A weighted method for estimation of receptor occupancy for pharmacodynamic measurements in drug development.

BACKGROUND: Flow cytometry-based receptor occupancy (RO) assessments for pharmacodynamic (PD) response measurements along with drug pharmacokinetic (PK) measurements represent a cornerstone in mechanism based PK/PD modeling of drugs against cell surface targets. This report describes the utility of using a "Free" and a "Bound" assay in combination to derive RO estimations through a weighted calculation method. METHODS: Data from a RO assay validation study in human samples was used to explore the performance of various RO data calculation methods. The calculation methods were subsequently applied to investigate the best method to generate RO data in a first in human phase 1 clinical trial. Finally, the outcome of the analysis was used for PK/PD modeling of a prospective phase 2a trial. RESULTS: The validation data assessment demonstrated that a weighted RO calculation method had a better performance in terms of precision, accuracy and dynamic range. In the phase 1 clinical trial data analysis the weighted method again demonstrated a better performance resulting in a more robust RO estimation, and subsequently also generating a more reliable PK/PD simulation for the phase 2a trial. CONCLUSIONS: This report demonstrated the utility of using a combined "Free" and "Bound" RO assessment together with a weighted calculation method to better support mechanism-based PK/PD modeling activities.

Accuracy evaluation of a new real-time continuous glucose monitoring algorithm in hypoglycemia.

BACKGROUND: The purpose of this study was to evaluate the performance of a new continuous glucose monitoring (CGM) calibration algorithm and to compare it with the Guardian(®) REAL-Time (RT) (Medtronic Diabetes, Northridge, CA) calibration algorithm in hypoglycemia. SUBJECTS AND METHODS: CGM data were obtained from 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. Data were obtained in two separate sessions using the Guardian RT CGM device. Data from the same CGM sensor were calibrated by two different algorithms: the Guardian RT algorithm and a new calibration algorithm. The accuracy of the two algorithms was compared using four performance metrics. RESULTS: The median (mean) of absolute relative deviation in the whole range of plasma glucose was 20.2% (32.1%) for the Guardian RT calibration and 17.4% (25.9%) for the new calibration algorithm. The mean (SD) sample-based sensitivity for the hypoglycemic threshold of 70 mg/dL was 31% (33%) for the Guardian RT algorithm and 70% (33%) for the new algorithm. The mean (SD) sample-based specificity at the same hypoglycemic threshold was 95% (8%) for the Guardian RT algorithm and 90% (16%) for the new calibration algorithm. The sensitivity of the event-based hypoglycemia detection for the hypoglycemic threshold of 70 mg/dL was 61% for the Guardian RT calibration and 89% for the new calibration algorithm. Application of the new calibration caused one false-positive instance for the event-based hypoglycemia detection, whereas the Guardian RT caused no false-positive instances. The overestimation of plasma glucose by CGM was corrected from 33.2 mg/dL in the Guardian RT algorithm to 21.9 mg/dL in the new calibration algorithm. CONCLUSIONS: The results suggest that the new algorithm may reduce the inaccuracy of Guardian RT CGM system within the hypoglycemic range; however, data from a larger number of patients are required to compare the clinical reliability of the two algorithms.

Evaluation of an Algorithm for Retrospective Hypoglycemia Detection Using Professional Continuous Glucose Monitoring Data.

BACKGROUND: People with type 1 diabetes (T1D) are unable to produce insulin and thus rely on exogenous supply to lower their blood glucose. Studies have shown that intensive insulin therapy reduces the risk of late-diabetic complications by lowering average blood glucose. However, the therapy leads to increased incidence of hypoglycemia. Although inaccurate, professional continuous glucose monitoring (PCGM) can be used to identify hypoglycemic events, which can be useful for adjusting glucose-regulating factors. New pattern classification approaches based on identifying hypoglycemic events through retrospective analysis of PCGM data have shown promising results. The aim of this study was to evaluate a new pattern classification approach by comparing the performance with a newly developed PCGM calibration algorithm. METHODS: Ten male subjects with T1D were recruited and monitored with PCGM and self-monitoring blood glucose during insulin-induced hypoglycemia. A total of 19 hypoglycemic events occurred during the sessions. RESULTS: The pattern classification algorithm detected 19/19 hypoglycemic events with 1 false positive, while the PCGM with the new calibration algorithm detected 17/19 events with 2 false positives. CONCLUSIONS: We can conclude that even after the introduction of new calibration algorithms, the pattern classification approach is still a valuable addition for improving retrospective hypoglycemia detection using PCGM.

