Pharmacogenomics最新文献

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a poor prognosis and limited effective treatments. Standard chemotherapy offers modest survival benefits, and actionable genetic alterations are rare. RET gene fusions occur in less than 1% of PDAC cases but present a potential target for therapy. We describe a case of a 62-year-old woman with metastatic PDAC who progressed on two lines of chemotherapy. A RET fusion (TPR - RET) was identified via liquid biopsy and confirmed on tissue biopsy. Treatment with the selective RET inhibitor selpercatinib led to rapid symptom improvement, normalization of tumor markers, and significant tumor regression. A partial radiographic response of approximately 60% was achieved, with manageable toxicity. This case highlights the importance of comprehensive molecular profiling, including noninvasive methods like liquid biopsy, in identifying rare but actionable mutations. Emerging evidence from clinical trials supports the efficacy of RET inhibitors in RET-altered PDAC. Broader integration of genomic testing in PDAC may uncover new therapeutic opportunities and improve patient outcomes.

Introduction: Supportive care in oncology aims to prevent and manage cancer-related side effects. This study reviews supportive medications for gastrointestinal cancer patients, analyzes usage patterns, biomarker frequencies, and feasibility of dose adjustments.

Methodology: A 10-year retrospective study was performed at Parirenyatwa group of Hospitals, Radiotherapy & Oncology Center evaluating the supportive care medicines prescribed for all gastrointestinal cancer patients.

Results: A total of 607 patients were analyzed, 42% receiving cancer-specific treatment and 58% best supportive care. Patients who receive cancer-specific treatment, 200 participants (78%) received at least one supportive care medicine compared to 250 (71%) in the best supportive care group (OR 1.4; 95% CI 0.96-2; p = 0.08).Seventeen different supportive care medications were prescribed, seven (41%) of these have pharmacogenomic biomarkers testing recommendation. More patients in the cancer-treated group received medicines with actionable pharmacogenomic biomarkers than in the best supportive care group (56% versus 40%; OR 1.9; 95% CI 1.38-2.66; p = 0.0001). PGX guidelines were fully implemented for tramadol, codeine, omeprazole, and ondansetron; partially for ibuprofen and celecoxib; and unimplementable for amitriptyline.

Conclusion: There is an opportunity to improve supportive care in gastrointestinal cancer patients through pharmacogenomics though some specific medicines challenges to full implementations are due to limited registered medicines.

Metoprolol, a β1-selective blocker, is widely used for treating cardiovascular and non-cardiovascular conditions. Its pharmacokinetics (PK) and pharmacodynamics (PD) vary significantly between individuals, in part due to CYP2D6 genetic polymorphisms. Co-administration of CYP2D6 inhibitors can lead to phenotype-genotype discordance, known as phenoconversion, resulting in clinically significant changes in metoprolol exposure and response. This review examines studies of the combined impact of CYP2D6 genotype and inhibitor use on metoprolol PK and PD. A literature search of PubMed, Embase, and Scopus identified 17 relevant clinical studies. Strong inhibitors such as paroxetine and fluoxetine increased metoprolol exposure by up to 8-fold and induced phenoconversion in extensive metabolizers. Moderate inhibitors, including mirabegron and amiodarone, increased metoprolol exposure by 2- to 3-fold, particularly in those with high baseline CYP2D6 activity. These inhibitors also reduced heart rate and blood pressure, mimicking the response seen in poor metabolizers. Weak inhibitors caused minimal PK changes without notable PD effects. Severe adverse drug reactions, including bradycardia and hypotension, occurred with strong inhibitors, especially in patients with cardiovascular disease and reduced CYP2D6 function. CYP2D6 phenoconversion is a clinically important but often overlooked contributor to metoprolol response variability. Incorporating phenoconversion into clinical practice can lead to more effective pharmacotherapy with metoprolol.

