Tran Na Ly, Kim Hyerim, Shin Cheol-Hee, Ko Eun, Oh Seung Ja
Department of Genetics and Biotechnology, College of Life Sciences, Kyung Hee University, Yongin-Si, 17104, Gyeonggi-Do, Korea.
Program in Nanoscience and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.
Cancer Cell Int. 2023 Dec 12;23(1):321. doi: 10.1186/s12935-023-03176-2.
Artificial intelligence (AI) is capable of integrating a large amount of related information to predict therapeutic relationships such as disease treatment with known drugs, gene expression, and drug-target binding. AI has gained increasing attention as a promising tool for next-generation drug development.
An AI method was used for drug repurposing and target identification for cancer. Among 8 survived candidates after background checking, N-(1-propyl-1H-1,3-benzodiazol-2-yl)-3-(pyrrolidine-1-sulfonyl) benzamide (Z29077885) was newly selected as an new anti-cancer drug, and the anti-cancer efficacy of Z29077885 was confirmed using cell viability, western blot, cell cycle, apoptosis assay in MDA-MB 231 and A549 in vitro. Then, anti-tumor efficacy of Z29077885 was validated in an in vivo A549 xenograft in BALB/c nude mice.
First, we discovered an antiviral agent, Z29077885, as a new anticancer drug candidate using the AI deep learning method. Next, we demonstrated that Z29077885 inhibits Serine/threonine kinase 33 (STK33) enzymatic function in vitro and showed the anticancer efficacy in various cancer cells. Then, we found enhanced apoptosis via S-phase cell cycle arrest as the mechanism underlying the anticancer efficacy of Z29077885 in both lung and breast cancer cells. Finally, we confirmed the anti-tumor efficacy of Z29077885 in an in vivo A549 xenograft.
In this study, we used an AI-driven screening strategy to find a novel anticancer medication targeting STK33 that triggers cancer cell apoptosis and cell cycle arrest at the s phase. It will pave a way to efficiently discover new anticancer drugs.
人工智能（AI）能够整合大量相关信息，以预测治疗关系，如已知药物与疾病治疗、基因表达以及药物 - 靶点结合之间的关系。作为下一代药物开发的一种有前景的工具，人工智能已受到越来越多的关注。
采用一种人工智能方法进行癌症药物的重新利用和靶点识别。在背景审查后的8个存活候选物中，新选择N-(1-丙基-1H-1,3-苯并二氮杂卓-2-基)-3-(吡咯烷-1-磺酰基)苯甲酰胺（Z29077885）作为一种新型抗癌药物，并在体外使用MDA-MB 231和A549细胞的细胞活力、蛋白质免疫印迹、细胞周期、凋亡检测等方法确认了Z29077885的抗癌效果。然后，在BALB/c裸鼠体内的A549异种移植模型中验证了Z29077885的抗肿瘤效果。
首先，我们使用人工智能深度学习方法发现了一种抗病毒剂Z29077885作为新型抗癌药物候选物。其次，我们证明Z29077885在体外抑制丝氨酸/苏氨酸激酶33（STK33）的酶功能，并在各种癌细胞中显示出抗癌效果。然后，我们发现通过S期细胞周期阻滞增强凋亡是Z29077885在肺癌和乳腺癌细胞中抗癌效果的潜在机制。最后，我们在体内A549异种移植模型中确认了Z29077885的抗肿瘤效果。
在本研究中，我们使用人工智能驱动的筛选策略发现了一种靶向STK33的新型抗癌药物，该药物可触发癌细胞凋亡并使细胞周期停滞在S期。这将为高效发现新型抗癌药物铺平道路。
