OBJECTIVE: The recognition of adverse drug reactions (ADRs) is an important part of daily clinical work. However, medical education in this field is mostly drug-based and does not address adequately the complexity of this field regarding individual risk factors and polypharmacy. This study investigates the potential of the web-based serious game SeeMe (side-effect exposure-medical education) in pharmacological education of medical students to improve the recognition of relevant ADRs.
METHODS: One hundred fifty-seven medical students were recruited to evaluate the serious game SeeMe. SeeMe was developed to improve knowledge and recognition of ADRs in clinical practice. Players take on the role of a physician trying to understand fictional patients with ADRs. Before and after an 8-week playing period, an evaluation was carried out through a pre- and post-questionnaire and a pre- and post- knowledge test.
RESULTS: The students achieved significantly better results in the knowledge test, as almost twice as many exam-relevant questions were answered correctly (p < 0.001). The serious game had a positive effect on the students\' perception of the importance of ADRs.
CONCLUSIONS: This study demonstrates the potential of web- and case-based fictional serious games in medical education. The improved recognition of side effects represents a crucial step for education and training in clinical pharmacology. Future versions of the serious game may take this further and focus on training in the treatment of ADRs and their relevance in various healthcare professions.

BACKGROUND: Sacituzumab govitecan (sacituzumab) emerged as an important agent in metastatic and locally recurrent HER2-negative breast cancer treatment. UGT1A1 polymorphisms have also been shown to predict sacituzumab toxicity.
METHODS: In this retrospective study, we sought to evaluate the associations between UGT1A1 status, toxicity, and therapeutic outcomes in sacituzumab recipients with advanced breast cancer who underwent genotype testing for UGT1A1 alleles (N = 68).
RESULTS: We found 17 (25%) of our patients to be homozygous for UGT1A1*28 and 24 (35.3%) were heterozygous. Of seven African American patients with triple-negative breast cancer, five were homozygous for UGT1A1*28 and two were heterozygous. Patients with a homozygous UGT1A1*28 genotype were significantly more likely to have treatment terminated because of adverse effects. However, the polymorphism was not associated with treatment discontinuation because of disease progression.
CONCLUSIONS: This retrospective, real-world analysis suggests potential clinical utility in UGT1A1 testing for patients receiving sacituzumab, but future trials are needed to confirm the association between genotypes and treatment outcomes.

Cardiovascular diseases (CVDs) have a high mortality rate, and despite the several available therapeutic targets, non-response to antihypertensives remains a common problem. Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are important classes of drugs recommended as first-line therapy for several CVDs. However, response to ACEIs and ARBs varies among treated patients. Pharmacogenomics assesses how an individual\'s genetic characteristics affect their likely response to drug therapy. Currently, numerous studies suggest that genetic polymorphisms may contribute to variability in drug response. Moreover, further studies evaluating gene-gene interactions within signaling pathways in response to antihypertensives might help to unravel potential genetic predictors for antihypertensive response. This review summarizes the pharmacogenetic data for ACEIs and ARBs in patients with CVD, and discusses the potential pharmacogenetics of these classes of antihypertensives in clinical practice. However, replication studies in different populations are needed. In addition, studies that evaluate gene-gene interactions that share signaling pathways in the response to antihypertensive drugs might facilitate the discovery of genetic predictors for antihypertensive response.

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.

BACKGROUND: The Alpe-DPD study (NCT02324452) demonstrated that prospective genotyping and dose-individualization using four alleles in DPYD (DPYD*2A/rs3918290, c.1236G > A/rs75017182, c.2846A > T/rs67376798 and c.1679 T > G/rs56038477) can mitigate the risk of severe fluoropyrimidine toxicity. However, this could not prevent all toxicities. The goal of this study was to identify additional genetic variants, both inside and outside DPYD, that may contribute to fluoropyrimidine toxicity.
METHODS: Biospecimens and data from the Alpe-DPD study were used. Exon sequencing was performed to identify risk variants inside DPYD. In silico and in vitro analyses were used to classify DPYD variants. A genome-wide association study (GWAS) with severe fluoropyrimidine-related toxicity was performed to identify variants outside DPYD. Association with severe toxicity was assessed using matched-pair analyses for the exon sequencing and logistic, Cox, and ordinal regression analyses for GWAS.
RESULTS: Twenty-four non-synonymous, frameshift, and splice site DPYD variants were detected in ten of 986 patients. Seven of these variants (c.1670C > T, c.1913 T > C, c.1925 T > C, c.506delC, c.731A > C, c.1740 + 1G > T, c.763 - 2A > G) were predicted to be deleterious. The carriers of either of these variants showed a trend towards a 2.14-fold (95% CI, 0.41-11.3, P = 0.388) increased risk of severe toxicity compared to matched controls (N = 30). After GWAS of 942 patients, no individual single nucleotide polymorphisms achieved genome-wide significance (P ≤ 5 × 10-8), however, five variants were suggestive of association (P < 5 × 10-6) with severe toxicity.
CONCLUSIONS: Results from DPYD exon sequencing and GWAS analysis did not identify additional genetic variants associated with severe toxicity, which suggests that testing for single markers at a population level currently has limited clinical value. Identifying additional variants on an individual level is still promising to explain fluoropyrimidine-related severe toxicity. In addition, studies with larger samples sizes, in more diverse cohorts are needed to identify potential clinically relevant genetic variants related to severe fluoropyrimidine toxicity.

