Kim Sun Kyung, Goughnour Peter C, Lee Eui Jin, Kim Myeong Hyun, Chae Hee Jin, Yun Gwang Yeul, Kim Yi Rang, Choi Jin Woo
College of Pharmacy, Kyung Hee University, Seoul, Republic of Korea.
Center for Research and Development, Oncocross Ltd., Seoul, Republic of Korea.
PLoS One. 2021 Jan 28;16(1):e0246106. doi: 10.1371/journal.pone.0246106. eCollection 2021.
Aging is a multifactorial process that involves numerous genetic changes, so identifying anti-aging agents is quite challenging. Age-associated genetic factors must be better understood to search appropriately for anti-aging agents. We utilized an aging-related gene expression pattern-trained machine learning system that can implement reversible changes in aging by linking combinatory drugs. In silico gene expression pattern-based drug repositioning strategies, such as connectivity map, have been developed as a method for unique drug discovery. However, these strategies have limitations such as lists that differ for input and drug-inducing genes or constraints to compare experimental cell lines to target diseases. To address this issue and improve the prediction success rate, we modified the original version of expression profiles with a stepwise-filtered method. We utilized a machine learning system called deep-neural network (DNN). Here we report that combinational drug pairs using differential expressed genes (DEG) had a more enhanced anti-aging effect compared with single independent treatments on leukemia cells. This study shows potential drug combinations to retard the effects of aging with higher efficacy using innovative machine learning techniques.
衰老是一个涉及众多基因变化的多因素过程，因此识别抗衰老药物极具挑战性。为了合理地寻找抗衰老药物，必须更好地理解与年龄相关的遗传因素。我们利用了一个经过衰老相关基因表达模式训练的机器学习系统，该系统可以通过联合药物实现衰老的可逆变化。基于计算机模拟基因表达模式的药物重新定位策略，如连接图谱，已被开发为一种独特的药物发现方法。然而，这些策略存在局限性，例如输入基因和药物诱导基因的列表不同，或者在将实验细胞系与目标疾病进行比较时存在限制。为了解决这个问题并提高预测成功率，我们用逐步过滤法修改了原始版本的表达谱。我们使用了一种名为深度神经网络（DNN）的机器学习系统。在此我们报告，与对白血病细胞的单一独立治疗相比，使用差异表达基因（DEG）的联合药物对具有更强的抗衰老作用。这项研究展示了利用创新的机器学习技术，以更高的疗效延缓衰老影响的潜在药物组合。
