Am J Pharm Educ. 2022 Oct 21:8899. doi: 10.5688/ajpe8899. Online ahead of print.

ABSTRACT

Objective: To describe the development and assessment of an integrated virtual escape room in a cardiology course.Methods: A virtual escape room was developed to reinforce therapeutics, pharmacology, pharmacokinetics, medicinal chemistry, pharmacogenomics, and calculations related to cardiology in an integrated pharmacy course and was completed by two student cohorts. Groups of 4-5 students had 40 minutes to complete virtual escape room puzzles, and each puzzle had to be solved correctly prior to advancing. After completion of the activity, learners met with facilitators to debrief. Students completed pre- and post-surveys to assess knowledge changes and perceptions of the experience.Results: One hundred and twenty-six second-year PharmD students completed the escape room, and 79% (n=55) and 93% (n=52) of students completed pre- and post-surveys for the 2020 and 2021 cohorts, respectively. McNemar's paired test indicates a statistically significant improvement in student knowledge on pre- and post-survey knowledge questions (M=43.1, SD=22.6; M=74.1, SD=19.6, and M=52.0, SD=15.8; M=67.1, SD=19.2 for 2020 and 2021, respectively). Most students in both cohorts (88%) agreed that logistics of the escape rooms were amenable to learning and applying information, and 86% enjoyed working through puzzles.Implications: The virtual escape room was well-received by students and served as an effective tool for reinforcing and integrating cardiology concepts. The virtual nature of the activity makes it practical and easily replicable to implement at other institutions, which can benefit from using the format, logistics and materials described in this study to decrease faculty workload and costs associated with implementing this educational technique.

PMID:36270662 | DOI:10.5688/ajpe8899

Annu Rev Pharmacol Toxicol. 2022 Oct 21. doi: 10.1146/annurev-pharmtox-091222-022612. Online ahead of print.

ABSTRACT

Investigations in pharmacology and toxicology range from molecular studies to clinical care. Studies in basic and clinical pharmacology and in preclinical and clinical toxicology are all essential in bringing new knowledge and new drugs into clinical use. The 30 reviews in Volume 63 of the Annual Review of Pharmacology and Toxicology explore topics across this spectrum. Examples include "Zebrafish as a Mainstream Model for In Vivo Systems Pharmacology and Toxicology" and "Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond." Other reviews discuss components important for drug discovery and development and the use of pharmaceuticals in a variety of diseases. Air pollution continues to increase globally; accordingly, "Air Pollution-Related Neurotoxicity Across the Life Span" is a timely and forward-thinking review. Volume 63 also explores the use of contemporary technologies such as electronic health records, pharmacogenetics, and new drug delivery systems that help enhance and improve the utility of new therapies. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 63 is January 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

PMID:36270297 | DOI:10.1146/annurev-pharmtox-091222-022612

Anesth Analg. 2022 Nov 1;135(5):926-928. doi: 10.1213/ANE.0000000000006054. Epub 2022 Oct 21.

NO ABSTRACT

PMID:36269983 | DOI:10.1213/ANE.0000000000006054

OMICS. 2022 Oct 21. doi: 10.1089/omi.2022.0105. Online ahead of print.

ABSTRACT

The current pandemic has markedly shifted the focus of the global research and development ecosystem toward infectious agents such as SARS-CoV-2, the causative agent for COVID-19. A case in point is the chronic liver disease associated with hepatitis B virus (HBV) infection that continues to be a leading cause of severe liver disease and death globally. The burden of HBV infection is highest in the World Health Organization designated western Pacific and Africa regions. Tenofovir disoproxil fumarate (TDF) is a nucleoside analogue used in treatment of HBV infection but carries a potential for kidney toxicity. TDF is not metabolized by the cytochrome P450 enzymes and, therefore, its clearance in the proximal tubule of the renal nephron is controlled mostly by membrane transport proteins. Clinical pharmacogenomics of TDF with a focus on drug transporters, discussed in this perspective article, offers a timely example where resource-limited countries and regions of the world with high prevalence of HBV can strengthen the collective efforts to fight both COVID-19 and liver diseases impacting public health. We argue that precision/personalized medicine is invaluable to guide this line of research inquiry. In all, our experience in Ghana tells us that it is important not to forget the burden of chronic diseases while advancing research on infectious diseases such as COVID-19. For the long game with COVID-19, we need to address the public health burden of infectious agents and chronic diseases in tandem.

