Summary Ruxolitinib is a potent Janus kinase (JAK) 1/JAK2 inhibitor approved for the treatment of myelofibrosis (MF). Ruxolitinib was assessed in JUMP, a large (N = 2233), phase 3b, expanded‐access study in MF in countries without access to ruxolitinib outside a clinical trial, which included patients with low platelet counts (<100 × 109/l) and patients without splenomegaly – populations that have not been extensively studied. The most common adverse events (AEs) were anaemia and thrombocytopenia, but they rarely led to discontinuation (overall, 5·4%; low‐platelet cohort, 12·3%). As expected, rates of worsening thrombocytopenia were higher in the low‐platelet cohort (all grades, 73·2% vs. 53·5% overall); rates of anaemia were similar (all grades, 52·9% vs. 59·5%). Non‐haematologic AEs, including infections, were mainly grade 1/2. Overall, ruxolitinib led to meaningful reductions in spleen length and symptoms, including in patients with low platelet counts, and symptom improvements in patients without splenomegaly. In this trial, the largest study of ruxolitinib in patients with MF to date, the safety profile was consistent with previous reports, with no new safety concerns identified. This study confirms findings from the COMFORT studies and supports the use of ruxolitinib in patients with platelet counts of 50–100 × 109/l. (ClinicalTrials.gov identifier NCT01493414).
Ovarian biopsy specimens from ten girls (three postmenarcheal) who had undergone antiblastic treatment for acute lymphoblastic leukemia (ALL) and were in complete remission were examined by light microscope. The biopsy specimens from four of these patients (three postmenarcheal) were also observed by electron microscope. The structural and ultrastructural analysis showed a reduction in the number of follicles which were otherwise normal. No follicles were found in the thin sections from two of the three postmenarcheal girls, whereas normal follicles were observed in the third. The cortical stroma showed moderate to severe signs of fibrosis and changes of capillaries. All of these alterations were more evident in patients where ALL was diagnosed at an older age and this finding suggests that they are at a higher risk for low fertility or early menopause.
BackgroundTuberculosis (TB) represents a worldwide cause of mortality (it infects one third of the world’s population) affecting mostly developing countries, including India, and recently also developed ones due to the increased mobility of the world population and the evolution of different new bacterial strains capable to provoke multi-drug resistance phenomena. Currently, antitubercular drugs are unable to eradicate subpopulations of Mycobacterium tuberculosis (MTB) bacilli and therapeutic vaccinations have been postulated to overcome some of the critical issues related to the increase of drug-resistant forms and the difficult clinical and public health management of tuberculosis patients. The Horizon 2020 EC funded project “In Silico Trial for Tuberculosis Vaccine Development” (STriTuVaD) to support the identification of new therapeutic interventions against tuberculosis through novel in silico modelling of human immune responses to disease and vaccines, thereby drastically reduce the cost of clinical trials in this critical sector of public healthcare.ResultsWe present the application of the Universal Immune System Simulator (UISS) computational modeling infrastructure as a disease model for TB. The model is capable to simulate the main features and dynamics of the immune system activities i.e., the artificial immunity induced by RUTI® vaccine, a polyantigenic liposomal therapeutic vaccine made of fragments of Mycobacterium tuberculosis cells (FCMtb). Based on the available data coming from phase II Clinical Trial in subjects with latent tuberculosis infection treated with RUTI® and isoniazid, we generated simulation scenarios through validated data in order to tune UISS accordingly to STriTuVaD objectives. The first case simulates the establishment of MTB latent chronic infection with some typical granuloma formation; the second scenario deals with a reactivation phase during latent chronic infection; the third represents the latent chronic disease infection scenario during RUTI® vaccine administration.ConclusionsThe application of this computational modeling strategy helpfully contributes to simulate those mechanisms involved in the early stages and in the progression of tuberculosis infection and to predict how specific therapeutical strategies will act in this scenario. In view of these results, UISS owns the capacity to open the door for a prompt integration of in silico methods within the pipeline of clinical trials, supporting and guiding the testing of treatments in patients affected by tuberculosis.
As of today, 20 disease-modifying drugs (DMDs) have been approved for the treatment of relapsing multiple sclerosis (MS) and, based on their efficacy, they can be grouped into moderate-efficacy DMDs and high-efficacy DMDs. The choice of the drug mostly relies on the judgment and experience of neurologists and the evaluation of the therapeutic response can only be obtained by monitoring the clinical and magnetic resonance imaging (MRI) status during follow up. In an era where therapies are focused on personalization, this study aims to develop a modeling infrastructure to predict the evolution of relapsing MS and the response to treatments. We built a computational modeling infrastructure named Universal Immune System Simulator (UISS), which can simulate the main features and dynamics of the immune system activities. We extended UISS to simulate all the underlying MS pathogenesis and its interaction with the host immune system. This simulator is a multi-scale, multi-organ, agent-based simulator with an attached module capable of simulating the dynamics of specific biological pathways at the molecular level. We simulated six MS patients with different relapsing–remitting courses. These patients were characterized based on their age, sex, presence of oligoclonal bands, therapy, and MRI lesion load at the onset. The simulator framework is made freely available and can be used following the links provided in the availability section. Even though the model can be further personalized employing immunological parameters and genetic information, we generated a few simulation scenarios for each patient based on the available data. Among these simulations, it was possible to find the scenarios that realistically matched the real clinical and MRI history. Moreover, for two patients, the simulator anticipated the timing of subsequent relapses, which occurred, suggesting that UISS may have the potential to assist MS specialists in predicting the course of the disease and the response to treatment.
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