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Abstract Background Older patients with coronary artery disease (CAD) are particularly vulnerable to the efficacy and adverse drug reactions, and may therefore particularly benefit from personalized medication. Drug–gene interactions (DGIs) occur when an individual’s genotype affects the pharmacokinetics and/or pharmacodynamics of a victim drug. Objectives This study aimed to investigate the impact of cardiovascular-related DGIs on the clinical efficacy and safety outcomes in older patients with CAD. Methods Hospitalized older patients (≥ 65 years old) with CAD were consecutively recruited from August 2018 to May 2022. Eligible patients were genotyped for the actionable pharmacogenetic variants of CYP2C9, CYP2C19, CYP2D6, CYP3A5, and SLCO1B1, which had clinical annotations or implementation guidelines for cardiovascular drugs. Allele frequencies and DGIs were determined in the cohort for the 5 actionable PGx genes and the prescribed cardiovascular drugs. All patients were followed up for at least 1 year. The influence of DGIs on the cardiovascular drug-related efficacy outcomes (all-cause mortality and/or major cardiovascular events, MACEs) and drug response phenotypes of “drug-stop” and “dose-decrease” were evaluated. Results A total of 1,017 eligible older patients with CAD were included, among whom 63.2% were male, with an average age of 80.8 years old, and 87.6% were administrated with polypharmacy (≥ 5 medications). After genotyping, we found that 96.0% of the older patients with CAD patients had at least one allele of the 5 pharmacogenes associated with a therapeutic change, indicating a need for a therapeutic change in a mean of 1.32 drugs of the 19 cardiovascular-related drugs. We also identified that 79.5% of the patients had at least one DGI (range 0–6). The median follow-up interval was 39 months. Independent of age, negative association could be found between the number of DGIs and all-cause mortality (adjusted HR: 0.84, 95% CI: 0.73–0.96, P = 0.008), and MACEs (adjusted HR: 0.84, 95% CI: 0.72–0.98, P = 0.023), but positive association could be found between the number of DGIs and drug response phenotypes (adjusted OR: 1.24, 95% CI: 1.05–1.45, P = 0.011) in the elderly patients with CAD. Conclusions The association between cardiovascular DGIs and the clinical outcomes emphasized the necessity for the integration of genetic and clinical data to enhance the optimization of cardiovascular polypharmacy in older patients with CAD. The causal relationship between DGIs and the clinical outcomes should be established in the large scale prospectively designed cohort study.

Background: Older patients with coronary artery disease (CAD) are particularly vulnerable to the efficacy and adverse drug reactions, and may therefore particularly benefit from personalized medication. Drug–gene interactions (DGIs) occur when an individual's genotype affects the pharmacokinetics and/or pharmacodynamics of a victim drug. Objectives: This study aimed to investigate the impact of cardiovascular-related DGIs on the clinical efficacy and safety outcomes in older patients with CAD. Methods: Hospitalized older patients (≥ 65 years old) with CAD were consecutively recruited from August 2018 to May 2022. Eligible patients were genotyped for the actionable pharmacogenetic variants of CYP2C9, CYP2C19, CYP2D6, CYP3A5, and SLCO1B1, which had clinical annotations or implementation guidelines for cardiovascular drugs. Allele frequencies and DGIs were determined in the cohort for the 5 actionable PGx genes and the prescribed cardiovascular drugs. All patients were followed up for at least 1 year. The influence of DGIs on the cardiovascular drug-related efficacy outcomes (all-cause mortality and/or major cardiovascular events, MACEs) and drug response phenotypes of "drug-stop" and "dose-decrease" were evaluated. Results: A total of 1,017 eligible older patients with CAD were included, among whom 63.2% were male, with an average age of 80.8 years old, and 87.6% were administrated with polypharmacy (≥ 5 medications). After genotyping, we found that 96.0% of the older patients with CAD patients had at least one allele of the 5 pharmacogenes associated with a therapeutic change, indicating a need for a therapeutic change in a mean of 1.32 drugs of the 19 cardiovascular-related drugs. We also identified that 79.5% of the patients had at least one DGI (range 0–6). The median follow-up interval was 39 months. Independent of age, negative association could be found between the number of DGIs and all-cause mortality (adjusted HR: 0.84, 95% CI: 0.73–0.96, P = 0.008), and MACEs (adjusted HR: 0.84, 95% CI: 0.72–0.98, P = 0.023), but positive association could be found between the number of DGIs and drug response phenotypes (adjusted OR: 1.24, 95% CI: 1.05–1.45, P = 0.011) in the elderly patients with CAD. Conclusions: The association between cardiovascular DGIs and the clinical outcomes emphasized the necessity for the integration of genetic and clinical data to enhance the optimization of cardiovascular polypharmacy in older patients with CAD. The causal relationship between DGIs and the clinical outcomes should be established in the large scale prospectively designed cohort study. [ABSTRACT FROM AUTHOR]