PMID:36269614 | DOI:10.1089/omi.2022.0105

Expert Rev Mol Diagn. 2022 Oct 21. doi: 10.1080/14737159.2022.2139602. Online ahead of print.

NO ABSTRACT

PMID:36268756 | DOI:10.1080/14737159.2022.2139602

Pharmacogenomics. 2022 Oct 21. doi: 10.2217/pgs-2022-0113. Online ahead of print.

ABSTRACT

Aim: We sought to identify the variants that could predict the risk of nivolumab-induced immune-related adverse events (irAEs) in patients with cancer. Patients & methods: We enrolled 622 Japanese patients and carried out a genome-wide association study. The associations for 507 single nucleotide polymorphisms (SNPs) showing p < 0.001 were further investigated using an independent cohort. Results: In the combined analysis, possible associations were found for a total of 90 SNPs. Although no SNPs were identified to be significantly associated with nivolumab-induced irAEs, the SNP most strongly associated with nivolumab-induced irAEs was rs469490. Conclusion: This study is an important hypothesis-generating study to guide future studies in larger and/or other ethnic cohorts.

PMID:36268685 | DOI:10.2217/pgs-2022-0113

Ann Transl Med. 2022 Sep;10(18):983. doi: 10.21037/atm-22-3976.

ABSTRACT

BACKGROUND: Microvascular angina (MVA) is a group of clinical manifestations of angina pectoris or angina-like chest pain, positive exercise test, and exclusion of epicardial coronary artery spasm, wherein coronary angiography (CAG) does not present obvious epicardial vascular stenosis. Shexiang Tongxin dropping pill (STDP) has the effect of benefiting the Qi and opening the blood vessels, activating blood circulation, and resolving blood stasis. We explored the mechanism of STDP against MVA by network pharmacology and molecular docking.

METHODS: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), literature search, SwissTargetPrediction database, and high-throughput experiment- and reference-guided database of traditional Chinese medicine (HERB) were applied to identify the active ingredients and targets of STDP. The MVA targets were searched in the databases of GeneCards, Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB), DisGeNET, Online Mendelian Inheritance in Man (OMIM), and Therapeutic Target Database (TTD). The common targets of STDP and MVA were screened. The software RStudio 4.1.3 was used to analyze the enrichment of these targets using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Protein-protein interaction (PPI) network analysis of the common targets was performed using the Search Tool for the Retrieval of Interacting Genes/Genomes (STRING) database. The cytoHubba plug-in of Cytoscape 3.9.1 software was employed to analyze the PPI network and obtain the core targets. Molecular docking was performed to verify the relationship between the core compounds and proteins with AutoDock Tools 1.5.7 and Pymol 2.4.0.

RESULTS: We identified 93 effective components of STDP, 310 potential targets, 981 MVA targets, and 138 intersectional targets. The potential anti-MVA mechanism of STDP may involve the advanced glycation end products/receptor for advanced glycation end products (AGE-RAGE) signaling pathway in diabetic complications; lipids and atherosclerosis; fluid shear stress; atherosclerosis; the tumor necrosis factor (TNF), interleukin (IL)-17, hypoxia-inducible factor (HIF)-1, and C-type lectin receptor signaling pathways. Further, STDP mainly acts on its targets IL-6, AKT1, STAT3, JUN, and IL-1β to against MVA.

CONCLUSIONS: The STDP may exert its therapeutic effects through processes, such as anti-inflammation, promotion of smooth muscle cell proliferation and differentiation, lipid metabolism, immunomodulation, and regulation of cellular autophagy.

PMID:36267750 | PMC:PMC9577736 | DOI:10.21037/atm-22-3976

Pharmacogenomics J. 2022 Oct 20. doi: 10.1038/s41397-022-00293-5. Online ahead of print.

ABSTRACT

Using concentration-time data from the NEAT001/ARNS143 study (single sample at week 4 and 24), we determined raltegravir pharmacokinetic parameters using nonlinear mixed effects modelling (NONMEM v.7.3; 602 samples from 349 patients) and investigated the influence of demographics and SNPs (SLC22A6 and UGT1A1) on raltegravir pharmacokinetics and pharmacodynamics. Demographics and SNPs did not influence raltegravir pharmacokinetics and no significant pharmacokinetic/pharmacodynamic relationships were observed. At week 96, UGT1A1*28/*28 was associated with lower virological failure (p = 0.012), even after adjusting for baseline CD4 count (p = 0.048), but not when adjusted for baseline HIV-1 viral load (p = 0.082) or both (p = 0.089). This is the first study to our knowledge to assess the influence of SNPs on raltegravir pharmacodynamics. The lack of a pharmacokinetic/pharmacodynamic relationship is potentially an artefact of raltegravir's characteristic high inter and intra-patient variability and also suggesting single time point sampling schedules are inadequate to thoroughly assess the influence of SNPs on raltegravir pharmacokinetics.

