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Predicting Potential Drug-Drug-Gene Interactions in a Population of Individuals Utilizing a Community-Based Pharmacy
- Daniel Dowd, Gabriela Williams, David Krause, Stephen Clarke, Eric Crumbaugh, Jeffrey Botbyl, Stephen R. Saklad
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- Journal:
- CNS Spectrums / Volume 27 / Issue 2 / April 2022
- Published online by Cambridge University Press:
- 28 April 2022, pp. 236-237
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Introduction
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.
MethodsA 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.
ResultsBased 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.
ConclusionsIn 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.
FundingGenomind, Inc.
A Novel Real-Time PCR Assay for Detection of HLA-A*31:01 in Individuals Being Considered for Carbamazepine Therapy
- David S. Krause, Kathleen Davis, Daniel Dowd, David J. Robbins
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- Journal:
- CNS Spectrums / Volume 26 / Issue 2 / April 2021
- Published online by Cambridge University Press:
- 10 May 2021, pp. 154-155
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Background
Carbamazepine, an anticonvulsant also used as a mood stabilizer and for trigeminal neuralgia, is associated with serious, sometimes fatal cutaneous adverse drug reactions, including Stevens Johnson Syndrome and toxic epidermal necrolysis1. Current literature demonstrates a genetic predisposition linked to specific class I and II human leukocyte antigen (HLA) types in various ethnic populations2. HLA-A*31:01 is one such HLA type, and is routinely identified by the tag SNP rs1061235. However, rs1061235 has poor specificity for HLA*31:01 due to interference of HLA-A*33 types3. We investigated the false positive rate in our population and developed a novel real-time PCR assay that distinguishes HLA-A*31:01 from other HLA-A types including HLA-A*33.
Methods120 unique samples were tested in triplicate during the validation of this assay and were sent to a reference lab for HLA next generation sequencing (NGS) typing, including 89 in-house samples and 31 Coriell samples with documented HLA typing results. The results from our real-time PCR assay were compared to the HLA typing results. HLA typing results were also compared to the tag SNP rs1061235 results to calculate the false positive rate.
ResultsThere was 100% concordance between our real-time PCR results and expected results based on HLA typing. 89 sample results for tag SNP rs1061235 were compared to HLA typing results. 75/89 samples had a rs1061235 variant, but 31/75 (41%) samples did not have the HLA-A*31:01 type, thus defining the false positive rate of the tag SNP for our population. We theorized there would be a small subset of rare HLA-A types that would interfere with the assay and we tested the three types available to us. We confirmed that 3 of the HLA types (HLA-A*31:04, 31:12, and 31:16) result falsely positive due to sequence homology with 31:01. There is no known literature indicating whether these rare HLA-A*31 subtypes are associated with cutaneous adverse reactions. These 3 HLA types and the other suspected interfering HLA types have limited frequency data sets and are expected to occur rarely in our patient population; we expect these HLA types make up less than 0.003% of the our population. Our assay specificity for the validation is >99%.
ConclusionsOur custom real-time PCR assay for detection of HLA-A*31:01 is significantly more specific than the commonly used tag SNP rs1061235. Clinicians considering carbamazepine therapy for their patients will have a better understanding of cutaneous adverse reaction risk and can make improved personalized treatment decisions. This quick, cost effective assay allows more patients in need of carbamazepine treatment to benefit from its use.
FundingGenomind, Inc.
Use of a Consultation Service Following Pharmacogenomic Testing in Psychiatry
- Daniel Dowd, David S. Krause
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- Journal:
- CNS Spectrums / Volume 26 / Issue 2 / April 2021
- Published online by Cambridge University Press:
- 10 May 2021, pp. 179-180
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Background
There is a plethora of drugs available to psychiatrists for treatment of mental illness, which can vary in efficacy, tolerability, metabolic pathways and drug-drug interactions. Psychotropics are the second most commonly listed therapeutic class mentioned in the FDA’s Table of Pharmacogenomic Biomarkers in Drug Labeling. Pharmacogenomic (PGx) assays are increasingly used in psychiatry to help select safe and appropriate medication for a variety of mental illnesses. Our commercial laboratory offers PGx expert consultations by PharmDs and PhDs to clinician-users. Our database contains valuable information regarding the treatment of a diverse and challenging population.
