Courcelles Eulalie, Boissel Jean-Pierre, Massol Jacques, Klingmann Ingrid, Kahoul Riad, Hommel Marc, Pham Emmanuel, Kulesza Alexander
Novadiscovery SA, Lyon, France.
Phisquare Institute, Transplantation Foundation, Paris, France.
Front Med Technol. 2022 Feb 24;4:810315. doi: 10.3389/fmedt.2022.810315. eCollection 2022.
Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps-impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.
卫生技术评估（HTA）旨在对医疗产品（MPs）的临床疗效、安全性和价值进行系统、透明、无偏见的综合分析，以帮助政策制定者、支付方、临床医生和行业做出明智的决策。可用于卫生技术评估的证据存在差距，这阻碍了在实际临床实践中及时预测个体的长期效果。此外，对一种医疗产品的评估需要跨利益相关方的沟通与参与。这两个方面都可能受益于建模与仿真的更多应用。建模在卫生技术评估中用于数据综合和卫生经济预测。与此同时，对模型引导的药物开发（MIDD）的监管考量已使人们关注到一些机制建模技术，而这些技术实际上可能与卫生技术评估相关。外推和生成个性化预测的能力使机制性的模型引导的药物开发方法适合支持将临床试验数据转化为真实世界证据。因此，从这个角度出发，我们讨论机制模型如何解决与卫生技术评估相关问题的具体例子。我们阐明了不同利益相关方在评估阶段的贡献和需求，并提出机制建模策略和报告如何能为此做出贡献。在建模方面，卫生技术评估领域和临床开发领域之间仍然存在障碍：缺乏适用于决策过程的模型验证框架、利益相关方的支持不一致且不明确、可推广的用例有限以及缺乏适当的激励措施。为应对这一挑战，我们建议加强主管部门、药物开发者和建模者之间的合作，以便在卫生技术评估的证据生成、综合和评估中实施核心的机制模型，从而使所有相关利益方能够利用机制性和临床证据的整体。
