Machine-Learning-Driven Discovery of -Phenylbenzenesulfonamides as a Novel Chemotype for Lactate Dehydrogenase A Inhibition with Anti-Pancreatic Cancer Activity.

Journal: Journal of medicinal chemistry
Published Date: Jul 11, 2025

Abstract

Lactate dehydrogenase A (LDHA) is a promising target for cancer therapy due to its crucial role in aerobic glycolysis. Despite extensive efforts, the structural diversity of LDHA inhibitors remains limited. Here, we utilized machine learning techniques, particularly reinforcement learning models, along with molecular dynamics simulations and biological validation, to identify -phenylbenzenesulfonamides as a novel chemotype for LDHA inhibition. Compound was generated and identified as a hit (IC = 720 nM), with its sulfonamide moiety forming crucial hydrogen-bonding interactions with LDHA. Structural optimization led to derivatives with enhanced LDHA inhibitory activity, exemplified by compound (IC = 156 nM). Compound significantly reduced lactate production and induced apoptosis in pancreatic Mia PaCa-2 cells and demonstrated robust antitumor effects following oral administration at 100 mg/kg, with no apparent toxicity observed. These findings position as a promising LDHA inhibitor with a novel chemotype for pancreatic cancer treatment.

Authors

  • Qijie Gong
    State Key Laboratory of Natural Medicines, and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing 211198, China.
  • Ying Dong
    School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, Beijing, China.
  • Jiaqi Liang
    State Key Laboratory of Natural Medicines, and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing 211198, China.
  • Jian Zhou
    CTIQ, Canon Medical Research USA, Inc., Vernon Hills, 60061, USA.
  • Guowei Zhang
    Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, China.
  • Le Yang
    Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA.
  • Tiande Bing
    State Key Laboratory of Natural Medicines, and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing 211198, China.
  • Fidiniaina Rina Juliana
    State Key Laboratory of Natural Medicines, and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing 211198, China.
  • Ziyuan Zhu
    Faculties of Pharmacy and Pharmaceutical Sciences, Monash University, Clayton VIC 3800, Australia.
  • Yue Wu
    Key Laboratory of Luminescence and Real-Time Analytical Chemistry (Ministry of Education), College of Pharmaceutical Sciences, Southwest University, Chongqing 400716, China.
  • Fulai Yang
    Department of Chemistry, China Pharmaceutical University, Nanjing 211198, China.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Xiaojin Zhang
    State Key Laboratory of Natural Medicines, and Jiangsu Key Laboratory of Drug Design and Optimization, China Pharmaceutical University, Nanjing 211198, China.

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