MethodsGenomind offers a PGx assay currently measuring variants of 24 genes relevant for selection of drugs with a mental illness indication. Since 2012 we have analyzed > 250,000 DNA samples. Between 10/18 - 8/20 6,401 reports received a consult. The data contained herein are derived from those consults. Consultants record information on prior meds, reason for failure or intolerability, potential risk-associated or useful drugs based on the genetic variants. Consultants only recommend specific drugs and doses consistent with a published PGx guideline.
ResultsThe 5 most commonly discussed genes were SLC6A4, MTHFR, CACNA1C, COMT and BDNF. The 3 most commonly discussed drugs were fluoxetine, lithium and duloxetine. The most common reasons for drug failure were inefficacy and drug induced “agitation, irritability and/or anxiety”. SSRIs were the most common class of discontinued drug; sertraline, escitalopram and fluoxetine were the three most commonly reported discontinuations and were also the 3 most likely to be associated with “no improvement”. Aripiprazole was the most commonly reported discontinued atypical antipsychotic. The providers rated 94% of consultations as extremely or very helpful at the time of consult. An independent validation survey of 128 providers confirmed these ratings, with 96% reporting a rating of “very helpful” or “extremely helpful”. In addition, 94% reported that these consults were superior to PGx consults provided through other laboratories. Patient characteristics captured during consults via a Clinical Global Impressions-Severity (CGI-S) scale revealed that the majority of patients were moderately (54%) or markedly ill (23%). The most frequent symptoms reported were depression, anxiety, insomnia and inattentiveness.
DiscussionThe large variety of psychotropic drugs available to providers, and their highly variable response rates, tolerability, capacity for drug-drug interactions and metabolic pathways present a challenge for even expert psychopharmacologists. Consultation with experts in PGx provides additional useful information that may improve outcomes and decrease healthcare resource utilization. This database may provide future opportunities for machine learning algorithms to further inform implications of included gene variants.
FundingGenomind, Inc.
19 Real World Patient-Reported Outcomes Following Pharmacogenomic Testing
- Nichole Rigby, Jennifer Ma, Joseh Boland, Danile Van Dorm, Daniel Dowd, David Krause
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- Journal:
- CNS Spectrums / Volume 24 / Issue 1 / February 2019
- Published online by Cambridge University Press:
- 12 March 2019, p. 183
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Background
The use of pharmacogenomics (PGx) testing has the potential to accelerate response to psychopharmacologic therapy (Rx) and improve outcomes; accordingly PGx use to select appropriate Rx is increasing. One such commercially available test is the Genecept Assay (the Assay [Genomind]) which measures variants of 18 genes (12 pharmacodynamic and 6 CYP450) for which response, tolerability or exposure to various Rx has been reported. Recent interest in genetics has led patients (pts) to be stewards of their own genetic data. In 2017 we launched a Patient Gateway to allow pts to retrieve their genetic results, have access to mental health information, and record outcomes following use of the Assay.
ObjectiveTo assess the effectiveness of Rx recommendations following use of the Assay, as reported on a purpose-built patient portal.
MethodPts receiving the Assay were invited to visit an online, interactive portal. Pts providing informed consent (IC) were asked to record their baseline overall health using a 4- point modified patient global index (m-PGI) of severity. Pts also recorded their conditions, medications, and supplements, and various symptoms. Pts were invited to visit the portal ad libitum and re-rate their overall health using the m-PGI. These data were then combined with the pts’ genetic results using custom scripts in Python (v 3.6.4) and R (v 3.5.1). All identifying data were removed. Pts included in this analysis responded (at least) twice to the health questionnaire. New medications were subsequently scored as concordant, discordant, or indeterminate with the Assay’s recommendations, using predetermined criteria. We report the initial results for this subgroup herein.