AbstractThis 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. [ABSTRACT FROM AUTHOR]

Background: Venlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug. Purpose: A physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK). Methods: The parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim® and MoBi®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model's performance was evaluated by comparing predicted and observed values of plasma concentration–time (PCT) curves and PK parameters values. Results: In the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC0-96 h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups. Conclusions: In clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment. [ABSTRACT FROM AUTHOR]

Aim: The objective of this registry study is to assess the utilization of pharmacogenomic (PGx) drugs among patients with chronic kidney disease (CKD). Methods: This study was a retrospective study of patients affiliated with the Department of Nephrology, Aalborg University Hospital, Denmark in 2021. Patients diagnosed with CKD were divided into CKD without dialysis and CKD with dialysis. PGx prescription drugs were retrieved from the Patient Administration System. Actionable dosing guidelines (AG) for specific drug–gene pairs for CYP2D6, CYP2C9, CYP2C19 and SLCO1B1 were retrieved from the PharmGKB homepage. Results: Out of 1241 individuals, 25.5% were on dialysis. The median number of medications for each patient was 9 within the non‐dialysis group and 16 within the dialysis group. Thirty‐one distinct PGx drugs were prescribed. Altogether, 76.0% (943 individuals) were prescribed at least one PGx drug and the prevalence of prescriptions of PGx drugs was higher in the dialysis group compared to the non‐dialysis group. The most frequently prescribed drugs with AG were metoprolol, pantoprazole, atorvastatin, simvastatin and warfarin. Conclusion: This study demonstrated that a substantial proportion of patients with CKD are exposed to drugs or drug combinations for which there exists AG related to PGx of CYP2D6, CYP2C19, CYP2C9 and SLCO1B1. [ABSTRACT FROM AUTHOR]

Katie Pizzolato,1 David Thacker,2 Nicole Marie Del Toro-Pagán,1 Nishita S Amin,1 Abeer Hanna,3 Jacques Turgeon,2,4 Veronique Michaud2,4,5 1Tabula Rasa Healthcare, Office of Translational Research and Residency Programs, Moorestown, NJ, 08057, USA; 2Tabula Rasa HealthCare, Precision Pharmacotherapy Research and Development Institute, Orlando, FL, 32827, USA; 3VieCare Butler, Program of All-Inclusive Care for the Elderly (PACE), Butler, PA, 16001, USA; 4Faculty of Pharmacy, Université de Montréal, Montréal, QC, H2L, Canada; 5University of Montreal Hospital Research Center (CRCHUM), Montréal, QC, H2X 0A9, CanadaCorrespondence: Veronique Michaud, Precision Pharmacotherapy Research and Development Institute, 13485 Veterans Way, Orlando, FL, 32827, USA, Tel +856-938-8697, Email vmichaud@trhc.comAbstract: The opioid epidemic in the United States has exposed the need for providers to limit opioid dispensing and identify at-risk patients prior to prescribing opioids. With pharmacogenomic testing, clinicians can analyze hundreds of medications—including commonly prescribed opioids—against genetic results to understand and predict risk and response. Moreover, knowledge of genotypic variants and altered function can help decrease trial and error prescribing, identify patients at-risk for adverse drug events, and improve pain control. This patient case demonstrates how pharmacogenomic test results identified drug–gene interactions and provided insight about a patient’s inadequate opioid therapy response. With pharmacogenomic information, the patient’s healthcare team discontinued opioid therapy and selected a more appropriate regimen for osteoarthritis (ie, celecoxib), resulting in improved pain control and quality of life.Keywords: CYP2D6, CYP2C9, drug–gene interactions, opioids, pain management, pharmacogenomics

