Direct delivery of chemotherapy agents to the brain via degradable polymer delivery systems—such as Gliadel®—is a clinically proven method for treatment of glioblastoma multiforme, but there are important limitations with the current technology—including the requirement for surgery, profound local tissue toxicity, and limitations in diffusional penetration of agents—that limit its application and effectiveness. Here, we demonstrate another technique for direct, controlled delivery of chemotherapy to the brain that provides therapeutic benefit with fewer limitations. In our new approach, camptothecin (CPT)-loaded poly(lacticco-glycolic acid) (PLGA) nanoparticles are infused via convection-enhanced delivery (CED) to a stereotactically defined location in the brain, allowing simultaneous control of location, spread, and duration of drug release. To test this approach, CPT-PLGA nanoparticles (~100 nm in diameter) were synthesized with 25% drug loading. When these nanoparticles were incubated in culture with 9L gliosarcoma cells, the IC50 of CPT-PLGA nanoparticles was 0.04 µM, compared to 0.3 µM for CPT alone. CPT-PLGA nanoparticles stereotactically delivered by CED improved survival in rats with intracranial 9L tumors: the median survival for rats treated with CPT-PLGA nanoparticles (22 days) was significantly longer than unloaded nanoparticles (15 days) and free CPT infusion (17 days). CPT-PLGA nanoparticle treatment also produced significantly more long-term survivors (30% of animals were free of disease at 60 days) than any other treatment. CPT was present in tissues harvested up to 53 days post-infusion, indicating prolonged residence at the local site of administration. These are the first results to demonstrate the effectiveness of combining polymer-controlled release nanoparticles with CED in treating fatal intracranial tumors.
Summary
In this paper, a unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes is proposed. A feasible reference trajectory taking constraints into consideration is first generated offline by the symplectic pseudospectral optimal control method. Then, a trajectory tracking model predictive controller also based on the symplectic pseudospectral method is developed to track the reference trajectory. At each sampling instant, the trajectory tracking controller works by solving an open‐loop optimal control problem where linearized system dynamics is used instead to improve the computational efficiency. Since the symplectic pseudospectral optimal control method is the core algorithm for both offline trajectory planning and online trajectory tracking, constraints on state variables and control inputs can be easily imposed and hence theoretically guaranteed in solutions. By selecting proper weighted matrices on tracking error and control, the developed controller could achieve control objectives in both accurate trolley positioning and fast suppressing of residual swing angles. Simulations for 3D overhead crane systems in the presence of perturbations in initial conditions, an abrupt variation of system parameter, and various external disturbances demonstrate that the developed controller is robust and of excellent control performance.
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