Predicting the Long-Term Effects of Therapeutic Neutralization of Oncostatin M on Human Hematopoiesis.

Therapeutic neutralization of Oncostatin M (OSM) causes mechanism-driven anemia and thrombocytopenia, which narrows the therapeutic window complicating the selection of doses (and dosing intervals) that optimize efficacy and safety. We utilized clinical data from studies of an anti-OSM monoclonal antibody (GSK2330811) in healthy volunteers (n = 49) and systemic sclerosis patients (n = 35), to quantitatively determine the link between OSM and alterations in red blood cell (RBC) and platelet production. Longitudinal changes in hematopoietic variables (including RBCs, reticulocytes, platelets, erythropoietin, and thrombopoietin) were linked in a physiology-based model, to capture the long-term effects and variability of therapeutic OSM neutralization on human hematopoiesis.

Biological and clinical insights from a randomized phase 2 study of an anti-oncostatin M monoclonal antibody in systemic sclerosis.

Objectives: The cytokine oncostatin M (OSM) is implicated in the pathology of SSc. Inhibiting OSM signalling using GSK2330811 (an anti-OSM monoclonal antibody) in patients with SSc has the potential to slow or stop the disease process.

Methods: This multicentre, randomized, double-blind, placebo-controlled study enrolled participants ≥18 years of age with active dcSSc.

In vivo affinity and target engagement in skin and blood in a first-time-in-human study of an anti-oncostatin M monoclonal antibody.

Aims: The oncostatin M (OSM) pathway drives fibrosis, inflammation and vasculopathy, and is a potential therapeutic target for inflammatory and fibrotic diseases. The aim of this first-time-in-human experimental medicine study was to assess the safety, tolerability, pharmacokinetics and target engagement of single subcutaneous doses of GSK2330811, an anti-OSM monoclonal antibody, in healthy subjects.

Methods: This was a phase I, randomized, double-blind, placebo-controlled, single-dose escalation, first-time-in-human study of subcutaneously administered GSK2330811 in healthy adults (NCT02386436).

How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study.

Background: In type 1 diabetes (T1D) management, short-term glucose prediction can allow to anticipate therapeutic decisions when hypo/hyperglycemia is imminent. Literature prediction methods mainly use past continuous glucose monitoring (CGM) readings. Sophisticated algorithms can use information on insulin delivered and meal carbohydrate (CHO) content.

Models for change in tumour size, appearance of new lesions and survival probability in patients with advanced epithelial ovarian cancer.

Aims: The aims of this study were (i) to develop a modelling framework linking change in tumour size during treatment to survival probability in metastatic ovarian cancer; and (ii) to model the appearance of new lesions and investigate their relationship with survival and disease characteristics.

Methods: Data from a randomized Phase III clinical trial comparing carboplatin monotherapy to gemcitabine plus carboplatin combotherapy in 336 patients with metastatic ovarian cancer were used. A population model describing change in tumour size based on drug treatment information was established and its relationship with time to appearance of new lesions and survival were investigated with time to event models.

Jump neural network for real-time prediction of glucose concentration.

Prediction of the future value of a variable is of central importance in a wide variety of fields, including economy and finance, meteorology, informatics, and, last but not least important, medicine. For example, in the therapy of Type 1 Diabetes (T1D), in which, for patient safety, glucose concentration in the blood should be maintained in a defined normoglycemic range, the ability to forecast glucose concentration in the short-term (with a prediction horizon of around 30 min) might be sufficient to reduce the incidence of hypoglycemic and hyperglycemic events. Neural Network (NN) approaches are suitable for prediction purposes because of their ability to model nonlinear dynamics and handle in their inputs signals coming from different domains.

Dexcom G4AP: an advanced continuous glucose monitor for the artificial pancreas.

Input from continuous glucose monitors (CGMs) is a critical component of artificial pancreas (AP) systems, but CGM performance issues continue to limit progress in AP research. While G4 PLATINUM has been integrated into AP systems around the world and used in many successful AP controller feasibility studies, this system was designed to address the needs of ambulatory CGM users as an adjunctive use system. Dexcom and the University of Padova have developed an advanced CGM, called G4AP, to specifically address the heightened performance requirements for future AP studies.

Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring.

Background: In type 1 diabetes mellitus (T1DM), physical activity (PA) lowers the risk of cardiovascular complications but hinders the achievement of optimal glycemic control, transiently boosting insulin action and increasing hypoglycemia risk. Quantitative investigation of relationships between PA-related signals and glucose dynamics, tracked using, for example, continuous glucose monitoring (CGM) sensors, have been barely explored.

Subjects And Methods: In the clinic, 20 control and 19 T1DM subjects were studied for 4 consecutive days.

Reduction of number and duration of hypoglycemic events by glucose prediction methods: a proof-of-concept in silico study.

Background: Hypoglycemia prevention is one of the major challenges in diabetes research. Recently, it has been suggested that continuous glucose monitoring (CGM)-based short-term glucose prediction algorithms could be exploited to generate alerts when hypoglycemia is forecasted, allowing the patient to take appropriate countermeasures to avoid/mitigate the event. However, quantifying the potential benefits of prediction in terms of reduction of number/duration of hypoglycemia requires an in silico assessment that is the object of the present article.

Italian contributions to the development of continuous glucose monitoring sensors for diabetes management.

Monitoring glucose concentration in the blood is essential in the therapy of diabetes, a pathology which affects about 350 million people around the World (three million in Italy), causes more than four million deaths per year and consumes a significant portion of the budget of national health systems (10% in Italy). In the last 15 years, several sensors with different degree of invasiveness have been proposed to monitor glycemia in a quasi-continuous way (up to 1 sample/min rate) for relatively long intervals (up to 7 consecutive days). These continuous glucose monitoring (CGM) sensors have opened new scenarios to assess, off-line, the effectiveness of individual patient therapeutic plans from the retrospective analysis of glucose time-series, but have also stimulated the development of innovative on-line applications, such as hypo/hyper-glycemia alert systems and artificial pancreas closed-loop control algorithms.

Neural network incorporating meal information improves accuracy of short-time prediction of glucose concentration.

Diabetes mellitus is one of the most common chronic diseases, and a clinically important task in its management is the prevention of hypo/hyperglycemic events. This can be achieved by exploiting continuous glucose monitoring (CGM) devices and suitable short-term prediction algorithms able to infer future glycemia in real time. In the literature, several methods for short-time glucose prediction have been proposed, most of which do not exploit information on meals, and use past CGM readings only.