我们目前所见证的对个性化医疗的不断需求引发了在生物标志物发现和生物库领域更深入的研究，因此在处理、收集、存储和分发“高质量”生物样本的能力上取得了长足的进展。然而，“样本质量”这一概念随着技术进步而发生变化。在这方面，我们提议在精准医学研究中将样本质量的概念从“高质量”的广义定义转变为更适合的“适合用途”的概念。数字孪生是真实实体的数字副本，在任何数字化领域都被广泛采用，并正在生物医学领域找到应用。本文提出对生物样本采用数字孪生的采用，可以提供有关物理孪生体（即生物样本）的整个生命周期的即时信息，并大大扩大可用样本与研究人员和医生需求之间的可能匹配标准。这种微调匹配可以极大地有助于提高“适合用途”的质量，不仅适用于基于当前需求的研究，而且还可以改善未来可能面临的最佳可用样本的识别，这取决于技术和生命科学的发展。在我们的生物库视角中，假设并利用数据科学观点，可用的数据越多（准确），则可以从中提取的信息越多，未来（当前未知的）需求的匹配机会就越多。这应该是所谓的时间机器——生物库应该遵循的必须原则。版权所有©2023年，国际抗癌研究所（George J. Delinasios 博士），保留所有权利。

The growing demand for personalized medicine we are currently witnessing has given rise to more in-depth research in the field of biomarker discovery and, thus, in biological banks that hold the ability to process, collect, store, and distribute "high-quality" biological specimens. However, the notion of "specimen quality" is subject to change with technological advancements. In this perspective, we propose that the notion of sample quality should shift from a broad definition of "high-quality" to a "fit-for-purpose" concept more suitable for precision medicine studies. Digital twins are a digital replica of real entities. These are largely adopted in any digitalized domain and are currently finding applications in biomedicine. The adoption of digital twins for biosamples, proposed in this paper, can provide prompt information about the whole lifecycle of the physical twin (i.e., the biosample) and substantially extend the possible matching criteria between the available samples and the researchers' and physicians' requests. This fine-tuning matching could greatly contribute to improving the "fit-for-purpose" quality, not only for studies based on current needs, but also to improve the identification of the best available samples in future situations, determined by the evolution of technologies and biosciences. Assuming and exploiting a data-science view in our biobank perspective, the more (accurate) data there are available, the more information can be extrapolated from them, the more opportunities there are for matching future, currently unknown, needs. This should be a mandatory principle that the 'time machines' called biobanks should follow.Copyright © 2023, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.