Rationale and design for a pragmatic randomized trial to assess gene-based prescribing for SSRIs in the treatment of depression.

Specific selective serotonin reuptake inhibitors (SSRIs) metabolism is strongly influenced by two pharmacogenes, CYP2D6 and CYP2C19. However, the effectiveness of prospectively using pharmacogenetic variants to select or dose SSRIs for depression is uncertain in routine clinical practice. The objective of this prospective, multicenter, pragmatic randomized controlled trial is to determine the effectiveness of genotype-guided selection and dosing of antidepressants on control of depression in participants who are 8 years or older with ≥3 months of depressive symptoms who require new or revised therapy. Those randomized to the intervention arm undergo pharmacogenetic testing at baseline and receive a pharmacy consult and/or automated clinical decision support intervention based on an actionable phenotype, while those randomized to the control arm have pharmacogenetic testing at the end of 6-months. In both groups, depression and drug tolerability outcomes are assessed at baseline, 1 month, 3 months (primary), and 6 months. The primary end point is defined by change in Patient-Reported Outcomes Measurement Information System (PROMIS) Depression score assessed at 3 months versus baseline. Secondary end points include change inpatient health questionnaire (PHQ-8) measure of depression severity, remission rates defined by PROMIS score < 16, medication adherence, and medication side effects. The primary analysis will compare the PROMIS score difference between trial arms among those with an actionable CYP2D6 or CYP2C19 genetic result or a CYP2D6 drug-drug interaction. The trial has completed accrual of 1461 participants, of which 562 were found to have an actionable phenotype to date, and follow-up will be complete in April of 2024.

Individualized Dose-Response to Statins Associated with Cardiovascular Disease Outcomes.

Background: Statins reduce low-density lipoprotein cholesterol (LDL-C) and are efficacious in the prevention of atherosclerotic cardiovascular disease (ASCVD). Dose-response to statins varies among patients and can be modeled using three distinct pharmacological properties: (1) E0 (baseline LDL-C), (2) ED50 (potency: median dose achieving 50% reduction in LDL-C); and (3) Emax (efficacy: maximum LDL-C reduction). However, individualized dose-response and its association with ASCVD events remains unknown. Objective: We analyze the relationship between ED50 and Emax with real-world cardiovascular disease outcomes. Method: We leveraged de-identified electronic health record data to identify individuals exposed to multiple doses of the three most commonly prescribed statins (atorvastatin, simvastatin, or rosuvastatin) within the context of their longitudinal healthcare. We derived ED50 and Emax to quantify the relationship with a composite outcome of ASCVD events and all-cause mortality. Results: We estimated ED50 and Emax for 3,033 unique individuals (atorvastatin: 1,632, simvastatin: 1,089, and rosuvastatin: 312) using a nonlinear, mixed effects dose-response model. Time-to-event analyses revealed that ED50 and Emax are independently associated with the primary endpoint. Hazard ratios were 0.85 (p < 0.01), 0.83 (p < 0.01), and 0.87 (p = 0.10) for ED50 and 1.13 (p < 0.001), 1.06 (p < 0.001), and 1.15 (p = 0.009) for Emax in the atorvastatin, simvastatin, and rosuvastatin cohorts, respectively. Conclusion: The class-wide association of ED50 and Emax with clinical outcomes indicates that these measures influence the risk for ASCVD events in patients on statins.

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.

Drug-Induced Liver Injury with Commonly Used Antibiotics in the All of Us Research Program.