Innocent G Asiimwe, Munir Pirmohamed The Wolfson Centre for Personalized Medicine, MRC Centre for Drug Safety Science, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UKCorrespondence: Innocent G Asiimwe; Munir Pirmohamed, Tel +44 151 7955387 ; +44 151 794 5549, Fax +44 151 794 5059, Email i.asiimwe@liverpool.ac.uk; munirp@liverpool.ac.ukAbstract: Cardiovascular disease remains a leading cause of both morbidity and mortality worldwide. It is widely accepted that both concomitant medications (drug–drug interactions, DDIs) and genomic factors (drug–gene interactions, DGIs) can influence cardiovascular drug-related efficacy and safety outcomes. Although thousands of DDI and DGI (aka pharmacogenomic) studies have been published to date, the literature on drug–drug–gene interactions (DDGIs, cumulative effects of DDIs and DGIs) remains scarce. Moreover, multimorbidity is common in cardiovascular disease patients and is often associated with polypharmacy, which increases the likelihood of clinically relevant drug-related interactions. These, in turn, can lead to reduced drug efficacy, medication-related harm (adverse drug reactions, longer hospitalizations, mortality) and increased healthcare costs. To examine the extent to which DDGIs and other interactions influence efficacy and safety outcomes in the field of cardiovascular medicine, we review current evidence in the field. We describe the different categories of DDIs and DGIs before illustrating how these two interact to produce DDGIs and other complex interactions. We provide examples of studies that have reported the prevalence of clinically relevant interactions and the most implicated cardiovascular medicines before outlining the challenges associated with dealing with these interactions in clinical practice. Finally, we provide recommendations on how to manage the challenges including but not limited to expanding the scope of drug information compendia, interaction databases and clinical implementation guidelines (to include clinically relevant DDGIs and other complex interactions) and work towards their harmonization; better use of electronic decision support tools; using big data and novel computational techniques; using clinically relevant endpoints, preemptive genotyping; ensuring ethnic diversity; and upskilling of clinicians in pharmacogenomics and personalized medicine.Keywords: drug–drug interactions, drug–gene interactions, drug–drug–gene interactions, drug–gene–gene interactions, pharmacogenomics

Introduction: Pharmacogenetic (PGx) screening is intended to optimize drug efficacy and reduce adverse drug reactions. Current screening options include genotyping assays for preselected PGx variants and broader next-generation sequencing panels (NGS). Few studies have directly compared preemptive PGx screening methods., Materials and Methods: The two PGx methods were compared in a cross-sectional study of adult Military Health System (MHS) clinic beneficiaries. Participants had initial targeted CYP2C19/CYP2D6 genotyping at a Military Health System Laboratory. Genotyping was followed by multi-gene NGS testing. Current prescriptions were recorded and potential drug-drug interactions screened to evaluate prescribing risk., Results: All participants (100%) had at least one clinically actionable NGS panel result compared to 81% with targeted CYP2C19/CYP2D6 genotyping. Participants ( n  = 162) had an average of 6.6 (range 0-22) prescriptions and 2.7 (range 0-24) drug-drug interactions. Among those with at least one clinically actionable NGS result, 42% were currently taking medication with actionable CPIC guidelines (Level A/B), compared with 24% with CYP2C19/CYP2D6 genotyping. Sixteen participants (10%) had uncertain NGS panel results, with none for CYP2C19/CYP2D6 genotyping., Conclusions: Preemptive multi-gene NGS detected more clinically actionable PGx results than targeted CYP2C19/CYP2D6 genotyping. Effective PGx screening in the MHS may decrease preventable adverse effects and improve military readiness.

