherence. Multiple testing will be addressed using the Hochberg method, and a parallel gated analysis will account for non-actionable genotypes. Secondary analysis will estimate minimal clinically important differences for symptom and role-functioning change with PGx-guided therapy. Discussion: At the time of writing, 36 participants have consented and been randomized to an intervention. This trial will evaluate whether PGx-guided prescribing improves symptom remission in adolescents treated with SSRIs. If efficacious, results should be interpreted with existing pediatric pharmacokinetic, observational, and adult trial data to inform PGx use in managing pediatric anxiety and depressive disorders. Full article

Background/Objectives: Prompt integration of molecular and clinical data into electronic medical records, with a sustainable workflow that supports clinicians in rendering genomics-guided care, is critical. We sought to expand the implementation of in-house NGS at our community-based academic cancer center to operationalize the utilization of molecular diagnostic studies to optimize cancer care for all patients, including those outside this study, through broader adoption and diffusion. Methods: In this prospective IRB-approved study, the Kellogg Cancer Genomic Initiative (KCGI), patients with advanced cancers underwent in-house NGS, including tumor mutational burden (TMB) and pharmacogenomics. In-house bioinformatics (Flype) was used for structured reporting and served as a molecular knowledgebase. A multidisciplinary molecular tumor board (MTB) was created to provide precision therapy recommendations. Results: In-house NGS, completed within 11 business days on average, was performed in 90% (251) of the 279 patients in the KCGI with advanced cancers. RNA and TMB analyses were successful in 89.2% and 86.5% of patients, respectively. A total of 54.2% of patients were identified as candidates for use of on- or off-label FDA-approved therapies, and 99.6% of patients who underwent pharmacogenomics testing had at least one gene alteration associated with medication dose adjustment/avoidance. An MTB was established to discuss these and other molecularly challenging cases continues to function as a consultative service that provides actionable recommendations. Conclusions: In this real-world trial, the utilization of in-house NGS with an adaptable bioinformatics pipeline and the establishment of an MTB enabled the refinement of institutional processes and created an environment that enhanced clinician interest in genomics and improved genomics-guided care for patients with advanced cancers. Full article

Artificial Intelligence (AI) and Precision Medicine are increasingly influencing pediatric pharmacotherapy, where age-dependent pharmacokinetic variability demands highly individualized therapeutic strategies. This review examines current applications of AI in pediatric precision medicine and evaluates their clinical relevance and translational challenges. Recent evidence shows substantial progress across multiple domains. In pharmacogenomics, predictive models have reached R² = 0.95 for drug exposure. Tools for adverse drug reaction detection report sensitivities of 81.5% and specificities of 79.5%. Clinical decision support systems for pediatric epilepsy have achieved diagnostic accuracies of 93.4%. Real-world implementations have been associated with a 75% reduction in prescription distribution errors and a 65% improvement in adverse drug reaction detection. Despite these advances, clinical translation remains limited: only 0.38% of pediatric AI models progress to testing in real patients, and 77% of published studies carry a high risk of bias. These gaps highlight the need for rigorous validation, improved data quality, and careful consideration of ethical and algorithmic constraints. Overall, AI has the potential to shift pediatric pharmacotherapy from empirically driven decisions toward predictive, precision-based approaches. Achieving this goal will require well-designed pediatric studies and sustained interdisciplinary collaboration to ensure safe and effective integration into clinical practice. Full article

Over the past decades, remarkable progress in multimodal therapy has significantly improved survival outcomes for children with cancer. Yet, considerable variability in treatment response and toxicity persists, often driven by underlying genetic differences that affect the pharmacokinetics and pharmacodynamics of anticancer drugs. Pharmacogenomics, the study of genetic determinants of drug response, offers a powerful approach to personalize pediatric cancer therapy by optimizing efficacy while minimizing adverse effects. This review synthesizes current evidence on key pharmacogenetic variants influencing the response to major classes of antineoplastic agents used in children, including thiopurines, methotrexate, anthracyclines, alkylating agents, vinca alkaloids, and platinum compounds. Established gene–drug associations such as TPMT, NUDT15, DPYD, SLC28A3, and RARG are discussed alongside emerging biomarkers identified through genome-wide and multi-omics studies. The review also examines the major challenges that impede clinical implementation, including infrastructural limitations, cost constraints, population-specific variability, and ethical considerations. Furthermore, it highlights how integrative multi-omics, systems pharmacology, and artificial intelligence may accelerate the translation of pharmacogenomic data into clinical decision-making. The integration of pharmacogenomic testing into pediatric oncology protocols has the potential to transform cancer care by improving drug safety, enhancing treatment precision, and paving the way toward ethically grounded, personalized therapy for children. Full article

Background/Objectives: Vedolizumab (VDZ), a monoclonal antibody targeting α4β7 integrin, is used in Crohn’s disease (CD) management, yet patients’ responses vary, underscoring the need for pharmacogenomic (PGx) markers. This study aimed to identify PGx pathways associated with suboptimal VDZ response using a rare-variant analytical framework. Methods: DNA from 63 CD patients treated with VDZ as first-line advanced therapy underwent whole-exome sequencing. Clinical response at week 14 classified patients as optimal responders (ORs) or suboptimal responders (SRs). Sequencing data were processed using GATK Best Practices, annotated with variant effect predictors, and filtered for rare damaging variants (damaging missense and high-confidence loss-of-function; minor allele frequency < 0.05). Variants were mapped to genes specific for SRs and ORs, and analyzed for pathway enrichment using the Reactome database. Rare-variant burden and composition differences were assessed with Fisher’s exact test and SKAT-O gene-set association analysis. Results: Suboptimal VDZ response was associated with pathways related to membrane transport (ABC-family proteins, ion channels), L1–ankyrin interactions, and bile acid recycling, while optimal response was associated with pathways involving MET signaling. SKAT-O identified lipid metabolism-related pathways as significantly different—SRs harbored variants in pro-inflammatory lipid signaling and immune cell trafficking genes (e.g., PIK3CG, CYP4F2, PLA2R1), whereas ORs carried variants in fatty acid oxidation and detoxification genes (e.g., ACADM, CYP1A1, ALDH3A2, DECR1, MMUT). Conclusions: This study underscores the potential of exome-based rare-variant analysis to stratify CD patients and guide precision medicine approaches. The identified genes and pathways are potential PGx markers for CD patients treated with VDZ. Full article

