BACKGROUND: Genetic variants of UGT1A1, involved in glucuronidation and clearance of bilirubin, are associated with reduced bilirubin metabolization and drug-induced isolated hyperbilirubinemia. We studied the impact of the UGT1A1*28 polymorphism on drug-induced isolated hyperbilirubinemia in metastatic renal cell carcinoma patients treated with pazopanib, cabozantinib, and axitinib.
METHODS: We genotyped the UGT1A1*28 TA6/TA6-TA6/TA7-TA7/TA7 polymorphism and correlated with median baseline, on-treatment and peak bilirubin levels during therapy, incidence of grade-1- or -2 (G1/2)-hyperbilirubinemia and time-to-G1-hyperbilirubinemia.
RESULTS: Of the 66 patients treated with pazopanib, 29 received axitinib and 28 cabozantinib upon progression. Median baseline bilirubin was higher in TA7/TA7-carriers versus TA6/TA6+TA6/TA7-carriers at start of pazopanib (P < .0001), cabozantinib (P < .0001), and axitinib (P = .007). During pazopanib therapy, median bilirubin increased 1.4-fold in TA7/TA7+TA6/TA7-carriers but not in TA6/TA6-carriers. On cabozantinib, bilirubin increased 1.5-fold in TA7/TA7-carriers but not in TA6/TA6+TA6/TA7-carriers. Axitinib did not increase bilirubin in any genotype. Peak bilirubin in TA7/TA7- versus TA6/TA6+TA6/TA7-carriers was higher on pazopanib (P < .0001) or cabozantinib (P < .0001). With pazopanib, G1-hyperbilirubinemia occurred in 57% of TA7/TA7- and 12% of TA6/TA6+TA6/TA7-carriers (P = .0009) and G2-hyperbilirubinemia in 36% and 6% of the patients, respectively (P = .004). On cabozantinib, G1-hyperbilirubinemia occurred in 100% of TA7/TA7- and 5% of TA6/TA6+TA6/TA7-carriers (P < .0001) and G2-hyperbilirubinemia in 33% and 0% of the patients, respectively (P = .04). On axitinib, no correlation between the genotypes and G1/2-hyperbilirubinemia was observed.
CONCLUSIONS: We validate the previously described impact of the UGT1A1*28 polymorphism on isolated bilirubin increase on pazopanib. We report for the first time that cabozantinib also interferes with UGT1A1 and causes isolated bilirubin increase.

This review explores genetic contributors to drug interactions, known as drug-gene and drug-drug-gene interactions (DGI and DDGI, respectively). This article is part of a mini-review issue led by the International Society for the Study of Xenobiotics (ISSX) New Investigators Group. Pharmacogenetics (PGx) is the study of the impact of genetic variation on pharmacokinetics (PK), pharmacodynamics (PD), and adverse drug reactions. Genetic variation in pharmacogenes, including drug metabolizing enzymes and drug transporters, is common and can increase the risk of adverse drug events or contribute to reduced efficacy. In this review, we summarize clinically actionable genetic variants, and touch on methodologies such as genotyping patient DNA to identify genetic variation in targeted genes, and deep mutational scanning as a high-throughput in vitro approach to study the impact of genetic variation on protein function and/or expression in vitro. We highlight the utility of physiologically based pharmacokinetic (PBPK) models to integrate genetic and chemical inhibitor and inducer data for more accurate human PK simulations. Additionally, we analyze the limitations of historical ethnic descriptors in pharmacogenomics research. Altogether, the work herein underscores the importance of identifying and understanding complex DGI and DDGIs with the intention to provide better treatment outcomes for patients. We also highlight current barriers to wide-scale implementation of PGx-guided dosing as standard or care in clinical settings.

Tacrolimus, a calcineurin inhibitor, is a highly effective immunosuppressant used in solid organ transplantation (SOT). However, it is characterized by a narrow therapeutic range and high inter-patient variability in pharmacokinetics. Standard weight-based dosing followed by empiric dose titration is suboptimal in controlling drug concentrations, increasing risk of rejection or toxicity, particularly in the initial months post transplantation. This review explores the potential of combined pre-transplant genotyping and pharmacokinetic (PK) modelling to improve tacrolimus dosing in paediatric SOT recipients. A systematic search of Medline, Embase and Cochrane databases identified studies published between March 2013 and March 2023 that investigated genotype- and PK model-informed tacrolimus dosing in children post-SOT. The Newcastle-Ottawa Scale assessed study quality. Seven studies encompassing paediatric kidney, heart, liver and lung transplants reported using genotype and model-informed dosing. A combination of clinical and genetic factors significantly impacts tacrolimus clearance and thus initial dose recommendation. Body size, transplant organ and co-medications were consistently important, while either time post-transplant or haematocrit emerged in some studies. Several models were identified, however, with limitations evident in some and with absence of evidence for their effectiveness in optimizing initial and subsequent dosing. This review highlights the development of PK models in paediatric SOT that integrate genotype and clinical covariates to personalize early tacrolimus dosing. While promising, prospective studies are needed to validate and confirm their effectiveness in improving time to therapeutic concentrations and reducing under- or overexposure. This approach has the potential to optimize tacrolimus therapy in paediatric SOT, thereby improving outcomes.