Introduction: Minority groups are underrepresented in pharmacogenomics (PGx) research. Recent sub-analysis of CYP-GUIDES showed reduced length of stay (LOS) in depressed patients with CYP2D6 sub-functional status. Our primary objective was to determine whether PGx guided (G) versus standard treatment (S) influenced LOS among different race/ethnic groups. Secondary objectives included prevalence of drug-gene interactions (DGIs) and readmission rates (RAR). Methods: Retrospective sub-analysis of CYP-GUIDES data comprising CYP2D6 phenotypes was reclassified using standardized CYP2D6 genotype to phenotype recommendations from the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Dutch Pharmacogenetics Working Group (DPWG). The Mann-Whitney test was used to determine differences in LOS between groups G and S and Kruskal Wallis test to compare LOS among different race/ethnic groups. Logistic regression was used to determine covariates associated with RAR. Results: This study included 1,459 patients with 67.3% in G group (n = 982). The majority of patients were White (57.5%), followed by Latinos (25.6%) and Blacks (12.3%). Although there were no differences in LOS between G and S groups, Latinos had significant shorter LOS than Whites (p = 0.002). LOS was significantly reduced by 5.6 days in poor metabolizers in group G compared to S (p = 0.002). The proportion of supra functional and ultra-rapid metabolizers (UMs) were 6 and 20.3% using CYP-GUIDES and CPIC/DPWG definitions, respectively. Prevalence of DGIs was 40% with significantly fewer DGIs in Blacks (p < 0.001). Race/ethnicity was significantly associated with RAR (aOR 1.30; p = 0.003). Conclusion: A greater number of patients were classified as CYP2D6 UMs using CPIC/DPWG definitions as compared to CYP-GUIDES definitions. This finding may have clinical implications for using psychotropics metabolized by CYP2D6. [ABSTRACT FROM AUTHOR]

Introduction: Minority groups are underrepresented in pharmacogenomics (PGx) research. Recent sub-analysis of CYP-GUIDES showed reduced length of stay (LOS) in depressed patients with CYP2D6 sub-functional status. Our primary objective was to determine whether PGx guided (G) versus standard treatment (S) influenced LOS among different race/ethnic groups. Secondary objectives included prevalence of drug-gene interactions (DGIs) and readmission rates (RAR).Methods: Retrospective sub-analysis of CYP-GUIDES data comprising CYP2D6 phenotypes was reclassified using standardized CYP2D6 genotype to phenotype recommendations from the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Dutch Pharmacogenetics Working Group (DPWG). The Mann-Whitney test was used to determine differences in LOS between groups G and S and Kruskal Wallis test to compare LOS among different race/ethnic groups. Logistic regression was used to determine covariates associated with RAR.Results: This study included 1,459 patients with 67.3% in G group (n = 982). The majority of patients were White (57.5%), followed by Latinos (25.6%) and Blacks (12.3%). Although there were no differences in LOS between G and S groups, Latinos had significant shorter LOS than Whites (p = 0.002). LOS was significantly reduced by 5.6 days in poor metabolizers in group G compared to S (p = 0.002). The proportion of supra functional and ultra-rapid metabolizers (UMs) were 6 and 20.3% using CYP-GUIDES and CPIC/DPWG definitions, respectively. Prevalence of DGIs was 40% with significantly fewer DGIs in Blacks (p < 0.001). Race/ethnicity was significantly associated with RAR (aOR 1.30; p = 0.003).Conclusion: A greater number of patients were classified as CYP2D6 UMs using CPIC/DPWG definitions as compared to CYP-GUIDES definitions. This finding may have clinical implications for using psychotropics metabolized by CYP2D6.

Objective: Periodontitis, characterized by inflammation linked to apoptosis dysregulation, underscores the role of inhibitors of apoptosis proteins (IAPs) like survivin and cIAP1, implicated in disease progression and treatment resistance across various conditions. Our study aims to analyze the prediction of drug-gene interactions by machine learning techniques, combining regularized logistic regression and stochastic gradient descent (SGD) for efficient classification., Methods: Data from Probes-Drugs.org on IAP-based drug-protein interactions underwent rigorous annotation and outlier removal. A data robot tool trained machine learning models, regularized logistic regression and SGD (https://app.datarobot.com/new). Network analysis employed Cytoscape to construct and analyze the IAP network, identifying key hub nodes crucial in periodontitis pathogenesis., Results: The constructed IAP network comprised 376 nodes and 556 edges, revealing intricate drug-gene interactions with an average of 2957 neighbors per node. Ten hub nodes were identified as pivotal in regulating biological processes specific to periodontitis, suggesting their potential as therapeutic targets and biomarkers. Predictive models demonstrated high accuracy, with gradient descent achieving 93% and regularized logistic regression achieving 92% in identifying drug-gene interactions within the IAP network., Conclusions: These findings highlight the utility of computational methods in elucidating molecular mechanisms underlying periodontitis, offering insights into potential therapeutic strategies targeting IAP-related pathways. Future research should focus on validating hub genes experimentally and integrating multi-omics data to advance precision medicine approaches in periodontitis treatment., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Harris et al.)

