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Pharmacogenomics (PGx) implementation in clinical practice is steadily increasing. PGx uses genetic information to personalize medication use, which increases medication efficacy and decreases side effects. The availability of clinical PGx guidelines is essential for its implementation in clinical settings. Currently, there are few organizations/associations responsible for releasing those guidelines, including the Clinical Pharmacogenetics Implementation Consortium, Dutch Pharmacogenetics Working Group, the Canadian Pharmacogenomics Network for Drug Safety and the French National Network of Pharmacogenetics. According to the US FDA, oncology medications are highly correlated to PGx biomarkers. Therefore, summarizing the PGx guidelines for oncology drugs will positively impact the clinical decisions for cancer patients. This review aims to scrutinize side-by-side available clinical PGx guidelines in oncology.

First, evaluate if patients carrying putatively diminished activity genotype have longer paclitaxel exposure (e.g., time above threshold concentration of 0.05 μM [T ]). Second, screen additional pharmacogenes for associations with T . Pharmacogene panel genotypes were translated into genetic phenotypes for associations with T (n = 58). Patients with predicted low-activity CYP2C8 had shorter T after adjustment for age, body surface area and race (9.65 vs 11.03 hrs, β = 5.47, p = 0.02). This association was attributed to (p = 0.006), not (p = 0.58). Patients with predicted low-activity SLCO1B1 had longer T (12.12 vs 10.15 hrs, β = 0.85, p = 0.012). Contrary to previous publications, may confer increased paclitaxel metabolic activity. and genotype may explain some paclitaxel pharmacokinetic variability.

Interindividual variability in drug efficacy and toxicity is a major challenge in clinical practice. Variations in drug pharmacokinetics (PKs) and pharmacodynamics (PDs) can be, in part, explained by polymorphic variants in genes encoding drug metabolizing enzymes and transporters (absorption, distribution, metabolism, and excretion) or in genes encoding drug receptors. Pharmacogenomics (PGx) has allowed the identification of predictive biomarkers of drug PKs and PDs and the current knowledge of genome‐disease and genome‐drug interactions offers the opportunity to optimize tailored drug therapy. High‐throughput PGx genotyping, from targeted to more comprehensive strategies, allows the identification of PK/PD genotypes to be developed as clinical predictive biomarkers. However, a biomarker needs a robust process of validation followed by clinical‐grade assay development and must comply to stringent regulatory guidelines. We here discuss the methodological challenges and the emerging technological tools in PGx biomarker discovery and validation, at the crossroad among molecular genetics, bioinformatics, and clinical medicine.

CYP2C19 loss of function (LOF) carriers undergoing percutaneous coronary intervention (PCI) have an increased risk of ischemic events when treated with clopidogrel. PCI patients in TAILOR-PCI were randomized to clopidogrel or genotype-guided (GG) therapy in which LOF carriers received ticagrelor and non-carriers clopidogrel. Direct medical costs associated with a GG approach have not been described before. TAILOR-PCI participants for whom direct medical costs were available for the duration from the date of PCI to one-year post PCI were included. Primary cost estimates were obtained from the Mayo Clinic Cost Data Warehouse. There were no differences in direct medical costs between the GG and clopidogrel groups (mean $20,682 versus $19,747, p  = 0.11) however total costs were greater in the GG group (mean $21,245 versus $19,891, p  = 0.02) which was primarily driven by ticagrelor costs. In conclusion the increased expense of a GG strategy post PCI as compared to clopidogrel for all is primarily driven by the cost of ticagrelor.

Genomic medicine, which uses DNA variation to individualise and improve human health, is the subject of this Series of papers. The idea that genetic variation can be used to individualise drug therapy—the topic addressed here—is often viewed as within reach for genomic medicine. We have reviewed general mechanisms underlying variability in drug action, the role of genetic variation in mediating beneficial and adverse effects through variable drug concentrations (pharmacokinetics) and drug actions (pharmacodynamics), available data from clinical trials, and ongoing efforts to implement pharmacogenetics in clinical practice.

Prediction of phenotypic consequences of mutations constitutes an important aspect of precision medicine. Current computational tools mostly rely on evolutionary conservation and have been calibrated on variants associated with disease, which poses conceptual problems for assessment of variants in poorly conserved pharmacogenes. Here, we evaluated the performance of 18 current functionality prediction methods leveraging experimental high-quality activity data from 337 variants in genes involved in drug metabolism and transport and found that these models only achieved probabilities of 0.1–50.6% to make informed conclusions. We therefore developed a functionality prediction framework optimized for pharmacogenetic assessments that significantly outperformed current algorithms. Our model achieved 93% for both sensitivity and specificity for both loss-of-function and functionally neutral variants, and we confirmed its superior performance using cross validation analyses. This novel model holds promise to improve the translation of personal genetic information into biological conclusions and pharmacogenetic recommendations, thereby facilitating the implementation of Next-Generation Sequencing data into clinical diagnostics.