Antibiotics are a known cause of idiosyncratic drug-induced liver injury (DILI). According to the Centers for Disease Control and Prevention, the five most commonly prescribed antibiotics in the United States are azithromycin, ciprofloxacin, cephalexin, amoxicillin, and amoxicillin-clavulanate. We quantified the frequency of acute DILI for these common antibiotics in the All of Us Research Program, one of the largest electronic health record (EHR)-linked research cohorts in the United States. Retrospective analyses were conducted applying a standardized phenotyping algorithm to de-identified clinical data available in the All of Us database for 318,598 study participants. Between February 1984 and December 2022, more than 30% of All of Us participants (n = 119,812 individuals) had been exposed to at least 1 of our 5 study drugs. Initial screening identified 591 potential case patients that met our preselected laboratory-based phenotyping criteria. Because DILI is a diagnosis of exclusion, we then used phenome scanning to narrow the case counts by (i) scanning all EHRs to identify all alternative diagnostic explanations for the laboratory abnormalities, and (ii) leveraging International Classification of Disease 9th revision (ICD)-9 and ICD 10th revision (ICD)-10 codes as exclusion criteria to eliminate misclassification. Our final case counts were 30 DILI cases with amoxicillin-clavulanate, 24 cases with azithromycin, 24 cases with ciprofloxacin, 22 cases with amoxicillin alone, and < 20 cases with cephalexin. These findings demonstrate that data from EHR-linked research cohorts can be efficiently mined to identify DILI cases related to the use of common antibiotics.

Rural Land Management and Kidney Health.

There is wide geographic variability in the prevalence of chronic kidney disease, and much of this variability is unexplained by known clinical risk determinants such as diabetes and hypertension. Additional factors contributing to this geographic variability include social determinants of kidney health, as well as genetic factors (ancestry) and non-genetic factors (the environment). Environmental nephrotoxins can accelerate the progression of kidney disease in some patients at risk. Examples of environmental nephrotoxins that have previously been associated with changes in glomerular filtration rate include chlorotriazine herbicides (e.g., atrazine) and trace metals (e.g., arsenic, cadmium, lead, and mercury). Land management practices influence the concentration of these nephrotoxins in our soil and water. In this review, we explore sustainable approaches to agriculture and the preservation of natural landscapes as land management practices that can optimize kidney health in a variety of communities.

The Clinical Pharmacogenetics Implementation Consortium Guideline for SLCO1B1, ABCG2, and CYP2C9 genotypes and Statin-Associated Musculoskeletal Symptoms.

Statins reduce cholesterol, prevent cardiovascular disease, and are among the most commonly prescribed medications in the world. Statin-associated musculoskeletal symptoms (SAMS) impact statin adherence and ultimately can impede the long-term effectiveness of statin therapy. There are several identified pharmacogenetic variants that impact statin disposition and adverse events during statin therapy. SLCO1B1 encodes a transporter (SLCO1B1; alternative names include OATP1B1 or OATP-C) that facilitates the hepatic uptake of all statins. ABCG2 encodes an efflux transporter (BCRP) that modulates the absorption and disposition of rosuvastatin. CYP2C9 encodes a phase I drug metabolizing enzyme responsible for the oxidation of some statins. Genetic variation in each of these genes alters systemic exposure to statins (i.e., simvastatin, rosuvastatin, pravastatin, pitavastatin, atorvastatin, fluvastatin, lovastatin), which can increase the risk for SAMS. We summarize the literature supporting these associations and provide therapeutic recommendations for statins based on SLCO1B1, ABCG2, and CYP2C9 genotype with the goal of improving the overall safety, adherence, and effectiveness of statin therapy. This document replaces the 2012 and 2014 Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for SLCO1B1 and simvastatin-induced myopathy.

SLCO1B1*5 Allele Is Associated With Atorvastatin Discontinuation and Adverse Muscle Symptoms in the Context of Routine Care.

