scholarly journals Replay Simulations with Personalized Metabolic Model for Treatment Design and Evaluation in Type 1 Diabetes

2020 ◽  
pp. 193229682097319
Author(s):  
Jonathan Hughes ◽  
Thibault Gautier ◽  
Patricio Colmegna ◽  
Chiara Fabris ◽  
Marc D Breton
Keyword(s):  
Type 1 Diabetes ◽  
In Silico ◽  
Real Life ◽  
Treatment Optimization ◽  
Method Performance ◽  
In Silico Studies ◽  
Wide Range ◽  
Subject Specific ◽  
Model Personalization

Background: The capacity to replay data collected in real life by people with type 1 diabetes mellitus (T1DM) would lead to individualized (vs population) assessment of treatment strategies to control blood glucose and possibly true personalization. Patek et al introduced such a technique, relying on regularized deconvolution of a population glucose homeostasis model to estimate a residual additive signal and reproduce the experimental data; therefore, allowing the subject-specific replay of what-if scenarios by altering the model inputs (eg, insulin). This early method was shown to have a limited domain of validity. We propose and test in silico a similar approach and extend the method applicability. Methods: A subject-specific model personalization of insulin sensitivity and meal-absorption parameters is performed. The University of Virginia (UVa)/Padova T1DM simulator is used to generate experimental scenarios and test the ability of the methodology to accurately reproduce changes in glucose concentration to alteration in meal and insulin inputs. Method performance is assessed by comparing true (UVa/Padova simulator) and replayed glucose traces, using the mean absolute relative difference (MARD) and the Clarke error grid analysis (CEGA). Results: Model personalization led to a 9.08 and 6.07 decrease in MARD over a prior published method of replaying altered insulin scenarios for basal and bolus changes, respectively. Replay simulations achieved high accuracy, with MARD <10% and more than 95% of readings falling in the CEGA A-B zones for a wide range of interventions. Conclusions: In silico studies demonstrate that the proposed method for replay simulation is numerically and clinically valid over broad changes in scenario inputs, indicating possible use in treatment optimization.

scholarly journals The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day

2018 ◽  
Vol 12 (2) ◽  
pp. 273-281 ◽  
Author(s):  
Roberto Visentin ◽  
Enrique Campos-Náñez ◽  
Michele Schiavon ◽  
Dayu Lv ◽  
Martina Vettoretti ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
In Silico ◽  
Artificial Pancreas ◽  
Real Life ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Self Monitoring ◽  
Inhaled Insulin ◽  
Single Meal

Background: A new version of the UVA/Padova Type 1 Diabetes (T1D) Simulator is presented which provides a more realistic testing scenario. The upgrades to the previous simulator, which was accepted by the Food and Drug Administration in 2013, are described. Method: Intraday variability of insulin sensitivity (SI) has been modeled, based on clinical T1D data, accounting for both intra- and intersubject variability of daily SI. Thus, time-varying distributions of both subject’s basal insulin infusion and insulin-to-carbohydrate ratio were calculated and made available to the user. A model of “dawn” phenomenon based on clinical T1D data has been also included. Moreover, the model of subcutaneous insulin delivery has been updated with a recently developed model of commercially available fast-acting insulin analogs. Models of both intradermal and inhaled insulin pharmacokinetics have been included. Finally, new models of error affecting continuous glucose monitoring and self-monitoring of blood glucose devices have been added. Results: One hundred in silico adults, adolescent, and children have been generated according to the above modifications. The new simulator reproduces the intraday glucose variability observed in clinical data, also describing the nocturnal glucose increase, and the simulated insulin profiles reflect real life data. Conclusions: The new modifications introduced in the T1D simulator allow to extend its domain of validity from “single-meal” to “single-day” scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.