Glaucoma is a chronic, degenerative eye disease for which personalized diagnostic and therapeutic biomarkers are still lacking. Precision medicine offers a promising strategy to improve glaucoma management, and pharmacogenomics may play a key role in optimizing treatment response, minimizing side effects, and enhancing adherence. Advances in genetics have introduced potential tools such as polygenic risk scores (PRS) for adult-onset glaucoma and CRISPR/Cas9 for early-onset monogenic forms. Although still in its early stages, pharmacogenetic research in glaucoma has identified several genetic variants that may influence drug efficacy and security. Notably, single nucleotide polymorphisms in genes such as PTGFR, ADRB2 and CYP2D6 have been associated with varied responses to beta-blockers and prostaglandin analogues. Other candidate genes include ABCB1, SLCO2A1, AFAP1, and ADRB1, though findings remain preliminary. Despite the appeal of genetically guided therapy, broader implementation in clinical practice requires larger, multicenter validation studies, functional analysis of variants, and consensus on actionable biomarkers. Until then, early diagnosis, adherence, and timely treatment remain the pillars of glaucoma care. This narrative review provides an updated overview of the current evidence, knowledge gaps, and future directions in the pharmacogenomics of glaucoma.

The CYP2C19 gene encodes one of the main enzymes responsible for clopidogrel bioactivation, a widely prescribed antiplatelet prodrug. Due to its high polymorphism, clopidogrel response varies considerably, especially in carriers of nonfunctional alleles, such as CYP2C192 and  * 3. Meta-analyses have associated CYP2C19 loss-of-function alleles with worse cardiovascular outcomes, and genotype-guided strategies have demonstrated feasibility and clinical utility, supporting the rationale for locally validated implementation in Brazil. Personalized treatment strategies, based on preemptive genotyping and genotype-guided recommendations, have been proposed to improve clopidogrel efficacy and safety. However, extrapolating international guidelines to genetically diverse and underrepresented populations, such as Brazilians, poses challenges. Therefore, to discuss the clinical application of this testing in Brazil, it is also necessary to explore strategies that promote national pharmacogenomic studies, enhance infrastructure and training, and better align public policies. This study followed a contextual synthesis approach, with evidence identified through PubMed (last updated July 2025), and discusses structural and scientific barriers to implementing precision medicine in a local context of Brazil, proposing strategies for CYP2C19-clopidogrel pharmacogenetics at the Hospital de Clínicas of the Universidade Federal do Triângulo Mineiro (HC-UFTM), Minas Gerais, Brazil, considering both local realities and broader systemic limitations.

Ovarian cancers (OCs) are often defined as poorly immunogenic tumors that have low response rates to current immunotherapies and frequently develop resistance to chemotherapies. Oncolytic viruses (OVs) are an emerging therapeutic approach that is favored due to its multifactorial mechanism of action; OVs aim to enhance immune cell recovery and infiltration into the tumor, in addition to assisting the immune system to identify and target evasive tumors. While many different OVs have been studied, this review focuses on the four that have been extensively tested in preclinical models and clinical trials with OC patients: vaccinia viruses, vesicular stomatitis virus, herpes simplex 1, and adenoviruses. We will first explore how these viruses have been developed, modified and tested as monotherapies in OCs, with limited success. The various combinatorial approaches involving OVs that are currently being investigated to improve the outcomes for OC patients will then be addressed. Attention will be given to how the genetics of OC cells may influence response to OVs and how that has led to genetic modifications of OVs that improve the cancer specificity and efficacy of these therapies.

At the crossroads of genomics and pharmacology, pharmacogenomics is revolutionizing healthcare by tailoring drug therapies to individual genetic profiles thereby reducing the risk of adverse drug reactions and propelling the field of precision medicine forward. This review delves into the role of pharmacogenomics in uncovering genetic variations, including single nucleotide polymorphisms, that affects how drugs are metabolized and effective. Artificial intelligence (AI) has ameliorated the discovery of biomarkers and the drug development process; enabled real-time clinical decision-making, expanding the possibilities of personalized medicine. AI-powered models, especially in machine learning and deep learning have demonstrated potential in forecasting drug responses and enhancing the precision of genetic variant identification, exemplified by tools like DeepVariant and AlphaFold. However, the diversity of data, the clarity of model interpretations, and the ethical issues surrounding data privacy and genetic discrimination remain as major hurdles. Efforts are underway to address these challenges through multi-omics integration, federated learning, and explainable AI, all aimed at improving clinical translation and promoting fair access to personalized treatments. This review enunciates the existing applications, translational pathways, and prospects of AI in pharmacogenomics, its promise in achieving the goal of precision medicine ensuring the proper treatment of right patient at the right moment.