PMID:36266537 | DOI:10.1038/s41397-022-00293-5

Pulm Pharmacol Ther. 2022 Oct 17:102172. doi: 10.1016/j.pupt.2022.102172. Online ahead of print.

ABSTRACT

COVID-19 medicines, such as molnupiravir are beginning to emerge for public health and clinical practice. On the other hand, drugs display marked variability in their efficacy and safety. Hence, COVID-19 medicines, as with all drugs, will be subject to the age-old maxim "one size prescription does not fit all". In this context, pharmacogenomics is the study of genome-by-drug interactions and offers insights on mechanisms of patient-to-patient and between-population variations in drug efficacy and safety. Pharmacogenomics information is crucial to tailoring the patients' prescriptions to achieve COVID-19 preventive and therapeutic interventions that take into account the host biology, patients' genome, and variable environmental exposures that collectively influence drug efficacy and safety. This expert review critically evaluates and summarizes the pharmacogenomics and personalized medicine aspects of the emerging COVID-19 drugs, and other selected drug interventions deployed to date. Here, we aim to sort out the hope, hype, and reality and suggest that there are veritable prospects to advance COVID-19 medicines for public health benefits, provided that pharmacogenomics is considered and implemented adequately. Pharmacogenomics is an integral part of rational and evidence-based medical practice. Scientists, health care professionals, pharmacists, pharmacovigilance practitioners, and importantly, patients stand to benefit by expanding the current pandemic response toolbox by the science of pharmacogenomics, and its applications in COVID-19 medicines and clinical trials.

PMID:36265833 | DOI:10.1016/j.pupt.2022.102172

Br J Clin Pharmacol. 2022 Oct 20. doi: 10.1111/bcp.15573. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Moore`s law predicts the doubling of complexity of integrated circuits every two years; Kryder's corollary assumes a doubling of data storage every thirteen months. With the increasing volume of legislation, pharmacovigilance systems today are inherently complex, and the emphasis has shifted from reactive (responding to emerging risks), to planned, active, risk-proportionate approaches operating throughout the lifecycle of medicines.

EXPERIMENTAL APPROACH: Exploration of the drivers for increasing complexity of pharmacovigilance systems, focusing on regulatory environment, data management, and evaluation.

KEY RESULTS: Evaluation of post-marketing data plays an increasingly important role in pharmacovigilance. There is great interest on the part of all stakeholders in optimizing the use of these data. Innovative approaches, including pharmacogenetics and passive measures (sensors), will lead to increased complexity and volumes of data, and inevitably to an increase in the volume of case reports. There is a multiplicity of regulations and guidelines on how to manage these data, with an inherent lack of harmonization.

CONCLUSION AND IMPLICATIONS: We summarize the current characterization of safety data types, sources, and the classification of these data. Using this benchmark, we discuss the future requirements of an effective pharmacovigilance ecosystem, keeping the principle of parsimony in mind. In this complex, continuously and rapidly changing environment, there is a need for a return to simplicity and pragmatism. The application of Ockam's razor could help to support the rapid provision of new, affordable medicines with a positive benefit to risk profile.

PMID:36264908 | DOI:10.1111/bcp.15573

Pharmacotherapy. 2022 Oct 20. doi: 10.1002/phar.2736. Online ahead of print.

ABSTRACT

AIMS: East Asians have a higher risk of bleeding than Europeans when treated with ticagrelor. This study aimed to explore genetic indicators related to the high bleeding propensity in East Asian patients with acute coronary syndrome (ACS) using ticagrelor.

METHODS: Between March 2018 and July 2021, 208 patients with ACS were administered ticagrelor and underwent genetic testing. These patients were enrolled and followed up for bleeding events for 12 months. Single nucleotide polymorphisms (SNPs) were detected using whole-exome sequencing. SNPs significantly associated with cumulative bleeding events within 1-, 6-, and 12-month follow-up were selected (p <0.01). Among these, SNPs showing a difference of ≥2 fold in their distribution frequency among East Asians and Europeans were selected.

