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. Free serum OSM stimulated precursor cell production through sigmoidal relations, with higher maximum suppression (Imax ) and OSM concentration for 50% suppression (IC50 ) for platelets (89.1% [95% confidence interval: 83.4-93.0], 6.03 pg/mL [4.41-8.26]) than RBCs (57.0% [49.7-64.0], 2.93 pg/mL [2.55-3.36]). Reduction in hemoglobin and platelets increased erythro- and thrombopoietin, respectively, prompting reticulocytosis and (partially) alleviating OSM-restricted hematopoiesis. The physiology-based model was substantiated by preclinical data and utilized in exploration of once-weekly or every other week dosing regimens. Predictions revealed an (for the indication) unacceptable occurrence of grade 2 (67% [58-76], 29% [20-38]) and grade 3 (17% [10-25], 3% [0-7]) anemias, with limited thrombocytopenia. Individual extent of RBC precursor modulation was moderately correlated to skin mRNA gene expression changes. The physiological basis and consideration of interplay among hematopoietic variables makes the model generalizable to other drug and nondrug scenarios, with adaptations for patient populations, diseases, and therapeutics that modulate hematopoiesis or exhibit risk of anemia and/or thrombocytopenia.

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. Participants were randomized 3:1 (GSK2330811:placebo) in one of two sequential cohorts to receive GSK2330811 (cohort 1: 100 mg; cohort 2: 300 mg) or placebo s.c. every other week for 12 weeks. The primary endpoint was safety; blood and skin biopsy samples were collected to explore mechanistic effects on inflammation and fibrosis. Clinical efficacy was an exploratory endpoint. RESULTS: Thirty-five participants were randomized to placebo (n = 8), GSK2330811 100 mg (n = 3) or GSK2330811 300 mg (n = 24). Proof of mechanism, measured by coordinate effects on biomarkers of inflammation or fibrosis, was not demonstrated following GSK2330811 treatment. There were no meaningful differences between GSK2330811 and placebo for any efficacy endpoints. The safety and tolerability of GSK2330811 were not favourable in the 300 mg group, with on-target, dose-dependent adverse events related to decreases in haemoglobin and platelet count that were not observed in the 100 mg or placebo groups. CONCLUSION: Despite a robust and novel experimental medicine approach and evidence of target engagement, anticipated SSc-related biologic effects of GSK2330811 were not different from placebo and safety was unfavourable, suggesting OSM inhibition may not be a useful therapeutic strategy in SSc. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov, NCT03041025; EudraCT, 2016-003417-95.

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). Safety and tolerability, GSK2330811 pharmacokinetic profile, OSM levels in blood and skin, and the potential for antidrug antibody formation were assessed. The in vivo affinity of GSK2330811 for OSM and target engagement in serum and skin blister fluid (obtained via a skin suction blister model) were estimated using target-mediated drug disposition (TMDD) models in combination with compartmental and physiology-based pharmacokinetic (PBPK) models. RESULTS: Thirty subjects were randomized to receive GSK2330811 and 10 to placebo in this completed study. GSK2330811 demonstrated a favourable safety profile in healthy subjects; no adverse events were serious or led to withdrawal. There were no clinically relevant trends in change from baseline in laboratory values, with the exception of a reversible dose-dependent reduction in platelet count. GSK2330811 exhibited linear pharmacokinetics over the dose range 0.1-6 mg kg-1 . The estimated in vivo affinity (nM) of GSK2330811 for OSM was 0.568 [95% confidence interval (CI) 0.455, 0.710] in the compartmental with TMDD model and 0.629 (95% CI 0.494, 0.802) using the minimal PBPK with TMDD model. CONCLUSIONS: Single subcutaneous doses of GSK2330811 were well tolerated in healthy subjects. GSK2330811 demonstrated sufficient affinity to achieve target engagement in systemic circulation and target skin tissue, supporting the progression of GSK2330811 clinical development.

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. The quantification of how much insulin and CHO information improves glucose prediction is missing in the literature and is investigated, in an open-loop setting, in this proof-of-concept study. METHODS: We adopted a versatile literature prediction methodology able to utilize a variety of inputs. We compared predictors that use (1) CGM; (2) CGM and insulin; (3) CGM and CHO; and (4) CGM, insulin, and CHO. Data of 15 T1D subjects in open-loop setup were used. Prediction was evaluated via absolute error and temporal gain focusing on meal/night periods. The relative importance of each individual input of the predictor was evaluated with a sensitivity analysis. RESULTS: For a prediction horizon (PH) ≥ 30 minutes, insulin and CHO information improves prediction accuracy of 10% and double the temporal gain during the 2 hours following the meal. During the night the 4 methods did not give statistically different results. When PH ≥ 45 minutes, the influence of CHO information on prediction is 5-fold that of insulin. CONCLUSIONS: In an open-loop setting, with PH ≥ 30 minutes, information on CHO and insulin improves short-term glucose prediction in the 2-hour time window following a meal, but not during the night. CHO information improves prediction significantly more than insulin.

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. RESULTS: The tumour size profiles were well characterized as evaluated by visual predictive checks. Metastasis in the liver at enrolment and change in tumour size up to week 12 were predictors of time to appearance of new lesions. Survival was predicted based on the patient tumour size and ECOG performance status at enrolment and on appearance of new lesions during treatment and change in tumour size up to week 12. Tumour size and survival data from a separate study were adequately predicted. CONCLUSIONS: The proposed models simulate tumour dynamics following treatment and provide a link to the probability of developing new lesions as well as to survival. The models have potential to be used for optimizing the design of late phase clinical trials in metastatic ovarian cancer based on early phase clinical study results and simulation.

