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Pharmacogenetic Testing in Primary Care and Prevention

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A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Pharmacogenetics".

Deadline for manuscript submissions: closed (1 November 2021) | Viewed by 29357

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Special Issue Editors

Dr. Latha Palaniappan

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Guest Editor

Stanford Concierge and Executive Medicine, Stanford University, 900 Blake Wilbur Dr Rm W200, 2nd Fl MC 5358, Stanford, CA 94304, USA
Interests: precision genomics; pharmacogenomics; primary care; diverse populations; chronic disease; prevention

Dr. Sean P. David

E-Mail Website
Co-Guest Editor

1. Outcomes Research Network, NorthShore Research Institute, NorthShore University HealthSystem, Evanston, IL 60201, USA
2. Department of Family Medicine, Pritzker School of Medicine, University of Chicago, Chicago, IL 60637, USA
Interests: genetics and omics; pharmacogenetics; personalized medicine; health policy

Dr. Deepak Voora

E-Mail Website
Co-Guest Editor

Center for Applied Genomics & Precision Medicine, Duke University, 2187 CIEMAS, Campus Box 3382, Durham, NC 27708 USA
Interests: pharmacogenomics; precision medicine; cardiovascular disease; primary care

Special Issue Information

Dear Colleagues,

The increasing implementation of pharmacogenetic (PGx) testing brings challenges and excitement. Like other testing applications in personalized and precision medicine, PGx testing is increasingly being used in healthy individuals and in patients with common, chronic diseases and to affect risk factors for cardiovascular disease and cancer. Can PGx testing be used for precision prescribing, and ultimately improve clinical outcomes in healthy populations and for primary and for secondary prevention in patients with conditions commonly managed in primary care -- like hypertension, hyperlipidemia, type 2 diabetes, and depression. Although feasible guidelines have been developed to promote the clinical practice of these tests, performing meaningful PGx tests requires the correlation of pharmacogenetic variation with clinical effectiveness data, including measurement of outcomes and costs at the patient, provider, system, or economic levels. This Special Issue focuses on the clinical application of pharmacogenetic testing in primary care and prevention contexts. It includes, but is not limited to, population-based studies on genetic variation in the toxicity and efficacy of commonly used drugs, multi-omics studies for determining therapeutic and toxicity response, determination of epigenetic changes in drug response/resistance, and the implementation of pharmacogenetic testing results into routine clinical interactions.

Dr. Latha Palaniappan
Guest Editor

Dr. Sean P. David
Dr. Deepak Voora
Co-Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Pharmacogenetics
Pharmacokinetics
Pharmacodynamics
Personalized medicine
Biomarkers
Cost effectiveness
Clinical effectiveness

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Published Papers (8 papers)

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13 pages, 663 KiB

Open AccessArticle

Assessment of a Manual Method versus an Automated, Probability-Based Algorithm to Identify Patients at High Risk for Pharmacogenomic Adverse Drug Outcomes in a University-Based Health Insurance Program

by Kendra J. Grande, Rachel Dalton, Nicolas A. Moyer, Meghan J. Arwood, Khoa A. Nguyen, Jill Sumfest, Kristine C. Ashcraft and Rhonda M. Cooper-DeHoff

J. Pers. Med. 2022, 12(2), 161; https://doi.org/10.3390/jpm12020161 - 26 Jan 2022

Cited by 5 | Viewed by 3268

Abstract

We compared patient cohorts selected for pharmacogenomic testing using a manual method or automated algorithm in a university-based health insurance network. The medication list was compiled from claims data during 4th quarter 2018. The manual method selected patients by number of medications by the health system’s list of medications for pharmacogenomic testing. The automated method used YouScript’s pharmacogenetic interaction probability (PIP) algorithm to select patients based on the probability that testing would result in detection of one or more clinically significant pharmacogenetic interactions. A total of 6916 patients were included. Patient cohorts selected by each method differed substantially, including size (manual n = 218, automated n = 286) and overlap (n = 41). The automated method was over twice as likely to identify patients where testing may reveal a clinically significant pharmacogenetic interaction than the manual method (62% vs. 29%, p < 0.0001). The manual method captured more patients with significant drug–drug or multi-drug interactions (80.3% vs. 40.2%, respectively, p < 0.0001), higher average number of significant drug interactions per patient (3.3 vs. 1.1, p < 0.0001), and higher average number of unique medications per patient (9.8 vs. 7.4, p < 0.0001). It is possible to identify a cohort of patients who would likely benefit from pharmacogenomic testing using manual or automated methods. Full article

(This article belongs to the Special Issue Pharmacogenetic Testing in Primary Care and Prevention)

