Pharmacogenomics in Clinical Practice for Older People.

Older people are over-represented among individuals that experience adverse drug reactions (ADR) and adverse drug events (ADE). Furthermore, older people are over-represented among individuals that visit emergency departments and are hospitalized because of ADRs. Moreover, older people are overrepresented among those who suffer ADEs while hospitalized. Finally, older people are among those most likely to have an anaphylactic response to prescription medications. Therefore, older people are prime candidates for efforts aimed at optimizing pharmacotherapeutic outcomes. Pharmacogenomics is an approach of using genetic data to optimize pharmacotherapeutic outcomes. Over the last two decades, pharmacogenomics grew from research initiatives into the current environment of pharmacogenomics implementation. Specifically, implementing pharmacogenomics into clinical settings or within health care systems has proven beneficial in optimizing pharmacotherapeutic outcomes. Therefore, pharmacists focused on optimizing pharmacotherapeutic outcomes for older people should be aware of the approaches to and resources available for implementing pharmacogenomics. KEY WORDS: Drug labeling biomarkers, Genes, Older adults, Pharmacogenomics.

A review of real-world evidence on preemptive pharmacogenomic testing for preventing adverse drug reactions: a reality for future health care.

Adverse drug reactions (ADRs) are a significant public health concern and a leading cause of hospitalization; they are estimated to be the fourth leading cause of death and increasing healthcare costs worldwide. Carrying a genetic variant could alter the efficacy and increase the risk of ADRs associated with a drug in a target population for commonly prescribed drugs. The use of pre-emptive pharmacogenetic/omic (PGx) testing can improve drug therapeutic efficacy, safety, and compliance by guiding the selection of drugs and/or dosages. In the present narrative review, we examined the current evidence of pre-emptive PGx testing-based treatment for the prevention of ADRs incidence and hospitalization or emergency department visits due to serious ADRs, thus improving patient safety. We then shared our perspective on the importance of preemptive PGx testing in clinical practice for the safe use of medicines and decreasing healthcare costs.

[Pharmacogenetic testing for prevention of severe cutaneous adverse drug reactions].

Pharmacogenetic testing benefits patients by predicting drug efficacy and risk of adverse drug reactions (ADRs). Pharmacogenetic biomarkers useful in clinical practice include drug-metabolizing enzyme and drug transporter genes and human leukocyte antigen (HLA) genes. HLA genes, which are important molecules involved in human immunity, have long been analyzed for associations with ADRs, such as skin rash, drug-induced liver injury, and agranulocytosis. HLA is composed of many genes, each of which has dozens of different types (alleles), and many HLA alleles associated with ADRs have been reported. The odds ratios in the association of HLA alleles range from approximately 5 to several thousand, indicating a very large impact on the risk of ADRs. Thus, HLA genetic testing prior to initiation of drug therapy is expected to make a significant contribution to avoiding ADRs, but to demonstrate the clinical utility, it is necessary to prospectively show the effects of medical interventions based on the test results. We conducted the GENCAT study, a prospective, multicenter, single-arm clinical trial to investigate the impact of a therapeutic intervention based on the HLA-A*31:01 test on the incidence of carbamazepine-induced skin rash. HLA-A*31:01-positive patients were treated with an alternative drug such as valproic acid, and the study showed an approximately 60% reduction in the incidence of carbamazepine-induced skin rash. It is expected that the genetic test, which has demonstrated clinical utility, will lead to the establishment of safer and more appropriate stratified medicine by reflecting the information in clinical practice guidelines.

Update on HLA-B*15:02 allele associated with adverse drug reactions.

HLA alleles, part of the major histocompatibility complex, are strongly associated with adverse drug reactions (ADRs). This review focuses on HLA-B*15:02 and explores its association with ADRs in various ethnic populations and with different drugs, aiming to provide insights into the safe clinical use of drugs and minimize the occurrence of ADRs. Furthermore, the review explores the potential mechanisms by which HLA-B*15:02 may be associated with ADRs, aiming to gain new insights into drug modification and identification of haptens. In addition, it analyzes the frequency of the HLA-B*15:02, genotyping methods, cost-effectiveness and treatment measures for adverse reactions, thereby providing a theoretical basis for formulating clinical treatment plans.

CYP2D6 Genotyping and Inhibition as Predictors of Adverse Drug Reactions in Depressive Disorders.