T1D Hypoglycemia Prediction Algorithms: A Systematic Review (Preprint)

2021 ◽  
Author(s):  
Stella Tsichlaki ◽  
Lefteris Koumakis ◽  
Manolis Tsiknakis
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Digital Libraries ◽  
Predictive Models ◽  
Real Life ◽  
Blood Glucose Monitoring ◽  
Detection Techniques ◽  
Glucose Levels ◽  
Wide Range

BACKGROUND Diabetes is a chronic condition that necessitates regular monitoring and self-management of the patient's blood glucose levels. People with type 1 diabetes (T1D) can live a productive life if they receive proper diabetes care. Nonetheless, a loose glycemic control might increase the risk of developing hypoglycemia. This incident can occur due to a variety of causes, such as taking additional doses of insulin, skipping meals, or over-exercising. Mainly, the symptoms of hypoglycemia range from mild dysphoria to more severe conditions, if not detected in a timely manner. OBJECTIVE In this review, we report on innovative detection techniques and tactics for identifying and preventing hypoglycemic episodes, focusing on type 1 diabetes. METHODS A systematic literature search following the PRISMA guidelines was performed focusing on the “PUBMED”, “Google Scholar”, “IEEE Xplore” and “ACM” digital libraries to find articles about technologies related to hypoglycemia detection in type 1 diabetes patients. RESULTS The presented approaches have been utilized or devised to enhance blood glucose monitoring and boost its efficacy to forecast future glucose levels, which could aid the prediction of future episodes of hypoglycemia. We detected nineteen predictive models for hypoglycemia, specifically on type 1 diabetes, utilizing a wide range of algorithmic methodologies, spanning from statistics (10%) to machine learning (52%) and deep learning (38%). The algorithms employed most are the kalman filtering and classification models (SVM, KNN, random forests). The performance of the predictive models was found overall to be satisfactory, reaching accuracies between 70% and 99% which proves that such technologies are capable to facilitate the prediction of T1D hypoglycemia. CONCLUSIONS It is evident that CGM can improve the glucose control in diabetes but predictive models for hypo- and hyper- glycemia using only mainstream noninvasive sensors such as wristbands and smartwatches are foreseen to be the next step for mHealth in T1D. Prospective studies are required to demonstrate the value of such models in real-life mHealth interventions.

scholarly journals A “Slide Rule” to Adjust Insulin Dose Using Trend Arrows in Adults with Type 1 Diabetes: Test in Silico and in Real Life

2021 ◽  
Author(s):  
Daniela Bruttomesso ◽  
Federico Boscari ◽  
Giuseppe Lepore ◽  
Giulia Noaro ◽  
Giacomo Cappon ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
In Silico ◽  
Insulin Dose ◽  
Real Life ◽  
Slide Rule

1066-P: Improvement in Time-in-Range (TIR) with Real-Life Use of Hybrid Closed-Loop System in Patients with Type 1 Diabetes (T1D)

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1066-P
Author(s):  
HALIS K. AKTURK ◽  
DOMINIQUE A. GIORDANO ◽  
HAL JOSEPH ◽  
SATISH K. GARG ◽  
JANET K. SNELL-BERGEON
Keyword(s):  
Type 1 Diabetes ◽  
Closed Loop ◽  
Real Life ◽  
Loop System ◽  
Closed Loop System ◽  
Time In Range

Chalcones: As potent α-amylase enzyme inhibitors; synthesis, in vitro, and in silico studies

2020 ◽  
Vol 16 ◽  
Author(s):  
Mahboob Ali ◽  
Momin Khan ◽  
Khair Zaman ◽  
Abdul Wadood ◽  
Maryam Iqbal ◽  
...  
Keyword(s):  
In Silico ◽  
Enzyme Inhibitors ◽  
Biological Activities ◽  
Condensation Reaction ◽  
Type Ii Diabetes Mellitus ◽  
Spectroscopic Techniques ◽  
Chalcone Derivatives ◽  
In Silico Studies ◽  
Wide Range