An information and communication technology system to detect hypoglycemia in people with type 1 diabetes.

Continuous glucose monitoring (CGM) is a new technology with the potential to detect hypoglycemia in people with Type 1 diabetes. However, the inaccuracy of the device in the hypoglycemic range is unfortunately too large. The aim of this study was to develop an information and communication technology system for improving hypoglycemia detection in CGM. The system was developed as an Android application with a build-in pattern classification algorithm. The algorithm processes features from CGM and typed in data from the patient, then warns the patient about incoming hypoglycemia. The system improved the detection of hypoglycemic events by 29%, with only one 1 false alert compared to CGM alone. Furthermore, the algorithm increased the average lead-time by 14 minutes. These findings indicate that it is possible to improve the hypoglycemia detection with an information and communication technology system, but that the system must be validated on a larger dataset.

Real-time hypoglycemia detection from continuous glucose monitoring data of subjects with type 1 diabetes.

BACKGROUND: Hypoglycemia is a potentially fatal condition. Continuous glucose monitoring (CGM) has the potential to detect hypoglycemia in real time and thereby reduce time in hypoglycemia and avoid any further decline in blood glucose level. However, CGM is inaccurate and shows a substantial number of cases in which the hypoglycemic event is not detected by the CGM. The aim of this study was to develop a pattern classification model to optimize real-time hypoglycemia detection. MATERIALS AND METHODS: Features such as time since last insulin injection and linear regression, kurtosis, and skewness of the CGM signal in different time intervals were extracted from data of 10 male subjects experiencing 17 insulin-induced hypoglycemic events in an experimental setting. Nondiscriminative features were eliminated with SEPCOR and forward selection. The feature combinations were used in a Support Vector Machine model and the performance assessed by sample-based sensitivity and specificity and event-based sensitivity and number of false-positives. RESULTS: The best model was composed by using seven features and was able to detect 17 of 17 hypoglycemic events with one false-positive compared with 12 of 17 hypoglycemic events with zero false-positives for the CGM alone. Lead-time was 14 min and 0 min for the model and the CGM alone, respectively. CONCLUSIONS: This optimized real-time hypoglycemia detection provides a unique approach for the diabetes patient to reduce time in hypoglycemia and learn about patterns in glucose excursions. Although these results are promising, the model needs to be validated on CGM data from patients with spontaneous hypoglycemic events.

Professional continuous glucose monitoring in subjects with type 1 diabetes: retrospective hypoglycemia detection.

BACKGROUND: An important task in diabetes management is detection of hypoglycemia. Professional continuous glucose monitoring (CGM), which produces a glucose reading every 5 min, is a powerful tool for retrospective identification of unrecognized hypoglycemia. Unfortunately, CGM devices tend to be inaccurate, especially in the hypoglycemic range, which limits their applicability for hypoglycemia detection. The objective of this study was to develop an automated pattern recognition algorithm to detect hypoglycemic events in retrospective, professional CGM. METHOD: Continuous glucose monitoring and plasma glucose (PG) readings were obtained from 17 data sets of 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. The CGM readings were automatically classified into a hypoglycemic group and a nonhypoglycemic group on the basis of different features from CGM readings and insulin injection. The classification was evaluated by comparing the automated classification with PG using sample-based and event-based sensitivity and specificity measures. RESULTS: With an event-based sensitivity of 100%, the algorithm produced only one false hypoglycemia detection. The sample-based sensitivity and specificity levels were 78% and 96%, respectively. CONCLUSIONS: The automated pattern recognition algorithm provides a new approach for detecting unrecognized hypoglycemic events in professional CGM data. The tool may assist physicians and diabetologists in conducting a more thorough evaluation of the diabetes patient's glycemic control and in initiating necessary measures for improving glycemic control.

QT Measurement and Heart Rate Correction during Hypoglycemia: Is There a Bias?