The SLCO1B1 genotype is known to influence patient adherence to statin therapy, in part by increasing the risk for statin-associated musculoskeletal symptoms (SAMSs). The SLCO1B1*5 allele has previously been associated with simvastatin discontinuation and SAMSs. Prior analyses of the relationship between SLCO1B1*5 and atorvastatin muscle side effects have been inconclusive due to insufficient power. We now quantify the impact of SLCO1B1*5 on atorvastatin discontinuation and SAMSs in a large observational cohort using electronic medical record data from a single health care system. In our study cohort (n = 1,627 patients exposed to atorvastatin during the course of routine clinical care), 56% (n = 912 of 1,627 patients) discontinued atorvastatin and 18% (n = 303 of 1,627 patients) developed SAMSs. A univariate model revealed that SLCO1B1*5 increased the likelihood that patients would stop atorvastatin during routine care (odds ratio 1.2; 95% confidence interval (CI), 1.1-1.5; P = 0.04). A multivariate Cox proportional hazards model further demonstrated that this same variant was associated with time to atorvastatin discontinuation (hazard ratio 1.2; 95% CI, 1.1-1.4; P = 0.004). Additional time-to-event analyses also revealed that SCLO1B1*5 was associated with SAMSs (hazard ratio 1.4; 95% CI, 1.1-1.7; P = 0.02). Atorvastatin discontinuation was associated with SAMSs (odds ratio 1.67; P = 0.0001) in our cohort.

Cytochrome P450 2D6 Genotyping for Post-Operative Opioids.

Within most electronic medical records, automated decision support helps health care providers reduce the frequency of adverse drug reactions. Historically, this decision support has been used to prevent drug-drug interactions (DDIs). More recently, the clinical and scientific communities have been moving toward the use of this approach to predict and prevent drug-gene interactions (DGIs). Genetic variation in cytochrome P450 2D6 (CYP2D6) is known to impact clinical outcome for many drugs, including opioids. Randomized trials have been initiated to assess the utility of CYP2D6 gene-based dosing versus usual care. We review the use of this approach to guide opioid prescribing in the post-operative setting.

Potential Use of Pharmacogenetics to Reduce Drug-Induced Syndrome of Inappropriate Antidiuretic Hormone (SIADH).

Syndrome of inappropriate antidiuretic hormone (SIADH) is a common cause of hyponatremia, and many cases represent adverse reactions to drugs that alter ion channel conductance within the peptidergic nerve terminals of the posterior pituitary. The frequency of drug-induced SIADH increases with age; as many as 20% of patients residing in nursing homes have serum sodium levels below 135 mEq/L. Mild hyponatremia is associated with cognitive changes, gait instability, and falls. Severe hyponatremia is associated with cerebral edema, seizures, permanent disability, and/or death. Although pharmacogenetic tests are now being deployed for some drugs capable of causing SIADH (e.g., antidepressants, antipsychotics, and opioid analgesics), the implementation of these tests has been based upon the prior known association of these drugs with other serious adverse drug reactions (e.g., electrocardiographic abnormalities). Work is needed in large observational cohorts to quantify the strength of association between pharmacogene variants and drug-induced SIADH so that decision support can be developed to identify patients at high risk.

Air, Land, and Sea: Gene-Environment Interaction in Chronic Disease.

Each of us reflects a unique convergence of DNA and the environment. Over the past 2 decades, huge biobanks linked to electronic medical records have positioned the clinical and scientific communities to understand the complex genetic architecture underlying many common diseases. Although these efforts are producing increasingly accurate gene-based risk prediction algorithms for use in routine clinical care, the algorithms often fail to include environmental factors. This review explores the concept of heritability (genetic vs nongenetic determinants of disease), with emphasis on the role of environmental factors as risk determinants for common complex diseases influenced by air and water quality. Efforts to define patient exposure to specific toxicants in practice-based data sets will deepen our understanding of diseases with low heritability, and improved land management practices will reduce the burden of disease.

Relative Contribution of Genetic and Environmental Factors in CKD.

Chronic kidney disease affects nearly 15 percent of the U.S. population. Onset and rate of progression are influenced by a combination of genetic and non-genetic factors. Because health care systems across the U.S. are beginning to deploy automated decision support to stratify patients at risk, we review the relative impact of genetic factors (e.g., APOL1 gene polymorphisms) and non-genetic factors (e.g., clinical comorbidities and exposure to environmental nephrotoxins) contributing to this common disease. Overall, the impact of non-genetic factors appears to exceed the impact of genetic factors.

