The university of Virginia/Padova type 1 diabetes simulator matches the glucose traces of a clinical trial
Open accessAbstract
Background: In 2008, the Food and Drug Administration (FDA) accepted our type 1 diabetes mellitus (T1DM) simulator (S2008), equipped with 100 in silico adults, 100 adolescents, and 100 children, as a substitute for preclinical trials for certain insulin treatments, including closed-loop algorithms. Hypoglycemia was well described in the simulator, but recent closed-loop trials showed a much larger frequency of hypoglycemia events in patients compared with the in silico ones. In order to better describe the distribution of glucose concentration observed in clinical trials, the simulator has recently been updated, and modifications have been accepted by the FDA (S2013). The aim of this study is to assess the validity of the S2013 simulator against clinical data and compare its performance with that of the S2008. Subjects and Methods: The database consists of 24 T1DM subjects who received dinner (70.7±3.3 g of carbohydrate) and breakfast (52.9±0.1 g of carbohydrate) in two occasions (open- and closed-loop), for a total of 96 postmeal glucose profiles. Measured plasma glucose profiles were compared with those simulated in 100 in silico adults, and the continuous glucose error grid analysis (CG-EGA) was used to assess the validity of the simulated traces. Moreover, the most common outcome metrics have been compared. Results: The frequency of hypoglycemia episodes predicted by the S2013 well reproduces that observed during clinical trials as proven by the CG-EGA. In addition, the outcome metrics provided by the S2013 are similar to those observed in clinical trials in a set of T1DM subjects. Conclusions: We demonstrated that the virtual subjects of the S2013 are representative of the T1DM population observed in a clinical trial. We conclude that the S2013 is a valid tool usable to test the robustness of closed-loop control algorithms for artificial pancreas.
AI snapshot
Written from the paper’s full text.
TL;DR
The updated UVA/Padova type 1 diabetes simulator S2013 was assessed against clinical glucose data from 24 adults and compared with the earlier S2008. S2013 reproduced clinical glucose traces and hypoglycemia better, supporting its use for in silico testing of closed-loop algorithms.
Key findings
- CG-EGA accurate+benign percentages were 86.4% in hypoglycemia, 99.9% in euglycemia, and 99.2% in hyperglycemia for S2013, versus 40.7%, 99.8%, and 99.5% for S2008.
- S2013 outcome metrics were substantially similar to clinical data except for percentage of time in hypoglycemia (6.22±11.81 vs 4.04±7.93; P=0.006) and hypoglycemia area under the curve (5.00±14.50 vs 2.65±8.99 mg·h/dL; P=0.019); most S2008 metrics were significantly lower than clinical values (P<0.05).
- Hypoglycemia events numbered 37 in data, 32 with S2013, and 19 with S2008; hyperglycemia events were 72, 67, and 61, respectively.
- Real versus virtual parameter differences averaged 18% for body weight and 67% for carbohydrate ratio; a >50% carbohydrate-ratio difference occurred in 23 subjects, and 14 of them would have benefited from a ratio closer to the virtual one. No subject was matched by the same virtual patient at both dinner and breakfast.
Methods
- The clinical database included 24 T1DM adults from Virginia (n=11), Padova (n=7), and Montpellier (n=6), each studied during two 22-h admissions (open- and closed-loop), with dinner (70.7±3.3 g carbohydrate) and breakfast (52.9±0.1 g carbohydrate), yielding 96 postmeal traces plus 8 overnight portions with at least one hypoglycemic episode.
- Each measured plasma glucose profile was compared with 100 in silico adults undergoing the same meals, basal insulin, and boluses; the best FIT-matched simulated profile was evaluated by continuous glucose error grid analysis (CG-EGA).
- Outcome metrics were compared between clinical and simulated data using paired t tests or Wilcoxon signed rank tests, with significance at P=0.05; S2013 includes modified hypoglycemia glucose kinetics via the Low BG Risk Index, a glucagon counterregulation model, and refined virtual-subject generation.
Limitations
- Stated by the authors: the 24-subject dataset is not large despite 96 traces; only inpatient data were used, so outpatient free-living robustness is unproven; only adults were considered; the model does not yet account for intraindividual diurnal parameter variability; and glucagon secretion may not be appropriate for all long-duration T1DM patients.
- Evident from the text: the protocol did not include a physical activity session, so simulator validity under exercise is not tested.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 7 cited sources checked · 0 supported · 0 flagged
- Findingown data§ Discussion
A difference between real and virtual CR >50% was present in 23 subjects.
“This analysis revealed that a difference between real and virtual CR >50% was present in 23 subjects.”
Quote in the paperNot a substantial claim - Findingown data§ Results
S2008 CG-EGA performance was similar to S2013 in euglycemia and hyperglycemia (99.8% and 99.5% accurate+benign).
“The results of the S2008 (Table 1, second row) show that the performance is similar to that of S2013 in euglycemia and hyperglycemia (percentage in accurate+benign zones: 99.8% in euglycemia and 99.5% in hyperglycemia) but worse in hypoglycemia (percentage in accurate+benign zones: 40.7%).”
Quote in the paperSubstantial claim - Findingown data§ Abstract
The frequency of hypoglycemia episodes predicted by S2013 well reproduces that observed during clinical trials, as proven by CG-EGA.
“The frequency of hypoglycemia episodes predicted by the S2013 well reproduces that observed during clinical trials as proven by the CG-EGA.”
- Also stated in: § Discussion · boosted
“where the hypoglycemic episodes predicted by the simulator well reflect those observed in clinical trials (Fig. 2, left panel).”
Quote in the paperSubstantial claim - Also stated in: § Discussion · boosted
- Findingown data§ Results
S2013 well reproduces glucose patterns observed in clinical trials in all clinical zones, including rapid falls in glucose and hypoglycemic episodes.
“The S2013 well reproduces the glucose patterns observed in clinical trials in all clinical zones, in particular the rapid falls in glucose level and the hypoglycemic episodes.”
- Also stated in: § Discussion · boosted
“Our results show that the S2013 well reproduces the glucose patterns in eu-, hyper-, and, above all, hypoglycemia, where the hypoglycemic episodes predicted by the simulator well reflect those observed in clinical trials (Fig. 2, left panel).”