ResultsSince launch 9,401 unique patient profiles were created on the Gateway; 5,207 (55%) of these provided IC. Of these, 410 provided at least 2 m-PGI scores. Seventy-three (73) of these pts reported scores at least 4weeks apart and started 222 medications in the interim. 69.4% of pts identified as female; 70.8% had a diagnosis of generalized anxiety disorder, while 50.0% and 31.9% had diagnoses of major depressive disorder and post-traumatic stress disorder, respectively. 60.2% of pts reported improvement on the m-PGI of ≥1 unit; 20% had a ≥2-unit improvement. Pts reporting improvement were more likely (77% vs 66%); to have been placed on medication that were concordant with the assay than those who were not improved, although this difference did not reach statistical significance.
ConclusionIn this naturalistic, virtual trial of a PGx assay to guide pharmacotherapy in individuals with mental health illness, most users reported improvement in overall health. More pts whose medication was reported as concordant with the Assay reported improvement than those with discordant medications. Data collection is ongoing and updated data will be provided.
Funding Acknowledgements: Genomind
Election Ink and Turnout in a Partial Democracy
- Karen E. Ferree, Danielle F. Jung, Robert A. Dowd, Clark C. Gibson
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- Journal:
- British Journal of Political Science / Volume 50 / Issue 3 / July 2020
- Published online by Cambridge University Press:
- 13 July 2018, pp. 1175-1191
- Print publication:
- July 2020
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Modulation of the faecal microbiome of healthy adult dogs by inclusion of potato fibre in the diet
- Matthew R. Panasevich, Katherine R. Kerr, Ryan N. Dilger, George C. Fahey, Jr, Laetitia Guérin-Deremaux, Gary L. Lynch, Daniel Wils, Jan S. Suchodolski, Jörg M Steer, Scot E. Dowd, Kelly S. Swanson
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- Journal:
- British Journal of Nutrition / Volume 113 / Issue 1 / 14 January 2015
- Published online by Cambridge University Press:
- 24 November 2014, pp. 125-133
- Print publication:
- 14 January 2015
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Inclusion of fermentable fibres in the diet can have an impact on the hindgut microbiome and provide numerous health benefits to the host. Potato fibre (PF), a co-product of potato starch isolation, has a favourable chemical composition of pectins, resistant and digestible starch, cellulose, and hemicelluloses. The objective of the present study was to evaluate the effect of increasing dietary PF concentrations on the faecal microbiome of healthy adult dogs. Fresh faecal samples were collected from ten female dogs with hound bloodlines (6·13 (sem 0·17) years; 22·0 (sem 2·1) kg) fed five test diets containing graded concentrations of PF (0, 1·5, 3, 4·5 or 6 % as-fed; Roquette Frères) in a replicated 5 × 5 Latin square design. Extraction of DNA was followed by amplification of the V4–V6 variable region of the 16S rRNA gene using barcoded primers. Sequences were classified into taxonomic levels using Basic Local Alignment Search Tool (BLASTn) against a curated GreenGenes database. Inclusion of PF increased (P< 0·05) the faecal proportions of Firmicutes, while those of Fusobacteria decreased (P< 0·05). Similar shifts were observed at the genus level and were confirmed by quantitative PCR (qPCR) analysis. With increasing concentrations of PF, faecal proportions of Faecalibacterium increased (P< 0·05). Post hoc Pearson's correlation analysis showed positive (P< 0·05) correlations with Bifidobacterium spp. and butyrate production and Lactobacillus spp. concentrations. Overall, increases in the proportion of Faecalibacterium (not Lactobacillus/Bifidobacterium, as confirmed by qPCR analysis) and faecal SCFA concentrations with increasing dietary PF concentrations suggest that PF is a possible prebiotic fibre.