Search Results (121)

Genome-wide association studies (GWASs) play a central role in precision medicine, powering a range of clinical applications from pharmacogenomics to disease risk prediction. A critical component of GWASs is genotype imputation, a computational method used to infer untyped genetic variants. While imputation increases variant coverage by estimating genotypes at untyped loci, this expanded coverage can enhance the ability to detect genetic associations in some cases. However, imputation also introduces biases, particularly for rare variants and underrepresented populations, which may compromise clinical accuracy. This review examines the challenges and clinical implications of genotype imputation errors, including their impact on therapeutic decisions and predictive models, like polygenic risk scores (PRSs). In particular, the sources of imputation errors have been deeply explored, emphasizing the disparities in performance across ancestral populations and downstream effects on healthcare equity and addressing ethical considerations surrounding the access to equitable genomic resources. Based on the above, we propose evidence-based best practices for clinical GWAS implementation, including the direct genotyping of clinically actionable variants, the cross-population validation of imputation models, the transparent reporting of imputation quality metrics, and the use of ancestry-matched reference panels. As genomic data becomes increasingly adopted in healthcare systems worldwide, ensuring the accuracy and inclusivity of GWAS-derived insights is paramount. Here, we suggest a framework for the responsible clinical integration of imputed genetic data, paving the way for more reliable and equitable personalized medicine. Full article

Background: Pharmacogenomics (PGx), a pivotal branch of personalized medicine, studies how genetic variations influence drug responses. Despite its transformative potential, the adoption of PGx in Indian clinical practice faces challenges, such as the lack of population-specific data, evidence-based guidelines, and complexities in interpreting genomic reports. Comprehensive datasets tailored to Indian patients are essential to facilitate the integration of PGx into clinical settings. Methodology: The study collates pharmacogenomic data from multiple sources, including essential drugs listed by the World Health Organization (WHO), drugs used in neonatal intensive care units (NICUs), minimum sets of alleles recommended by the Association for Molecular Pathology (AMP), and catalogs the allele frequencies from the IndiGenomes database to address gaps in actionable PGx for the Indian population. Curated datasets were used to identify pharmacogenomic variants relevant to clinical practice. Results: Overall, 24 prime genes are essential for the outcomes of 57 drugs. In adults, 18 genes influence the metabolism of 44 drugs whereas, in pediatric populations, genotypes of 18 genes significantly impact the metabolism of 18 drugs. Two over-the-counter drugs with actionable PGx variants were identified: ibuprofen and omeprazole. These findings emphasize the clinical relevance of PGx for commonly used drugs, underscoring the need for population-specific data. Conclusions: As the data of several Indian human genome projects become available, an overarching need exists to establish and regulate the dynamic actionable PGx in Indian clinical practice. This will facilitate the integration of pharmacogenomic data into healthcare, enabling effective and personalized drug therapies. Full article

Background/Objectives: Tramadol is an opioid frequently prescribed for moderate to severe pain and has seen a global increase in use. This presents numerous challenges in clinical management. This study aims to elucidate metabolic signatures associated with tramadol consumption, enhancing predictive capabilities for therapeutic outcomes and optimizing patient-specific treatment plans. Methods: Data were obtained from the Qatar Biobank (QBB), focusing on pharmacogenomic variants associated with tramadol use and prescription trends. A cohort of 27 individuals who were administered daily tramadol doses between 100 and 400 mg with available metabolomic profiles were selected. The pharmacokinetics of tramadol were evaluated in relation to specific CYP2D6 genetic variants. Comparative pharmacometabolomic profiles were generated for tramadol users versus a control group of 54 non-users. Additionally, prescription data encompassing tramadol formulations were collected from the electronic medical records (EMR) system of the major public hospital network in Qatar (Hamad Medical Corporation) to discern prescribing patterns. Results: From January 2019 to December 2022, tramadol prescriptions varied, with chronic pain as the primary indication, followed by acute pain. Pharmacogenomic analysis indicated that CYP2D6 allele variations significantly impacted tramadol and O-desmethyltramadol glucuronide levels, notably in ‘normal metabolizers’. Metabolomic analysis revealed distinct metabolic profiles in tramadol users, with significant variations in phosphatidylcholine, histidine, and lysine pathways compared to controls, highlighting tramadol’s unique biochemical impacts. Conclusions: This study underscores the importance of integrating genetic and omics-based approaches to enhance tramadol’s efficacy and safety. These findings support personalized pain management strategies, enhancing treatment outcomes for both chronic and acute pain. Full article