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12 pages, 850 KiB

Open AccessArticle

Applicability of Pharmacogenomically Guided Medication Treatment during Hospitalization of At-Risk Minority Patients

by Loren Saulsberry, Keith Danahey, Merisa Middlestadt, Kevin J. O’Leary, Edith A. Nutescu, Thomas Chen, James C. Lee, Gregory W. Ruhnke, David George, Larry House, Xander M. R. van Wijk, Kiang-Teck J. Yeo, Anish Choksi, Seth W. Hartman, Randall W. Knoebel, Paula N. Friedman, Luke V. Rasmussen, Mark J. Ratain, Minoli A. Perera, David O. Meltzer and Peter H. O’Donnell Show full author list Hide full author list

J. Pers. Med. 2021, 11(12), 1343; https://doi.org/10.3390/jpm11121343 - 10 Dec 2021

Cited by 3 | Viewed by 2753

Abstract

Known disparities exist in the availability of pharmacogenomic information for minority populations, amplifying uncertainty around clinical utility for these groups. We conducted a multi-site inpatient pharmacogenomic implementation program among self-identified African-Americans (AA; n = 135) with numerous rehospitalizations (n = 341) from 2017 to 2020 (NIH-funded ACCOuNT project/clinicaltrials.gov#NCT03225820). We evaluated the point-of-care availability of patient pharmacogenomic results to healthcare providers via an electronic clinical decision support tool. Among newly added medications during hospitalizations and at discharge, we examined the most frequently utilized medications with associated pharmacogenomic results. The population was predominantly female (61%) with a mean age of 53 years (range 19–86). On average, six medications were newly prescribed during each individual hospital admission. For 48% of all hospitalizations, clinical pharmacogenomic information was applicable to at least one newly prescribed medication. Most results indicated genomic favorability, although nearly 29% of newly prescribed medications indicated increased genomic caution (increase in toxicity risk/suboptimal response). More than one of every five medications prescribed to AA patients at hospital discharge were associated with cautionary pharmacogenomic results (most commonly pantoprazole/suboptimal antacid effect). Notably, high-risk pharmacogenomic results (genomic contraindication) were exceedingly rare. We conclude that the applicability of pharmacogenomic information during hospitalizations for vulnerable populations at-risk for experiencing health disparities is substantial and warrants continued prospective investigation. Full article

(This article belongs to the Special Issue Pharmacogenetic Testing in Primary Care and Prevention)

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10 pages, 273 KiB

Open AccessCommunication

The Contribution of Pharmacogenetic Drug Interactions to 90-Day Hospital Readmissions: Preliminary Results from a Real-World Healthcare System

by Sean P. David, Lavisha Singh, Jaclyn Pruitt, Andrew Hensing, Peter Hulick, David O. Meltzer, Peter H. O’Donnell and Henry M. Dunnenberger

J. Pers. Med. 2021, 11(12), 1242; https://doi.org/10.3390/jpm11121242 - 23 Nov 2021

Cited by 3 | Viewed by 2541

Abstract

Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines exist for many medications commonly prescribed prior to hospital discharge, yet there are limited data regarding the contribution of gene-x-drug interactions to hospital readmissions. The present study evaluated the relationship between prescription of CPIC medications prescribed within 30 days of hospital admission and 90-day hospital readmission from 2010 to 2020 in a study population (N = 10,104) who underwent sequencing with a 14-gene pharmacogenetic panel. The presence of at least one pharmacogenetic indicator for a medication prescribed within 30 days of hospital admission was considered a gene-x-drug interaction. Multivariable logistic regression analyzed the association between one or more gene-x-drug interactions with 90-day readmission. There were 2211/2354 (93.9%) admitted patients who were prescribed at least one CPIC medication. Univariate analyses indicated that the presence of at least one identified gene-x-drug interaction increased the risk of 90-day readmission by more than 40% (OR = 1.42, 95% confidence interval (CI) 1.09–1.84) (p = 0.01). A multivariable model adjusting for age, race, sex, employment status, body mass index, and medical conditions slightly attenuated the effect (OR = 1.32, 95% CI 1.02–1.73) (p = 0.04). Our results suggest that the presence of one or more CPIC gene-x-drug interactions increases the risk of 90-day hospital readmission, even after adjustment for demographic and clinical risk factors. Full article

(This article belongs to the Special Issue Pharmacogenetic Testing in Primary Care and Prevention)

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12 pages, 1053 KiB

Open AccessArticle

Unmanaged Pharmacogenomic and Drug Interaction Risk Associations with Hospital Length of Stay among Medicare Advantage Members with COVID-19: A Retrospective Cohort Study

by Kristine Ashcraft, Chad Moretz, Chantelle Schenning, Susan Rojahn, Kae Vines Tanudtanud, Gwyn Omar Magoncia, Justine Reyes, Bernardo Marquez, Yinglong Guo, Elif Tokar Erdemir and Taryn O. Hall