Quote in the paperSubstantial claim - Also stated in: § Discussion · boosted
- Findingown data§ Results
For S2008, most outcome metrics are significantly lower in simulation than clinical data (P<0.05).
“For the S2008 (Table 2, third column), most of the outcome metrics are significantly lower in simulation (P<0.05).”
- Also stated in: § Discussion · plain
“Moreover, most of the in silico outcome metrics are significantly different from their clinical counterparts (Table 2, third column).”
Quote in the paperSubstantial claim - Also stated in: § Discussion · plain
- Findingown data§ Discussion
None of the subjects was matched by the same virtual patient at dinner and breakfast.
“In fact, we found that none of the subjects was matched by the same virtual patient at dinner and breakfast.”
Quote in the paperSubstantial claim - Findingown data§ Results
S2008 CG-EGA performance was worse in hypoglycemia (40.7% accurate+benign).
“The results of the S2008 (Table 1, second row) show that the performance is similar to that of S2013 in euglycemia and hyperglycemia (percentage in accurate+benign zones: 99.8% in euglycemia and 99.5% in hyperglycemia) but worse in hypoglycemia (percentage in accurate+benign zones: 40.7%).”
Quote in the paperSubstantial claim - Interpretationown data§ Results
The improvement of S2013 over S2008 is confirmed by the analysis of the distribution of outcome metrics.
“The improvement presented above is confirmed by the analysis of the distribution of outcome metrics, reported in Table 2.”
Quote in the paperSubstantial claim - Interpretationreasoning§ Discussion
The higher difference found for CR is due to the virtual CR being the "optimal" one for a given patient.
“The higher difference found for CR is due to the fact that the virtual CR is the “optimal” one for a given patient.”
Quote in the paperSubstantial claim - Interpretationown data§ Abstract
The S2013 is a valid tool usable to test the robustness of closed-loop control algorithms for artificial pancreas.
“We conclude that the S2013 is a valid tool usable to test the robustness of closed-loop control algorithms for artificial pancreas.”
- Also stated in: § Discussion · boosted
“In conclusion, this study proves the validity of the S2013 (Fig. 1B), which incorporates a novel nonlinear response to hypoglycemia and includes a counterregulation model.”
Quote in the paperSubstantial claim - Also stated in: § Discussion · boosted
- Interpretationreasoning§ Discussion
Of the 23 subjects with >50% CR difference, 14 would have benefitted by using a CR closer to the virtual one.
“However, 14 of them would have benefitted by using a CR closer to the virtual one.”
Quote in the paperSubstantial claim - Premisecitation§ Introduction
Real-life patient behavior does not have modest intra- and interday variability.
“Unfortunately, such a characteristic was proven to not occur in real life.3”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [3] Hinshaw L, Dalla Man C, Nandy DK, Saad A, Bharucha AE, Levine JA, Rizza RA, Basu R, Carter RE, Cobelli C, Kudva YC, Basu A: Diurnal pattern of insulin action in type 1 diabetes: implications for a closed loop system. Diabetes 2013;62:2223–2229 [DOI] [PMC free article] [PubMed] [Google Scholar] source
Not checked in the free run.
- Premisecitation§ Introduction
When glucose falls below a given threshold, insulin sensitivity increases, resulting in enhanced glucose utilization and further reduction of plasma glucose.
“Results in T1DM subjects suggest that, when glucose falls below a given threshold, insulin sensitivity increases, resulting in an enhanced glucose utilization, which further reduces the plasma glucose level.4–6”
- Also stated in: § Discussion · boosted
“In fact, in a previous study, it has been proved that, when the glucose level falls below a given threshold (i.e., during hypoglycemia), insulin sensitivity increases, producing an increase of glucose utilization and a consequent further reduction of plasma glucose level.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [4] Kovatchev BP, Farhy LS, Cox DJ, Straume M, Yankov VI, Gonder-Frederick LA, Clarke WL: Modeling insulin-glucose dynamics during insulin induced hypoglycemia. Evaluation of glucose counterregulation. J Theor Med 1999;1:313–323 [Google Scholar] source
Not checked in the free run.
- cites [5] Kovatchev BP, Straume M, Farhy LS, Cox DJ: Dynamic network model of glucose counterregulation in subjects with insulin-requiring diabetes. Methods Enzymol 2000;321:396–410 [DOI] [PubMed] [Google Scholar] source
Not checked in the free run.
- cites [6] Micheletto F, Dalla Man C, Vella A, Cobelli C: A model of glucagon secretion and action in healthy subjects [abstract A105]. In: Book of Abstracts. 10th Diabetes Technology Meeting. Bethesda, MD: Diabetes Technology Society, 2010 [Google Scholar]
Not checked in the free run.
- Also stated in: § Discussion · boosted
- Study design and conductreasoning§ Introduction
Because the same insulin schedule was administered to virtual subjects as to real subjects, the results are in principle independent from the control algorithm used in the trial.
“However, because we administered to the virtual subject the same insulin schedule as the one used by the real subject, the results are in principle independent from the algorithm used in the trial.”
Quote in the paperSubstantial claim - Interpretationreasoning§ Discussion
The updated distribution of virtual subjects allows in silico trials to be run in a more realistic scenario.
“The updated distribution of virtual subjects included in the simulator well represents the T1DM population observed in clinical trials, thus allowing in silico trials to be run in a more realistic scenario.”
Quote in the paperSubstantial claim - Interpretationown data§ Abstract
The virtual subjects of S2013 are representative of the T1DM population observed in a clinical trial.
“We demonstrated that the virtual subjects of the S2013 are representative of the T1DM population observed in a clinical trial.”
- Also stated in: § Discussion · boosted
“The updated distribution of virtual subjects included in the simulator well represents the T1DM population observed in clinical trials, thus allowing in silico trials to be run in a more realistic scenario.”
Quote in the paperSubstantial claim - Also stated in: § Discussion · boosted
- Interpretationown data§ Discussion
The frequency of hypoglycemia episodes predicted by S2008 does not fully reflect that observed during clinical trials.