This review aims to provide a comprehensive framework for implementing precision sports medicine, integrating genetics, pharmacogenomics, digital health solutions, and multi-omics data. Literature review was conducted using MEDLINE, EMBASE, Web of Science, and Cochrane Library databases (January 2018–April 2024), focusing on precision medicine applications in sports medicine, utilizing key terms including “precision medicine”, “sports medicine”, “genetics”, and “multi-omics”, with forward and backward citation tracking. The review identified key gene variants affecting athletic performance: endurance (AMPD1, PPARGC1A), power (ACTN3, NOS3), strength (PPARG), and injury susceptibility (COL5A1, MMP3), while also examining inherited conditions like cardiomyopathies (MYH7, MYBPC3). Pharmacogenomic guidelines were established for optimizing common sports medications, including NSAIDs (CYP2C9), opioids (CYP2D6), and cardiovascular drugs (SLCO1B1, CYP2C19). Digital health technologies, including wearables and predictive analytics, showed potential for enhanced athlete monitoring and injury prevention, while multi-omics approaches integrated various molecular data to understand exercise capacity and injury predisposition, enabling personalized assessments, training regimens, and therapeutic interventions based on individual biomolecular profiles. This review provides sports medicine professionals with a framework to deliver personalized care tailored to each athlete’s unique profile, promising optimized performance, reduced injury risks, and improved recovery outcomes. Full article

CYP2C9 and CYP2C19 are major CYP450 enzymes that heavily influence the hepatic metabolism and bioactivation of many medications, including over-the-counter and narrow therapeutic index drugs. Compared to the wild-type alleles, genetic variants in either gene could potentially alter the pharmacokinetics of widely used medications, affect the desired therapeutic outcomes of a drug therapy, or increase the risk of undesired adverse events. The frequency of genetic polymorphisms associated with CYP450 enzymes can widely differ across and between racial and ethnic groups. This narrative review highlights the differences in CYP2C9 and CYP2C19 allele frequencies among European and Asian population subgroups, using published literature. Identifying the substantial differences across European and Asian populations, as well as within Asian subgroups, indicates the need to further scrutinize general population data. Clinical scientists and healthcare providers should advocate for more inclusive clinical pharmacogenomic data and racially and ethnically diverse pharmacogenomic databases. Clinical trials of limited racial and geographical diversity may not necessarily have strong external generalizability for all populations. Furthermore, clinical trials that designate an all-inclusive Asian population consisting of multiple ethnicities may not be adequate due to the perceived genetic differences among Asian subgroups. Gravitating towards a more comprehensive approach to utilizing pharmacogenomic data necessitates granular population-level genetic information which can be leveraged to improve how drug therapies are prescribed, achieve health equity, and advance the future of precision medicine. Full article