Objective: The primary aim of this study was to examine the association between the different predicted phenotypes of the polymorphic CYP2D6 gene and the prevalence of adverse drug reactions in patients suffering from depressive disorders. The secondary aim was to investigate if comedication with CYP2D6 inhibitors resulted in more adverse drug reactions due to phenoconversion.Methods: Between January 2012 and December 2021, 415 patients with a depressive disorder and insufficient treatment response in secondary psychiatric care were included in the naturalistic observational study Genes, Depression, and Suicidality (GEN-DS). The patients were subjected to a semistructured interview and diagnosed according to DSM-IV. Patients were also required to complete the self-rating version of the UKU Side Effect Rating Scale. All patients were genotyped for CYP2D6 and assigned a corresponding predicted CYP2D6 phenotype.Results: Out of the 415 patients, 147 patients with available genotyping and UKU scale results were also prescribed 1 or more drugs metabolized by CYP2D6. We did not find any evidence of an effect of the predicted CYP2D6 phenotype on the total burden of adverse drug reactions or in any of the specific symptom domains as measured with the UKU scale among these patients. We also investigated if comedication with 1 or more substances that inhibited the effect of the CYP2D6 enzyme resulted in more reported adverse drug reactions due to phenoconversion. Even though the rate of phenotypic PMs increased from 13 to 38 patients, we did not find any support for increased adverse drug reactions in this group.Conclusions: We did not find that CYP2D6 phenotype could predict the occurrence of adverse drug reactions in patients with depressive disorders in this naturalistic setting. However, information about CYP2D6 genotype may still be important in antidepressant treatment for the selection of appropriate drugs, for dosing recommendations of certain medications, or when the patient is suffering from severe adverse reactions.

DAPredict: a database for drug action phenotype prediction.

The phenotypes of drug action, including therapeutic actions and adverse drug reactions (ADRs), are important indicators for evaluating the druggability of new drugs and repositioning the approved drugs. Here, we provide a user-friendly database, DAPredict (http://bio-bigdata.hrbmu.edu.cn/DAPredict), in which our novel original drug action phenotypes prediction algorithm (Yang,J., Zhang,D., Liu,L. et al. (2021) Computational drug repositioning based on the relationships between substructure-indication. Brief. Bioinformatics, 22, bbaa348) was embedded. Our algorithm integrates characteristics of chemical genomics and pharmacogenomics, breaking through the limitations that traditional drug development process based on phenotype cannot analyze the mechanism of drug action. Predicting phenotypes of drug action based on the local active structures of drugs and proteins can achieve more innovative drug discovery across drug categories and simultaneously evaluate drug efficacy and safety, rather than traditional one-by-one evaluation. DAPredict contains 305 981 predicted relationships between 1748 approved drugs and 454 ADRs, 83 117 predicted relationships between 1478 approved drugs and 178 Anatomical Therapeutic Chemicals (ATC). More importantly, DAPredict provides an online prediction tool, which researchers can use to predict the action phenotypic spectrum of more than 110 000 000 compounds (including about 168 000 natural products) and corresponding proteins to analyze their potential effect mechanisms. DAPredict can also help researchers obtain the phenotype-corresponding active structures for structural optimization of new drug candidates, making it easier to evaluate the druggability of new drug candidates and develop more innovative drugs across drug categories. Database URL:  http://bio-bigdata.hrbmu.edu.cn/DAPredict/.

The expanding role of HLA gene tests for predicting drug side effects.

Adverse drug reactions can be either dose-dependent (Type A) or idiosyncratic (Type B). Type B adverse drug reactions tend to be extremely rare and difficult to predict. They are usually immune-mediated. Examples include severe skin reactions and drug-induced liver injury. For many commonly prescribed drugs (such as antibiotics), the risk of developing an idiosyncratic adverse drug reaction is influenced by variability in the human leukocyte antigen (HLA) genes. Because these HLA-mediated adverse drug reactions can be lethal, there is growing interest in defining which specific drug-gene relationships might benefit from pre-emptive HLA genotyping and automated clinical decision support. This review summarizes the literature for HLA-mediated adverse reactions linked to common drugs.

Pharmacogenomics and adverse effects of anti-infective drugs in children.