This letter commends the article by Luzzi et al. on alternative neuroprotection strategies for aneurysmal subarachnoid hemorrhage (SAH). It highlights the pharmacological advantages of nicardipine, cilostazol, and clazosentan over nimodipine in managing cerebral vasospasm and delayed cerebral ischemia. Emphasizing the need for personalized medicine, it advocates for integrating genetic screening and advanced monitoring techniques to tailor treatments to individual patient profiles. This approach could significantly improve clinical outcomes by optimizing drug efficacy and minimizing adverse effects.

Digital health, an emerging scientific domain, attracts increasing attention as artificial intelligence and relevant software proliferate. Pharmacogenomics (PGx) is a core component of precision/personalized medicine driven by the overarching motto \"the right drug, for the right patient, at the right dose, and the right time.\" PGx takes into consideration patients\' genomic variations influencing drug efficacy and side effects. Despite its potentials for individually tailored therapeutics and improved clinical outcomes, adoption of PGx in clinical practice remains slow. We suggest that e-health tools such as clinical decision support systems (CDSSs) can help accelerate the PGx, precision/personalized medicine, and digital health emergence in everyday clinical practice worldwide. Herein, we present a systematic review that examines and maps the PGx-CDSSs used in clinical practice, including their salient features in both technical and clinical dimensions. Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines and research of the literature, 29 relevant journal articles were included in total, and 19 PGx-CDSSs were identified. In addition, we observed 10 technical components developed mostly as part of research initiatives, 7 of which could potentially facilitate future PGx-CDSSs implementation worldwide. Most of these initiatives are deployed in the United States, indicating a noticeable lack of, and the veritable need for, similar efforts globally, including Europe.

The field of pharmacogenetics, the investigation of the influence of one or more sequence variants on drug response phenotypes, is a special case of pharmacogenomics, a discipline that takes a genome-wide approach. Massively parallel, next generation sequencing (NGS), has allowed pharmacogenetics to be subsumed by pharmacogenomics with respect to the identification of variants associated with responders and non-responders, optimal drug response, and adverse drug reactions. A plethora of rare and common naturally-occurring GPCR variants must be considered in the context of signals from across the genome. Many fundamentals of pharmacogenetics were established for G protein-coupled receptor (GPCR) genes because they are primary targets for a large number of therapeutic drugs. Functional studies, demonstrating likely-pathogenic and pathogenic GPCR variants, have been integral to establishing models used for in silico analysis. Variants in GPCR genes include both coding and non-coding single nucleotide variants and insertion or deletions (indels) that affect cell surface expression (trafficking, dimerization, and desensitization/downregulation), ligand binding and G protein coupling, and variants that result in alternate splicing encoding isoforms/variable expression. As the breadth of data on the GPCR genome increases, we may expect an increase in the use of drug labels that note variants that significantly impact the clinical use of GPCR-targeting agents. We discuss the implications of GPCR pharmacogenomic data derived from the genomes available from individuals who have been well-phenotyped for receptor structure and function and receptor-ligand interactions, and the potential benefits to patients of optimized drug selection. Examples discussed include the renin-angiotensin system in SARS-CoV-2 (COVID-19) infection, the probable role of chemokine receptors in the cytokine storm, and potential protease activating receptor (PAR) interventions. Resources dedicated to GPCRs, including publicly available computational tools, are also discussed.

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Aim: To explore general practitioners\' (GPs) views on implementing pharmacogenomic testing in Australian general practice. Methods: Semi-structured interviews were conducted with nine GPs in Australia, recruited from primary care networks. Interviews were analyzed using thematic analysis. Themes were mapped onto the Consolidated Framework for Implementation Research domains. Results: Barriers to implementation included lack of knowledge, education, standardized pharmacogenomic reports and national clinical guidelines and financial inaccessibility. Facilitators included positive exposure to pharmacogenomics, peer influences, interdisciplinary collaboration and proven clinical utility. Current uptake was minimal; however, GPs shared positive perceptions of clinical use. Conclusion: Recommendations for successful implementation include building and disseminating clinical evidence, developing national guidelines and standardized reports, incorporation into formal education and increasing financial accessibility.
What is this article about? This article describes an original research study that examines the implementation of pharmacogenomic testing in Australian general practice. Pharmacogenomic testing applies personalized genomic information to medication prescribing, as genetic differences can affect how a person metabolizes certain medications. While there is excitement about the possibilities of using pharmacogenomics, the general uptake is slow. This study looked to understand the barriers and facilitators to implementation from the perspectives of general practitioners in Australia.What were the results? Through exploratory interviews with general practitioners, this study identified that barriers to implementation include a lack of knowledge, education, standardized reports and national clinical guidelines and financial inaccessibility. Facilitators include positive exposure to pharmacogenomic testing, peer influences, interdisciplinary collaboration and proven clinical utility. Current uptake was minimal; however, GPs shared positive perceptions of the potential of testing.What do the results of the study mean? Based on the results of this study, the following recommendations were generated for successful implementation: building and disseminating clinical evidence, developing national guidelines, incorporation into formal education, establishing accessible experts and improving financial accessibility.

