Design, synthesis and in vitro biological evaluation of 2-aminopyridine derivatives as novel PI3Kδ inhibitors for hematological cancer.

Phosphoinositide-3-kinase (PI3K) involves in regulation of proliferation, cell cycle, and apoptosis, and is overexpressed in most of human malignant tumors. Therefore, the development of PI3K inhibitors has attracted great interest in tumor treatment. In this study, we designed and synthesized a series of 2-aminopyridine derivatives via a bioisosterism strategy. Among them, compound MR3278 showed superior PI3Kδ inhibitory activity (IC50 = 30 nM), as well as higher inhibitory activity to most of AML cells (e.g., MOLM-16 and Mv-4-11 cells with IC50 values of 2.6 µM and 3.7 µM, respectively) than Idelalisib. Further cell studies indicated that MR3278 could induce G2/M phase arrests and cell apoptosis of Mv-4-11 cells via PI3K dependent pathway in a dose dependent manner. In addition, in silico physicochemical and ADMET evaluation revealed its drug-like properties with satisfactory toxicity profiles. These results indicate that MR3278 can be identified as a promising new lead compound to the current PI3Kδ inhibitor and is worthy of further profiling.

Automatic detection of adenoid hypertrophy on lateral nasopharyngeal radiographs of children based on deep learning.

Background: Adenoid hypertrophy is a prevalent cause of upper airway obstruction in children, potentially leading to various otolaryngological complications and even systemic sequelae. The lateral nasopharyngeal radiograph is routinely employed for the diagnosis of adenoid hypertrophy. This study aimed to evaluate the accuracy and reliability of deep learning, using lateral nasopharyngeal radiographs, for the diagnosis of adenoid hypertrophy in pediatric patients. Methods: In the retrospective study, the lateral nasopharyngeal X-ray images were collected from children receiving therapy in the Children's Hospital of Soochow University, the 983th Hospital of Joint Logistics Support Forces of Chinese PLA and the Suzhou Wujiang District Children's Hospital from January 2023 to November 2023. Five deep learning models, i.e., AlexNet, VGG16, Inception v3, ResNet50 and DenseNet121, were used for model training and validation. Receiver operating characteristic (ROC) curve analyses were used to evaluate the performance of each model. The best algorithm was compared with interpretations from three radiologists on 208 images in the internal validation group. Results: The lateral nasopharyngeal X-ray images were collected from 1,188 children, including 705 males (59.3%) and 483 females (40.7%), aged 8 months to 13 years, with a mean age of 5.57±2.66 years. Among the five deep learning models, DenseNet-121 performed the best, with area under the curve (AUC) values of 0.892 and 0.872, with accuracy of 0.895 and 0.878, sensitivity of 0.870 and 0.838, and specificity of 0.913 and 0.906 in the internal and external validation groups, respectively. The diagnostic performance of DenseNet-121 was higher than that of the junior and mid-level radiologists (0.892 vs. 0.836, 0.892 vs. 0.869), close to the senior radiologist (0.892 vs. 0.901). However, Delong's test revealed no significant difference between DenseNet121 and each radiologist in the validation group (P=0.24, P=0.52, P=0.79). Conclusions: All the five deep learning models in the study showed good performance for the diagnosis of adenoid hypertrophy, with DenseNet121 being the best, which was clinically relevant for the automatic identification of adenoid hypertrophy.

Discovery of N-(2-chloro-5-(3-(pyridin-4-yl)-1H-pyrazolo[3,4-b]pyridin-5-yl)pyridin-3-yl)-4-fluorobenzenesulfonamide (FD274) as a highly potent PI3K/mTOR dual inhibitor for the treatment of acute myeloid leukemia.

PI3K-Akt-mTOR pathway is a highly activated signal transduction pathway in human hematological malignancies and has been validated as a promising target for acute myeloid leukemia (AML) therapy. Herein, we designed and synthesized a series of 7-azaindazole derivatives as potent PI3K/mTOR dual inhibitors based on our previously reported FD223. Among them, compound FD274 showed excellent dual PI3K/mTOR inhibitory activity, with IC50 values against PI3Kα/ß/γ/δ and mTOR of 0.65 nM, 1.57 nM, 0.65 nM, 0.42 nM, and 2.03 nM, respectively, superior to compound FD223. Compared to the positive drug Dactolisib, FD274 exhibited significant anti-proliferation of AML cell lines (HL-60 and MOLM-16 with IC50 values of 0.092 µM and 0.084 µM, respectively) in vitro. Furthermore, FD274 demonstrated dose-dependent inhibition of tumor growth in the HL-60 xenograft model in vivo, with 91% inhibition of tumor growth at an intraperitoneal injection dose of 10 mg/kg and no observable toxicity. All of these results suggest that FD274 has potential for further development as a promising PI3K/mTOR targeted anti-AML drug candidate.