Guan Yuan, Inchai Jakkapong, Fang Zhuoqing, Law Jacky, Garcia Brito Alberto Alonzo, Pawlosky Annalisa, Gottweis Juraj, Daryin Alexander, Myaskovsky Artiom, Ramakrishnan Lakshmi, Palepu Anil, Kulkarni Kavita, Weng Wei-Hung, Natarajan Vivek, Karthikesalingam Alan, Rong Keran, Xu Yunhan, Tu Tao, Peltz Gary
Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University School of Medicine, Stanford CA.
Faculty of Medicine, Chiang Mai University 50200, Thailand.
bioRxiv. 2025 May 4:2025.04.29.651320. doi: 10.1101/2025.04.29.651320.
Liver fibrosis is a severe disease with few treatment options due to the poor quality of the available animal and models. To address this, we investigated whether a hypothesis generating multi-agent AI system (AI co-scientist) could assist in re-purposing drugs for treatment of liver fibrosis and direct their experimental characterization. A multi-parameter image analysis workflow, which enabled anti-fibrotic efficacy and drug toxicity to be serially assessed in multi-lineage human hepatic organoids grown in microwells (i.e., microHOs), was used to assess the effects of 14 drugs. Remarkably, two of the three AI co-scientist-recommended drugs that targeted epigenomic modifiers exhibited significant anti-fibrotic activity. Analysis of the anti-fibrotic effects of five drugs indicated that two inhibited TGFβ-induced intracellular signaling and three drugs altered TGFβ-induced mesenchymal cell differentiation. Since all five of the anti-fibrotic drugs reduced TGFβ-induced chromatin structural changes, epigenomic changes play an important role in the pathogenesis of liver fibrosis. One AI co-scientist recommended drug is an FDA-approved anti-cancer treatment (Vorinostat) that reduced TGFβ-induced chromatin structural changes by 91% and promoted liver parenchymal cell regeneration in microHOs. Hence, the use of AI co-scientist and this microHO platform identified a potential new generation of liver fibrosis treatments that also promote liver regeneration.
肝纤维化是一种严重疾病，由于现有动物模型和其他模型质量欠佳，治疗选择有限。为解决这一问题，我们研究了一种生成假设的多智能体人工智能系统（人工智能合作科学家）是否有助于重新利用药物来治疗肝纤维化，并指导其实验特性研究。我们使用了一种多参数图像分析工作流程来评估14种药物的效果，该流程能够在微孔中生长的多谱系人类肝类器官（即微型肝类器官）中连续评估抗纤维化功效和药物毒性。值得注意的是，人工智能合作科学家推荐的三种靶向表观基因组修饰剂的药物中有两种表现出显著的抗纤维化活性。对五种药物抗纤维化作用的分析表明，其中两种抑制了转化生长因子β（TGFβ）诱导的细胞内信号传导，三种药物改变了TGFβ诱导的间充质细胞分化。由于所有五种抗纤维化药物都减少了TGFβ诱导的染色质结构变化，因此表观基因组变化在肝纤维化发病机制中起重要作用。人工智能合作科学家推荐的一种药物是美国食品药品监督管理局（FDA）批准的抗癌治疗药物（伏立诺他），它使TGFβ诱导的染色质结构变化减少了91%，并促进了微型肝类器官中肝实质细胞的再生。因此，使用人工智能合作科学家和这个微型肝类器官平台确定了新一代潜在的肝纤维化治疗方法，这些方法还能促进肝脏再生。
