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Clinical Utility of Pharmacogenomic Data Collected by a Health-System Biobank to Predict and Prevent Adverse Drug Events

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Abstract

Introduction

Medication-related harm represents a significant issue for patient safety and quality of care. One strategy to avoid preventable adverse drug events is to utilize patient-specific factors such as pharmacogenomics (PGx) to individualize therapy.

Objective

We measured the number of patients enrolled in a health-system biobank with actionable PGx results who received relevant medications and assessed the incidence of adverse drug events (ADEs) that might have been prevented had the PGx results been used to inform prescribing.

Methods

Patients with actionable PGx results in the following four genes with Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines were identified: HLA-A*31:01, HLA-B*15:02, TPMT, and VKORC1. The patients who received interacting medications (carbamazepine, oxcarbazepine, thiopurines, or warfarin) were identified, and electronic health records were reviewed to determine the incidence of potentially preventable ADEs.

Results

Of 36,424 patients with PGx results, 2327 (6.4%) were HLA-A*31:01 positive; 3543 (9.7%) were HLA-B*15:02 positive; 2893 (7.9%) were TPMT intermediate metabolizers; and 4249 (11.7%) were homozygous for the VKORC1 c.1639 G>A variant. Among patients positive for one of the HLA variants who received carbamazepine or oxcarbazepine (n = 92), four (4.3%) experienced a rash that warranted drug discontinuation. Among the TPMT intermediate metabolizers who received a thiopurine (n = 56), 11 (19.6%) experienced severe myelosuppression that warranted drug discontinuation. Among patients homozygous for the VKORC1 c.1639 G>A variant who received warfarin (n = 379), 85 (22.4%) experienced active bleeding and/or international normalized ratio (INR) > 5 that warranted drug discontinuation or dose reduction.

Conclusion

Patients with actionable PGx results from a health-system biobank who received relevant medications experienced predictable ADEs. These ADEs may have been prevented if the patients’ PGx results were available in the electronic health record with clinical decision support prior to prescribing.

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Acknowledgements

We would like to acknowledge Wejdan Aljassas, Marina Amin, Youmi Son, Elaina Audi, Yihenew Ewnetu, Anthony Branco, Paige Desrosiers, Kevin Joseph, Rachel Carboni, Zeina Youssef, Adwoa Edusei, Bryanna Marie Pacheo, and Youngheon Choi for their contributions to data collection.

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Authors and Affiliations

  1. Department of Internal Medicine, Brigham and Women’s Hospital, 41 Avenue of Louis Pasteur, Office 103, Boston, MA, 02115, USA

    Sonam N. Shah, Mary G. Amato & David W. Bates

  2. Department of Pharmacy Practice, MCPHS University School of Pharmacy, Boston, MA, USA

    Sonam N. Shah, Roseann S. Gammal, Mary G. Amato, Maryam Alobaidly, Dariel Delos Reyes & Sarah Hasan

  3. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Roseann S. Gammal & Joel B. Krier

  4. Clinical Quality Analysis, Partners Healthcare, Somerville, MA, USA

    Diane L. Seger & David W. Bates

  5. Harvard Medical School, Boston, MA, USA

    Joel B. Krier & David W. Bates

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  1. Sonam N. Shah
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Corresponding author

Correspondence to Sonam N. Shah.

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Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Conflict of interest

Dr. Bates consults for EarlySense, which makes patient safety monitoring systems. He receives cash compensation from CDI (Negev), Ltd, which is a not-for-profit incubator for health IT startups. He receives equity from ValeraHealth, which makes software to help patients with chronic diseases. He receives equity from Clew, which makes software to support clinical decision making in intensive care. He receives equity from MD Clone, which takes clinical data and produces deidentified versions of it. He receives equity from AESOP, which makes software to reduce medication error rates. He receives research funding from IBM Watson Health. He serves as a Visiting Professor at Stavanger University. No other authors have any conflict of interest to declare.

Ethics approval

This study has been approved by our local Institutional Review Board.

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Contributorship statement

SNS, DLS, and RSG made substantial contributions to the conception of the design of the work; SNS, RSG, MGA, MA, DR, and SH assisted in data collection, chart review, and interpretation. DLS, MGA, RSG, and MA contributed to writing and revision of the manuscript. JBK and DWB supervised the research design and contributed to the writing and revision of the manuscript. All authors give approval for the final version to be published and agree to be accountable for all aspects of the work, ensuring questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Shah, S.N., Gammal, R.S., Amato, M.G. et al. Clinical Utility of Pharmacogenomic Data Collected by a Health-System Biobank to Predict and Prevent Adverse Drug Events. Drug Saf 44, 601–607 (2021). https://doi.org/10.1007/s40264-021-01050-6

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