78 Background: Circularly permuted TRAIL (CPT) is a recombinant mutant of human Apo2L/TRAIL developed by Beijing Sunbio Biotech Co., Ltd. as a targeted therapy for multiple myeloma and other hematologic malignancies. CPT is a dual pro-apoptotic receptor agonist that directly activates both pro-apoptotic receptors TRAIL-R1 (DR4) and TRAIL-R2 (DR5). CPT selectively induces apoptosis in a variety of cancer cells, while sparing most normal cells in preclinical models. Objective: CPT has shown preliminary activities for patients with relapsed or refractory multiple myeloma (Rel/Ref MM) in phase I study. The aim of this phase II study is to investigate the effect and safety of CPT for Rel/Ref MM patients. Methods: In this multi-center, open-label, single arm phase II study, twenty-seven Rel/Ref MM patients were enrolled to receive CPT treatment alone at the dose of 2.5 mg/kg/day intravenously, once daily for 14 consecutive days of each 21-day cycle. Clinical responses to CPT after 2 cycle's treatment were assessed by an independent review committee according to the criteria of the European Group for Blood and Marrow Transplantation (EBMT). Results: Among the 27 patients, one patient (3.7%) achieved near complete response (nCR) and eight patients (29.63%) exhibited partial response (PR). The overall response rate (ORR) was 33.3%. The median PFS was not reached within 2 cycle's treatment. The drug related adverse events (≥10%) included fever, AST/ALT elevation, leucopenia, neutropenia, rash, thrombocytopenia, and LDH elevation. The severe adverse events were occurred in 3 patients (11%), one of them was CPT-related liver injury. Anti-drug antibodies were detected in 6 patients without reduced efficacy or increased toxicities. Conclusions: The CPT is a very effective and well-tolerated new agent for Rel/Ref MM, and it is worthy to be further studied. Disclosures: Yang: Beijing Sunbio Biotech Co. Ltd.: Employment. Cui:Beijing Sunbio Biotech Co. Ltd.: Employment.

This paper realizes the simultaneous optimization of a vessel’s course and speed for a whole voyage within the estimated time of arrival (ETA), which can ensure the voyage is safe and energy-saving through proper planning of the route and speed. Firstly, a dynamic sea area model with meteorological and oceanographic data sets is established to delineate the navigable and prohibited areas; secondly, some data are extracted from the records of previous voyages, to train two artificial neural network models to predict fuel consumption rate and revolutions per minute (RPM), which are the keys to route optimization. After that, speed configuration is introduced to the optimization process, and a simultaneous optimization model for the ship’s course and speed is proposed. Then, based on a customized version of the A* algorithm, the optimization is solved in simulation. Two simulations of a ship crossing the North Pacific show that the proposed methods can make navigation decisions in advance that ensure the voyage’s safety, and compared with a naive route, the optimized navigation program can reduce fuel consumption while retaining an approximately constant time to destination and adapting to variations in oceanic conditions.

No abstract