Purpose: Sunitinib is a multikinase inhibitor used to treat metastatic renal cell carcinoma (mRCC), with an inter-individual variability of pharmacokinetics and toxicity. Our goal was to assess associations between the pharmacogenetics, pharmacokinetics and toxicity of sunitinib.

Methods: In this multi-center prospective study, 42 patients with mRCC were included. An NGS panel was used to identify variations in 19 genes involved in sunitinib pharmacokinetics and pharmacodynamics. Residual concentration of sunitinib and N-desethyl-sunitinib were used to estimate a composite AUC at steady-state.

Results: Fifty-seven percent of patients had a plasma exposure within the optimal therapeutic range. A higher composite AUC led to significantly greater risk of endocrine toxicity (p = 0.008) and neurologic toxicity (p = 0.024), and to a non-significantly greater risk of cardiovascular toxicity (p = 0.11). A alleles of FGFR2-rs2981582 and VEFGFR2-rs1870377 were associated with a lower risk of cardiovascular toxicity (OR = 0.22 [0.05-0.97], p = 0.04 and OR = 0.17 [0.04- 0.73], p = 0.01 respectively) and with a lower composite AUC (p = 0.02 and p = 0.0002 respectively).

Conclusion: We demonstrated for the first time, an association between genetics polymorphisms in sunitinib targets and extent of exposure to this molecule as well as their association associated with the risk of cardiovascular toxicity. We described the concentration dependence of neurological and endocrine toxicity.

Trial registration: NCT02404584, registered 26 March 2015.

Aims: We compared plasma adiponectin levels between patients with preeclampsia (PE) classified as responsive and nonresponsive to antihypertensive therapy; Moreover, we examined whether ADIPOQ SNPs rs17300539, rs266729,rs2241766, and rs1501299 and their haplotypes are associated with responsiveness to antihypertensive therapy, and whether they affect plasma adiponectin levels in 215 pregnant women with PE.

Patients & methods: Genotypes for ADIPOQ SNPs were determined using TaqMan allelic discrimination assays, and circulating adiponectin concentrations were measured by ELISA.

Results: Patients with PE classified as nonresponsive to antihypertensive therapy showed higher plasma adiponectin levels than responsive patients. The TG genotype of the rs2241766 SNP was more frequent in responsive patients with PE when compared to nonresponsive patients. Nonresponsive patients with PE carrying specific genotypes of the ADIPOQ SNPs rs17300539 (GG or GA+AA), rs266729 (CC), rs2241766(TT), and rs1501299 (GT+TT), respectively, as well as the 'G,C,T,G' and 'G,C,T,T' haplotypes showed higher plasma adiponectin levels than responsive patients with PE carrying the same genotypes/haplotypes.

Conclusions: Nonresponsive patients with PE showed higher plasma adiponectin levels than responsive patients, the TG genotype of the rs2241766 SNP was more frequent in the responsive patients with PE when compared to nonresponsive patients. Genotypes of rs17300539, rs266729, rs2241766, and rs1501299 SNPs and haplotypes may affect adiponectin levels in patients with PE classified as nonresponsive to antihypertensive therapy.

Aim: This study aimed to assess the impact of GSTP1, GSTM1, and GSTT1 polymorphisms on the response to oxaliplatin-based chemotherapy and survival outcomes in gastric cancer patients from Northwest China.

Patients and methods: Genotypes of GSTP1, GSTM1, and GSTT1 were analyzed in 185 gastric cancer patients receiving SOX/XELOX regimens. The associations of these genotypes with the objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were evaluated through logistic regression and Kaplan-Meier/Cox model analyses.

Results: Patients carrying the GSTP1(AG+GG) genotype exhibited a significantly better chemotherapy response (OR = 0.301, p = 0.022) compared to those with the AA genotype. Additionally, the GSTP1 (AG+GG) genotype conferred a reduced risk of disease progression (HR = 1.645, log-rank p = 0.003) and mortality (HR = 1.705, log-rank p = 0.007) relative to the AA genotype. No significant associations were observed for GSTM1 or GSTT1 individually. However, the combined GSTP1(AG+GG)/GSTM1- genotype was linked to an improved chemotherapy response and significantly better overall and progression-free survival.

Conclusion: The GSTP1 genotype status could be a valuable predictive biomarker for treatment response and survival, potentially facilitating more personalized therapeutic approaches for gastric cancer.