Pharmacogenomics-guided personalized medicine in a clinical setting: real-world data

Introduction: Combinatorial pharmacogenetic testing panels are widely available in clinical practice and often separate medications into columns/bins associated with low, medium, or high probability of gene-drug interactions. The objective of the Adolescent Management of Depression (AMOD) study was to determine the clinical utility of combinatorial pharmacogenetic testing in a double-blind, randomized, controlled effectiveness study by comparing patients who had genetic testing results at time of medication initiation to those that did not have results until week 8. The objective of this post hoc analysis was to assess and report additional outcomes with respect to significant gene-drug interactions (i.e., a medication in the high probability gene-drug interaction bin as defined by a proprietary algorithm) compared with patients taking a medication with minimal to moderate gene-drug interactions (i.e., a medication from the low or medium probability gene-drug interaction bin, respectively). Methods: Adolescents 13-18 years (N = 170) with moderate to severe major depressive disorder received pharmacogenetic testing. Symptom improvement and side effects were assessed at baseline, week 4, week 8, and 6 months. Patients were grouped into three categories based on whether the medication they were prescribed was associated with low, medium, or high risk for gene-drug interactions. Patients taking a medication from the low/medium gene-drug interaction bin were compared with patients taking a medication from the high gene-drug interaction bin. Results: Patients taking a medication from the high gene-drug interaction bin were more likely to endorse side effects compared with patients taking a medication in the low/medium gene-drug interaction bin at week 8 (p = 0.001) and 6 months (p < 0.0001). Depressive symptom severity scores did not differ significantly across the medication bins. Conclusions: This study demonstrates the utility of gene-drug interaction testing to guide medication decisions to minimize side effect burden rather than solely prioritizing the search for the most efficacious medication. (Clinical Trials Registration Identifier: NCT02286440).

BackgroundOlder 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.ObjectivesThis study aimed to investigate the impact of cardiovascular-related DGIs on the clinical efficacy and safety outcomes in older patients with CAD.MethodsHospitalized 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.ResultsA 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.ConclusionsThe 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.

Pharmacogenetics can improve medication-related outcomes by optimizing efficacy and minimizing adverse effects. It is unknown whether the presence of drug-gene interactions (DGIs) at the time of surgery results in adverse outcomes in the postoperative setting. To determine the association of active DGIs on postsurgical outcomes following vascular surgery procedures. This was a retrospective cohort study of Veterans Affairs (VA) hospital patients participating in the Million Veteran Program who had a vascular procedure documented in the VA Surgical Quality Improvement Program (VASQIP) from January 1, 2011, to December 31, 2022. Data analysis was performed from June 1, 2023, to October 31, 2024. Receipt of drugs impacted by pharmacogenetic variants 30 days prior to and up to 7 days following the vascular surgery procedure. Clinical outcomes collected as part of VASQIP, including length of stay (LOS), 30-day readmission, composite of myocardial infarction, stroke, and myocardial injury after noncardiac surgery, and 30-day postoperative death. Among 10 098 patients (mean [SD] age, 68.8 [8.3] years; 1581 [15.7%] Black [self-reported]; 9884 [97.9%] male), 5020 (49.7%) had a DGI. The most common DGIs included proton pump inhibitors with CYP2C19, statins with SLCO1B1, and clopidogrel with CYP2C19. Compared with 0 DGIs, the presence of 1, 2, or 3 or more DGIs was associated with a longer median (IQR) LOS: with 0 DGIs, 3 (1-6) days vs 1 DGI, 3 (1-7) days (adjusted incidence rate ratio [IRR], 1.12; 95% CI, 1.10-1.14; P < .001); 2 DGIs, 3 (1-7) days (adjusted IRR, 1.22; 95% CI, 1.19-1.25; P < .001); and 3 or more DGIs, 4 (2-8) days (adjusted IRR, 1.40; 95% CI, 1.35-1.44; P < .001). The 30-day readmission rate, which was 17.4% among those with 0 DGIs, was not significantly different in those with 1 DGI (17.6%; adjusted odds ratio [aOR], 1.01; 95% CI, 0.90-1.14; P = .84) but was significantly higher in those with 2 DGIs (21.2%; aOR, 1.26; 95% CI, 1.08-1.47; P = .004) and 3 or more DGIs (25.1%; aOR, 1.61; 95% CI, 1.30-1.99; P < .001). The risk of the composite outcome, which was 3.5% in those with 0 DGIs, was not significantly different in those with 1 DGI (4.1%; aOR, 1.15; 95% CI, 0.91-1.45; P = .24) but was significantly higher in those with 2 DGIs (5.7%; aOR, 1.62; 95% CI, 1.22-2.15; P = .001) and those with 3 or more DGIs (5.5%; aOR, 1.60; 95% CI, 1.04-2.36; P = .02). The findings suggest that patients with DGIs at the time of vascular surgery have increased risk of cardiovascular morbidity, increased readmission, and longer LOS. Further work is needed to determine which DGIs contribute to these outcomes and whether preoperative pharmacogenetic testing has the potential to mitigate these risks.