Osteoarthritis (OA) is a multifactorial, degenerative joint disease that significantly impairs mobility and quality of life, especially among older adults. The growing aging population and increasing obesity rates are expected to increase the incidence and prevalence of OA. In the absence of Disease-Modifying Antirheumatic Drugs (DMARDs) for OA, current treatment strategies largely focus on symptom relief rather than disease modification. These symptomatic treatments often fail to account for the substantial inter-individual variability in drug response. Pharmacogenomics (PGx), the study of how genetic variation influences drug response, offers a promising approach to personalize OA therapy. This review explores the clinical and pharmacogenomic considerations of commonly used OA medications—acetaminophen, nonsteroidal anti-inflammatory drugs (NSAIDs), duloxetine, and tramadol—focusing on gene–drug interactions that influence efficacy, safety, and metabolism. Evidence-based recommendations from the Clinical Pharmacogenetics Implementation Consortium guidelines are discussed, where applicable, to highlight actionable genetic variants in very important pharmacogenes such as CYP2D6, CYP2C9, and other important drug-metabolizing encoding genes such as CYP2E1 and UGT1A6. While PGx data are not currently embedded in OA clinical treatment guidelines, their integration into clinical practice may enhance therapeutic outcomes and minimize adverse drug events. This review underscores the potential of PGx as a clinical tool in OA pain management, paving the way toward truly personalized medicine. Full article

Background: Cephalosporins are considered safe antibiotics. However, serious hematological abnormalities may occur, although rarely, after their therapeutic use. Case Presentation: We present a case of pancytopenia in a 72-year-old female patient treated with ceftriaxone for a urinary tract infection. After five days of therapy, pancytopenia was observed. Other causes were excluded through extensive diagnostic evaluation, including immunological tests, viral serologies, bone marrow aspiration, and peripheral blood smear. The patient’s clinical condition significantly improved following the discontinuation of ceftriaxone and the administration of granulocyte colony-stimulating factor (G-CSF). Bone marrow findings revealed hypocellularity without malignant infiltration, and peripheral smear showed no dysplasia, blasts, or hemolysis. Conclusions: This case demonstrates that ceftriaxone, although widely regarded as a safe antibiotic, can induce rare but serious hematologic complications such as pancytopenia. A high index of suspicion is required when patients on antibiotic therapy develop unexplained cytopenias. Detailed medication history, exclusion of other causes, and prompt discontinuation of the suspected drug are essential. The patient’s favorable outcome supports the likelihood of an idiosyncratic, immune-mediated mechanism. Future research should explore pharmacogenomic screening in patients at increased risk, particularly involving HLA variants. Full article

Ineffective treatment and side effects are associated with high burdens for the patient and society. We investigated the application of graph representation learning (GRL) for predicting medication usage based on individual genetic data in the United Kingdom Biobank (UKBB). A graph convolutional network (GCN) was used to integrate interconnected biomedical entities in the form of a knowledge graph as part of a machine learning (ML) prediction model. Data from The Pharmacogenomics Knowledgebase (PharmGKB) was used to construct a biomedical knowledge graph. Individual genetic data (n = 485,754) from the UKBB was obtained and preprocessed to match with pharmacogenetic variants in the PharmGKB. Self-reported medication usage labels were obtained from UKBB data field 20003. We hypothesize that pharmacogenetic variants can predict the impact of medications on individuals. We assume that an individual using a medication on a regular basis experiences a net benefit (vs. side-effects) from the medication. ML models were trained to predict medication usage for 264 medications. The GCN model significantly outperformed both a baseline logistic regression model (p-value: 1.53 × 10⁻⁹) and a deep neural network model (p-value: 8.68 × 10⁻⁸). The GCN model also significantly outperformed a GCN model trained using a random graph (GCN-random) (p-value: 5.44 × 10⁻⁹). A consistent trend of medications with higher sample sizes having better performance was observed, and for several medications, a high relative rank of the medication (among multiple medications) was associated with greater than 2-fold higher odds of usage of the medication. In conclusion, a graph-based ML approach could be useful in advancing precision medicine by prioritizing medications that a patient may need based on their genetic data. However, further research is needed to improve the quality and quantity of genetic data and to validate our approach using more reliable medication labels. Full article