“Thus, the frequency of hypoglycemia episodes predicted by the S2008 does not fully reflect that observed during clinical trials.”
- Also stated in: § Introduction · plain
“However, the frequency of hypoglycemia episodes predicted by the simulator was not completely reflecting that observed during clinical trials (see Results).”
Quote in the paperSubstantial claim - Also stated in: § Introduction · plain
- Premisecitation§ Abstract
In 2008, the FDA accepted the S2008 simulator, equipped with 100 in silico adults, 100 adolescents, and 100 children, as a substitute for preclinical trials for certain insulin treatments, including closed-loop algorithms.
“In 2008, the Food and Drug Administration (FDA) accepted our type 1 diabetes mellitus (T1DM) simulator (S2008), equipped with 100 in silico adults, 100 adolescents, and 100 children, as a substitute for preclinical trials for certain insulin treatments, including closed-loop algorithms.”
- Also stated in: § Introduction · plain
“In 2008, we introduced a T1DM simulator1 (S2008). This simulator, equipped with 100 in silico adults, 100 adolescents, and 100 children, was accepted by the Food and Drug Administration (FDA) as a substitute for preclinical trials for certain insulin treatments, including closed-loop algorithms.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] Kovatchev BP, Breton M, Dalla Man C, Cobelli C: In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes. J Diabetes Sci Technol 2009;3:44–55 [DOI] [PMC free article] [PubMed] [Google Scholar] source
Not checked in the free run.
- Also stated in: § Introduction · plain
- May combine two claims. 8
- Classified as probably not a substantial claim. 6
May combine two claims.
Findingown data§ ResultsS2013 CG-EGA accurate+benign percentages were 86.4% in hypoglycemia, 99.9% in euglycemia, and 99.2% in hyperglycemia.
“The average results of CG-EGA of the S2013 (Table 1, first row) are very good in hypo-, eu-, and hyperglycemia (percentage in accurate+benign zones: 86.4% in hypoglycemia, 99.9% in euglycemia and 99.2% in hyperglycemia).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ DiscussionThe percentage difference between subject and virtual parameters was 18% on average for body weight and 67% for CR.
“We found that the percentage difference between subject and virtual parameters was 18% on average for body weight and 67% for CR.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ DiscussionWhen matching was limited to virtual subjects with CR similar to the real one, a match was always found except for eight traces, which presented hypoglycemia and thus had suboptimal CR.
“In this case a match was always found except for eight traces, which, however, presented hypoglycemia, and thus the corresponding CR were suboptimal, as explained above.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ ResultsS2008 simulated profiles reproduce data well in euglycemia and hyperglycemia but not hypoglycemic episodes.
“The simulated profiles obtained with the S2008 reproduce quite well the data in euglycemia and hyperglycemia but not the hypoglycemic episodes.”
- Also stated in: § Discussion · plain
“On the other hand, we observed that the S2008 reproduces quite well the glucose patterns in euglycemia and hyperglycemia, but not the hypoglycemic episodes, which are difficult to predict (Fig. 2, right panel).”
Quote in the paperSubstantial claim- Also stated in: § Discussion · plain
May combine two claims.
Findingown data§ ResultsS2013 outcome metrics are substantially similar to clinical data, except percentage of time in hypoglycemia and area under the BG curve for hypoglycemia.
“The outcome metrics of S2013 simulations (Table 2, second column) are substantially similar to those obtained from the data, except for the percentage of time in hypoglycemia and the area under the BG curve for hypoglycemia.”
- Also stated in: § Abstract · plain
“In addition, the outcome metrics provided by the S2013 are similar to those observed in clinical trials in a set of T1DM subjects.”
Quote in the paperSubstantial claim- Also stated in: § Abstract · plain
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone given§ DiscussionThe S2013 model does not yet take into account intraindividual diurnal parameter variability.
“The most important one is that the model does not yet take into account the intraindividual diurnal parameter variability.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown data§ DiscussionThe dataset used for model assessment only includes inpatient data, and there are no data to prove that performance would be as robust in outpatient free-living individuals.
“Second, the dataset used for model assessment only includes inpatient data. Unfortunately, we do not have data to prove that the performance would be as robust in outpatient free-living individuals.”
Quote in the paperSubstantial claimMay combine two claims.
Premisecitation§ IntroductionS2008 was successfully validated against data from T1DM subjects, reproducing distribution of insulin correction factors, glucose traces in children, and glucose traces of induced moderate hypoglycemia in adults.
“The simulator was successfully validated against data of different experiments performed in T1DM subjects, reproducing (1) distribution of insulin correction factors in the T1DM population of children and adults, (2) glucose traces in children with T1DM, and (3) glucose traces of induced moderate hypoglycemia observed in adults in clinical trials at the University of Virginia.1”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] Kovatchev BP, Breton M, Dalla Man C, Cobelli C: In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes. J Diabetes Sci Technol 2009;3:44–55 [DOI] [PMC free article] [PubMed] [Google Scholar] source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone given§ DiscussionOnly the adult population has been considered in this article.
“Third, only the adult population has been considered in this article.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown data§ DiscussionThe simulator is not designed to do prospective studies on a given individual, given the large intrasubject variability of real life.
“However, it is important to stress that the simulator is not designed to do prospective studies on a given individual, given the large intrasubject variability of real life.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [3] Hinshaw L, Dalla Man C, Nandy DK, Saad A, Bharucha AE, Levine JA, Rizza RA, Basu R, Carter RE, Cobelli C, Kudva YC, Basu A: Diurnal pattern of insulin action in type 1 diabetes: implications for a closed loop system. Diabetes 2013;62:2223–2229 [DOI] [PMC free article] [PubMed] [Google Scholar] source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone given§ DiscussionThe dataset used for assessment, consisting of 24 subjects and 96 traces, cannot be considered a large database.
“First, the dataset used for the assessment, consisting of 24 subjects, cannot be considered a large database, despite subjects being studied twice with two meals per trial for a total of 96 traces.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone given§ DiscussionIf information on the exact timing of meals and carbohydrate content is available, the method proposed here can be applied to outpatient free-living individuals.