UNASSIGNED: Pharmacogenomics (PGx) is a well-established concept of how genes impact medication response, with many studies demonstrating reductions in medication side effects, improved efficacy and cost effectiveness. Despite these benefits, implementation of PGx in daily practice remains limited. Studies on the implementation of PGx in clinical practice have previously found that inadequate knowledge is one of the main barriers. Details regarding specifically which educational needs exist among family medicine clinicians requires further study.
UNASSIGNED: The aim of this study was to identify both the perceived role that pharmacogenomics (PGx) could play in primary care practice, the knowledge gaps that family medicine clinicians experience, and the skills they require to use PGx in their daily practice.
UNASSIGNED: To achieve this aim, the attitudes, knowledge, barriers, skills needed, and preferred educational program were explored in a family medicine clinician focus group study via a semi-structured interview and knowledge quiz. Second, multidisciplinary focus groups provided information on the level of knowledge and necessary skills to use PGx in patient care. After gathering key recorded information from both focus groups, the perceived role pharmacogenomics could possibly play in primary care, the predominant knowledge gaps, and the most appropriate educational program was determined by qualitative analysis.
UNASSIGNED: Four themes emerged regarding the PGx educational needs and the role of PGx in family medicine: 1) need for PGx competences, 2) insight into the roles and responsibilities of PGx services, 3) optimization of PGx workflow through artificial intelligence integrated in the electronic health record, and 4) the ethical dilemmas and psychological effects related to PGx. These themes reflect a shift in the role of PGx in family medicine with implications for education.
UNASSIGNED: The results obtained from this study will help improve the implementation of PGx in daily practice, and consequently, may result in increased utilization of PGx, thereby resulting in improved medication efficacy and reduced side effects.

UNASSIGNED: Warfarin is the only approved anticoagulant for antithrombotic treatment in patients with mechanical prosthetic heart valves (MPHV). However, dosing warfarin is challenging due to its narrow therapeutic window and highly variable clinical outcomes. Both low and high doses of warfarin can lead to thrombotic and bleeding events, respectively, with these complications being more severe in individuals with sensitive genetic polymorphisms. Incorporating genetic testing could enhance the accuracy of warfarin dosing and minimize its adverse events.
UNASSIGNED: This study aims to evaluate the utilities and cost-effectiveness of pharmacogenomics-guided versus standard dosing of warfarin in patients with MPHV in Iran.
UNASSIGNED: In this economic evaluation study, a cost-effectiveness analysis was conducted to compare pharmacogenomics-guided versus standard warfarin dosing. Data related to quality of life (QoL) were collected through a cross-sectional study involving 105 randomly selected MPHV patients using the EuroQol-5D (EQ-5D) Questionnaire. Costs were calculated with input from clinical experts and a review of relevant guidelines. Additional clinical data were extracted from published literature. The pharmacoeconomic threshold set for medical interventions within Iran\'s healthcare system was $1,500. A decision tree model was designed from the perspective of Iran\'s healthcare system with a one-year study horizon. Sensitivity analyses were also performed to assess the uncertainty of input parameters.
UNASSIGNED: The utility scores derived from the questionnaire for standard and pharmacogenomics-guided warfarin treatments were 0.68 and 0.76, respectively. Genotype-guided dosing of warfarin was more costly compared to the standard dosing ($246 vs $69), and the calculated incremental cost-effectiveness ratio (ICER) was $2474 per quality-adjusted life year (QALY) gained. One-way sensitivity analyses showed that our model is sensitive to the percentage of time in the therapeutic range (PTTR), the cost of genetic tests, and the utility of both pharmacogenomics-guided and standard dosing arms. However, the probabilistic sensitivity analysis demonstrates the robustness of our model.
UNASSIGNED: Warfarin dosing with pharmacogenomics testing is currently not cost-effective. However, if the cost of genotyping tests decreases to $118, the ICER would become cost-effective.