Quantifying the Benefits of Digital Biomarkers and Technology-Based Study Endpoints in Clinical Trials: Project Moneyball.

Q1 Computer Science
Digital Biomarkers Pub Date : 2022-06-29 eCollection Date: 2022-05-01 DOI:10.1159/000525255
Hiromasa Mori, Stig Johan Wiklund, Jason Yuren Zhang
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引用次数: 4

Abstract

Introduction: Digital biomarkers have significant potential to transform drug development, but only a few have contributed meaningfully to bring new treatments to market. There are uncertainties in how they will generate quantifiable benefits in clinical trial performance and ultimately to the chances of phase 3 success. Here we have proposed a statistical framework and ran a proof-of-concept model with hypothetical digital biomarkers and visualized them in a familiar manner to study power calculation.

Methods: A Monte Carlo simulation for Parkinson's disease (PD) was performed using the Captario SUM® platform and illustrative study technology impact calculations were generated. We took inspiration from the EMA-qualified wearable-derived digital endpoint stride velocity 95th centile (SV95C) for Duchenne muscular dystrophy, and we imagined a similar measurement for PD would be available in the future. DaTscan enrichment and "SV95C-like" endpoint biomarkers were assumed on a hypothetical disease-modifying drug pivotal trial aiming for an 80% probability of achieving a study p value of less than 0.05.

Results: Four scenarios with different combinations of technologies were illustrated. The model illustrated a way to quantify the magnitude of the contributions that enrichment and endpoint technologies could make to drug development studies.

Discussion/conclusion: Quantitative models could be valuable not only for the study sponsors but also as an interactive and collaborative engagement tool for technology players and multi-stakeholder consortia. Establishing values of digital biomarkers could also facilitate business cases and financial investments.

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量化临床试验中数字生物标志物和基于技术的研究终点的益处:Moneyball项目。
数字生物标志物具有改变药物开发的巨大潜力,但只有少数生物标志物为将新疗法推向市场做出了有意义的贡献。它们将如何在临床试验中产生可量化的效益,并最终在3期成功的机会方面存在不确定性。在这里,我们提出了一个统计框架,并运行了一个假设的数字生物标志物的概念验证模型,并以一种熟悉的方式将它们可视化,以研究功率计算。方法:使用Captario SUM®平台对帕金森病(PD)进行蒙特卡罗模拟,并生成说明性研究技术影响计算。我们从杜氏肌营养不良症(Duchenne muscular dystrophy)通过ema认证的可穿戴式数字终端跨步速度95百分位(SV95C)中获得灵感,并设想未来可以使用类似的PD测量方法。DaTscan富集和“sv95c样”终点生物标志物是在假设的疾病改善药物关键试验中假设的,目标是实现研究p值小于0.05的80%概率。结果:展示了四种不同技术组合的场景。该模型说明了一种量化富集和终点技术对药物开发研究的贡献程度的方法。讨论/结论:定量模型不仅对研究发起人有价值,而且对技术参与者和多利益相关者联盟来说,它是一种互动和协作的参与工具。建立数字生物标志物的价值也可以促进商业案例和金融投资。
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来源期刊
Digital Biomarkers
Digital Biomarkers Medicine-Medicine (miscellaneous)
CiteScore
10.60
自引率
0.00%
发文量
12
审稿时长
23 weeks
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