Children, as a special group, have their own peculiarities in terms of individualized medication use compared to adults. Adverse drug reactions have been an important issue that needs to be addressed in the hope of safe medication use in children, and the occurrence of adverse drug reactions is partly due to genetic factors. Anti-infective drugs are widely used in children, and they have always been an important cause of the occurrence of adverse reactions in children. Pharmacogenomic technologies are becoming increasingly sophisticated, and there are now many guidelines describing the pharmacogenomics of anti-infective drugs. However, data from paediatric-based studies are scarce. This review provides a systematic review of the pharmacogenomics of anti-infective drugs recommended for gene-guided use in CPIC guidelines by exploring the relationship between pharmacogenetic frequencies and the incidence of adverse reactions, which will help inform future studies of individualized medication use in children.

Structure-activity relationships andz interindividual variability of drug responses: pharmacogenomics with antimicrobial drugs as a paradigm.

Adverse drug reactions represent a major health burden because they cause notable patient morbidity and mortality. From this viewpoint, several strategies have been developed to prevent or reduce adverse drug reactions. One such strategy is the use of pharmacogenomics. Interindividual variability in drug response and adverse effects is mainly attributable to genetic variation in enzymes such as sulfotransferases and cytochrome P450s. The current narrative review discusses the relationship between the structure and activity of drugs. Specifically, the activity of drugs can be increased and/or their adverse effects can be reduced by altering specific positions in their structures.

Impact of CYP2C9*2 and *3 polymorphisms on valproate-associated adverse drug reactions in individuals living with epilepsy: a case-control study.

Epilepsy is characterized by repeated seizure activity. Valproate, a commonly used antiepileptic drug, shows large inter-individual variation in plasma valproic levels and causes many adverse drug reactions. Aim: To find the influence of CYP2C9*2 and *3 polymorphisms on valproate-associated adverse drug reactions and plasma valproic acid levels in people with epilepsy. Methods: We recruited 158 people with epilepsy (79 cases and 79 controls) from an epilepsy clinic. Steady-state plasma valproic acid levels were measured using liquid chromatography-mass spectrometry and genotyping of CYP2C9 variants was carried out with helps of RT-PCR. Results: The presence of a mutant heterozygous genotype showed an odds ratio (OR) of 2.82 (95% CI: 1.10-7.24) and the adjusted OR was 5.39 (95% CI: 1.69-17.16). There was no significant difference in steady-state plasma valproate concentration between genotypes. Conclusion: The presence of a mutant heterozygous CYP2C9 genotype possesses five-times the risk of developing adverse drug reactions to valproate in people with epilepsy.

The INGENIOUS trial: Impact of pharmacogenetic testing on adverse events in a pragmatic clinical trial.

Adverse drug events (ADEs) account for a significant mortality, morbidity, and cost burden. Pharmacogenetic testing has the potential to reduce ADEs and inefficacy. The objective of this INGENIOUS trial (NCT02297126) analysis was to determine whether conducting and reporting pharmacogenetic panel testing impacts ADE frequency. The trial was a pragmatic, randomized controlled clinical trial, adapted as a propensity matched analysis in individuals (N = 2612) receiving a new prescription for one or more of 26 pharmacogenetic-actionable drugs across a community safety-net and academic health system. The intervention was a pharmacogenetic testing panel for 26 drugs with dosage and selection recommendations returned to the health record. The primary outcome was occurrence of ADEs within 1 year, according to modified Common Terminology Criteria for Adverse Events (CTCAE). In the propensity-matched analysis, 16.1% of individuals experienced any ADE within 1-year. Serious ADEs (CTCAE level ≥ 3) occurred in 3.2% of individuals. When combining all 26 drugs, no significant difference was observed between the pharmacogenetic testing and control arms for any ADE (Odds ratio 0.96, 95% CI: 0.78-1.18), serious ADEs (OR: 0.91, 95% CI: 0.58-1.40), or mortality (OR: 0.60, 95% CI: 0.28-1.21). However, sub-group analyses revealed a reduction in serious ADEs and death in individuals who underwent pharmacogenotyping for aripiprazole and serotonin or serotonin-norepinephrine reuptake inhibitors (OR 0.34, 95% CI: 0.12-0.85). In conclusion, no change in overall ADEs was observed after pharmacogenetic testing. However, limitations incurred during INGENIOUS likely affected the results. Future studies may consider preemptive, rather than reactive, pharmacogenetic panel testing.

Prescription medications with actionable pharmacogenomic recommendations in Veterans Health Administration patients.