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. In this chapter we illustrate the design of a jump NN glucose prediction algorithm that exploits past glucose concentration data, measured in real-time by a minimally invasive continuous glucose monitoring (CGM) sensor, and information on ingested carbohydrates, supplied by the patient himself or herself. The methodology is assessed by tuning the NN on data of ten T1D individuals and then testing it on a dataset of ten different subjects. Results with a prediction horizon of 30 min show that prediction of glucose concentration in T1D via NN is feasible and sufficiently accurate. The average time anticipation obtained is compatible with the generation of preventive hypoglycemic and hyperglycemic alerts and the improvement of artificial pancreas performance.

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. The G4AP employs the same sensor and transmitter as the G4 PLATINUM but contains updated denoising and calibration algorithms for improved accuracy and reliability. These algorithms were applied to raw data from an existing G4 PLATINUM clinical study using a simulated prospective procedure. The results show that mean absolute relative difference (MARD) compared with venous plasma glucose was improved from 13.2% with the G4 PLATINUM to 11.7% with the G4AP. Accuracy improvements were seen over all days of sensor wear and across the plasma glucose range (40-400 mg/dl). The greatest improvements occurred in the low glucose range (40-80 mg/dl), in euglycemia (80-120 mg/dl), and on the first day of sensor use. The percentage of sensors with a MARD <15% increased from 69% to 80%. Metrics proposed by the AP research community for addressing specific AP requirements were also computed. The G4AP consistently exhibited improved sensor performance compared with the G4 PLATINUM. These improvements are expected to enable further advances in AP research.

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. They underwent low-intensity PA sessions daily. PA was tracked by the PA monitoring system (PAMS), a system comprising accelerometers and inclinometers. Variations on glucose dynamics were tracked estimating first- and second-order time derivatives of glucose concentration from CGM via Bayesian smoothing. Short-time effects of PA on glucose dynamics were quantified through the partial correlation function in the interval (0, 60 min) after starting PA. RESULTS: Correlation of PA with glucose time derivatives is evident. In T1DM, the negative correlation with the first-order glucose time derivative is maximal (absolute value) after 15 min of PA, whereas the positive correlation is maximal after 40-45 min. The negative correlation between the second-order time derivative and PA is maximal after 5 min, whereas the positive correlation is maximal after 35-40 min. Control subjects provided similar results but with positive and negative correlation peaks anticipated of 5 min. CONCLUSIONS: Quantitative information on correlation between mild PA and short-term glucose dynamics was obtained. This represents a preliminary important step toward incorporation of PA information in more realistic physiological models of the glucose-insulin system usable in T1DM simulators, in development of closed-loop artificial pancreas control algorithms, and in CGM-based prediction algorithms for generation of hypoglycemic alerts.

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. MATERIALS AND METHODS: Data for 50 virtual subjects were generated by using the University of Virginia/Padova type 1 diabetes simulator (54-h monitoring), made more credible by adding realistic measurement noise and perturbations of meals and insulin injections. CGM was assumed to be well calibrated. Occurrence and duration of hypoglycemic events were compared in three scenarios: (1) hypoglycemia was not recognized and not dealt with; (2) 15 g of carbohydrates was ingested when CGM crossed the hypoglycemia threshold; or (3) 15 g of carbohydrates was ingested when the 30-min ahead-of-time CGM prediction crossed the hypoglycemia threshold. The effectiveness of alerts was investigated also in the case of delayed/absent ingestion of carbohydrates. RESULTS: In Scenario 1, each virtual subject spent 17.7% of the time in the hypoglycemic range, with a median of four events of 120 min in the 54-h period monitored. In Scenario 2, the time spent in hypoglycemia was reduced to 4.7% (four events of 40 min). In Scenario 3, the time spent in hypoglycemia was further reduced to 1.2% (one event of 15 min). Absent/delayed patient's responses to alerts slightly increase these percentages, but improvements remain significant. CONCLUSIONS: This in silico proof-of-concept study demonstrates that using predicted rather than measured CGM allows a significant reduction of the number of hypoglycemic events and the time spent in hypoglycemic range both by 75%, stimulating further research and clinical investigation on the generation of preventive hypoglycemic alerts exploiting glucose prediction methods.

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. In this review, we illustrate some significant Italian contributions, both from industry and academia, to the growth of the CGM sensors research area. In particular, technological, algorithmic and clinical developments performed in Italy will be discussed and put in relation with the advances obtained in the field in the wider international research community.

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. In this paper, we propose an algorithm for short-time glucose prediction using past CGM sensor readings and information on carbohydrate intake. The predictor combines a neural network (NN) model and a first-order polynomial extrapolation algorithm, used in parallel to describe, respectively, the nonlinear and the linear components of glucose dynamics. Information on the glucose rate of appearance after a meal is described by a previously published physiological model. The method is assessed on 20 simulated datasets and on 9 real Abbott FreeStyle Navigator datasets, and its performance is successfully compared with that of a recently proposed NN glucose predictor. Results suggest that exploiting meal information improves the accuracy of short-time glucose prediction.