“In principle, if information on the exact timing of the meals and carbohydrates content is available, we are pretty confident that the method proposed here can be applied to outpatient free-living individuals.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone given§ IntroductionThe database used for model assessment was not used for the update of S2013.
“This database was not used for the update of S2013.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone given§ DiscussionFinding a match between real and simulated subjects is likely easier if only three or four BG values are available in a day, as happens in real life.
“However, it is likely that finding a match between real and simulated subjects is easier if only three or four BG values are available in a day, as happens in real life.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
The virtual subjects well span the variability of the T1DM population observed in vivo.
“The virtual subjects well span the variability of the T1DM population observed in vivo.”
The paper relies on this without support.
Recent closed-loop trials showed a much larger frequency of hypoglycemia events in patients compared with the in silico ones.
“Hypoglycemia was well described in the simulator, but recent closed-loop trials showed a much larger frequency of hypoglycemia events in patients compared with the in silico ones.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The UVA/Padova T1D simulator consists of a fully identified maximal model of glucose metabolism in T1D and a virtual population of 100 adult PwDs displaying key metabolic behaviors observed in the general population of individuals with T1D.
“we used the University of Virginia (UVA)/Padova T1D simulator, which consists of a fully identified maximal model of glucose metabolism in T1D 18 and a virtual population of 100 adult PwDs displaying key metabolic behaviors observed in the general population of individuals with T1D.[18][19][20][21]”
From Time to Moderate and Severe Hyperglycemia and Ketonemia Following an Insulin Pump Occlusion (2024) · cites this paper as [20]
The Subjects and Methods passage describes a glucose–insulin model and 100 virtual adults, and the Discussion says the updated virtual population represents the T1D population observed in clinical trials. Our copy does not describe the model as “fully identified” or “maximal,” and its evidence for population representativeness is framed around clinical-trial observations rather than the general T1D population. (LLM, conf=0.87)
Experimental — accuracy not yet measuredVisentin et al. [7] found that the S2013 simulator overestimated percent time spent in hypoglycemia significantly.
“unlike the S2008 simulator, the S2013 simulator overestimated the percent time spent in hypoglycemia significantly [7].”
From A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model (2019) · cites this paper as Visentin et al. [7]
Cites
The UVA/PADOVA Type 1 Diabetes Simulator: New Features
Man, C. D., Micheletto, F., Lv, D., Breton, M., Kovatchev, B., & Cobelli, C. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296813514502 · Cited by 791 · cited 3× · see citing works
“updated version of the T1DM simulator (S2013), incorporating a model of counterregulation and a new description of glucose dynamics in hypoglycemia, has been developed.(2) Here our aim is to assess the validity of S2013 versus S2008 compared with T1DM data obtained in a clinical trial.”
MethodInfluentialSelf-citation<i>In Silico</i> Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes
Kovatchev, B. P., Breton, M., Dalla Man, C., & Cobelli, C. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300106 · Cited by 760 · see citing works
Self-citationEvaluating the Accuracy of Continuous Glucose-Monitoring Sensors
Kovatchev, B. P., Gonder-Frederick, L. A., Cox, D. J., & Clarke, W. L. (2004) · Diabetes Care · doi:10.2337/diacare.27.8.1922 · Cited by 312 · see citing works
Self-citationKovatchev, B. P., Cox, D. J., Gonder-Frederick, L. A., Young-Hyman, D. L., Schlundt, D. G., & Clarke, W. L. (1998) · Diabetes Care · doi:10.2337/diacare.21.11.1870 · Cited by 309 · see citing works
Self-citationThe Original Clarke Error Grid Analysis (EGA)
Clarke, W. L. (2005) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2005.7.776 · Cited by 282 · see citing works
Kovatchev, B., Cobelli, C., Renard, E., Anderson, S., Breton, M., Patek, S., Clarke, W., Bruttomesso, D., Maran, A., Costa, S., et al. (2010) · Journal of Diabetes Science and Technology · doi:10.1177/193229681000400611 · Cited by 200 · see citing works
Self-citationKovatchev, B. P., Straume, M., Cox, D. J., & Farhy, L. S. (2000) · Computational and Mathematical Methods in Medicine · doi:10.1080/10273660008833060 · Cited by 125 · see citing works
Self-citationDiurnal Pattern of Insulin Action in Type 1 Diabetes
Hinshaw, L., Dalla Man, C., Nandy, D. K., Saad, A., Bharucha, A. E., Levine, J. A., Rizza, R. A., Basu, R., Carter, R. E., Cobelli, C., et al. (2013) · Diabetes · doi:10.2337/db12-1759 · Cited by 125 · see citing works
Self-citationKovatchev, B. P., Cox, D. J., Gonder-Frederick, L. A., & Clarke, W. L. (2002) · Diabetes Technology & Therapeutics · doi:10.1089/152091502760098438 · Cited by 83 · see citing works
Self-citationKovatchev, B. P., Farhy, L. S., Cox, D. J., Straume, M., Yankov, V. I., Gonder-Frederick, L. A., & Clarke, W. L. (1999) · Computational and Mathematical Methods in Medicine · doi:10.1080/10273669908833028 · Cited by 17 · see citing works
Self-citationDynamic network model of glucose counterregulation in subjects with insulin-requiring diabetes
Kovatchev, B. P., Straume, M., Farhy, L. S., & Cox, D. J. (2000) · Methods in Enzymology · doi:10.1016/s0076-6879(00)21204-4 · Cited by 7 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Micheletto F, , Dalla Man C, , Vella A, , Cobelli C: A model of glucagon secretion and action in healthy subjects [abstract A105]. In: Book of Abstracts. 10th Diabetes Technology Meeting. Bethesda, MD: Diabetes Technology Society, 2010.