Aim: To evaluate the prevalence of medications with actionable pharmacogenomic (PGx) safety and efficacy recommendations in patients receiving care from the Veterans Health Administration. Materials & methods: Outpatient prescription data from 2011 to 2021 and any documented adverse drug reactions (ADRs) were reviewed for those who received PGx testing at one Veterans Administration location between November 2019 and October 2021. Results: Among the reviewed prescriptions, 381 (32.8%) were associated with an actionable recommendation based on the Clinical Pharmacogenetics Implementation Consortium (CPIC) prescribing guidelines, with 205 (17.7%) for efficacy concerns and 176 (15.2%) for safety concerns. Among those with a documented ADR for a PGx-impacted medication, 39.1% had PGx results that aligned with CPIC recommendations. Conclusion: Medications with actionable PGx recommendations for safety and efficacy concerns are received with similar frequency, and most patients who have undergone PGx testing at the Phoenix Veterans Administration have received medications that may be impacted by PGx testing.

Predictive modeling of adverse drug reactions to tamoxifen therapy for breast cancer on base of pharmacogenomic testing.

OBJECTIVES: The present study investigated the analysis of adverse drug reactions (ADRs) to tamoxifen (TAM) in breast cancer patients in relation to the carriage of genetic polymorphisms of genes encoding enzymes of CYP system and transporters of P-glycoprotein (Pg) and predictive models based on it. METHODS: A total of 120 women with breast cancer taking adjuvant TAM were examined for the gene polymorphisms such as CYP2D6*4, CYP3A5*3, CYP2C9*2, CYP2C9*3, CYP2C19*2, CYP2C19*3 and ABCB1 (C3435T). Allelic variants were determined using the real-time polymerase chain reaction method. The research material was double sampling of buccal epithelium. Medical history data and extracts from case histories were used as sources of medical information, on the basis of which questionnaires specially created by us were filled out. RESULTS: An associative analysis showed association with the development of ADRs to TAM indicating their clinical significance from different genetic polymorphisms of CYP2D6, CYP3A5, CYP2C9 and ABCB1. The complex associative analysis performed using mathematical modeling made it possible to build predictive risk models for the development of ADRs such as hot flashes, dyspepsia, bone pain, and asthenia. CONCLUSIONS: Models that include both genetic and non-genetic determinants of ADRs of TAM may further improve the prediction of individual response to TAM.

Genotype-guided drug prescribing vs. usual care reduced clinically relevant adverse drug reactions at 12 wk.

SOURCE CITATION: Swen JJ, van der Wouden CH, Manson LE, et al; Ubiquitous Pharmacogenetics Consortium. A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study. Lancet. 2023;401:347-356. 36739136.

Advanced statistics identification of participant and treatment predictors associated with severe adverse effects induced by fluoropyrimidine-based chemotherapy.

PURPOSE: Adverse effects following fluoropyrimidine-based chemotherapy regimens are common. However, there are no current accepted diagnostic markers for prediction prior to treatment, and the underlying mechanisms remain unclear. This study aimed to determine genetic and non-genetic predictors of adverse effects. METHODS: Genomic DNA was analyzed for 25 single nucleotide polymorphisms (SNPs). Demographics, comorbidities, cancer and fluoropyrimidine-based chemotherapy regimen types, and adverse effect data were obtained from clinical records for 155 Australian White participants. Associations were determined by bivariate analysis, logistic regression modeling and Bayesian network analysis. RESULTS: Twelve different adverse effects were observed in the participants, the most common severe adverse effect was diarrhea (12.9%). Bivariate analysis revealed associations between all adverse effects except neutropenia, between genetic and non-genetic predictors, and between 8 genetic and 12 non-genetic predictors with more than 1 adverse effect. Logistic regression modeling of adverse effects revealed a greater/sole role for six genetic predictors in overall gastrointestinal toxicity, nausea and/or vomiting, constipation, and neutropenia, and for nine non-genetic predictors in diarrhea, mucositis, neuropathy, generalized pain, hand-foot syndrome, skin toxicity, cardiotoxicity and fatigue. The Bayesian network analysis revealed less directly associated predictors (one genetic and six non-genetic) with adverse effects and confirmed associations between six adverse effects, eight genetic predictors and nine non-genetic predictors. CONCLUSION: This study is the first to link both genetic and non-genetic predictors with adverse effects following fluoropyrimidine-based chemotherapy. Collectively, we report a wealth of information that warrants further investigation to elucidate the clinical significance, especially associations with genetic predictors and adverse effects.

Use of a multi-gene pharmacogenetic panel reduces adverse drug effects.

