MUCOADHESIVE NANOPARTICLE COMPOSITION COMPRISING IMMUNOSUPPRESANT AND METHODS OF USE THEREOF
    16.
    发明申请
    MUCOADHESIVE NANOPARTICLE COMPOSITION COMPRISING IMMUNOSUPPRESANT AND METHODS OF USE THEREOF 有权
    包含免疫抑制剂的MUCOADHESIVE纳米颗粒组合物及其使用方法

    公开(公告)号:US20160243189A1

    公开(公告)日:2016-08-25

    申请号:US15142709

    申请日:2016-04-29

    Abstract: Disclosed is a mucoadhesive nanoparticle delivery system for delivering an immunosuppressant, such as cyclosporine A, to a mucosal site for treatment of a disease or condition involving inflammation or excess immune activity. The system comprises nanoparticles formed from a plurality of linear amphiphilic block copolymers, each having a hydrophobic block comprising polylactide (PLA) and a hydrophilic block comprising dextran. The nanoparticles are surface-functionalized with a mucosal targeting moiety, such as a phenylboronic acid derivative, for targeted delivery and enhanced retention at the mucosal site. Pharmaceutical compositions, methods, and uses thereof comprising the mucoadhesive nanoparticle delivery system are disclosed. The compositions can be administered in an effective amount for treating the disease or condition while substantially preserving or restoring the function and/or integrity of the mucosal lining. The composition may be formulated as an aqueous suspension for administration to an anterior surface of the eye in the treatment of dry eye syndrome.

    Abstract translation: 公开了一种用于将免疫抑制剂如环孢霉素A递送至粘膜部位的粘膜粘附纳米颗粒递送系统,用于治疗涉及炎症或过量免疫活性的疾病或病症。 该系统包含由多个线性两亲嵌段共聚物形成的纳米颗粒,每个嵌段共聚物均具有包含聚丙交酯(PLA)的疏水嵌段和包含葡聚糖的亲水嵌段。 纳米颗粒用粘膜靶向部分如苯基硼酸衍生物进行表面官能化,用于靶向递送,并增强粘膜部位的保留。 公开了包含粘膜粘附纳米颗粒递送系统的药物组合物,方法和用途。 组合物可以以有效量施用以治疗疾病或病症,同时基本上保留或恢复粘膜内层的功能和/或完整性。 组合物可以配制成水性悬浮液,用于在干眼综合征的治疗中给予眼前表面。

    Learning-model predictive control with multi-step prediction for vehicle motion control

    公开(公告)号:US12296839B2

    公开(公告)日:2025-05-13

    申请号:US18060023

    申请日:2022-11-30

    Abstract: A system for learning-model predictive control (LMPC) with multi-step prediction for motion control of a vehicle includes sensors and actuators. One or more control modules each having a processor, a memory, and input/output (I/O) ports are in communication with the sensors and actuators, the processor executing program code portions stored in the memory. The program code portions cause the sensors and actuators to obtain vehicle state information, receive a driver input, and generate a desired dynamic output based on the driver input and the vehicle state information. A program code portion estimates actions of the actuators based on the vehicle state information and the driver input, and utilizes the vehicle state information, the driver input, and the estimated actions of the actuators to select one or more models of a physics-based vehicle model and a machine-learning model of the vehicle to selectively adjust commands to the actuators.

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