CYP2C19 genotype-guided antiplatelet therapy: promises and pitfalls.

Pharmacogenetic variants can alter the mechanism of action (pharmacodynamic gene variants) or kinetic processes such as absorption, distribution, metabolism and elimination (pharmacokinetic gene variants). Many initial successes in precision medicine occurred in the context of genes encoding the cytochromes P450 (CYP enzymes). CYP2C19 activates the antiplatelet drug clopidogrel, and polymorphisms in the CYP2C19 gene are known to alter the outcome for patients taking clopidogrel in the context of cardiovascular disease. CYP2C19 loss-of-function alleles are specifically associated with increased risk for coronary stent thrombosis and major adverse cardiovascular events in patients taking clopidogrel following percutaneous coronary intervention. We explore successes and challenges encountered as the clinical and scientific communities advance CYP2C19 genotyping in the context of routine patient care.

Implementation of wide-scale pharmacogenetic testing in primary care.

The convergence of translational genomics and biomedical informatics has changed healthcare delivery. Institutional consortia have begun implementing lab testing and decision support for drug-gene interactions. Aggregate datasets are now revealing the impact of clinical decision support for drug-gene interactions. Given the pleiotropic nature of pharmacogenes, interdisciplinary teams and robust clinical decision support tools must exist within an informatics framework built to be flexible and capable of cross-talk between clinical specialties. Navigation of the challenges presented with the implementation of five steps to build a genetics program infrastructure requires the expertise of multiple healthcare professionals. Ultimately, this manuscript describes our efforts to place pharmacogenomics in the hands of the primary care provider integrating this information into a patient's healthcare over their lifetime.

CYP2C19 genotype, physician prescribing pattern, and risk for long QT on serotonin selective reuptake inhibitors.

Aim: To examine the impact of CYP2C19 genotype on selective serotonin reuptake inhibitor (SSRI) prescribing patterns. Patients & methods: Observational cohort containing 507 unique individuals receiving an SSRI prescription with CYP2C19 genotype already in their electronic medical record. Genotype was distributed as follows: n = 360 (71%) had no loss of function alleles, 136 (26.8%) had one loss of function allele and 11 (2.2%) had two loss of function alleles. Results & conclusion: For poor metabolizers exposed to sertraline, citalopram or escitalopram, providers changed prescribing patterns in response to alerts in the electronic medical record by either changing the drug, changing the dose or monitoring serial EKGs longitudinally. For intermediate metabolizers exposed to sertraline, citalopram or escitalopram, no alert was needed (mean QTc = 440.338 ms [SD = 31.1273] for CYP2C19*1/*1, mean QTc = 440.371 ms [SD = 29.2706] for CYP2C19*1/*2; p = 0.995).

Chronic Kidney Disease in Agricultural Communities.

Patients residing in agricultural communities have a high risk of developing chronic kidney disease. In the Great Plains, geo-environmental risk factors (eg, variable climate, temperature, air quality, water quality, and drought) combine with agro-environmental risk factors (eg, exposure to fertilizers, soil conditioners, herbicides, fungicides, and pesticides) to increase risk for toxic nephropathy. However, research defining the specific influence of agricultural chemicals on the progression of kidney disease in rural communities has been somewhat limited. By linking retrospective clinical data within electronic medical records to environmental data from sources like US Environmental Protection Agency, analytical models are beginning to provide insight into the impact of agricultural practices on the rate of progression for kidney disease in rural communities.

Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction.