Introduction. Several studies show that hypoglycemia causes QT interval prolongation. The aim of this study was to investigate the effect of QT measurement methodology, heart rate correction, and insulin types during hypoglycemia. Methods. Ten adult subjects with type 1 diabetes had hypoglycemia induced by intravenous injection of two insulin types in a cross-over design. QT measurements were done using the slope-intersect (SI) and manual annotation (MA) methods. Heart rate correction was done using Bazett's (QTcB) and Fridericia's (QTcF) formulas. Results. The SI method showed significant prolongation at hypoglycemia for QTcB (42(6) ms; P < .001) and QTcF (35(6) ms; P < .001). The MA method showed prolongation at hypoglycemia for QTcB (7(2) ms, P < .05) but not QTcF. No difference in ECG variables between the types of insulin was observed. Discussion. The method for measuring the QT interval has a significant impact on the prolongation of QT during hypoglycemia. Heart rate correction may also influence the QT during hypoglycemia while the type of insulin is insignificant. Prolongation of QTc in this study did not reach pathologic values suggesting that QTc prolongation cannot fully explain the dead-in-bed syndrome.

A physiological model of the effect of hypoglycemia on plasma potassium.

BACKGROUND: Adrenaline release and excess insulin during hypoglycemia stimulate the uptake of potassium from the bloodstream, causing low plasma potassium (hypokalemia). Hypokalemia has a profound effect on the heart and is associated with an increased risk of malignant cardiac arrhythmias. It is the aim of this study to develop a physiological model of potassium changes during hypoglycemia to better understand the effect of hypoglycemia on plasma potassium. METHOD: Potassium counterregulation to hypokalemia was modeled as a linear function dependent on the absolute potassium level. An insulin-induced uptake of potassium was modeled using a negative exponential function, and an adrenaline-induced uptake of potassium was modeled as a linear function. Functional expressions for the three components were found using published data. RESULTS: The performance of the model was evaluated by simulating plasma potassium from three published studies. Simulations were done using measured levels of adrenaline and insulin. The mean root mean squared error (RMSE) of simulating plasma potassium from the three studies was 0.09 mmol/liter, and the mean normalized RMSE was 14%. The mean difference between nadirs in simulated and measured potassium was 0.12 mmol/liter. CONCLUSIONS: The presented model simulated plasma potassium with good accuracy in a wide range of clinical settings. The limited number of hypoglycemic episodes in the test set necessitates further tests to substantiate the ability of the model to simulate potassium during hypoglycemia. In conclusion, the model is a good first step toward better understanding of changes in plasma potassium during hypoglycemia.

Sources of glycemic variability--what type of technology is needed?

People on insulin therapy are challenged with evaluation of numerous factors affecting the blood glucose in order to select the optimal dose for reaching their glucose target. Following medical recommendations precisely still results in considerable blood glucose unpredictability, often resulting in frustration in the short term due to hypoglycemia and hyperglycemia, and, in the long term, will likely result in complications. The kinetics of insulin do indeed vary significantly and have become an important focus when developing new insulin analogues and delivery systems; however, numerous of other factors impact glycemic variability. These have different dependences and interactions and are therefore difficult to characterize. Some of the factors are highly dependent and influenced by the type of insulin and devices used in therapy. Development of future therapy products is therefore highly focused on how to minimize glycemic variability.

Modeling the effect of blood glucose and physical exercise on plasma adrenaline in people with type 1 diabetes.

BACKGROUND: Adrenaline is often studied in people with type 1 diabetes during hypoglycemic episodes. Adrenaline is difficult and costly to measure, and therefore a pharmacokinetic model of adrenaline can be a supportive tool that adds information and saves measurements resources. METHODS: We have developed a compartment model of adrenaline secretion and elimination. It is based on input on physical exercise, blood glucose level, and optional infused adrenaline. The model parameters are identified using least square regression on published data of adrenaline kinetics measured in a number of different clinical studies. RESULTS: Simulation of published adrenaline measurements shows agreement with data of adrenaline infusion (R(2) = 0.9), exercise (R(2) = 0.97), and hypoglycemic episodes (R(2) = 0.93-0.97). The identified function describing adrenaline secretion during hypoglycemia shows an exponential increase for a blood glucose decreasing below 3.5 mmol/L and an approaching maximum around 1 mmol/L. Exercise intensity increasing above 50% of maximal oxygen uptake maximum causes approximately exponential increase in adrenaline secretion. CONCLUSION: The model is a simple tool that can be used to simulate and predict adrenaline concentrations in situations of hypoglycemia, physical exercise, and adrenaline infusion. In conclusion, the developed model, although simple, seems to be useful for simulating adrenaline dynamics in situations with hypoglycemic episodes, physical exercise, or infusion.