Phase I dose escalation study of BI 836826 (CD37 antibody) in patients with relapsed or refractory B-cell non-Hodgkin lymphoma

BI 836826 is a chimeric immunoglobulin G1 antibody targeting CD37, a tetraspanin transmembrane protein predominantly expressed on normal and malignant B cells. This phase I, open-label study used a modified 3 + 3 design to evaluate the safety, maximum tolerated dose (MTD), pharmacokinetics, and preliminary activity of BI 836826 in patients with relapsed/refractory B cell non-Hodgkin lymphoma (NHL; NCT01403948). Eligible patients received up to three courses comprising an intravenous infusion (starting dose: 1 mg) once weekly for 4 weeks followed by an observation period of 27 (Course 1, 2) or 55 days (Course 3). Patients had to demonstrate clinical benefit before commencing treatment beyond course 2. Forty-eight patients were treated. In the dose escalation phase (1-200 mg) involving 37 Caucasian patients, the MTD was 100 mg. Dose-limiting toxicities occurred in four patients during the MTD evaluation period, and included stomatitis, febrile neutropenia, hypocalcemia, hypokalemia, and hypophosphatemia. The most common adverse events were neutropenia (57%), leukopenia (57%), and thrombocytopenia (41%), and were commonly of grade 3 or 4. Overall, 18 (38%) patients experienced infusion-related reactions, which were mostly grade 1 or 2. Preliminary evidence of anti-tumor activity was seen; three patients responded to treatment, including one complete remission in a Korean patient with diffuse large B cell lymphoma. BI 836826 plasma exposure increased more than proportionally with increasing doses. BI 836826 demonstrated preliminary activity; the most frequent adverse events were hematotoxicity and infusion-related reactions which were manageable after amending the infusion schedule. Although BI 856826 will not undergo further clinical development, these results confirm CD37 as a valid therapeutic target in B cell NHL

An Assessment of the diversity in scenario-based tsunami forecasts for the Indian Ocean

This work examines the extent to which tsunami forecasts from different numerical forecast systems might be expected to differ under real-time conditions. This is done through comparing tsunami amplitudes from a number of existing tsunami scenario databases for eight different hypothetical tsunami events within the Indian Ocean. Forecasts of maximum tsunami amplitude are examined at ten output points distributed throughout the Indian Ocean at a range of depths. The results show that there is considerable variability in the forecasts and on average, the standard deviation of the maximum amplitudes is approximately 62% of the mean value. It is also shown that a significant portion of this diversity can be attributed to the different lengths of the scenario time series. These results have implications for the interoperability of Regional Tsunami Service Providers in the Indian Ocean

Accuracy of resource selection functions across spatial scales

International audienceResource selection functions (RSFs) can be used to map suitable habitat of a species based on predicted probability of use. The spatial scale may affect accuracy of such predictions. To provide guidance as to which spatial extent or grain is appropriate and most accurate for animals, we used the concept of hierarchical selection orders to dictate extent and grain. We conducted a meta-analysis from 123 RSF studies of 886 species to identify differences in prediction success that might be expected for five selection orders. Many studies do not constrain spatial extent to the grain of the next broader selection order in the hierarchy, mixing scaling effects. Thus, we also compared accuracy of single- vs. multiple-grain RSFs developed at the unconstrained extent of an entire study area. Results suggested that the geographical range of a species was the easiest to predict of the selection orders. At smaller scales within the geographical range, use of a site was easier to predict when environmental variables were measured at a grain equivalent to the home-range size or a microhabitat feature required for reproduction or resting. Selection of patches within home ranges and locations of populations was often more difficult to predict. Multiple-grain RSFs were more predictive than single-grain RSFs when the entire study area was considered available. Models with variables measured at both small and large (> 100 ha) grains were usually most predictive, even for many species with small home ranges. Multiple-grain models may be particularly important for species with moderate dispersal abilities in habitat fragments surrounded by an unsuitable matrix. We recommend studies should no longer address only one grain to map animal species distributions