Background/Objectives: Pharmacogenomic screening plays a crucial role in optimizing chemotherapy outcomes and minimizing toxicity. Characterizing the baseline distribution of genetic variants in specific populations is essential to inform the prioritization of drug–gene combinations for clinical implementation. The objective of this study was to investigate the distribution of pharmacogenetic variants in 36 genes related to the fluoropyrimidine (FP) pathway among healthy Omani individuals, forming a foundation for future studies in cancer patients receiving FP-based chemotherapy. Methods: Ninety-eight healthy Omani participants aged ≥18 years were recruited at the Sultan Qaboos Comprehensive Cancer Care and Research Center. Whole-blood samples were collected, and genomic DNA was extracted. Targeted next-generation sequencing was performed using a custom Ion AmpliSeq panel covering coding exons and splice-site regions of 36 genes involved in FP metabolism and response. Results: A total of 999 variants were detected across the 36 genes, with 63.3% being heterozygous. The ABCC4 gene had the highest mutation frequency (76 mutations), while DHFR and SMUG1 had the lowest (<10 mutations). In DPYD, four functionally significant variants were found at frequencies ranging from 1 to 8.2% of the population. Missense mutations were also observed in MTHFR and UGT1A1. Three actionable variants in DPYD and MTHFR, associated with 5-fluorouracil and/or capecitabine response, were identified. Additionally, 27 novel single-nucleotide polymorphisms of unknown clinical significance were detected. Conclusions: This study reveals key pharmacogenetic variants in the Omani population, underscoring the importance of integrating pharmacogenomic testing into routine care to support safer, more personalized chemotherapy in the region. Full article

Background/Objectives: Lung cancer has a high mortality rate worldwide, with non-small cell lung cancer (NSCLC) being the most prevalent. Carboplatin and paclitaxel are key treatments for NSCLC; however, adverse drug reactions (ADRs) pose significant challenges. This study examined the impact of genetic variations in ABCB1 and ABCC2 genes on the incidence of ADRs and survival in NSCLC patients treated with carboplatin and paclitaxel. Methods: Variants were identified using RT-PCR, and ADRs classified according to the Common Toxicity Criteria for Adverse Events, Version 4.03. Results: The ABCB1 rs1128503 (c.1236C>T) CC genotype was associated with a higher chance of nausea (OR: 3.5, 95% CI 1.367–9.250, p = 0.0093), vomiting (OR: 13.553, 95% CI 1.705–107.723, p = 0.0137), and a higher risk of death in CT or TT genotypes (HR: 1.725, 95% CI 1.036–2.871, p = 0.0361). The ABCC2 rs717620 (c.-24C>T) TT genotype was associated with increased ALP levels (OR: 14.6, 95% CI 1.234–174.236, p = 0.0335). The ABCB1 rs2032582 non-CC genotypes (TT+AA+TA+CA+CT) were associated with an increased risk of death (HR: 1.922, 95% CI 1.093–3.377, p = 0.0232). Patients with hypocalcemia (HR: 2.317, 95% IC 1.353–3.967, p = 0.022), vomiting (HR: 3.047, 95% IC 1.548–5.997, p = 0.0013), and diarrhea (HR: 2.974, 95% IC 1.590–5.562, p = 0.0006) were associated with lower overall survival. Conclusions: The data suggest that ABCB1 variants may influence gastrointestinal ADRs and patient survival, highlighting the importance of pharmacogenomics in predicting ADRs and drug resistance. This approach offers more precise pharmacotherapy, reduces ADRs, and enhances the patients’ quality of life and survival. Full article

