Preclinical and early clinical studies of a novel compound SYHA1813 that efficiently crosses the blood-brain barrier and exhibits potent activity against glioblastoma

Glioblastoma (GBM) is the most common and aggressive malignant brain tumor in adults and is poorly controlled. Previous studies have shown that both macrophages and angiogenesis play significant roles in GBM progression, and co-targeting of CSF1R and VEGFR is likely to be an effective strategy for GBM treatment. Therefore, this study developed a novel and selective inhibitor of CSF1R and VEGFR, SYHA1813, possessing potent antitumor activity against GBM. SYHA1813 inhibited VEGFR and CSF1R kinase activities with high potency and selectivity and thus blocked the cell viability of HUVECs and macrophages and exhibited anti-angiogenetic effects both in vitro and in vivo. SYHA1813 also displayed potent in vivo antitumor activity against GBM in immune-competent and immune-deficient mouse models, including temozolomide (TMZ) insensitive tumors. Notably, SYHA1813 could penetrate the blood-brain barrier (BBB) and prolong the survival time of mice bearing intracranial GBM xenografts. Moreover, SYHA1813 treatment resulted in a synergistic antitumor efficacy in combination with the PD-1 antibody. As a clinical proof of concept, SYHA1813 achieved confirmed responses in patients with recurrent GBM in an ongoing first-in-human phase I trial. The data of this study support the rationale for an ongoing phase I clinical study (ChiCTR2100045380).

Design and implementation of a modular pulse wave preprocessing and analysis system based on a new detection algorithm / 生物医学工程学杂志

As one of the standard electrophysiological signals in the human body, the photoplethysmography contains detailed information about the blood microcirculation and has been commonly used in various medical scenarios, where the accurate detection of the pulse waveform and quantification of its morphological characteristics are essential steps. In this paper, a modular pulse wave preprocessing and analysis system is developed based on the principles of design patterns. The system designs each part of the preprocessing and analysis process as independent functional modules to be compatible and reusable. In addition, the detection process of the pulse waveform is improved, and a new waveform detection algorithm composed of screening-checking-deciding is proposed. It is verified that the algorithm has a practical design for each module, high accuracy of waveform recognition and high anti-interference capability. The modular pulse wave preprocessing and analysis software system developed in this paper can meet the individual preprocessing requirements for various pulse wave application studies under different platforms. The proposed novel algorithm with high accuracy also provides a new idea for the pulse wave analysis process.