Patients with chronic kidney disease (CKD) stage 3–5 are polypharmacy patients. Many of these drugs are metabolized by cytochrome P450 (CYP450) and CYP450. Genetic polymorphism is well known to result in altered drug metabolism capacity. This study determined the added value of pharmacogenetic testing to the routine medication evaluation in polypharmacy patients with CKD. In adult outpatient polypharmacy patients with CKD3-5 disease, a pharmacogenetic profile was determined. Then, automated medication surveillance for gene–drug interactions was performed based on the pharmacogenetic profile and the patients’ current prescriptions. Of all identified gene–drug interactions, the hospital pharmacist and the treating nephrologist together assessed clinical relevance and necessity of a pharmacotherapeutic intervention. The primary endpoint of the study was the total number of applied pharmacotherapeutic interventions based on a relevant gene–drug interaction. A total of 61 patients were enrolled in the study. Medication surveillance resulted in a total of 66 gene–drug interactions, of which 26 (39%) were considered clinically relevant. This resulted in 26 applied pharmacotherapeutic interventions in 20 patients. Systematic pharmacogenetic testing enables pharmacotherapeutic interventions based on relevant gene–drug interactions. This study showed that pharmacogenetic testing adds to routine medication evaluation and could lead to optimized pharmacotherapy in CKD patients.

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A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study

To investigate whether it is feasible to perform pharmacogenetic testing and implement the test results as part of medication reviews during hospitalization of multimorbid patients. Patients with ≥2 chronic conditions and ≥5 regular drugs with at least one potential gene-drug interaction (GDI) were included from one geriatric and one cardiology ward for pharmacogenetic testing. After inclusion by the study pharmacist, blood samples were collected and shipped to the laboratory for analysis. For patients still hospitalized at the time when the pharmacogenetic test results were available, the information was used in medication reviews. Recommendations from the pharmacist on actionable GDIs were communicated to the hospital physicians, who subsequently decided on potential immediate changes or forwarded suggestions in referrals to general practitioners. The pharmacogenetic test results were available for medication review in 18 of the 46 patients (39.1%), where median length of hospital stay was 4.7days (1.6-18.3). The pharmacist recommended medication changes for 21 of 49 detected GDIs (42.9%). The hospital physicians accepted 19 (90.5%) of the recommendations. The most commonly detected GDIs involved metoprolol (CYP2D6 genotype), clopidogrel (CYP2C19 genotype) and atorvastatin (CYP3A4/5 and SLCOB1B1 genotype). The study shows that implementation of pharmacogenetic testing for medication review of hospitalized patients has the potential to improve drug treatment before being transferred to primary care. However, the logistics workflow needs to be further optimized, as test results were available during hospitalization for less than half of the patients included in the study.

Should we consider panel-based pharmacogenetic testing in gynecologic oncology patients? (224)

Logistics and (cost-)effectiveness of pharmacogenetic (PGx)-testing may be optimized when delivered through a pre-emptive panel-based approach, within a clinical decision support system (CDSS). Here, clinical recommendations are automatically deployed by the CDSS when a drug-gene interaction (DGI) is encountered. However, this requires record of PGx-panel results in the electronic medical record (EMR). Several studies indicate promising clinical utility of panel-based PGx-testing in polypharmacy and psychiatry, but is undetermined in primary care. Therefore, we aim to quantify both the feasibility and the real-world impact of this approach in primary care. Within a prospective pilot study, community pharmacists were provided the opportunity to request a panel of eight pharmacogenes to guide drug dispensing within a CDSS for 200 primary care patients. In this side-study, this cohort was cross-sectionally followed-up after a mean of 2.5-years. PGx-panel results were successfully recorded in 96% and 68% of pharmacist and general practitioner (GP) EMRs, respectively. This enabled 97% of patients to (re)use PGx-panel results for at least one, and 33% for up to four newly initiated prescriptions with possible DGIs. A total of 24.2% of these prescriptions had actionable DGIs, requiring pharmacotherapy adjustment. Healthcare utilization seemed not to vary among those who did and did not encounter a DGI. Pre-emptive panel-based PGx-testing is feasible and real-world impact is substantial in primary care.

