Purpose Sphingosine kinases (SK1 and SK2) regulate tumor growth by generating the mitogenic and pro-inflammatory lipid sphingosine 1-phosphate (S1P). This phase I study investigated the safety, pharmacokinetics, pharmacodynamics and anti-tumor activity of ABC294640, a first-in-class orally-available inhibitor of SK2. Experimental Design Escalating doses of ABC294640 were administered orally to patients with advanced solid tumors in sequential cohorts at the following dose levels: 250 mg qd, 250 mg bid, 500 mg bid and 750 mg bid, continuously in cycles of 28 days. Serial blood samples were obtained to measure ABC294640 concentrations and sphingolipid profiles. Results 22 patients were enrolled, and 21 received ABC294640. The most common drug-related toxicities were nausea, vomiting and fatigue. Among the four patients at 750 mg bid, one had dose-limiting grade 3 nausea and vomiting, and two were unable to complete Cycle 1 due to diverse drug-related toxicities. The 500 mg bid dose level was established as the Recommended Phase II Dose. ABC294640 administration resulted in decreases in S1P levels over the first 12 hours, with return to baseline at 24 hours. The best response was a partial response in a patient with cholangiocarcinoma at 250 mg qd, and stable disease was observed in 6 patients with various solid tumors across dose levels. Conclusions At 500 mg bid, ABC294640 is well tolerated and achieves biologically-relevant plasma concentrations. Changes in plasma sphingolipid levels may provide a useful pharmacodynamic biomarker for ABC294640.

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The use of neural network techniques to localize an acoustic point source in a homogeneous medium is demonstrated. The input data are the cosines of the phase difference measurements at an array with N detectors. Only the most fundamental types of neural network systems will be considered. Use will be made of linear and sigmoid-type neurons in a single-layer network. The performance of the single-layer network is very satisfactory for a wide range of configuration parameters if the resolution and sampling conditions are satisfied. Once the parameters of the neural network are determined, the computational effort to determine a new source location is minimal. However, when a source/detector configuration is considered that does not satisfy the resolution and sampling conditions, the single-layer network will not consistently perform well.