Cardiovascular disease (CVD) is the leading cause of mortality worldwide, often involving a strong genetic background. Polygenic risk scores (PRSs) combine the cumulative effects of multiple genetic variants to quantify an individual’s susceptibility to CVD. Pharmacogenomics (PGx) can further personalize treatment by tailoring medication choices to an individual’s genetic profile. Even with these potential benefits, the extent to which PRS can be integrated into the PGx of CVD remains unclear. Our review provides an overview of current evidence on the application of PRS in the PGx of CVD, examining clinical utility and limitations and providing directions for future research. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews protocol, we conducted a comprehensive literature search in PubMed, EMBASE, and the Web of Science. Studies investigating the relationship between PRS in predicting the efficacy, adverse effects, or cost-effectiveness of cardiovascular medications were selected. Of the 1894 articles identified, 32 met the inclusion criteria. These studies predominantly examined lipid-lowering therapies, antihypertensives, and antiplatelets, although other medication classes (e.g., rate-control drugs, ibuprofen/acetaminophen, diuretics, and antiarrhythmics) were also included. Our findings showed that PRS is most robustly validated in lipid-lowering therapies, especially statins, where studies reported that individuals with higher PRSs derived the greatest reduction in lipids while on statins. Studies analyzing antihypertensives, antiplatelets, and antiarrhythmic medications demonstrated more variable outcomes, though certain PRSs did identify subgroups with significantly improved response rates or a higher risk of adverse events. Though PRS was a strong tool in many cases, we found some key limitations in its applicability in research, such as the under-representation of non-European-ancestry cohorts in the examined studies and a lack of standardized outcome reporting. In conclusion, though PRS offers promise in improving the efficacy of PGx of CVD by enhancing the personalization of medication on an individual level, several obstacles, such as the need for including a broader ancestral diversity and more robust cost-effectiveness data remain. Future research must (i) prioritize validating PRS in ethnically diverse populations, (ii) refine PRS derivation methods to tailor them for drug response phenotypes, and (iii) establish clear and attainable guidelines for standardizing the reporting of outcomes. Full article

Background/Objectives: This review examines the role of pharmacogenomics in individual responses to the pharmacotherapy of various drugs of abuse, including alcohol, cocaine, and opioids, to identify genetic variants that contribute to variability in substance use disorder treatment outcomes. In addition, it explores the pharmacomicrobiomic aspects of substance use, highlighting the impact of the gut microbiome on bioavailability, drug metabolism, pharmacodynamics, and pharmacokinetics. Results: Research on pharmacogenetics has identified several promising genetic variants that may contribute to the individual variability in responses to existing pharmacotherapies for substance addiction. However, the interpretation of these findings remains limited. It is estimated that genetic factors may account for 20–95% of the variability in individual drug responses. Therefore, genetic factors alone cannot fully explain the differences in drug responses, and factors such as gut microbiome diversity may also play a significant role. Drug microbial biotransformation is produced by microbial exoenzymes that convert low molecular weight organic compounds into analogous compounds by oxidation, reduction, hydrolysis, condensation, isomerization, unsaturation, or by the introduction of heteroatoms. Despite significant advances in pharmacomicrobiomics, challenges persist including the lack of standardized methodologies, inter-individual variability, limited understanding of drug biotransformation mechanisms, and the need for large-scale validation studies to develop microbiota-based biomarkers for clinical use. Conclusions: Progress in the pharmacogenomics of substance use disorders has provided biological insights into the pharmacological needs associated with common genetic variants in drug-metabolizing enzymes. The gut microbiome and its metabolites play a pivotal role in various stages of drug addiction including seeking, reward, and biotransformation. Therefore, integrating pharmacogenomics with pharmacomicrobiomics will form a crucial foundation for significant advances in precision and personalized medicine. Full article

