Researchers atStanford Universityhave used 3D printing to develop a strain sensor that enables the wireless monitoring of tumor regression in cancer patients.
该设备由50 nm的黄金层组成，滴入3D印刷外壳中的柔性聚合物上，该设备设计为直接包裹在肿瘤周围。位置后，该团队的可拉伸传感器会随着肿瘤的进展而扩展和收缩，以收集数据，这可以为医生提供低成本，可扩展的新方法，以评估患者对治疗的反应。
Targeting in-vivo drug testing
临床翻译、科学家的一个过程s seek to apply lab findings on a disease or potential treatment within human studies, can be arduous to say the least. Through this trial and error approach, the Stanford team say “thousands of potential cancer drugs are tested for every one drug that makes it to patients.”
Currently, to assess such cancer treatments, oncology researchers deploy a suite of high-throughput screening models. Often involving the use of algorithms alongside genomic testing, cell culture and organoid systems, these are capable of evaluating the efficacy of several drugs quickly against a given type of cancer.
That said, in-vivo models tend to yield results that more closely resemble clinical outcomes, as testing with them allows for the comparison of tumor growth between replicates of treated and untreated controls, but biological variation, low-resolution and small sample sizes still make adopting them tricky and expensive.
Given that “determining treatment response is critical to clinical translation,” the Stanford team say developing a tool to automate this process could enable the “gathering of high-resolution continuous datasets.” In their paper, the scientists add that such data quality and automation advances may even eventually unlock “high throughput in-vivo drug testing setups.”
A ‘FAST’ tumor-sensing approach
为了提高体内肿瘤感应的功效，斯坦福大学的科学家提出了一种灵活的自主传感器测量肿瘤体积回归或“快速”技术。该团队的过程基于一个传感器，该传感器由金色层上的弹性体制成，该传感器旨在与肿瘤串联成长或收缩。
When it does so, microcracks occur in the device’s gold layer, causing parts of its surface to lose contact with each other, and for the tortuosity of its electrons to increase. These changes can then theoretically be monitored via a custom PCB and mobile app, and initial testing showed that at a strain of 0-75%, their sensor was capable of detecting size differences down to a 10-μm resolution.
While the scientists’ sensing tool can be applied internally or externally, they went on to assess its efficacy in-vivo within a mouse test subject. Held in place via a 3D printed housing mechanism, designed to allow it to accurately track tumor volume changes, the sensor proved capable in these tests of identifying differently-treated tumors in a space of just five hours.
What’s more, when deployed over a week, each cheap and easy-to-construct sensor demonstrated the ability to consistently and accurately measure tumors via resistance variations. As such, the device’s developers say it could now “expedite and automate the process of screening cancer therapies,” while “eliminating the need for guesswork” around their efficacy.
3D printing’s drug discovery potential
Accelerating drug discovery has also become one of the main target applications of emerging 3D bioprinting technologies. Earlier this year, start-upNucleraraised $42.5 million towards the launch of a newEOPROTEIN药物发现3D生物生产商, designed to produce the samples needed to study protein-to-protein interactions, and gain a better understanding of deadly diseases.
Prellis Biologics, meanwhile, attracted $14.5 million in Series B funding in December 2021 to support its development of a novel drug discovery platform. As the firm’sEXIS 3D bioprinted lymph nodeis capable of breaking the natural cell failsafes that prevent auto-antibody development, it could soon expedite therapeutic R&D.
在最近的新闻中3D Systems has spun-off Systemic Bio, a 3D bioprinting subsidiary with similar goals of developing a drug discovery tool. Already, the company has unveiled a h-VIOS organ-on-a-chip platform, and it’s currently working to establish partnerships with potential pharmaceutical adopters.
More on the researchers’ findings can be found in their paper titled “A flexible electronic strain sensor for the real-time monitoring of tumor regression。”这项研究由Alex Abramson，Carmel T. Chan，Yasser Khan，Alana Mermin-Bunnell，Naoji Matsuhisa，Robyn Fong，Rohan Shad，William Heisinger，Parag Mallick，Sanjiv Samjiv Sam Gambhir和Zhenan Bao。
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Featured image shows the team’s sensor installed into a mouse test subject. Image via Stanford University.
