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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.

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.

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.

Type 2 diabetes accounts for nearly 90% of the approximately 537 million cases of diabetes worldwide. The number affected is increasing rapidly with alarming trends in children and young adults (up to age 40 years). Early detection and proactive management are crucial for prevention and mitigation of microvascular and macrovascular complications and mortality burden. Access to novel therapies improves person-centred outcomes beyond glycaemic control. Precision medicine, including multiomics and pharmacogenomics, hold promise to enhance understanding of disease heterogeneity, leading to targeted therapies. Technology might improve outcomes, but its potential is yet to be realised. Despite advances, substantial barriers to changing the course of the epidemic remain. This Seminar offers a clinically focused review of the recent developments in type 2 diabetes care including controversies and future directions.

ABSTRACT Purpose Biomedical databases combining electronic medical records and phenotypic and genomic data constitute a powerful resource for the personalization of treatment. To leverage the wealth of information provided, algorithms are required that systematically translate the contained information into treatment recommendations based on existing genotype–phenotype associations. Methods We developed and tested algorithms for translation of preexisting genotype data of over 44,000 participants of the Estonian biobank into pharmacogenetic recommendations. We compared the results obtained by genome sequencing, exome sequencing, and genotyping using microarrays, and evaluated the impact of pharmacogenetic reporting based on drug prescription statistics in the Nordic countries and Estonia. Results Our most striking result was that the performance of genotyping arrays is similar to that of genome sequencing, whereas exome sequencing is not suitable for pharmacogenetic predictions. Interestingly, 99.8% of all assessed individuals had a genotype associated with increased risks to at least one medication, and thereby the implementation of pharmacogenetic recommendations based on genotyping affects at least 50 daily drug doses per 1000 inhabitants. Conclusion We find that microarrays are a cost-effective solution for creating preemptive pharmacogenetic reports, and with slight modifications, existing databases can be applied for automated pharmacogenetic decision support for clinicians.