Cited by
Sorted by citations
In silico clinical trials: how computer simulation will transform the biomedical industry
Viceconti, M., HENNEY, A. M., & Morley‐Fletcher, E. (2016) · International Journal of Clinical Trials · doi:10.18203/2349-3259.ijct20161408 · Cited by 302 · see citing works
The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day
Visentin, R., Campos-Náñez, E., Schiavon, M., Lv, D., Vettoretti, M., Breton, M., Kovatchev, B. P., Dalla Man, C., & Cobelli, C. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818757747 · Cited by 280 · see citing works
Self-citationThe Artificial Pancreas: How Closed-Loop Control Is Revolutionizing Diabetes
Haidar, A. (2016) · IEEE Control Systems · doi:10.1109/mcs.2016.2584318 · Cited by 137 · see citing works
A Primer on Reinforcement Learning in Medicine for Clinicians
Jayaraman, P., Desman, J., Sabounchi, M., Nadkarni, G., & Sakhuja, A. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01316-0 · Cited by 130 · see citing works
“Additionally, RL has been shown to optimize insulin dosingfortype1diabetics 51 usingtheFDAapprovedUniversityofVirginia/ Padovatype-1diabetessimulator.”
Vettoretti, M., Facchinetti, A., Sparacino, G., & Cobelli, C. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2746340 · Cited by 110 · see citing works
Self-citationCircadian Variability of Insulin Sensitivity: Physiological Input for In Silico Artificial Pancreas
Visentin, R., Dalla Man, C., Kudva, Y. C., Basu, A., & Cobelli, C. (2015) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2014.0192 · Cited by 113 · see citing works
“These were generated by randomly extracting different realizations of the parameter vector from an appropriate joint parameter distribution, a process that has been demonstrated to well span the variability of the T1DM population observed in vivo.(14,15) However, as already mentioned, an important limitation of the si…”
Self-citationMessori, M., Incremona, G. P., Cobelli, C., & Magni, L. (2018) · IEEE Control Systems · doi:10.1109/mcs.2017.2766314 · Cited by 112 · see citing works
Self-citationToward a Run-to-Run Adaptive Artificial Pancreas: In Silico Results
Toffanin, C., VISENTIN, R., Messori, M., Palma, F. D., Magni, L., & Cobelli, C. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2652062 · Cited by 107 · see citing works
Self-citationVisentin, R., Man, C. D., & Cobelli, C. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2535241 · Cited by 86 · see citing works
“A clinical validation of the UVA/Padova T1DM simulator has been reported [6], where it has been shown that the virtual adult population of the simulator is representative of a T1DM population observed in a clinical trial by comparing 96 measured postprandial glucose profiles with those simulated in 100 in silico subje…”
MethodSelf-citationResalat, N., El Youssef, J., Tyler, N., Castle, J., & Jacobs, P. G. (2019) · PLOS ONE · doi:10.1371/journal.pone.0217301 · Cited by 78 · see citing works
Contreras, I., Oviedo, S., Vettoretti, M., VISENTIN, R., & Vehı́, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187754 · Cited by 80 · see citing works
Self-citationAn Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning
Zhu, T., Li, K., Kuang, L., Herrero, P., & Georgiou, P. (2020) · Sensors · doi:10.3390/s20185058 · Cited by 78 · see citing works
Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care
Dickson, J. L., Stewart, K. W., Pretty, C. G., Flechet, M., Desaive, T., Penning, S., Lambermont, B. C., Benyó, B. I., Shaw, G. M., & Chase, J. G. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2686432 · Cited by 72 · see citing works
Automatic regulatory control in type 1 diabetes without carbohydrate counting
Colmegna, P. H., Garelli, F., Battista, H. D., & Sánchez‐Peña, R. (2018) · Control Engineering Practice · doi:10.1016/j.conengprac.2018.02.003 · Cited by 68 · see citing works
Model predictive control with integral action for artificial pancreas
Incremona, G. P., Messori, M., Toffanin, C., Cobelli, C., & Magni, L. (2018) · Control Engineering Practice · doi:10.1016/j.conengprac.2018.05.006 · Cited by 65 · see citing works
Self-citationViceconti, M., Cobelli, C., Haddad, T. D., Himes, A. K., Kovatchev, B. P., & Palmer, M. (2017) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1177/0954411917702931 · Cited by 63 · see citing works
Self-citationLinear parameter-varying model to design control laws for an artificial pancreas
Colmegna, P., Peña, R., & Gondhalekar, R. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.09.021 · Cited by 57 · see citing works
Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control
Lee, J., Dassau, E., Gondhalekar, R., Seborg, D., Pinsker, J., & Doyle, F. (2016) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.6b02718 · Cited by 53 · see citing works
Personalized Adaptive CBR Bolus Recommender System for Type 1 Diabetes
Torrent‐Fontbona, F., & López, B. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2813424 · Cited by 52 · see citing works
“The 33 virtual subjects are supposed to encompass variations in the population with T1DM [29].”
BackgroundMehmood, S., Ahmad, I., Arif, H., Ammara, U. E., & Majeed, A. (2020) · Applied System Innovation · doi:10.3390/asi3030031 · Cited by 50 · see citing works
Therapy-driven Deep Glucose Forecasting
Aiello, E. M., Lisanti, G., Magni, L., Musci, M., & Toffanin, C. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.103255 · Cited by 49 · see citing works
Multicenter Closed-Loop/Hybrid Meal Bolus Insulin Delivery with Type 1 Diabetes
Chase, H. P., Doyle, F. J., Zisser, H., et al. (2014) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2014.0050 · Cited by 41 · see citing works
“31 In summary, this large multicenter inpatient study showed the CL system handled the four bolus situations safely.”
Self-citationModel-based analysis of postprandial glycemic response dynamics for different types of food
Rozendaal, Y., Maas, A., van Pul, C., Cottaar, E., Haak, H., Hilbers, P., & van Riel, N. (2018) · Clinical Nutrition Experimental · doi:10.1016/j.yclnex.2018.01.003 · Cited by 37 · see citing works
Visentin, R., Giegerich, C., Jäger, R., Dahmen, R., Boss, A., Grant, M., Dalla Man, C., Cobelli, C., & Klabunde, T. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2016.0128 · Cited by 36 · see citing works
“These were generated by randomly extracting different realizations of the parameter vector from an appropriate joint parameter distribution; a process that has been demonstrated to span the variability of the T1D population observed in vivo.(7,9) The current version of the UVA/Padova T1D simulator describes the subcut…”
MethodSelf-citationSynthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues
Pasculli, G., Virgolin, M., Myles, P. R., Vidovszky, A. A., Fisher, C. K., Biasin, E., Mourby, M. J., Pappalardo, F., D’Amico, S., Torchia, M., et al. (2025) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.70021 · Cited by 32 · see citing works
“…an additional layer of complexity with the introduction of the term “virtual controls” as in Switchenko et [85] and model generated data, respectively [86], whereas Dutta et al. adopted the “simulated historical control” when referring to data obtained via bootstrap of a Phase III trial data [87].”