Context: Pharmacogenomic analysis may improve the efficacy or safety of the drugs used in palliative care. Decision support systems may promote clinical integration of this information. Objectives: To determine the feasibility and acceptability of a pharmacist-directed pharmacogenomic decision support system in the care of patients with advanced illness and explore the drug–gene and drug–drug interactions that occur in this population. Methods: Physicians or nurse practitioners from two U.S. hospice agencies identified opioid-treated patients receiving multiple other drugs. Buccal samples and clinical data were obtained from consenting patients. A pharmacist used the proprietary MedWise™ platform to evaluate the current medications in terms of genotype and phenotype, created a standardized report describing potential interactions and recommended actions that may reduce the associated risk. Clinicians could access the report online and completed Likert-type scales to assess use and satisfaction with the system. Results: Twenty clinicians and 100 patients participated. The reports revealed that 74 drugs were subject to 462 drug–gene interactions and 77 were involved in 691 drug–drug interactions; only 4 and 16 patients, respectively, had no drug–gene or drug–drug interactions. Clinicians routinely checked the reports and used the information to change ≥1 treatments in 55 (55%) patients. Almost all clinicians rated the system likely to improve the quality of care and all "agreed" or "strongly agreed" to recommend the system to colleagues. Conclusion: This pharmacist-directed pharmacogenomic decision support system was perceived positively and was integrated into practice. Further studies are warranted to its clinical integration and its outcomes. [ABSTRACT FROM AUTHOR]

Polypharmacy therapies, quite frequent in older populations, pose significant health risks for patients due to a high possibility of their cumulative adverse drug reactions. We present Personalized Medicine Therapy Optimization Method (PM-TOM) for the discovery of polypharmacy therapies with minimal drug- drug interactions, drug-gene interactions, and drug-condition interactions. The inputs of the PM-TOM heuristic algorithm are the patient’s conditions, genome, and the drug products and therapy considered by a clinician. Its output is a comprehensive report that explains details of the optimal therapies for considered drug products and all drugs that treat the patient’s conditions. PM-TOM was developed by using the Electronic Medical Records repository of the Personal Genome Project (PGP), and the public repositories: DrugBank and Comprehensive Toxicogenomics Database (CTD). Testing of PM-TOM showed potential for significant reduction of the cumulative adverse drug interactions in personalized polypharmacy therapies. In the group of patients with 8 to 17 conditions, PMTOM reduced the average cumulative drug interactions from 22 to 5.83, and the group with 6 to 7 conditions from 11.17 to 3. These results encourage further research and development of clinical decision support tools like PM-TOM. [ABSTRACT FROM AUTHOR]

Background: This study measures the use of drugs within the therapeutic areas of antithrombotic agents (B01), the cardiovascular system (C), analgesics (N02), psycholeptics (N05), and psychoanaleptics (N06) among the general population (GP) in comparison to persons with diabetes in Denmark. The study focuses on drugs having pharmacogenomics (PGx) based dosing guidelines for CYP2D6, CYP2C19, and SLCO1B1 to explore the potential of applying PGx-based decision-making into clinical practice taking drug–drug interactions (DDI) and drug–gene interactions (DGI) into account. Methods: This study is cross-sectional, using The Danish Register of Medicinal Product Statistics as the source to retrieve drug consumption data. Results: The prevalence of use in particular for antithrombotic agents (B01) and cardiovascular drugs (C) increases significantly by 4 to 6 times for diabetic users compared to the GP, whereas the increase for analgesics (N02), psycoleptics, and psychoanaleptics (N06) was somewhat less (2–3 times). The five most used PGx drugs, both in the GP and among persons with diabetes, were pantoprazole, simvastatin, atorvastatin, metoprolol, and tramadol. The prevalence of use for persons with diabetes compared to the GP (prevalence ratio) increased by an average factor of 2.9 for all PGx drugs measured. In addition, the prevalence of use of combinations of PGx drugs was 4.6 times higher for persons with diabetes compared to GP. In conclusion, the findings of this study clearly show that a large fraction of persons with diabetes are exposed to drugs or drug combinations for which there exist PGx-based dosing guidelines related to CYP2D6, CYP2C19, and SLCO1B1. This further supports the notion of accessing and accounting for not only DDI but also DGI and phenoconversion in clinical decision-making, with a particular focus on persons with diabetes.