RESULTS: Among all patients, 96.60% received ticagrelor plus aspirin or cilostazol, and 42.3% suffered from bleeding events during 12-month follow-up. Further, 22 SNPs of 15 genes were found to have a significant association with cumulative bleeding events within 1-, 6-, and 12-month follow-up. Among these SNPs, FIG4 rs2295837 (A>T) variant had the strongest association with bleeding events within 1 month (p = 1.28 × 10-4 ), with an increased risk of bleeding in T allele carriers (odds ratio [OR]: 3.07, 95% confidence interval [CI]: 1.68-5.63). PROK2 rs3796224 (C>T) variant was most strongly associated with cumulative bleeding events within 6 months (p = 4.57 × 10-4 ) with an increased risk of bleeding in T allele carriers (OR: 2.16, 95% CI: 1.20-3.89). Moreover, HRNR rs6662450 (C>T) variant showed the strongest relation with cumulative bleeding events within 12 months (p = 4.86 × 10-4 ) with a reduced risk of bleeding in T allele carriers (OR: 0.48, 95% CI: 0.24-0.95).

CONCLUSIONS: Fifteen genes, including PROK2, HRNR, and FIG4, were potential biomarkers of high bleeding propensity in East Asian patients with ACS using ticagrelor.

PMID:36263704 | DOI:10.1002/phar.2736

Clin Psychopharmacol Neurosci. 2022 Nov 30;20(4):762-767. doi: 10.9758/cpn.2022.20.4.762.

ABSTRACT

OBJECTIVE: Psychiatric symptoms and mental disorders are common after Coronavirus Disease-19 (COVID-19). Some drugs used to treat acute COVID-19 have psychiatric side effects. We assessed the psychiatric symptoms and mental disorders of patients treated for acute COVID-19 with hydroxychloroquine (HCQ), interleukin-6 receptor antagonists (anti-IL-6), and corticoids (CTC).

METHODS: We evaluated 177 patients in a day hospital 4 months after acute infection.

RESULTS: In a multivariate analysis, HCQ was associated with significant anxiety symptoms (odds ratio [OR] = 5.9, 95% confidence interval [95% CI] = 1.8-20.0, p = 0.003) and mental disorders (OR = 4.1, 95% CI = 1.2-13.9, p = 0.02). In a bivariate analysis with propensity matched cohorts, HCQ was associated with significant anxiety symptoms (9 patients [50.0%] with significant symptoms in the HCQ group versus 15 [20.1%] in the control group, OR = 3.8, 95% CI = 1.3-11.3, p = 0.01). Anti-IL-6 and CTC were not associated with significant psychiatric symptoms or mental disorders.

CONCLUSION: We recommend monitoring psychiatric symptoms, especially anxiety, in patients treated with HCQ during COVID-19 infection. Further studies with larger samples and prospective assessments are needed to confirm our results.

PMID:36263650 | DOI:10.9758/cpn.2022.20.4.762

Recent Pat Anticancer Drug Discov. 2022 Oct 18. doi: 10.2174/1574892818666221018091415. Online ahead of print.

ABSTRACT

Many cancer patients die due to their treatment failing because of their disease's resistance to chemotherapy and other forms of radiation therapy. Resistance may develop at any stage of therapy, even at the beginning. Several factors influence current therapy, including the type of cancer and the existence of genetic abnormalities. The response to treatment is not always predicted by the existence of a genetic mutation and might vary for various cancer subtypes. It is clear that cancer patients must be assigned a particular treatment or combination of drugs based on prediction models. Preliminary studies utilizing artificial intelligence-based prediction models have shown promising results. Building therapeutically useful models is still difficult despite enormous increases in computer capacity due to the lack of adequate clinically important pharmacogenomics data. Machine learning is the most widely used branch of artificial intelligence. Here, we review the current state in the area of using machine learning to predict treatment response. In addition, examples of machine learning algorithms being employed in clinical practice are offered.

PMID:36263487 | DOI:10.2174/1574892818666221018091415

Front Pharmacol. 2022 Oct 3;13:984383. doi: 10.3389/fphar.2022.984383. eCollection 2022.

ABSTRACT

Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD. Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 'Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)' and 103 'Combining Medications to Enhance Depression Outcomes (CO-MED)' patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt. Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt. Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.