J. Pers. Med. 2021, 11(11), 1192; https://doi.org/10.3390/jpm11111192 - 12 Nov 2021

Cited by 6 | Viewed by 3261

Abstract

Unmanaged pharmacogenomic and drug interaction risk can lengthen hospitalization and may have influenced the severe health outcomes seen in some COVID-19 patients. To determine if unmanaged pharmacogenomic and drug interaction risks were associated with longer lengths of stay (LOS) among patients hospitalized with COVID-19, we retrospectively reviewed medical and pharmacy claims from 6025 Medicare Advantage members hospitalized with COVID-19. Patients with a moderate or high pharmacogenetic interaction probability (PIP), which indicates the likelihood that testing would identify one or more clinically actionable gene–drug or gene–drug–drug interactions, were hospitalized for 9% (CI: 4–15%; p < 0.001) and 16% longer (CI: 8–24%; p < 0.001), respectively, compared to those with low PIP. Risk adjustment factor (RAF) score, a commonly used measure of disease burden, was not associated with LOS. High PIP was significantly associated with 12–22% longer LOS compared to low PIP in patients with hypertension, hyperlipidemia, diabetes, or chronic obstructive pulmonary disease (COPD). A greater drug–drug interaction risk was associated with 10% longer LOS among patients with two or three chronic conditions. Thus, unmanaged pharmacogenomic risk was associated with longer LOS in these patients and managing this risk has the potential to reduce LOS in severely ill patients, especially those with chronic conditions. Full article

(This article belongs to the Special Issue Pharmacogenetic Testing in Primary Care and Prevention)

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17 pages, 1236 KiB

Open AccessArticle

A Cost–Consequence Analysis of Preemptive SLCO1B1 Testing for Statin Myopathy Risk Compared to Usual Care

by Charles A. Brunette, Olivia M. Dong, Jason L. Vassy, Morgan E. Danowski, Nicholas Alexander, Ashley A. Antwi and Kurt D. Christensen

J. Pers. Med. 2021, 11(11), 1123; https://doi.org/10.3390/jpm11111123 - 31 Oct 2021

Cited by 6 | Viewed by 3321

Abstract

There is a well-validated association between SLCO1B1 (rs4149056) and statin-associated muscle symptoms (SAMS). Preemptive SLCO1B1 pharmacogenetic (PGx) testing may diminish the incidence of SAMS by identifying individuals with increased genetic risk before statin initiation. Despite its potential clinical application, the cost implications of SLCO1B1 testing are largely unknown. We conducted a cost–consequence analysis of preemptive SLCO1B1 testing (PGx+) versus usual care (PGx−) among Veteran patients enrolled in the Integrating Pharmacogenetics in Clinical Care (I-PICC) Study. The assessment was conducted using a health system perspective and 12-month time horizon. Incremental costs of SLCO1B1 testing and downstream medical care were estimated using data from the U.S. Department of Veterans Affairs’ Managerial Cost Accounting System. A decision analytic model was also developed to model 1-month cost and SAMS-related outcomes in a hypothetical cohort of 10,000 Veteran patients, where all patients were initiated on simvastatin. Over 12 months, 13.5% of PGx+ (26/193) and 11.2% of PGx− (24/215) participants in the I-PICC Study were prescribed Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline-concordant statins (Δ2.9%, 95% CI −4.0% to 10.0%). Differences in mean per-patient costs for lipid therapy prescriptions, including statins, for PGx+ compared to PGx− participants were not statistically significant (Δ USD 9.53, 95% CI −0.86 to 22.80 USD). Differences in per-patient costs attributable to the intervention, including PGx testing, lipid-lowering prescriptions, SAMS, laboratory and imaging expenses, and primary care and cardiology services, were also non-significant (Δ− USD 1004, 95% CI −2684 to 1009 USD). In the hypothetical cohort, SLCO1B1-informed statin therapy averted 109 myalgias and 3 myopathies at 1-month follow up. Fewer statin discontinuations (78 vs. 109) were also observed, but the SLCO1B1 testing strategy was 96 USD more costly per patient compared to no testing (124 vs. 28 USD). The implementation of SLCO1B1 testing resulted in small, non-significant increases in the proportion of patients receiving CPIC-concordant statin prescriptions within a real-world primary care context, diminished the incidence of SAMS, and reduced statin discontinuations in a hypothetical cohort of 10,000 patients. Despite these effects, SLCO1B1 testing administered as a standalone test did not result in lower per-patient health care costs at 1 month or over 1 year of treatment. The inclusion of SLCO1B1, among other well-validated pharmacogenes, into preemptive panel-based testing strategies may provide a better balance of clinical benefit and cost. Full article

