AI 治癌卡在数据缺口：Precigenetics 用活细胞动态测量补上关键一环
这条挺有意思，作者指出了个真问题：现有细胞数据全是快照，肿瘤耐药恰恰发生在快照拍不到的那几天。Precigenetics 能测活细胞的连续变化，还扩展到 3D 类器官，正好补上 virtual cell 模型的训练数据缺口。注意作者持股，属于利益相关的推荐。
作者认为 AI 要在癌症治疗上真正发挥作用，缺的不是模型而是训练数据。现有生物数据集多为单时刻快照，因为测序等测量手段会杀死细胞，只能拿到用药前和用药后两个状态，而肿瘤细胞恰恰是在几个小时到几天的时间窗口内产生耐药性，这个过程被完全漏掉。
Precigenetics 的做法是对活细胞进行无标记的动态测量，在不破坏细胞的前提下捕捉形态和生化指标随时间的连续变化，覆盖细胞对药物等扰动的完整响应路径。第二个问题是现有数据多来自 2D 细胞系或动物模型，与人体组织真实响应差异很大，这也是大量药物在临床前数据亮眼却倒在人体试验的原因之一。该公司正在把平台扩展到 3D 类器官模型，更接近真实人体生物学。
两者结合后，产出的数据正好是训练下一代 AI "virtual cell" 模型所需的形态。文中也披露作者本人是该公司的顾问并持有股权，创始人 Parmita 目前正在寻求天使投资。
I don't think AI can cure cancer until it has the right data to learn from. Right now, that data mostly doesn't exist. I believe Precigenetics can change that.
Most large biological datasets used to train AI "virtual cell" models show cells at a single moment, usually because the measurement (ex: sequencing) destroys the cell. You get a "before" and an "after" treatment, but not the path in between.
That path can matter a lot though. For example, tumor cells adapt to drugs over hours and days, and resistance often emerges in exactly the window a snapshot misses.
Precigenetics addresses this challenge by measuring the dynamic response of cells to drugs and other perturbations. They can measure morphological and biochemical changes inside living cells, without labels or killing the cell.
The second problem is that most of this kind of data comes from 2D cell lines or animal models, which often don't represent how human tissue will respond. That's one reason so many drugs that look promising in preclinical studies fail in human trials.
Precigenetics is extending its platform to human organoids, 3D tissue models that more closely resemble real human biology.
Put those together and you get exactly the kind of data that is needed to train the next generation of AI virtual cells to solve cancer (and other diseases!).
Parmita is a formidable founder and she is now looking for investors to support her mission. If you are an angel investor, I highly recommend reaching out!
Full disclosure: I am an advisor for, and hold equity in Precigenetics.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Precigenetics, virtual cell, 类器官, 药物研发.
Source attribution: curated from Tanishq Abraham (论文推介) (@iScienceLuvr).
