Ferris State University College of Pharmacy, Big Rapids, MI.
North Dakota State University School of Pharmacy, Fargo ND.
J Manag Care Spec Pharm. 2020 Dec;26(12):1597-1602. doi: 10.18553/jmcp.2020.26.12.1597.
Opioid use and misuse are continued issues facing clinicians across all aspects of health care. As clinicians struggle to effectively manage opioid prescribing, pharmacogenomics (PGx) further offers the prescriber an improved ability to understand the potential for an individual patient's genetics to influence opioid efficacy and safety. When PGx data are available at the point of initial prescribing, clinicians can apply that data to drug therapy selection. However, barriers continue to exist relative to PGx data sharing and interpretation, which have created difficulties for widespread PGx implementation. This article briefly describes potential barriers to PGx data integration, strategies to overcome those barriers, and the potential positive effect of successful data sharing on opioid prescribing. Prescription drug monitoring programs (PDMPs) have been successfully operationalized to share controlled substance prescribing data across health care settings. Such data sharing enables clinicians to, among other things, better understand risks associated with misuse. Because a relatively limited volume of PGx data is currently pertinent to opioid prescribing, such PGx data could be added to PDMPs as a way to communicate genetic information within current technology platforms. Not only would this integrate into existing clinical workflow models where PDMP data are accessed at this point of prescribing and/or dispensing, but associated clinical guidance for PGx data interpretation in the context of opioids could be integrated into the workflow process. Such clinical decision support could be provided directly through the PDMP interface for uniformity or could be provided via systems that access PDMP data. Clinical, economic, and policy implications of the inclusion of PGx data within PDMPs are also discussed. Through harnessing PDMP for data sharing, multiple barriers to PGx implementation could be mitigated, and clinicians may have better access to PGx data to optimize opioid prescribing. No outside funding supported this study. Bright has a patent pending related to opioid use disorder risk assessment that includes genetic information and was a collaborator on funded research projects with pharmacogenomics-related companies. Petry has been a consultant to the North Dakota Department of Health and has received grants from IGNITE I and IGNITE II (NIH), unrelated to this work. The other authors are aware of no financial conflicts of interest.
阿片类药物的使用和滥用仍然是所有医疗保健领域的临床医生面临的持续问题。随着临床医生努力有效地管理阿片类药物的处方，药物基因组学（PGx）进一步为临床医生提供了更好的能力，以了解个体患者的基因对阿片类药物疗效和安全性的潜在影响。当 PGx 数据在初始处方时可用时，临床医生可以将该数据应用于药物治疗选择。然而，PGx 数据共享和解释方面仍然存在障碍，这给 PGx 的广泛实施带来了困难。本文简要描述了 PGx 数据集成的潜在障碍、克服这些障碍的策略，以及成功的数据共享对阿片类药物处方的潜在积极影响。处方药物监测计划（PDMP）已经成功实施，以在医疗保健环境中共享受控物质的处方数据。这种数据共享使临床医生能够更好地了解与滥用相关的风险。由于目前与阿片类药物处方相关的 PGx 数据相对有限，因此可以将此类 PGx 数据添加到 PDMP 中，作为在当前技术平台内传递遗传信息的一种方式。这不仅将整合到目前在处方和/或配药时访问 PDMP 数据的现有临床工作流程模型中，而且还可以将与阿片类药物相关的 PGx 数据解释的相关临床指导整合到工作流程中。这种临床决策支持可以通过 PDMP 接口直接提供，以实现一致性，也可以通过访问 PDMP 数据的系统提供。还讨论了将 PGx 数据纳入 PDMP 对临床、经济和政策的影响。通过利用 PDMP 进行数据共享，可以减轻 PGx 实施的多个障碍，并且临床医生可能更方便地获得 PGx 数据，以优化阿片类药物的处方。本研究没有外部资金支持。Bright 拥有一项与包括遗传信息在内的阿片类药物使用障碍风险评估相关的专利申请，并且是与药物基因组学相关公司合作的资助研究项目的合作者。Petry 一直担任北达科他州卫生部的顾问，并获得了 IGNITE I 和 IGNITE II（NIH）的赠款，与这项工作无关。其他作者没有财务利益冲突。
