Hydroxychloroquine and short-course radiotherapy in elderly patients with newly diagnosed high-grade glioma: a randomized phase II trial

Background: Effective treatment for patients at least 70 years with newly diagnosed glioblastoma remains challenging and alternatives to conventional cytotoxics are appealing. Autophagy inhibition has shown promising efficacy and safety in small studies of glioblastoma and other cancers. Methods: We conducted a randomized phase II trial to compare radiotherapy with or without hydroxychloroquine (2:1 allocation). Patients aged at least 70 years with newly diagnosed high-grade glioma deemed suitable for short-course radiotherapy with an ECOG performance status of 0–1 were included. Radiotherapy treatment consisted of 30 Gy, delivered as 6 fractions given over 2 weeks (5 Gy per fraction). Hydroxychloroquine was given as 200 mg orally b.d. from 7 days prior to radiotherapy until disease progression. The primary endpoint was 1-year overall survival (OS). Secondary endpoints included progression-free survival (PFS), quality of life, and toxicity. Results: Fifty-four patients with a median age of 75 were randomized between May 2013 and October 2016. The trial was stopped early in 2016. One-year OS was 20.3% (95% confidence interval [CI] 8.2–36.0) hydroxychloroquine group, and 41.2% (95% CI 18.6–62.6) radiotherapy alone, with a median survival of 7.9 and 11.5 months, respectively. The corresponding 6-month PFS was 35.3% (95% CI 19.3–51.7) and 29.4% (95% CI 10.7–51.1). The outcome in the control arm was better than expected and the excess of deaths in the hydroxychloroquine group appeared unrelated to cancer. There were more grade 3–5 events in the hydroxychloroquine group (60.0%) versus radiotherapy alone (38.9%) without any clear common causation. Conclusions: Hydroxychloroquine with short-course radiotherapy did not improve survival compared to radiotherapy alone in elderly patients with glioblastoma

RMBNToolbox: random models for biochemical networks

<p>Abstract</p> <p>Background</p> <p>There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models.</p> <p>Results</p> <p>We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language.</p> <p>Conclusion</p> <p>While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.</p