Prevalence of predicted gene-drug interactions for antidepressants in the treatment of major depressive disorder in the Precision Medicine in Mental Health Care Study

Selecting effective antidepressants for the treatment of major depressive disorder (MDD) is an imprecise practice, with remission rates of about 30% at the initial treatment. To determine whether pharmacogenomic testing affects antidepressant medication selection and whether such testing leads to better clinical outcomes. A pragmatic, randomized clinical trial that compared treatment guided by pharmacogenomic testing vs usual care. Participants included 676 clinicians and 1944 patients. Participants were enrolled from 22 Department of Veterans Affairs medical centers from July 2017 through February 2021, with follow-up ending November 2021. Eligible patients were those with MDD who were initiating or switching treatment with a single antidepressant. Exclusion criteria included an active substance use disorder, mania, psychosis, or concurrent treatment with a specified list of medications. Results from a commercial pharmacogenomic test were given to clinicians in the pharmacogenomic-guided group (n = 966). The comparison group received usual care and access to pharmacogenomic results after 24 weeks (n = 978). The co-primary outcomes were the proportion of prescriptions with a predicted drug-gene interaction written in the 30 days after randomization and remission of depressive symptoms as measured by the Patient Health Questionnaire-9 (PHQ-9) (remission was defined as PHQ-9 ≤ 5). Remission was analyzed as a repeated measure across 24 weeks by blinded raters. Among 1944 patients who were randomized (mean age, 48 years; 491 women [25%]), 1541 (79%) completed the 24-week assessment. The estimated risks for receiving an antidepressant with none, moderate, and substantial drug-gene interactions for the pharmacogenomic-guided group were 59.3%, 30.0%, and 10.7% compared with 25.7%, 54.6%, and 19.7% in the usual care group. The pharmacogenomic-guided group was more likely to receive a medication with a lower potential drug-gene interaction for no drug-gene vs moderate/substantial interaction (odds ratio [OR], 4.32 [95% CI, 3.47 to 5.39]; P < .001) and no/moderate vs substantial interaction (OR, 2.08 [95% CI, 1.52 to 2.84]; P = .005) (P < .001 for overall comparison). Remission rates over 24 weeks were higher among patients whose care was guided by pharmacogenomic testing than those in usual care (OR, 1.28 [95% CI, 1.05 to 1.57]; P = .02; risk difference, 2.8% [95% CI, 0.6% to 5.1%]) but were not significantly higher at week 24 when 130 patients in the pharmacogenomic-guided group and 126 patients in the usual care group were in remission (estimated risk difference, 1.5% [95% CI, -2.4% to 5.3%]; P = .45). Among patients with MDD, provision of pharmacogenomic testing for drug-gene interactions reduced prescription of medications with predicted drug-gene interactions compared with usual care. Provision of test results had small nonpersistent effects on symptom remission. ClinicalTrials.gov Identifier: NCT03170362.

Pharmacogenetic tests in Belgian care: (how) do we get started? Personalized medicine attempts to take all the information about an individual into account, and this also includes characteristics that differ from the presumed ‘average patient’. This approach includes pharmacogenetics, where the influence of genetic variation in various biomolecules on drug response is studied. By performing preemptive pharmacogenetic testing, drug therapies can be optimized, and serious side effects can be avoided. In order to implement pharmacogenetic testing in practice, some hurdles still need to be overcome. For example, scientific information needs to be translated into practical clinical guidelines that are applicable in the local context and reimbursement issues also need to be resolved. In this paper, a current list of gene-drug interactions is presented that could be prioritized during the implementation process in Belgium. The list only contains clinically relevant interactions for which there is sufficient scientific evidence. In addition, a tool is described that takes into account the drug consumption in a specific healthcare environment, to prioritize the most interesting gene-drug interactions. International implementation initiatives show that the obstacles are surmountable. It is therefore time to start a dialogue on accelerating the implementation of pharmacogenetic testing in Belgium. We hope that this prioritized list, together with a discussion of some hurdles that need to be overcome, can inform this debate.