Despite advances in the treatment of cervical cancer (CC), the prognosis of patients with CC remains to be improved. This study aimed to explore candidate gene targets for CC. CC datasets were downloaded from the Gene Expression Omnibus database. Genes with similar expression trends in varying steps of CC development were clustered using Short Time-series Expression Miner (STEM) software. Gene functions were then analyzed using the Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Protein interactions among genes of interest were predicted, followed by drug-target genes and prognosis-associated genes. The expressions of the predicted genes were determined using real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting. Red and green profiles with upward and downward gene expressions, respectively, were screened using STEM software. Genes with increased expression were significantly enriched in DNA replication, cell-cycle-related biological processes, and the p53 signaling pathway. Based on the predicted results of the Drug-Gene Interaction database, 17 drug-gene interaction pairs, including 3 red profile genes (TOP2A, RRM2, and POLA1) and 16 drugs, were obtained. The Cancer Genome Atlas data analysis showed that high POLA1 expression was significantly correlated with prolonged survival, indicating that POLA1 is protective against CC. RT-qPCR and Western blotting showed that the expressions of TOP2A, RRM2, and POLA1 gradually increased in the multistep process of CC. TOP2A, RRM2, and POLA1 may be targets for the treatment of CC. However, many studies are needed to validate our findings. [ABSTRACT FROM AUTHOR]

Drug interactions are a well-known cause of adverse drug events, and drug interaction databases can help the clinician to recognize and avoid such interactions and their adverse events. However, not every interaction leads to an adverse drug event. This is because the clinical relevance of drug–drug interactions also depends on the genetic profile of the patient. If inhibitors or inducers of drug metabolising enzymes (e.g., CYP and UGT) are added to the drug therapy, phenoconcversion can occur. This leads to a genetic phenotype that mismatches the observable phenotype. Drug–drug–gene and drug–gene–gene interactions influence the toxicity and/or ineffectivness of the drug therapy. To date, there have been limited published studies on the impact of genetic variations on drug–drug interactions. This review discusses the current evidence of drug–drug–gene interactions, as well as drug–gene–gene interactions. Phenoconversion is explained, the and methods to calculate the phenotypes are described. Clinical recommendations are given regarding the integratation of the PGx results in the assessment of the relevance of drug interactions in the future.

Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug–drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victims. Predicted concentration–time profiles accurately described a validation dataset, including data from patients with genetic polymorphisms, demonstrating that the models characterized the CYP2C19 and CYP1A2 network over the whole range of DDI studies investigated. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA), expanding the library of publicly available qualified whole-body PBPK models for DDI predictions, and they are thereby available to support potential recommendations for dose adaptations, support labeling, inform the design of clinical DDI trials, and potentially waive those.

Despite extensive efforts to identify patients with cardiovascular disease (CVD) who could most benefit from the treatment approach, patients vary in their benefit from therapy and propensity for adverse drug events. Genetic variability in individual responses to drugs (pharmacogenetics) is considered an essential determinant in responding to a drug. Thus, understanding these pharmacogenomic relationships has led to a substantial focus on mechanisms of disease and drug response. In turn, understanding the genomic and molecular bases of variables that might be involved in drug response is the main step in personalized medicine. There is a growing body of data evaluating drug-gene interactions in recent years, some of which have led to FDA recommendations and detection of markers to predict drug responses (e.g., genetic variant in VKORC1 and CYP2C9 genes for prediction of drug response in warfarin treatment). Also, statins are widely prescribed drugs for the prevention of CVD. Atorvastatin, fluvastatin, rosuvastatin, simvastatin, and lovastatin are the most common statins used to manage dyslipidemia. This review provides an overview of the current knowledge on the pharmacogenetics of statins, which are being used to treat cardiovascular diseases., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