: Background: The inhibition of α-amylase enzyme is one of the best therapeutic approach for the management of type II diabetes mellitus. Chalcone possesses a wide range of biological activities. Objective: In the current study chalcone derivatives (1-17) were synthesized and evaluated their inhibitory potential against α-amylase enzyme. Method: For that purpose, a library of substituted (E)-1-(naphthalene-2-yl)-3-phenylprop-2-en-1-ones was synthesized by ClaisenSchmidt condensation reaction of 2-acetonaphthanone and substituted aryl benzaldehyde in the presence of base and characterized via different spectroscopic techniques such as EI-MS, HREI-MS, 1H-, and 13C-NMR. Results: Sixteen synthetic chalcones were evaluated for in vitro porcine pancreatic α-amylase inhibition. All the chalcones demonstrated good inhibitory activities in the range of IC50 = 1.25 ± 1.05 to 2.40 ± 0.09 μM as compared to the standard commercial drug acarbose (IC50 = 1.34 ± 0.3 μM). Conclusion: Chalcone derivatives (1-17) were synthesized, characterized, and evaluated for their α-amylase inhibition. SAR revealed that electron donating groups in the phenyl ring have more influence on enzyme inhibition. However, to insight the participation of different substituents in the chalcones on the binding interactions with the α-amylase enzyme, in silico (computer simulation) molecular modeling analyses were carried out.

Neuropharmacological Screening of Chiral and Non-chiral Phthalimide- Containing Compounds in Mice: in vivo and in silico Experiments

2019 ◽  
Vol 15 (1) ◽  
pp. 102-118 ◽  
Author(s):  
Carolina Campos-Rodríguez ◽  
José G. Trujillo-Ferrara ◽  
Ameyali Alvarez-Guerra ◽  
Irán M. Cumbres Vargas ◽  
Roberto I. Cuevas-Hernández ◽  
...  
Keyword(s):  
Locomotor Activity ◽  
In Silico ◽  
Biological Properties ◽  
Chiral Molecules ◽  
Chiral Compounds ◽  
Plus Maze ◽  
In Silico Studies ◽  
The Central Nervous System

Background: Thalidomide, the first synthesized phthalimide, has demonstrated sedative- hypnotic and antiepileptic effects on the central nervous system. N-substituted phthalimides have an interesting chemical structure that confers important biological properties. Objective: Non-chiral (ortho and para bis-isoindoline-1,3-dione, phthaloylglycine) and chiral phthalimides (N-substituted with aspartate or glutamate) were synthesized and the sedative, anxiolytic and anticonvulsant effects were tested. Method: Homology modeling and molecular docking were employed to predict recognition of the analogues by hNMDA and mGlu receptors. The neuropharmacological activity was tested with the open field test and elevated plus maze (EPM). The compounds were tested in mouse models of acute convulsions induced either by pentylenetetrazol (PTZ; 90 mg/kg) or 4-aminopyridine (4-AP; 10 mg/kg). Results: The ortho and para non-chiral compounds at 562.3 and 316 mg/kg, respectively, decreased locomotor activity. Contrarily, the chiral compounds produced excitatory effects. Increased locomotor activity was found with S-TGLU and R-TGLU at 100, 316 and 562.3 mg/kg, and S-TASP at 316 and 562.3 mg/kg. These molecules showed no activity in the EPM test or PTZ model. In the 4-AP model, however, S-TGLU (237.1, 316 and 421.7 mg/kg) as well as S-TASP and R-TASP (316 mg/kg) lowered the convulsive and death rate. Conclusion: The chiral compounds exhibited a non-competitive NMDAR antagonist profile and the non-chiral molecules possessed selective sedative properties. The NMDAR exhibited stereoselectivity for S-TGLU while it is not a preference for the aspartic derivatives. The results appear to be supported by the in silico studies, which evidenced a high affinity of phthalimides for the hNMDAR and mGluR type 1.

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