PMID:36263124 | PMC:PMC9573988 | DOI:10.3389/fphar.2022.984383

Nucleic Acids Res. 2022 Oct 19:gkac911. doi: 10.1093/nar/gkac911. Online ahead of print.

ABSTRACT

Extensive in vitro cancer drug screening datasets have enabled scientists to identify biomarkers and develop machine learning models for predicting drug sensitivity. While most advancements have focused on omics profiles, cancer drug sensitivity scores precalculated by the original sources are often used as-is, without consideration for variabilities between studies. It is well-known that significant inconsistencies exist between the drug sensitivity scores across datasets due to differences in experimental setups and preprocessing methods used to obtain the sensitivity scores. As a result, many studies opt to focus only on a single dataset, leading to underutilization of available data and a limited interpretation of cancer pharmacogenomics analysis. To overcome these caveats, we have developed CREAMMIST (https://creammist.mtms.dev), an integrative database that enables users to obtain an integrative dose-response curve, to capture uncertainty (or high certainty when multiple datasets well align) across five widely used cancer cell-line drug-response datasets. We utilized the Bayesian framework to systematically integrate all available dose-response values across datasets (>14 millions dose-response data points). CREAMMIST provides easy-to-use statistics derived from the integrative dose-response curves for various downstream analyses such as identifying biomarkers, selecting drug concentrations for experiments, and training robust machine learning models.

PMID:36259664 | DOI:10.1093/nar/gkac911

Med J Aust. 2022 Oct 18. doi: 10.5694/mja2.51759. Online ahead of print.

NO ABSTRACT

PMID:36259142 | DOI:10.5694/mja2.51759

Drug Metab Pers Ther. 2022 Oct 19. doi: 10.1515/dmpt-2022-0123. Online ahead of print.

ABSTRACT

OBJECTIVES: Clinical Pharmacogenetics Implementation Consortium (CPIC) is a platform that advances the pharmacogenomics (PGx) practice by developing evidence-based guidelines. The purpose of this study was to analyze the CPIC database for ADME related genes and their corresponding drugs, and evidence level for drug-gene pairs; and to determine the presence of these drug-gene pairs in the highest mortality diseases in the United States.

METHODS: CPIC database was evaluated for drug-gene pairs related to absorption, distribution, metabolism, and excretion (ADME) properties. National Vital Statistics from Centers for Disease Control and Prevention was used to identify the diseases with the highest mortality. CPIC levels are assigned to different drug-gene pairs based on varying levels of evidence as either A, B, C, or D. All drug-gene pairs assigned with A/B, B/C, or C/D mixed levels were excluded from this study. A stepwise exclusion process was followed to determine the prevalence of various ADME drug-gene pairs among phase I/II enzymes or transporters and stratify the drug-gene pairs relevant to different disease conditions most commonly responsible for death in the United States.

RESULTS: From a total of 442 drug-gene pairs in the CPIC database, after exclusion of 86 drug-gene pairs with levels A/B, B/C, or C/D, and 211 non-ADME related genes, 145 ADME related drug-gene pairs resulted. From the 145 ADME related drug-genes pairs, the following were the distribution of levels: Level A: 43 (30%), Level B: 22 (15%), Level C: 59 (41%), Level D: 21 (14%). The most prevalent ADME gene with CPIC level A classification was cytochrome P450 2C9 (CYP2C9) (26%) and overall, the most prevalent ADME gene in the CPIC database was CYP2D6 (30%). The most prevalent diseases related to the CPIC evidence related drugs were cancer and depression.

CONCLUSIONS: We found that there is an abundance of ADME related genes in the CPIC database, including in the high mortality disease states of cancer and depression. There is a differential level of pharmacogenomic evidence in drug-gene pairs enlisted in CPIC where levels A and D having the greatest number of drug-gene pairs. CYP2D6 was the most common ADME gene with CPIC evidence for drug-gene pairs. Pharmacogenomic applications of CPIC evidence can be leveraged to individualize patient therapy and lower adverse effect events.

PMID:36257916 | DOI:10.1515/dmpt-2022-0123

Psychiatry Res. 2022 Oct 8;317:114891. doi: 10.1016/j.psychres.2022.114891. Online ahead of print.