This paper discusses difficulties of pharmacogenomic data integration into clinical practice. It emphasizes the need for developing simple and easy to use bioinformatics tools to help prescribers to rapidly access and use genetic data in clinical decision-making at the point of encounter. [ABSTRACT FROM AUTHOR]

Cytochrome P450 (CYP) enzymes are commonly involved in drug metabolism, and genetic variation in the genes encoding CYPs are associated with variable drug response. While genotype-guided therapy has been clinically implemented in adults, these associations are less well established for pediatric patients. In order to understand the frequency of pediatric exposures to drugs with known CYP interactions, we compiled all actionable drug-CYP interactions with a high level of evidence using Clinical Pharmacogenomic Implementation Consortium (CPIC) data and surveyed 10 years of electronic health records (EHR) data for the number of children exposed to CYP-associated drugs. Subsequently, we performed a focused literature review for drugs commonly used in pediatrics, defined asmore than 5000 pediatric patients exposed in the decade-long EHR cohort. There were 48 drug-CYP interactions with a high level of evidence in the CPIC database. Of those, only 10 drugs were commonly used in children (ondansetron, oxycodone, codeine, omeprazole, lansoprazole, sertraline, amitriptyline, citalopram, escitalopram and risperidone). For these drugs, reports of the drug-CYP interaction in cohorts including children were sparse. There are adequate data for implementation of genotype-guided therapy for children for three of the 10 commonly used drugs (codeine, omeprazole and lansoprazole). For the majority of commonly used drugs with known CYP interactions, more data are required to support pharmacogenomic implementation in children. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Methodological standards for clinical pharmacogenetic studies should be developed to improve reporting of studies and facilitate their inclusion in systematic reviews. The essence of these studies lies within the concept of effect modification. Study Design and Setting: A narrative review discussing methodological issues in the design and reporting of pharmacogenetic studies. Results: Studying effect modification within a trial leads to the comparison of subgroups based on genotype. Differences in effect based on genotype should preferably be expressed in absolute terms (risk differences) to facilitate clinical decisions on treatment. Information on the distribution of potential effect modifiers or prognostic factors should be available to prevent a biased comparison of differences in effect between genotypes. The distribution of genotypes should also be presented and compared to Hardy–Weinberg equilibrium to check for selection bias. Additional points of interest include the possibility of selective nonavailability of biomaterial and the choice of a statistical model to study effect modification. Conclusion: Additional methodological issues should be taken into account when designing and reporting pharmacogenetic studies, to ensure high study quality. We present several important issues for future studies investigating drug–gene interactions that can serve as a basis for further discussion on methodology in pharmacogenetics. [Copyright &y& Elsevier]

Drug interactions are a well-known cause of adverse drug events, and drug interaction databases can help the clinician to recognize and avoid such interactions and their adverse events. However, not every interaction leads to an adverse drug event. This is because the clinical relevance of drug–drug interactions also depends on the genetic profile of the patient. If inhibitors or inducers of drug metabolising enzymes (e.g., CYP and UGT) are added to the drug therapy, phenoconcversion can occur. This leads to a genetic phenotype that mismatches the observable phenotype. Drug–drug–gene and drug–gene–gene interactions influence the toxicity and/or ineffectivness of the drug therapy. To date, there have been limited published studies on the impact of genetic variations on drug–drug interactions. This review discusses the current evidence of drug–drug–gene interactions, as well as drug–gene–gene interactions. Phenoconversion is explained, the and methods to calculate the phenotypes are described. Clinical recommendations are given regarding the integratation of the PGx results in the assessment of the relevance of drug interactions in the future. [ABSTRACT FROM AUTHOR]

Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug–drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victims. Predicted concentration–time profiles accurately described a validation dataset, including data from patients with genetic polymorphisms, demonstrating that the models characterized the CYP2C19 and CYP1A2 network over the whole range of DDI studies investigated. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA), expanding the library of publicly available qualified whole-body PBPK models for DDI predictions, and they are thereby available to support potential recommendations for dose adaptations, support labeling, inform the design of clinical DDI trials, and potentially waive those. [ABSTRACT FROM AUTHOR]