Deep Reinforcement Learning for Closed-Loop Blood Glucose ControlPreprint
Fox, I., Lee, J. M., Pop‐Busui, R., & Wiens, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.09051 · Cited by 52 · see citing works
Reinforcement Learning for Blood Glucose Control: Challenges and Opportunities
Fox, I., & Wiens, J. (2019) · Cited by 33 · see citing works
Zhu, T., Li, K., & Georgiou, P. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3303367 · Cited by 32 · see citing works
“…from the version S2008 by incorporating a nonlinear hypoglycemia model and counter-regulation (i.e., glucagon administration), which was accepted by the US Food and Drug Administration for pre-clinical in silico trials and has been proved to match the observations in actual clinical trials [37].”
Design and In Silico Evaluation of an Exercise Decision Support System Using Digital Twin Models
Young, G. M., Dodier, R. H., Youssef, J. E., Castle, J. R., Wilson, L. M., Riddell, M. C., & Jacobs, P. G. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231223217 · Cited by 26 · see citing works
Hybrid Knowledge and Data Driven Synthesis of Runtime Monitors for Cyber-Physical Systems
Zhou, X., Ahmed, B., Aylor, J. H., Asare, P., & Alemzadeh, H. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3242653 · Cited by 24 · see citing works
“The state-of-the-art UVA-Padova Type 1 Diabetes Simulator S2013 (T1DS2013) contains 30 virtual patients, which were demonstrated to be representative of the T1DM population observed in a clinical trial [65], and has been approved by the FDA for pre-clinical testing of APS [63], [66].”
BackgroundVisentin, R., Schiavon, M., Giegerich, C., Klabunde, T., Man, C. D., & Cobelli, C. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2897851 · Cited by 22 · see citing works
Self-citationGutiérrez-Casares, J. R., Quintero, J., Jorba, G., Junet, V., Martínez, V., Pozo-Rubio, T., Oliva, B., Daura, X., Mas, J. M., & Montoto, C. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.741170 · Cited by 22 · see citing works
“The FDA has accepted a type 1 diabetes simulator to replace animal testing in pre-clinical trials (112).”
BackgroundPersonalized LSTM-based alarm systems for hypoglycemia and hyperglycemia prevention
Iacono, F. D., Magni, L., & Toffanin, C. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105167 · Cited by 22 · see citing works
Herrgårdh, T., Li, H., Nyman, E., & Cedersund, G. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.619254 · Cited by 19 · see citing works
An adaptive back-stepping control for blood glucose regulation in type 1 diabetes
Sepasi, S., Kalat, A. A., & Seyedabadi, M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102498 · Cited by 19 · see citing works
Diaz C., J. L., Fabris, C., Breton, M. D., & Cengiz, E. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0154 · Cited by 19 · see citing works
“19,20 The simulation platform was modified to include cycle-related IS variability, modeled based on data collected during euglycemic clamp studies performed in FP and LP in a group of women with T1D of fertile age.”
Cobelli, C., & Man, C. D. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211015268 · Cited by 18 · see citing works
Self-citationTorrent‐Fontbona, F., Massana, J., & López, B. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.12.036 · Cited by 18 · see citing works
In silico thrombectomy trials for acute ischemic stroke
Miller, C., Konduri, P. R., Bridio, S., et al. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107244 · Cited by 17 · see citing works
Sensor-Augmented Pump-Based Customized Mathematical Model for Type 1 Diabetes
Grosman, B., Wu, D., Miller, D. M., LINTEREUR, L. J., Roy, A., Parikh, N., & KAUFMAN, F. R. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0333 · Cited by 17 · see citing works
Visser, S. A. G., Kandala, B., Fancourt, C., Krug, A. W., & Cho, C. R. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1729 · Cited by 18 · see citing works
Adaptive basal insulin recommender system based on Kalman filter for type 1 diabetes
Torrent‐Fontbona, F. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.02.015 · Cited by 17 · see citing works
Application of genetic algorithm for hemodialysis schedule optimization
Choi, J. W., Lee, H., Lee, J. C., et al. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.04.003 · Cited by 16 · see citing works
Olçomendy, L., Cassany, L., Pirog, A., Franco, R., Puginier, E., Jaffredo, M., Gucik‐Derigny, D., Ríos, H., de Loza, A. F., Gaitan, J., et al. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.795225 · Cited by 15 · see citing works
“In particular, the UVA/Padova T1DM Simulator accurately models the interpatient variability observed in response to meal intake in real T1D patients (52).”
Raheb, M. A., Niazmand, V. R., Eqra, N., & Vatankhah, R. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105860 · Cited by 15 · see citing works
Development platform for artificial pancreas algorithms
Smaoui, M., Rabasa-Lhoret, R., & Haidar, A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0243139 · Cited by 14 · see citing works
“The latter two simulators have been validated against results of clinical studies, providing fair simulation models for AP algorithm developers [12, 20].”
MethodSala-Mira, I., Siket, M., Kovács, L., Eigner, G., & Bondía, J. (2021) · IEEE Access · doi:10.1109/access.2021.3120880 · Cited by 14 · see citing works
“Finally, it includes a realistic cohort of virtual subjects [25].”