Pharmacological interventions for treating posttraumatic stress disorder in Canadian Armed Forces (CAF) members and Veterans often achieve modest results. The field of pharmacogenetics, or the study of how genes influence...

The implementation of pharmacogenetic testing in psychiatry is underway but is not yet standard protocol. Barriers to pharmacogenetics becoming standard practice are the lack of translation of evidence-based recommendations and standardization of genetic testing panels. As for the latter, there are currently no regulatory standards related to the gene and allele content of testing panels used to derive medication selection and dosing advice. To address these barriers, we summarize the current gene-drug interaction knowledgebase and proposed a minimum gene and allele set for pharmacogenetic testing in psychiatry. The Pharmacogenomics Knowledgebase has cataloged 448 gene-drug interactions relevant to psychiatry based on the current scientific literature, drug labels, and pharmacogenetic-based implementation guidelines. A majority of these interactions involved two cytochrome P450 enzymes (CYP2D6 and CYP2C19) and antidepressant medications, however, CYP2C9, HLA-A, and HLA-B are relevant to mood stabilizers/anticonvulsants. On the basis of evidence base, we proposed a minimum gene and allele set for pharmacogenetic testing in psychiatry that includes 16 variant alleles within five genes (CYP2C9, CYP2C19, CYP2D6, HLA-A, HLA-B). The intent is to assist clinicians in judging the gene and allele content of pharmacogenetic tests and to facilitate pharmacogenetic testing as a standard protocol and companion tool for psychotropic medication selection and dosing.

Adverse drug reactions (ADRs) are associated with increased morbidity, mortality, and resource utilization. Drug interactions (DDIs) are among the most common causes of ADRs, and estimates have cited that up to 22% of patients take interacting medications. DDIs are often due to the propensity for agents to induce or inhibit enzymes responsible for the metabolism of concomitantly administered drugs. However, this phenomenon is further complicated by genetic variants of such enzymes. The aim of this study is to quantify and describe potential drug-drug, drug-gene, and drug-drug-gene interactions in a community-based patient population. A regional pharmacy with retail outlets in Arkansas provided deidentified prescription data from March 2020 for 4761 individuals. Drug-drug and drug-drug-gene interactions were assessed utilizing the logic incorporated into GenMedPro, a commercially available digital gene-drug interaction software program that incorporates variants of 9 pharmacokinetic (PK) and 2 pharmacodynamic (PD) genes to evaluate DDIs and drug-gene interactions. The data were first assessed for composite drug-drug interaction risk, and each individual was stratified to a risk category using the logic incorporated in GenMedPro. To calculate the frequency of potential drug-gene interactions, genotypes were imputed and allocated to the cohort according to each gene's frequency in the general population. Potential genotypes were randomly allocated to the population 100 times in a Monte Carlo simulation. Potential drug-drug, gene-drug, or gene-drug-drug interaction risk was characterized as minor, moderate, or major. Based on prescription data only, the probability of a DDI of any impact (mild, moderate, or major) was 26% [95% CI: 0.248-0.272] in the population. This probability increased to 49.6% [95% CI: 0.484-0.507] when simulated genetic polymorphisms were additionally assessed. When assessing only major impact interactions, there was a 7.8% [95% CI: 0.070-0.085] probability of drug-drug interactions and 10.1% [95% CI: 0.095-0.108] probability with the addition of genetic contributions. The probability of drug-drug-gene interactions of any impact was correlated with the number of prescribed medications, with an approximate probability of 77%, 85%, and 94% in patients prescribed 5, 6, or 7+ medications, respectively. When stratified by specific drug class, antidepressants (19.5%), antiemetics (21.4%), analgesics (16%), antipsychotics (15.6%), and antiparasitics (49.7%) had the highest probability of major drug-drug-gene interaction. In a community-based population of outpatients, the probability of drug-drug interaction risk increases when genetic polymorphisms are attributed to the population. These data suggest that pharmacogenetic testing may be useful in predicting drug interactions, drug-gene interactions, and severity of interactions when proactively evaluating patient medication profiles. Genomind, Inc.