Pharmacogenomics, the study of how genetic variations influence drug response, has become integral to cancer treatment as personalized medicine evolves. This review aims to explore key pharmacogenomic biomarkers relevant to cancer therapy and their clinical implications, providing an updated and comprehensive perspective on how genetic variations impact drug metabolism, efficacy, and toxicity in oncology. Genetic heterogeneity among oncology patients significantly impacts drug efficacy and toxicity, emphasizing the importance of incorporating pharmacogenomic testing into clinical practice. Genes such as CYP2D6, DPYD, UGT1A1, TPMT, EGFR, KRAS, and BRCA1/2 play pivotal roles in influencing the metabolism, efficacy, and adverse effects of various chemotherapeutic agents, targeted therapies, and immunotherapies. For example, CYP2D6 polymorphisms affect tamoxifen metabolism in breast cancer, while DPYD variants can result in severe toxicities in patients receiving fluoropyrimidines. Mutations in EGFR and KRAS have significant implications for the use of targeted therapies in lung and colorectal cancers, respectively. Additionally, BRCA1/2 mutations predict the efficacy of PARP inhibitors in breast and ovarian cancer. Ongoing research in polygenic risk scores, liquid biopsies, gene–drug interaction networks, and immunogenomics promises to further refine pharmacogenomic applications, improving patient outcomes and reducing treatment-related adverse events. This review also discusses the challenges and future directions in pharmacogenomics, including the integration of computational models and CRISPR-based gene editing to better understand gene–drug interactions and resistance mechanisms. The clinical implementation of pharmacogenomics has the potential to optimize cancer treatment by tailoring therapies to an individual’s genetic profile, ultimately enhancing therapeutic efficacy and minimizing toxicity. Full article

Background: Over the past decade, TNF inhibitors such as Infliximab and Adalimumab have become central to Inflammatory Bowel Diseases treatment, greatly enhancing patient outcomes. However, immunogenicity—where anti-drug antibodies diminish effectiveness—remains an issue, often requiring dose changes or combination therapies. Pharmacogenomics is increasingly applied in IBD to personalise treatment, especially since genetic factors like the HLA-DQA1*05 variant heighten the immunogenicity risk with IFX. This study aims to examine the relationship between the HLA-DQA1*05 variant and response loss or antibody development in patients regularly monitored on IFX or ADA. Methods: Sixty-five paediatric IBD patients were enrolled, with therapeutic drug monitoring (TDM) of IFX and ADA, conducted using immunoenzymatic assays. The presence of the HLA-DQA1*05 T>C allele variant was also tested using a Biomole HLA-DQA1 Real-time PCR kit. Results: The HLA-DQA1*05 rs2097432 T>C allele was present in 54% of patients on IFX and 69% of those on ADA. No statistically significant differences were found between HLA carriers and non-carriers across any of the three analysed groups: IFX, ADA and the overall anti-TNFα. Conclusions: Our study suggests that the HLA-DQA1*05 allele does not increase the risk of secondary loss of response to anti-TNF therapy, likely because most patients were on a combination of anti-TNF agents and immunomodulators, which can lower anti-drug antibody production. Testing for HLA-DQA105 can aid in personalising treatment and optimising therapy to minimise immunogenicity risks. Full article

Neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, ALS, and Huntington’s, remain formidable challenges in medicine, with their relentless progression and limited therapeutic options. These diseases arise from a web of molecular disturbances—misfolded proteins, chronic neuroinflammation, mitochondrial dysfunction, and genetic mutations—that slowly dismantle neuronal integrity. Yet, recent scientific breakthroughs are opening new paths to intervene in these once-intractable conditions. This review synthesizes the latest insights into the underlying molecular dynamics of neurodegeneration, revealing how intertwined pathways drive the course of these diseases. With an eye on the most promising advances, we explore innovative therapies emerging from cutting-edge research: nanotechnology-based drug delivery systems capable of navigating the blood–brain barrier, gene-editing tools like CRISPR designed to correct harmful genetic variants, and stem cell strategies that not only replace lost neurons but foster neuroprotective environments. Pharmacogenomics is reshaping treatment personalization, enabling tailored therapies that align with individual genetic profiles, while molecular diagnostics and biomarkers are ushering in an era of early, precise disease detection. Furthermore, novel perspectives on the gut–brain axis are sparking interest as mounting evidence suggests that microbiome modulation may play a role in reducing neuroinflammatory responses linked to neurodegenerative progression. Taken together, these advances signal a shift toward a comprehensive, personalized approach that could transform neurodegenerative care. By integrating molecular insights and innovative therapeutic techniques, this review offers a forward-looking perspective on a future where treatments aim not just to manage symptoms but to fundamentally alter disease progression, presenting renewed hope for improved patient outcomes. Full article