暂无摘要。

Cancer drug resistance constitutes a severe limitation for the satisfactory outcome of these patients. This is a complex problem due to the co-existence in cancer cells of multiple and synergistic mechanisms of chemoresistance (MOC). These mechanisms are accounted for by the expression of a set of genes included in the so-called resistome, whose effectiveness often leads to a lack of response to pharmacological treatment. Additionally, genetic variants affecting these genes further increase the complexity of the question. This review focuses on a set of genes encoding members of the transportome involved in drug uptake, which have been classified into the MOC-1A subgroup of the resistome. These proteins belong to the solute carrier (SLC) superfamily. More precisely, we have considered here several members of families SLC2, SLC7, SLC19, SLC22, SLCO, SLC28, SLC29, SLC31, SLC46, and SLC47 due to the impact of their expression and genetic variants in anticancer drug uptake by tumor cells or, in some cases, general bioavailability. Changes in their expression levels and the appearance of genetic variants can contribute to the Darwinian selection of more resistant clones and, hence, to the development of a more malignant phenotype. Accordingly, to address this issue in future personalized medicine, it is necessary to characterize both changes in resistome genes that can affect their function. It is also essential to consider the time-dependent dimension of these features, as the genetic expression and the appearance of genetic variants can change during tumor progression and in response to treatment.

暂无摘要。

To reduce severe fluoropyrimidine-related toxicity, pharmacogenetic guidelines recommend a dose reduction for carriers of four high-risk variants in the DPYD gene (*2A, *13, c.2846A>T, HapB3). The polymorphism in the MIR27A gene has been shown to enhance the predictive value of these variants. Our study aimed to explore whether rs895819 in the MIR27A gene modifies the effect of five common DPYD variants: c.1129-5923C>G (rs75017182, HapB3), c.2194G>A (rs1801160, *6), c.1601G>A (rs1801158, *4), c.496A>G (rs2297595), and c.85T>C (rs1801265, *9A). The study included 370 Caucasian patients with gastrointestinal tumors who received fluoropyrimidine-containing chemotherapy. Genotyping was performed using high-resolution melting analysis. The DPYD*6 allele was associated with overall severe toxicity and neutropenia with an increased risk particularly pronounced in patients carrying the MIR27A variant. All carriers of DPYD*6 exhibited an association with asthenia regardless of their MIR27A status. The increased risk of neutropenia in patients with c.496G was only evident in those co-carrying the MIR27A variant. DPYD*4 was also significantly linked to neutropenia risk in co-carriers of the MIR27A variant. Thus, we have demonstrated the predictive value of the *6, *4, and c.496G alleles of the DPYD gene, considering the modifying effect of the MIR27A polymorphism.

Pharmacogenomics (PGx) investigates the influence of genetics on drug responses, enabling tailored treatments for personalized healthcare. This study assessed the accuracy of genotyping six genes using whole genome sequencing with four different computational tools and various sequencing depths. The effects of using different reference genomes (GRCh38 and GRCh37) and sequence aligners (BWA-MEM and Bowtie2) were also explored. The results showed generally minor variations in tool performance across most genes; however, more notable discrepancies were observed in the analysis of the complex CYP2D6 gene. Cyrius, a CYP2D6-specific tool, demonstrated the most robust performance, achieving the highest concordance rates for CYP2D6 in all instances, comparable to the consensus approach in most cases. There were rather small differences between the samples with 20× coverage depth and those with higher depth, but the decreased performance was more evident at lower depths, particularly at 5×. Additionally, variations in CYP2D6 results were observed when samples were aligned to different reference genomes using the same method, or to the same genome using different aligners, which led to reporting incorrect rare star alleles in several cases. These findings inform the selection of optimal PGx tools and methodologies as well as suggest that employing a consensus approach with two or more tools might be preferable for certain genes and tool combinations, especially at lower sequencing depths, to ensure accurate results. Additionally, we show how the upstream alignment can affect the performance of tools, an important factor to take into account.