Current approaches to predicting a cardiovascular disease (CVD) event rely on conventional risk factors and cross-sectional data. In this study, we applied machine learning and deep learning models to 10-year CVD event prediction by using longitudinal electronic health record (EHR) and genetic data. Our study cohort included 109, 490 individuals. In the first experiment, we extracted aggregated and longitudinal features from EHR. We applied logistic regression, random forests, gradient boosting trees, convolutional neural networks (CNN) and recurrent neural networks with long short-term memory (LSTM) units. In the second experiment, we applied a late-fusion approach to incorporate genetic features. We compared the performance with approaches currently utilized in routine clinical practice - American College of Cardiology and the American Heart Association (ACC/AHA) Pooled Cohort Risk Equation. Our results indicated that incorporating longitudinal feature lead to better event prediction. Combining genetic features through a late-fusion approach can further improve CVD prediction, underscoring the importance of integrating relevant genetic data whenever available.

LPA Variants Are Associated With Residual Cardiovascular Risk in Patients Receiving Statins.

BACKGROUND: Coronary heart disease (CHD) is a leading cause of death globally. Although therapy with statins decreases circulating levels of low-density lipoprotein cholesterol and the incidence of CHD, additional events occur despite statin therapy in some individuals. The genetic determinants of this residual cardiovascular risk remain unknown. METHODS: We performed a 2-stage genome-wide association study of CHD events during statin therapy. We first identified 3099 cases who experienced CHD events (defined as acute myocardial infarction or the need for coronary revascularization) during statin therapy and 7681 controls without CHD events during comparable intensity and duration of statin therapy from 4 sites in the Electronic Medical Records and Genomics Network. We then sought replication of candidate variants in another 160 cases and 1112 controls from a fifth Electronic Medical Records and Genomics site, which joined the network after the initial genome-wide association study. Finally, we performed a phenome-wide association study for other traits linked to the most significant locus. RESULTS: The meta-analysis identified 7 single nucleotide polymorphisms at a genome-wide level of significance within the LPA/PLG locus associated with CHD events on statin treatment. The most significant association was for an intronic single nucleotide polymorphism within LPA/PLG (rs10455872; minor allele frequency, 0.069; odds ratio, 1.58; 95% confidence interval, 1.35-1.86; P=2.6×10-10). In the replication cohort, rs10455872 was also associated with CHD events (odds ratio, 1.71; 95% confidence interval, 1.14-2.57; P=0.009). The association of this single nucleotide polymorphism with CHD events was independent of statin-induced change in low-density lipoprotein cholesterol (odds ratio, 1.62; 95% confidence interval, 1.17-2.24; P=0.004) and persisted in individuals with low-density lipoprotein cholesterol ≤70 mg/dL (odds ratio, 2.43; 95% confidence interval, 1.18-4.75; P=0.015). A phenome-wide association study supported the effect of this region on coronary heart disease and did not identify noncardiovascular phenotypes. CONCLUSIONS: Genetic variations at the LPA locus are associated with CHD events during statin therapy independently of the extent of low-density lipoprotein cholesterol lowering. This finding provides support for exploring strategies targeting circulating concentrations of lipoprotein(a) to reduce CHD events in patients receiving statins.

Multisite Investigation of Strategies for the Implementation of CYP2C19 Genotype-Guided Antiplatelet Therapy.

CYP2C19 genotype-guided antiplatelet therapy following percutaneous coronary intervention is increasingly implemented in clinical practice. However, challenges such as selecting a testing platform, communicating test results, building clinical decision support processes, providing patient and provider education, and integrating methods to support the translation of emerging evidence to clinical practice are barriers to broad adoption. In this report, we compare and contrast implementation strategies of 12 early adopters, describing solutions to common problems and initial performance metrics for each program. Key differences between programs included the test result turnaround time and timing of therapy changes, which are both related to the CYP2C19 testing model and platform used. Sites reported the need for new informatics infrastructure, expert clinicians such as pharmacists to interpret results, physician champions, and ongoing education. Consensus lessons learned are presented to provide a path forward for those seeking to implement similar clinical pharmacogenomics programs within their institutions.