BackgroundResultPersonalized LSTM models for glucose prediction in Type 1 diabetes subjects
Iacono, F. D., Magni, L., & Toffanin, C. (2022) · 2006 14th Mediterranean Conference on Control and Automation · doi:10.1109/med54222.2022.9837153 · Cited by 13 · see citing works
Grosman, B., Parikh, N., Roy, A., LINTEREUR, L. J., Vigersky, R. A., Cohen, O., & Rhinehart, A. S. (2022) · Annals of Biomedical Engineering · doi:10.1007/s10439-022-03079-9 · Cited by 12 · see citing works
Predicting Insulin Treatment Scenarios with the Net Effect Method: Domain of Validity
Vettoretti, M., Facchinetti, A., Sparacino, G., & Cobelli, C. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2016.0148 · Cited by 12 · see citing works
Self-citationContreras, I., Vehı́, J., VISENTIN, R., & Vettoretti, M. (2017) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase.2017.61 · Cited by 10 · see citing works
“The software implements patient’s physiology using a mathematical model of glucose, insulin and glucagon dynamics which have demonstrated to describe effectively the inter-subject glucose variability observed in a clinical trial in T1D [11].”
MethodSelf-citationVisentin, R., Schiavon, M., Giegerich, C., Klabunde, T., Man, C. D., & Cobelli, C. (2018) · 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513234 · Cited by 9 · see citing works
Self-citationConstrained Versus Unconstrained Model Predictive Control for Artificial Pancreas
Toffanin, C., & Magni, L. (2023) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2023.3291564 · Cited by 9 · see citing works
“patterns observed in human studies on T1D patients [45].”
BackgroundDeployment of modular MPC for type 1 diabetes control: the Italian experience 2008–2016
Favero, S. D., Toffanin, C., Magni, L., et al. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815655-1.00016-8 · Cited by 9 · see citing works
Self-citationPrediction of Glucose Level Conditions from Sequential Data
Mordvanyuk, N., Torrent‐Fontbona, F., & Beatriz, L. (2017) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-806-8-227 · Cited by 11 · see citing works
“The experimentation has been carried out using the UVA-Padova type 1 diabetes simulator [16] and it compares the use of k-NN with sequential data with the k-NN without sequential data, only one-shot data.”
MethodApproaches to Generating Virtual Patient Cohorts with Applications in Oncology
Surendran, A., Sauteur-Robitaille, J. L., Kleimeier, D., Gevertz, J. L., Wilkie, K. P., Jenner, A. L., & Craig, M. (2023) · Springer eBooks · doi:10.1007/978-3-031-32614-1_8 · Cited by 16 · see citing works
A nonlinear time-delay realization for gastroparesis in patients with diabetes
Califano, C., Scharbarg, E., Magdelaine, N., & Moog, C. H. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.07.005 · Cited by 8 · see citing works
“This has to be compared with: - 35 mg/dl RMSE for 1 hour prediction in Zhao et al. (2014) - no cross-validation for the UVA/Padova simulator in Vinsentin et al. (2014).”
BackgroundMethodSAAM II : A general mathematical modeling rapid prototyping environment
Perazzolo, S. (2024) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.13181 · Cited by 8 · see citing works
“14,15 SAAM II is a widely used software platform for glucose-insulin Minimal Model Analysis, aiding in monitoring diabetes progression and evaluating the efficacy of antidiabetic drugs in clinical trials by estimating key parameters of glucose disposition in vivo.”
MethodA phenomenological-based semi-physical model of the kidneys and its role in glucose metabolism
Lema-Pérez, L., Builes-Montaño, C. E., & Álvarez, H. (2020) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2020.110489 · Cited by 7 · see citing works
Syafiie, S. (2020) · ISA Transactions · doi:10.1016/j.isatra.2020.11.012 · Cited by 7 · see citing works
Physiological models for artificial pancreas development
VISENTIN, R., Schiavon, M., Basu, R., Basu, A., Man, C. D., & Cobelli, C. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815655-1.00015-6 · Cited by 7 · see citing works
Self-citationApproaches to generating virtual patient cohorts with applications in oncologyPreprint
Surendran, A., Sauteur-Robitaille, J. L., Kleimeier, D., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.24.493265 · Cited by 7 · see citing works
“…tuberculosis (Pitcher et al. 2018), SARS-COV-2 (Jenner et al. 2021a), sepsis (Clermont et al. 2004; An 2004; Vodovotz and Billiar 2013), diabetes (Visentin et al. 2014; Gyuk et al. 2019), cardiovascular diseases (Corral-Acero et al. 2020), and different tumours (including breast (Switchenko et…”
BackgroundAhmadasas, M., Inanc, E., Ozkara, E., Siket, M., Rashid, M. M., Bilgic, M., Quinn, L., & Çınar, A. (2025) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2025.101033 · Cited by 6 · see citing works
In Silico Testing of a Control Algorithm for a Personalized Insulin Therapy System
Витольдович, П. К., Bazaev, N. A., & Litinskaia, E. L. (2021) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/elconrus51938.2021.9396210 · Cited by 6 · see citing works
Ongoing Debate About Models for Artificial Pancreas Systems and In Silico Studies
Forlenza, G. P. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0038 · Cited by 6 · see citing works
“The UVA/Padova simulator has since been validated in a clinical study 33 in addition to its wide usage by a diverse number of groups.”
Schiavon, M., Acciaroli, G., Vettoretti, M., Giaretta, A., & Visentin, R. (2018) · 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512257 · Cited by 5 · see citing works
Self-citationMetabolic Models, in Silico Trials, and Algorithms
Çınar, A., Basu, A., Bequette, B. W., Breton, M. D., Buckingham, B. A., Cengiz, E., Cobelli, C., Dassau, E., Doyle, F. J., Fabris, C., et al. (2025) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2025.18800.ac · Cited by 5 · see citing works
“63,64,66 In the early stages of MPC algorithm development, fi xed-parameter linear state-space models were used.”
Self-citationPersonalized LSTM-based alarm systems for hypoglycemia prevention
Toffanin, C., Iacono, F. D., & Magni, L. (2023) · 2006 14th Mediterranean Conference on Control and Automation · doi:10.1109/med59994.2023.10185789 · Cited by 5 · see citing works
“This metabolic simulator was approved by Food and Drug Administration (FDA) as a substitute of animal trials and largely used to test insulin therapies in silico, being demonstrated that its simulations reflect the glucose patterns observed in human studies on T1D patients [16], [18].”