The impact of genetic variation in sickle cell patients plays a significant role in opioid therapy individual response and pain management. This review aims to provide a comprehensive overview of the importance of exploring genetic variability and its impact on pain management in patients with sickle cell disease. It also explores opioid therapy variability and opioid Safety. With respect to literature, the polymorphisms in the key metabolic enzymes CYP2D6, UGT2B7, and COMT, as well as variations in the OPRM1, are important modifiers of the pharmacokinetics and pharmacodynamics of opioids. Variations in the COMT gene can influence how the body manages certain brain chemicals and how pain is experienced, while changes in the OPRM1 gene can alter how well opioids bind to their receptors. They help determine how opioids are broken down in the body, how well they attach to pain receptors, and how pain is felt by someone with sickle cell disease. Patients with reduced-function and ultra-rapid CYP2D6 alleles have a modified metabolism of codeine and tramadol, which presents either a reduced analgesic response or a risk for increased toxicity. These observations support the case for the need for tailored opioid prescriptions in a population that is genetically diverse, as well as the risk of not having standardized pain measurement, and the absence of clinical implementation. There remains the risk of unrecognized pharmacogenomics, lack of data, and personalized opioid descriptions persist. Future research should focus on integrating genetic testing into clinical practice to optimize opioid selection, personalize medicine, minimize adverse effects, and ensure each patient receives treatment that is both effective and safe to enhance quality of life for individuals with sickle cell disease. Full article

Major depressive disorder (MDD) was long framed as a single clinical entity arising from a linear stress–monoamine–hypothalamic–pituitary–adrenal (HPA) axis cascade. This view was shaped by forced swim and learned helplessness tests in animals and by short-term symptom-based trials using scales such as the Hamilton Depression Rating Scale (HAM-D) and the Montgomery–Åsberg Depression Rating Scale (MADRS). This “unitary cascade” view has been dismantled by advances in neuroimaging, immune–metabolic profiling, sleep phenotyping, and plasticity markers, which reveal divergent circuit-level, inflammatory, and chronobiological patterns across anxiety-linked, pain-burdened, and cognitively weighted depressive presentations, all characterized by high rates of non-response and relapse. Translationally, face-valid rodent assays that equated immobility with despair have yielded limited bedside benefit, whereas cross-species bridges—electroencephalography (EEG) motifs, rapid eye movement (REM) architecture, effort-based reward tasks, and inflammatory/metabolic panels—are beginning to provide mechanistically grounded, clinically actionable readouts. In current practice, depression care is shifting toward systems psychiatry: inflammation-high and metabolic-high archetypes, anhedonia- and circadian-dominant subgroups, formal treatment-resistant depression (TRD) staging, connectivity-guided neuromodulation, esketamine, selected pharmacogenomic panels, and early digital phenotyping, as endpoints broaden to functioning and durability. A central gap is that heterogeneity is acknowledged but rarely built into trial design or implementation. This perspective advances a plasticity-centered systems psychiatry in which a testable prediction is that manipulating defined prefrontal–striatal and prefrontal–limbic circuits in sex-balanced, chronic-stress models will reproduce human network-defined biotypes and treatment response, and proposes hybrid effectiveness–implementation platforms that embed immune–metabolic and sleep panels, circuit-sensitive tasks, and digital monitoring under a shared, preregistered data standard. Full article

Personalizing therapy using medical marijuana (MM) is based on understanding the pharmacogenomics (PGx) and drug–drug interactions (DDIs) involved, as well as identifying potential epigenetic risk markers. In this work, the evidence regarding the role of variants in phase I (CYP2C9, CYP2C19, CYP3A4/5) and II (UGT1A9/UGT2B7) genes, transporters (ABCB1), and selected neurobiological factors (AKT1/COMT) in differentiating responses to Δ⁹-tetrahydrocannabinol (THC) and cannabidiol (CBD) has been reviewed. Data indicating enzyme inhibition by CBD and the possibility of phenoconversion were also considered, which highlights the importance of a dynamic interpretation of PGx in the context of current pharmacotherapy. Simultaneously, the results of epigenetic studies (DNA methylation, histone modifications, and ncRNA) in various tissues and developmental windows were summarized, including the reversibility of some signatures in sperm after a period of abstinence and the persistence of imprints in blood. Based on this, practical frameworks for personalization are proposed: the integration of PGx testing, DDI monitoring, and phenotype correction into clinical decision support systems (CDS), supplemented by cautious dose titration and safety monitoring. The culmination is a proposal of tables and diagrams that organize the most important PGx–DDI–epigenetics relationships and facilitate the elimination of content repetition in the text. The paper identifies areas of implementation maturity (e.g., CYP2C9/THC, CBD-CYP2C19/clobazam, AKT1, and acute psychotomimetic effects) and those requiring replication (e.g., multigenic analgesic signals), indicating directions for future research. Full article