ABSTRACT

QT prolongation is a heart rhythm condition that impacts the lives of many people and when severe can be life-threatening. QT prolongation has been linked to variations in several genes, but it can also arise in the course of treatments with medications such as certain antipsychotics and antidepressants. However, it is unclear whether the risk of medication-induced QT prolongation (MIQTP) depends on specific genetic vulnerability. Here, we review the available literature on the interplay between genetic risk and medication exposure in the context of psychiatric treatment. A review was conducted on the genetic contribution to MIQTP in psychiatric patients. A literature search was conducted on the PubMed platform with 8 papers meeting criteria for review. A total of 3,838 patients from 8 studies meeting criteria for a psychotic or mood disorder were included in this review. All studies found evidence for the genetic contribution to MIQTP. The specific genes identified in these studies included the NOS1AP, ABCB1, KCNH2, SLC22A23, EPB41L4A, LEP, CACNA1C, CERKL, SLCO3A1, BRUNOL4, NRG3, NUBPL, PALLD, NDRG4 and PLN genes. The findings highlight both the importance of monitoring heart parameters in psychiatry and the possible role for genetic profiling to increase the treatment safety.

PMID:36257205 | DOI:10.1016/j.psychres.2022.114891

Pharmacogenet Genomics. 2022 Oct 17. doi: 10.1097/FPC.0000000000000483. Online ahead of print.

ABSTRACT

OBJECTIVE: The study of ABCB1 and CYP3A4/3A5 gene polymorphism genes is promising in terms of their influence on prothrombin time variability, the residual equilibrium concentration of direct oral anticoagulants (DOACs) in patients with atrial fibrillation and the development of new personalized approaches to anticoagulation therapy in these patients. The aim of the study is to evaluate the effect of ABCB1 (rs1045642) C>T; ABCB1 (rs4148738) C>T and CYP3A5 (rs776746) A>G, CYP3A4*22(rs35599367) C>T gene polymorphisms on prothrombin time level and residual equilibrium concentration of rivaroxaban in patients with atrial fibrillation.

METHODS: In total 86 patients (42 men and 44 female), aged 67.24 ± 1.01 years with atrial fibrillation were enrolled in the study. HPLC mass spectrometry analysis was used to determine rivaroxaban residual equilibrium concentration. Prothrombin time data were obtained from patient records.

RESULTS: The residual equilibrium concentration of rivaroxaban in patients with ABCB1 rs4148738 CT genotype is significantly higher than in patients with ABCB1 rs4148738 CC (P = 0.039). The analysis of the combination of genotypes did not find a statistically significant role of combinations of alleles of several polymorphic markers in increasing the risk of hemorrhagic complications when taking rivaroxaban.

CONCLUSION: Patients with ABCB1 rs4148738 CT genotype have a statistically significantly higher residual equilibrium concentration of rivaroxaban in blood than patients with ABCB1 rs4148738 CC genotype, which should be considered when assessing the risk of hemorrhagic complications and risk of drug-drug interactions. Further studies of the effect of rivaroxaban pharmacogenetics on the safety profile and efficacy of therapy are needed.

PMID:36256705 | DOI:10.1097/FPC.0000000000000483

Database (Oxford). 2022 Oct 18;2022:baac092. doi: 10.1093/database/baac092.

ABSTRACT

Pharmacogenomics (PGx) provides information about routine precision medicine, based on the patient's genotype. However, many of the available information about human allele frequencies, and about clinical drug-gene interactions, is based on American and European populations. PharmaKoVariome database was constructed to support genetic testing for safe prescription and drug development. It consolidated and stored 2507 diseases, 11 459 drugs and 61 627 drug-target or druggable genes from public databases. PharmaKoVariome precomputed ethnic-specific abundant variants for approximately 120 M single-nucleotide variants of drug-target or druggable genes. A user can search by gene symbol, drug name, disease and reference SNP ID number (rsID) to statistically analyse the frequency of ethnical variations, such as odds ratio and P-values for related genes. In an example study, we observed five Korean-enriched variants in the CYP2B6 and CYP2D6 genes, one of which (rs1065852) is known to be incapable of metabolizing drug. It is also shown that 4-6% of North and East Asians have risk factors for drugs metabolized by the CYP2D6 gene. Therefore, PharmaKoVariome is a useful database for pharmaceutical or diagnostic companies for developing diagnostic technologies that can be applied in the Asian PGx industry. Database URL: http://www.pharmakovariome.com/.

PMID:36255213 | DOI:10.1093/database/baac092