Background: Polypharmacy is most prevalent among the elderly population and in particular among nursing home residents. The frequency of the use of drugs with pharmacogenomics (PGx)-based dosing guidelines for CYP2D6, CYP2C9, CYP2C19 and SLCO1B1 were measured among nursing home residents in the Capital Region of Denmark as well as drug–drug interactions. The aim was to evaluate the potential of applying PGx-test as a supportive tool in medication reviews. Methods: Drug use among nursing home residents during 2017–2018 in the Capital Region of Copenhagen, for drugs with PGx-based dosing guidelines available through the PharmGKB website, were measured. Drug–drug interactions were scored in severity by using drug interaction checkers. Results: The number of residents using drugs with PGx-based actionable dosing guidelines (AG) were 119 out of 141 residents (84.3%). Of these 119 residents, 87 residents used drugs with AG for CYP2C19, 47 residents for CYP2D6, and 42 residents for SLCO1B1. In addition, 30 residents used two drugs with an AG for CYP2C19, and for CYP2D6, it was only seven residents. The most used drugs with AG were clopidogrel (42), pantoprazole (32), simvastatin (30), metoprolol (25), and citalopram (24). The most frequent drug interactions found with warnings were combinations of proton pump inhibitors and clopidogrel underscoring the potential for phenoconversion. Conclusion: this study clearly showed that the majority of the nursing home residents were exposed to drugs or drug combinations for which there exist PGx-based AG. This indeed supports the notion of accessing and accounting for not only drug–gene but also drug–drug–gene interactions as a supplement to medication review. [ABSTRACT FROM AUTHOR]

Introduction: Diffuse large B-cell lymphoma (DLBCL) is genetically and clinically heterogeneous. Despite advances in genomic subtyping, standard frontline chemoimmunotherapy has remained unchanged for years. As high-throughput analysis becomes more accessible, characterizing drug-gene interactions in DLBCL could support patient-specific treatment strategies., Materials and Methods: From our systematic literature review, we compiled a comprehensive list of somatic mutations implicated in DLBCL. We extracted reported and primary sequencing data for these mutations and assessed their association with signaling pathways, cell-of-origin subtypes, and clinical outcomes., Results: Twenty-two targetable mutations present in ≥ 5% of patients with DLBCL were associated with unfavorable outcomes, yielding a predicted population of 31.7% of DLBCL cases with poor-risk disease and candidacy for targeted therapy. A second review identified 256 studies that had characterized the drug-gene interactions for these mutations via in vitro studies, mouse models, and/or clinical trials., Conclusions: Our novel approach linking the data from our systematic reviews with informatics tools identified high-risk DLBCL subgroups, DLBCL-specific drug-gene interactions, and potential populations for precision medicine trials., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Aim: Evaluate results of pharmacogenomics testing for participants enrolled in the Program of All-inclusive Care for the Elderly (PACE). Materials & methods: A convenience sample of 100 participants from the PHARM-GENOME-PACE study. Genetic variants were determined by pharmacogenomics testing. Drug-gene interactions (DGIs), drug-drug-gene interactions (DDGIs) and phenoconversions were interrogated from a clinical decision support system. Results: In total, 146 genetic variants, 169 DGIs and 125 DDGIs were detected. DGIs and DDGIs occurred most commonly with the CYP2D6 gene (36.1 and 39.2%, respectively). There were 280 instances of phenoconversions; majority (62.9%) affecting the CYP3A4 isoenzyme. Conclusion: Prevalence of exposures to DGIs and DDGIs among PACE participants is high. Pharmacists using a clinical decision support system can support PACE practitioners with assessing multidrug simultaneous interactions. Clinical trial registration: NCT03257605.

The wide range of patient responses to surgical pain, opioids, and anesthetic agents has puzzled anesthesiologists for many years. Much of the variation has been attributed to differences in patient size, technique, or prior drug use. However, recent genetic testing has revealed exciting clues into the basis for these variances, allowing us to start to predict which patients may have difficulties and start to select medications more rationally. In this manuscript, we discuss genetics and pain perception, genetic predisposition to pain, drug metabolism interactions, ethnogenetics, opioid metabolism, opioid receptors, genetic-related peri-anesthetic toxicity, as well as a clinical approach and a discussion regarding the future of genetic testing and anesthesia., (Copyright © 2014 Elsevier Ltd. All rights reserved.)