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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.
128 Factors Associated with Cost Savings Following Use of a Pharmacogenetic Assay in Individuals with Mood and Anxiety Disorders
- Alison M Edwards, Roy H Perlis, David S Krause
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- Journal:
- CNS Spectrums / Volume 25 / Issue 2 / April 2020
- Published online by Cambridge University Press:
- 24 April 2020, pp. 281-282
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Background:
In a study conducted in the database of a large commercial healthcare insurer, we previously demonstrated that use of a commercial pharmacogenetic assay for individuals with mood disorders was associated with decreased resource utilization and cost in the 6 month period following use compared to propensity-score matched controls. We conducted a post hoc analysis to understand variables associated with high cost savings.
Methods:The results and methods of the initial study have previously been described. Cases were individuals with mood and anxiety disorders who received a commercial pharmacogenetic assay (Genomind, King of Prussia PA) to inform pharmacotherapy. 817 tested individuals (cases) with mood and/or anxiety disorders were matched to 2745 controls. Overall costs were estimated to be $1,948 lower in the tested group. The differences were largely the result of lesser emergency room and inpatient utilization for cases. In the present analysis, cost difference for cases compared to their matched controls was rank ordered by decile. High cost savers were arbitrarily defined a priori as the top 20% of savers. Using multivariable modeling techniques, an ordinal logistic regression model was generated in which baseline or follow-up variables were statistically tested for independent associations with high, low, and no cost savings.
Results:606 (74%) of cases were net cost savers compared to their controls (cost difference <0). High cost savers (n=121) saved on average $10,690 compared to their matched controls. They were statistically more likely to have been diagnosed with bipolar disorder (n=33/121) than low cost savers (n=57/485) or non-savers (n=31/211), and had a lower Charlson Comorbidity index. High cost savers had fewer mean number of antidepressants in the baseline period (mean=3.16) compared to non-savers (3.73) but more than low cost savers (2.72) (p<0.05 across groups). In a multivariable model, bipolar, count of antidepressants, outpatient visits, and inpatient visits were statistically associated with being a high cost saver; antidepressant count and all-cause inpatient and outpatient visits in the baseline period were inversely associated with cost savings.
Conclusions:Use of a pharmacogenetic assay was associated with cost-savings in the database of a large commercial insurer. Patients with bipolar disorder were more likely to be high cost savers than individuals with other mood and anxiety disorders.
Funding Acknowledgements:Genomind
The science of EChO
- Giovanna Tinetti, James Y-K. Cho, Caitlin A. Griffith, Olivier Grasset, Lee Grenfell, Tristan Guillot, Tommi T. Koskinen, Julianne I. Moses, David Pinfield, Jonathan Tennyson, Marcell Tessenyi, Robin Wordsworth, Alan Aylward, Roy van Boekel, Angioletta Coradini, Therese Encrenaz, Ignas Snellen, Maria R. Zapatero-Osorio, Jeroen Bouwman, Vincent Coudé du Foresto, Mercedes Lopez-Morales, Ingo Mueller-Wodarg, Enric Pallé, Franck Selsis, Alessandro Sozzetti, Jean-Philippe Beaulieu, Thomas Henning, Michael Meyer, Giuseppina Micela, Ignasi Ribas, Daphne Stam, Mark Swain, Oliver Krause, Marc Ollivier, Emanuele Pace, Bruce Swinyard, Peter A.R. Ade, Nick Achilleos, Alberto Adriani, Craig B. Agnor, Cristina Afonso, Carlos Allende Prieto, Gaspar Bakos, Robert J. Barber, Michael Barlow, Peter Bernath, Bruno Bézard, Pascal Bordé, Linda R. Brown, Arnaud Cassan, Céline Cavarroc, Angela Ciaravella, Charles Cockell, Athéna Coustenis, Camilla Danielski, Leen Decin, Remco De Kok, Olivier Demangeon, Pieter Deroo, Peter Doel, Pierre Drossart, Leigh N. Fletcher, Matteo Focardi, Francois Forget, Steve Fossey, Pascal Fouqué, James Frith, Marina Galand, Patrick Gaulme, Jonay I. González Hernández, Davide Grassi, Matt J. Griffin, Ulrich Grözinger, Manuel Guedel, Pactrick Guio, Olivier Hainaut, Robert Hargreaves, Peter H. Hauschildt, Kevin Heng, David Heyrovsky, Ricardo Hueso, Pat Irwin, Lisa Kaltenegger, Patrick Kervella, David Kipping, Geza Kovacs, Antonino La Barbera, Helmut Lammer, Emmanuel Lellouch, Giuseppe Leto, Mercedes Lopez Morales, Miguel A. Lopez Valverde, Manuel Lopez-Puertas, Christophe Lovi, Antonio Maggio, Jean-Pierre Maillard, Jesus Maldonado Prado, Jean-Baptiste Marquette, Francisco J. Martin-Torres, Pierre Maxted, Steve Miller, Sergio Molinari, David Montes, Amaya Moro-Martin, Olivier Mousis, Napoléon Nguyen Tuong, Richard Nelson, Glenn S. Orton, Eric Pantin, Enzo Pascale, Stefano Pezzuto, Ennio Poretti, Raman Prinja, Loredana Prisinzano, Jean-Michel Réess, Ansgar Reiners, Benjamin Samuel, Jorge Sanz Forcada, Dimitar Sasselov, Giorgio Savini, Bruno Sicardy, Alan Smith, Lars Stixrude, Giovanni Strazzulla, Gautam Vasisht, Sandrine Vinatier, Serena Viti, Ingo Waldmann, Glenn J. White, Thomas Widemann, Roger Yelle, Yuk Yung, Sergey Yurchenko
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- Journal:
- Proceedings of the International Astronomical Union / Volume 6 / Issue S276 / October 2010
- Published online by Cambridge University Press:
- 10 November 2011, pp. 359-370
- Print publication:
- October 2010
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The science of extra-solar planets is one of the most rapidly changing areas of astrophysics and since 1995 the number of planets known has increased by almost two orders of magnitude. A combination of ground-based surveys and dedicated space missions has resulted in 560-plus planets being detected, and over 1200 that await confirmation. NASA's Kepler mission has opened up the possibility of discovering Earth-like planets in the habitable zone around some of the 100,000 stars it is surveying during its 3 to 4-year lifetime. The new ESA's Gaia mission is expected to discover thousands of new planets around stars within 200 parsecs of the Sun. The key challenge now is moving on from discovery, important though that remains, to characterisation: what are these planets actually like, and why are they as they are?
In the past ten years, we have learned how to obtain the first spectra of exoplanets using transit transmission and emission spectroscopy. With the high stability of Spitzer, Hubble, and large ground-based telescopes the spectra of bright close-in massive planets can be obtained and species like water vapour, methane, carbon monoxide and dioxide have been detected. With transit science came the first tangible remote sensing of these planetary bodies and so one can start to extrapolate from what has been learnt from Solar System probes to what one might plan to learn about their faraway siblings. As we learn more about the atmospheres, surfaces and near-surfaces of these remote bodies, we will begin to build up a clearer picture of their construction, history and suitability for life.
The Exoplanet Characterisation Observatory, EChO, will be the first dedicated mission to investigate the physics and chemistry of Exoplanetary Atmospheres. By characterising spectroscopically more bodies in different environments we will take detailed planetology out of the Solar System and into the Galaxy as a whole.
EChO has now been selected by the European Space Agency to be assessed as one of four M3 mission candidates.