Effectivenes, Safety, and Patient Preferences of Infliximab Biosimilar Medications for Inflammatory Bowel Disease

背景：生物药物(生物制品)对免疫系统疾病、癌症、 和其他疾病；然而，他们高昂的费用是医疗保健的障碍和医疗系统的负担。 生物制品不能被完全复制为“非专利”药物。生物仿制药-相似但不完全相同的版本 生物药物--获得批准，有可能节省大量成本。然而，退伍军人管理局的提供者和患者 对疾病特异性随机对照的生物相似转换安全性和有效性的关注 试验不需要获得批准。意义/影响：肿瘤坏死因子-α拮抗剂(抗肿瘤坏死因子) 是具有生物仿制药的最大类别的生物制品，在这些类别中，转换可能是可行的，以降低成本；然而 如何以患者可接受的方式安全有效地整合它们的使用尚不清楚。这项建议 解决退伍军人安全的退伍军人高铁优先事项，以及增加大量实际- 退伍军人事务部研究的世界影响，并通过 学习型医疗保健系统。创新：克罗恩病(CD)和溃疡性结肠炎(UC)分别位居第一和第二位 退伍军人中最常见的抗肿瘤坏死因子适应症，可作为学习医疗系统的模型 减轻与生物相似转换相关的不良事件的方法。具体目标：目标1a：比较 CD和UC患者继续使用抗肿瘤坏死因子发起者与改用抗肿瘤坏死因子发起者的不良事件发生率 生物相似。目的1b：比较继续使用抗肿瘤坏死因子启动剂的患者的CD或UC恶化的比率 那些转向生物相似的人。目的2：比较2a)传统回归方法的准确度和校准 模型与2b)机器学习模型用于预测与抗肿瘤坏死因子相关的药物不良事件 具有CD和UC的VA用户。目标3：使用协商民主方法让退伍军人参与，激发他们的 在他们知情和不知情的情况下，优先选择“喜欢”的药物转换计划并进行开发 关于治疗方法的共识。方法：目标1将通过回顾实现 2017-2019年从国家VA数据集接受抗肿瘤坏死因子治疗的CD和UC患者的队列研究。 不良事件和恶化将使用管理数据和手册的组合来确定 查看图表。对目标1的分析将通过使用GEE的泊松回归进行。调整后的事故率比率 与继续使用发起者生物相似的患者相比，转换为生物相似的患者的计算公式为 95%的可信区间和Wald p值将从回归模型估计中得出。预测 对抗肿瘤坏死因子有不良反应的患者，将告知选择合适的治疗方法，并指导 患者进行生物相似转换。对于目标2，无论是传统回归模型还是机器学习模型 将被构建以确定哪个模型将更好地预测抗肿瘤坏死因子相关的不良事件。 通过比较这些模型来开发尽可能最佳的风险分层工具，将使我们能够识别退伍军人 有发生不良事件的风险，以提高退伍军人护理的质量和效率。这是至关重要的 重要的是退伍军人政策纳入了退伍军人对影响 他们的健康。目标3将采用协商民主的方法，提供实用和可靠的方法 在复杂的政策问题上征求知情和深思熟虑的意见。民主议事利用教育 由专家和同行之间精心组织的审议，以提供明智的意见和政策 来自相关利益相关者的建议。下一步/实施：这项提议得到了 临床合作伙伴：国家退伍军人事务部炎症性肠病技术咨询小组和药房 福利管理层将通过VA特定的生物相似开关传播这项研究的结果 临床指南和退伍军人管理局处方政策。未来的研究将包括实用的临床试验 使用本提案中创建的学习保健平台的其他生物相似生物制剂。

Background: Biologic medications (biologics) are highly effective for diseases of the immune system, cancers, and other conditions; however, their high expense is a barrier to care and a burden to the healthcare system. Biologics cannot be exactly copied as “generic” medications. Biosimilars- similar, but not identical versions of biologic medications- are approved with large potential cost savings. However, VA providers and patients have concerns regarding biosimilar switching safety and effectiveness as disease-specific randomized controlled trials are not required for approval. Significance/Impact: Antagonists to tumor necrosis factor-α (Anti-TNFs) are the largest class of biologics with biosimilars where switching may be feasible to reduce costs; however how to safely and effectively integrate their use in a manner acceptable to patients is unknown. This proposal addresses the VA HSR priority of veteran safety, the ORD-wide research priority of increasing substantial real- world impact of VA research, and uses cross-cutting HSR methods of health systems engineering through a learning healthcare system. Innovation: Crohn’s disease (CD) and ulcerative colitis (UC) are the 1st and 2nd most common indications for Anti-TNFs in the VA and can serve as a model for a learning healthcare system approach for mitigation of adverse events related to biosimilar switching. Specific Aims: Aim 1a: To compare rates of adverse events in CD and UC patients continued on Anti-TNF originator to those switched to biosimilar. Aim 1b: To compare rates of CD or UC exacerbation in patients continued on Anti-TNF originator to those switched to biosimilar. Aim 2: To compare the accuracy and calibration of 2a) traditional regression models vs. 2b) machine learning models for predicting medication related adverse event related to Anti-TNF in VA users with CD and UC. Aim 3: To use deliberative democracy methods to engage Veterans, to elicit their preference regarding “like" medication switch programs with and without their knowledge and to develop consensus around treatment approaches. Methodology: Aim 1 will be achieved through a retrospective cohort study of CD and UC patients who received Anti-TNF from the national VA datasets from 2017-2019. Adverse events and exacerbations will be determined using a combination of administrative data and manual chart review. Analyses for Aim 1 will proceed by Poisson regression using GEE. Adjusted event rate ratios of patients switched to biosimilar compared to those who continued on originator biosimilar will be calculated with 95% confidence intervals and Wald p-values will be derived from the regression model estimates. Prediction of patients who have adverse events to Anti-TNF will inform selection of appropriate therapy, and guidance of patients for biosimilar switching. For Aim 2, both traditional regression models and machine learning models will be constructed to identify which model will be better for predicting Anti-TNF related adverse events. Developing the best possible risk stratification tool by comparing these models will allow us to identify veterans that are at risk of adverse events to improve both the quality and efficiency of veteran care. It is critically important that VA policies incorporate the opinions of Veterans on ethically controversial issues that impact their health. Aim 3 will employ deliberative democratic methods that offer a practical and reliable approach to soliciting informed and considered opinions in complex policy issues. Democratic deliberation uses education by experts and carefully structured deliberation among peers to deliver informed opinions and policy suggestions from concerned stakeholders. Next Steps/ Implementation: This proposal is supported with clinical partners: the national VA Inflammatory Bowel Disease Technical Advisory Group and Pharmacy Benefits Management who will disseminate findings from this study through VA-specific biosimilar switch clinical guidelines and VA-pharmacy prescription policy. Future studies will include pragmatic clinical trials of other biosimilar biologics using the learning healthcare platform created in this proposal.