Identify the functional status of the uridine-diphosphate glucuronyl transferase 1A1 ( ) -3279T>G ( ) variant. Retrospective review of clinically obtained serum bilirubin concentrations in pediatric patients to evaluate the association of the -3279T>G (* ) variant with bilirubin concentrations and assessed linkage disequilibrium of the -3279T>G ( ) and A(TA)7TAA ( ) variants. Total bilirubin concentration did not differ between patients who had a diplotype and patients homozygous for the -3279T>G ( ) variant. Total bilirubin concentration was lower in patients homozygous for the -3279T>G ( ) variant than in patients homozygous for the A(TA)7TAA ( ) variant (p < 0.01). The -3279T>G ( ) and A(TA)7TAA ( ) variants were in strong incomplete linkage disequilibrium in both black and white patients. The presence of the -3279T>G ( ) variant is not associated with increased bilirubin concentrations.

Latin America’s exceptional genetic diversity, shaped by centuries of admixture among Native American, European, and African ancestries, presents both challenges and opportunities for pharmacogenomic implementation. Current guidelines by CPIC and DPWG, though foundational, are largely based on European and East Asian data, limiting their applicability in highly admixed populations. This article presents the rationale and methodology of RELIVAF (Latin American Network for the Implementation and Validation of Pharmacogenomic Clinical Guidelines), which aims to produce region-specific recommendations aligned with local genetic profiles, healthcare systems, and regulatory landscapes. The framework integrates international standards with country- and ancestry-specific allele frequencies, effect sizes, drug availability, and implementation constraints. It also incorporates educational strategies to promote pharmacogenomic literacy among healthcare professionals. Three gene-drug pairs were prioritized for initial guideline development: DPYD -fluoropyrimidines, TPMT/NUDT15 –thiopurines (paediatric ALL), and CYP2C9/VKORC1 -coumarin anticoagulants (e.g., warfarin, acenocoumarol). Selection was based on clinical relevance, allele frequency variability, and potential public health impact. By leveraging regional data and collaborative expertise, RELIVAF aims to deliver actionable, equitable, and context-specific pharmacogenomic guidance, advancing precision medicine in Latin America and serving as a model for other underrepresented regions.

Background Pharmacogenomic testing guided treatment have been developed to guide drug selection or conversion in major depressive disorder patients. Whether patients benefit from pharmacogenetic testing remains unclear. We aim to evaluates the effect of pharmacogenomic testing guiding on clinical outcomes of major depressive disorder. Methods Pubmed, Embase, and Cochrane Library of Clinical Trials were searched from inception until August 2022. Key terms included pharmacogenomic and antidepressive. Odds ratios (RR) with 95% confidence intervals (95%CIs) were calculated using fixed-effects model for low or moderate heterogeneity or random-effects model for high heterogeneity. Results Eleven studies (5347 patients) were included. Compared with usual group, pharmacogenomic testing guided group was associated with an increased response rate at week 8 (OR 1.32, 95%CI 1.15–1.53, 8 studies, 4328 participants) and week 12 (OR 1.36, 95%CI 1.15–1.62, 4 studies, 2814 participants). Similarly, guided group was associated with an increased rate of remission at week 8 (OR 1.58, 95%CI 1.31–1.92, 8 studies, 3971 participants) and week 12 (OR 2.23, 95%CI 1.23–4.04, 5 studies, 2664 participants). However, no significant differences were found between the two groups in response rate at week 4 (OR 1.12, 95%CI 0.89–1.41, 2 studies, 2261 participants) and week 24 (OR 1.16, 95%CI 0.96–1.41, 2 studies, 2252 participants), and remission rate at week 4 (OR 1.26, 95%CI 0.93–1.72, 2 studies, 2261 participants) and week 24 (OR 1.06, 95%CI 0.83–1.34, 2 studies, 2252 participants). Medication congruence in 30 days was significantly reduced in the pharmacogenomic guided group compared with the usual care group (OR 2.07, 95%CI 1.69–2.54, 3 studies, 2862 participants). We found significant differences between subgroups of target population in response and remission rate. Conclusion Patients with major depressive disorder may benefit from pharmacogenomic testing guided treatment by achieving target response and remission rates more quickly.