Swen et al.1 examine the utility of multi-gene pharmacogenetic testing in a large multi-national cohort. They show fewer adverse drug reactions among patients receiving testing and prescribing recommendations based on genotype results compared with those receiving usual care.

Epigenetic Mechanisms Contribute to Intraindividual Variations of Drug Metabolism Mediated by Cytochrome P450 Enzymes.

Significant interindividual and intraindividual variations on cytochrome P450 (CYP)-mediated drug metabolism exist in the general population globally. Genetic polymorphisms are one of the major contribution factors for interindividual variations, but epigenetic mechanisms mainly contribute to intraindividual variations, including DNA methylation, histone modifications, microRNAs, and long non-coding RNAs. The current review provides analysis of advanced knowledge in the last decade on contributions of epigenetic mechanisms to intraindividual variations on CYP-mediated drug metabolism in several situations, including (1) ontogeny, the developmental changes of CYP expression in individuals from neonates to adults; (2) increased activities of CYP enzymes induced by drug treatment; (3) increased activities of CYP enzymes in adult ages induced by drug treatment at neonate ages; and (4) decreased activities of CYP enzymes in individuals with drug-induced liver injury (DILI). Furthermore, current challenges, knowledge gaps, and future perspective of the epigenetic mechanisms in development of CYP pharmacoepigenetics are discussed. In conclusion, epigenetic mechanisms have been proven to contribute to intraindividual variations of drug metabolism mediated by CYP enzymes in age development, drug induction, and DILI conditions. The knowledge has helped understanding how intraindividual variation are generated. Future studies are needed to develop CYP-based pharmacoepigenetics to guide clinical applications for precision medicine with improved therapeutic efficacy and reduced risk of adverse drug reactions and toxicity. SIGNIFICANCE STATEMENT: Understanding epigenetic mechanisms in contribution to intraindividual variations of CYP-mediated drug metabolism may help to develop CYP-based pharmacoepigenetics for precision medicine to improve therapeutic efficacy and reduce adverse drug reactions and toxicity for drugs metabolized by CYP enzymes.

Identification of adverse drug reactions that may be related to pharmacogenetics in a public hospital in the South of Brazil.

BACKGROUND: Adverse drug reactions (ADRs) are of great concern in clinical practice. Pharmacogenetics can identify individuals and groups at increased risk of developing ADRs, enabling treatment adjustments to improve outcomes. The study aimed to determine the prevalence of ADRs related to drugs with pharmacogenetic evidence level 1A in a public hospital in Southern Brazil. RESEARCH DESIGN AND METHODS: ADR information was collected from the pharmaceutical registries from 2017 to 2019. Drugs that have pharmacogenetic evidence level 1A were selected. Public genomic databases were used to estimate the genotypes/phenotypes frequency. RESULTS: During the period, 585 ADRs were spontaneously notified. Most were moderate (76.3%), whereas severe reactions accounted for 33.8%. Additionally, 109 ADRs caused by 41 drugs presented pharmacogenetic evidence level 1A, representing 18.6% of all notified reactions. Depending on the drug-gene pair, up to 35% of individuals from Southern Brazil could be at risk of developing ADRs. CONCLUSIONS: Relevant amount of ADRs were related to drugs with pharmacogenetic recommendations on drug labels and/or guidelines. Genetic information could guide and improve clinical outcomes, decreasing ADR incidence and reducing treatment costs.

DeepSide: A Deep Learning Approach for Drug Side Effect Prediction.

Drug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Side effect prediction algorithms have the potential to guide the drug design process. LINCS L1000 dataset provides a vast resource of cell line gene expression data perturbed by different drugs and creates a knowledge base for context specific features. The state-of-the-art approach that aims at using context specific information relies on only the high-quality experiments in LINCS L1000 and discards a large portion of the experiments. In this study, our goal is to boost the prediction performance by utilizing this data to its full extent. We experiment with 5 deep learning architectures. We find that a multi-modal architecture produces the best predictive performance among multi-layer perceptron-based architectures when drug chemical structure (CS), and the full set of drug perturbed gene expression profiles (GEX) are used as modalities. Overall, we observe that the CS is more informative than the GEX. A convolutional neural network-based model that uses only SMILES string representation of the drugs achieves the best results and provides 13.0% macro-AUC and 3.1% micro-AUC improvements over the state-of-the-art. We also show that the model is able to predict side effect-drug pairs that are reported in the literature but was missing in the ground truth side effect dataset. DeepSide is available at http://github.com/OnurUner/DeepSide.