(This article belongs to the Special Issue Pharmacogenetic Testing in Primary Care and Prevention)

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8 pages, 217 KiB

Open AccessArticle

Pharmacogenomic Testing and Patient Perception Inform Pain Pharmacotherapy

by Feng-Hua Loh, Brigitte Azzi, Alexander Weingarten and Zvi G. Loewy

J. Pers. Med. 2021, 11(11), 1112; https://doi.org/10.3390/jpm11111112 - 29 Oct 2021

Cited by 1 | Viewed by 3027

Abstract

(1) Background: Chronic pain is one of the most common reasons for individuals to seek medications. Historically, opioids have been the mainstay of chronic pain management. However, in some patient populations, opioids fail to demonstrate therapeutic efficacy, whereas in other populations, opioids may cause toxic effects, even at lower doses. Response to pain medication is affected by many factors, including an individual’s genetic variations. Pharmacogenomic testing has been designed to help achieve optimal treatment outcomes. This study aimed at assessing the impact of CYP2D6 pharmacogenomic testing on physicians’ choice in prescribing chronic pain medications and patient pain control. (2) Methods: This retrospective study reviewed 107 patient charts from a single site pain management center. All 107 patients received pharmacogenomic testing. The outcomes of interest were confirmation that the optimal pain medication is being administered or a change in the chronic pain medication is warranted as a result of the pharmacogenomic testing. The main independent variable was the pharmacogenomic test result. Other independent variables included patient gender, race, and comorbidities. The retrospective study was reviewed and approved by the Touro College and University System IRB, HSIRB1653E. (3) Results: Patients self-reported pain intensity on a scale of 1–10 before and after pharmacogenomic testing. Then, 100% of patients in the retrospective study were tested for their pain pharmacogenomic profile. Of the 107 patients participating in the study, more than 50% had their medications altered as a result of the pharmacogenomic testing. The percentage of patients with intense pain were decreased post-pharmacogenomic testing (5.6%) as compared to pre-pharmacogenomic testing (10.5%). Patients with intense, moderate, and mild pain categories were more likely to receive changes in pain medications. In contrast, patients with severe pain were less likely to receive a change in pain medication. Hispanic ethnicity was associated with a statistically significantly decrease in a pain scale category. Illegal drug abuse was associated with a decrease in pain scale category. Change in medication dose was associated with a decrease in pain scale category. (4) Conclusion: In this retrospective study, implementation of pharmacogenomic testing demonstrated significant benefits to patients with intense pain undergoing treatment. Full article

(This article belongs to the Special Issue Pharmacogenetic Testing in Primary Care and Prevention)

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8 pages, 549 KiB

Open AccessCommentary

Documenting Pharmacogenomic Test Results in Electronic Health Records: Practical Considerations for Primary Care Teams

by Roseann S. Gammal, Lucas A. Berenbrok, Philip E. Empey and Mylynda B. Massart

J. Pers. Med. 2021, 11(12), 1296; https://doi.org/10.3390/jpm11121296 - 4 Dec 2021

Cited by 9 | Viewed by 2950

Abstract

With increasing patient interest in and access to pharmacogenomic testing, clinicians practicing in primary care are more likely than ever to encounter a patient seeking or presenting with pharmacogenomic test results. Gene-based prescribing recommendations are available to healthcare providers through Food and Drug Administration-approved drug labeling and Clinical Pharmacogenetics Implementation Consortium guidelines. Given the lifelong utility of pharmacogenomic test results to optimize pharmacotherapy for commonly prescribed medications, appropriate documentation of these results in a patient’s electronic health record (EHR) is essential. The current “gold standard” for pharmacogenomics implementation includes entering pharmacogenomic test results into EHRs as discrete results with associated clinical decision support (CDS) alerts that will fire at the point of prescribing, similar to drug allergy alerts. However, such infrastructure is limited to the few institutions that have invested in the resources and personnel to develop and maintain it. For the majority of clinicians who do not practice at an institution with a dedicated clinical pharmacogenomics team and integrated pharmacogenomics CDS in the EHR, this report provides practical tips for documenting pharmacogenomic test results in the problem list and allergy field to maximize the visibility and utility of results over time, especially when such results could prevent the occurrence of serious adverse drug reactions or predict therapeutic failure. Full article

(This article belongs to the Special Issue Pharmacogenetic Testing in Primary Care and Prevention)

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8 pages, 832 KiB

Open AccessCommentary

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

by Russell A. Wilke

J. Pers. Med. 2021, 11(9), 853; https://doi.org/10.3390/jpm11090853 - 28 Aug 2021

Cited by 2 | Viewed by 6608

Abstract

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. Full article

(This article belongs to the Special Issue Pharmacogenetic Testing in Primary Care and Prevention)

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