Graduate School of Pharmaceutical Sciences, Kumamoto University, 5-1 Oe-honmachi, Chuo-ku, Kumamoto 862-0973, Japan.
International Research Center for Agricultural and Environmental Biology (IRCAEB), 2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, Japan.
J Nat Prod. 2024 Oct 25;87(10):2393-2397. doi: 10.1021/acs.jnatprod.4c00640. Epub 2024 Oct 4.
Natural products that inhibit osteoclast differentiation are promising therapeutic and preventive agents for osteoporosis. Conventionally, identifying osteoclast differentiation involves visual inspection of the microscope images of stained osteoclasts. In this study, a supervised machine learning model was developed to classify bright-field microscope images of osteoclasts without staining. The model was used to screen a compound library, and osteoclast differentiation inhibitors were identified, demonstrating the validity of our method. Next, an in-house library of fungal extracts was screened, and pinolidoxin was revealed as an inhibitor of osteoclast differentiation. Our machine learning method enabled accurate, objective, and high-throughput evaluation of osteoclast differentiation and efficient screening of the inhibitors from natural product extracts. This study represents the first machine learning classification developed to evaluate the inhibitory activity of natural products in osteoclast differentiation.
天然产物抑制破骨细胞分化是治疗和预防骨质疏松症的有前途的药物。传统上，鉴定破骨细胞分化需要通过观察染色破骨细胞的显微镜图像来进行。在这项研究中，开发了一种监督机器学习模型，用于对未染色的破骨细胞明场显微镜图像进行分类。该模型用于筛选化合物文库，并鉴定出破骨细胞分化抑制剂，证明了我们方法的有效性。接下来，对一个真菌提取物的内部文库进行了筛选，发现表松脂素是破骨细胞分化的抑制剂。我们的机器学习方法能够准确、客观、高通量地评估破骨细胞分化，并从天然产物提取物中有效筛选抑制剂。这项研究代表了第一个用于评估天然产物在破骨细胞分化中的抑制活性的机器学习分类方法。