MethodApplying Systems Medicine in the clinic
Maier, D. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.04.014 · Cited by 5 · see citing works
Faggionato, E., Guazzo, A., Pegolo, E., Ruggero Carli, A., Bruschetta, M., & Favero, S. D. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3261962 · Cited by 5 · see citing works
“In both the notable examples of closed-loop drug delivery systems mentioned above, in-silico testing has been widely employed: accurate simulators of subjects with type 1 diabetes were used for insulin delivery to control blood glucose [23], [24], while, an open-source simulator of patients’ physiology is available an…”
MethodTime to Moderate and Severe Hyperglycemia and Ketonemia Following an Insulin Pump Occlusion
Klonoff, D. C., Ayers, A. T., Ho, C. N., Fabris, C., Villa-Tamayo, M. F., Allen, E., Cengiz, E., Ekhlaspour, L., Wong, J. C., Heineman, L., et al. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241280386 · Cited by 4 · see citing works
A CBR-based bolus recommender system for type 1 diabetes
Torrent‐Fontbona, F., López, B., & Pozo-Alonso, A. (2017) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 4 · see citing works
Metabolic Models, in Silico Trials, and Algorithms
Çınar, A., Basu, A., Bequette, B. W., Breton, M. D., Buckingham, B. A., Cengiz, E., Cobelli, C., Dassau, E., Doyle, F. J., Fabris, C., et al. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251338300 · Cited by 4 · see citing works
“63,64,66 In the early stages of MPC algorithm development, fixed-parameter linear state-space models were used.”
Self-citationThe Digital Mouse: why computational modelling of mouse models of disease can improve translationPreprint
van Riel, N. A. W., Müller, R., & Dall’Ara, E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.04.075812 · Cited by 4 · see citing works
Roquemen-Echeverri, V., Kushner, T., Jacobs, P. G., & MOSQUERA-LOPEZ, C. M. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12018-x · Cited by 6 · see citing works
“Achieving optimal glycemic control in T1D remains challenging as insulin therapy needs to be adjusted over time based on each individual’s glucose response to meals, exercise, hormone cycles, stress, and other external disturbances [2, 3, 4].”
Alternative Frameworks for Personalized Insulin–Glucose Models
Kirchsteiger, H., Efendic, H., Reiterer, F., & del Re, L. (2015) · Lecture notes in bioengineering · doi:10.1007/978-3-319-25913-0_1 · Cited by 3 · see citing works
Automatic Regulation of Basal Insulin Infusion in a Personalized Insulin Therapy System
Litinskaia, E. L., Витольдович, П. К., & Polyakova, E. A. (2021) · Biomedical Engineering · doi:10.1007/s10527-021-10041-x · Cited by 3 · see citing works
SQUIREDL: Sparse Sequence-to-Sequence Uncertainty Estimation in Evidential Deep Learning
Vavaroutas, S., Dang, T., Rocheteau, E., et al. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3723049 · Cited by 2 · see citing works
“This simulator is accepted by the US FDA as a substitute of certain preclinical trials, and has been validated on type 1 diabetics [43] so its data can be considered as real-world for our purpose.”
Blood Glucose Regulation Models in Artificial Pancreas for Type-1 Diabetic Patients
Chandrasekhar, A., & Padhi, R. (2023) · Journal of the Indian Institute of Science · doi:10.1007/s41745-023-00362-z · Cited by 2 · see citing works
Modelado de la influencia del ciclo menstrual en el sistema glucosa-insulina en individuas sanas
Manrique-Córdoba, J., Romero-Ante, J. D., Vicente-Samper, J. M., & Sabater-Navarro, J. M. (2023) · Revista Iberoamericana de Automática e Informática Industrial RIAI · doi:10.4995/riai.2023.18533 · Cited by 2 · see citing works
Siket, M., Toth, R., Szász, L., Novák, K., Eigner, G., & Kovács, L. (2023) · IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI) · doi:10.1109/sami58000.2023.10044485 · Cited by 2 · see citing works
“This extended version of the simulator was published in 2013 and it i u m on A pp li e also been in the validation process [10] and continued working on this m po version of the simulator; in multiple steps [11] they managed The work was supported in part by the E¨otv¨os Lor´and Research Network…”
Modelado y simulación de tecnologías para el tratamiento de la diabetes
Rosales, N. (2020) · doi:10.35537/10915/97299 · Cited by 2 · see citing works
Robust two-degree-of-freedom control: A simulation-based approach for adults population with T1D
García-Rodríguez, J. A., Guy Yaoyotzin Cortés, S., Diaz-Velazco, R. C., Gudiño-Ochoa, A., & Navarrete‐Guzmán, A. (2024) · Results in Control and Optimization · doi:10.1016/j.rico.2024.100454 · Cited by 1 · see citing works
Islam, M. R., Yang, J., Li, M., Mokshagundam, S. P., Cai, L., & Li, J. (2025) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2025.112188 · Cited by 1 · see citing works
Periodic MPC for glucose control in Type 1 Diabetes
Mongini, P. A., Ragni, M., Magni, L., et al. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.06.005 · Cited by 1 · see citing works
Wen, S., Li, H., & Tao, R. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02866-3 · Cited by 1 · see citing works
A fuzzy state-dependent Riccati equation control: adaptive tuning of the state weighting matrix
Batmani, Y., Khodakaramzadeh, S., & Nekoo, S. R. (2024) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-024-05351-6 · Cited by 1 · see citing works
KnowSafe: Combined Knowledge and Data Driven Hazard Mitigation in Artificial Pancreas Systems
Zhou, X., Kouzel, M., Smith, C. W., & Alemzadeh, H. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3584870 · Cited by 1 · see citing works
“…contains models of 10 actual Type I diabetes patients [74], while the UVA-Padova T1DS features 30 virtual patients, shown to be representative of the Type 1 diabetes mellitus population observed in a clinical trial [75], and has been approved by the FDA for pre-clinical testing of APS [73].”
Walsh, J. S. A. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817719907
“In other words, theoretically would the iD BC have improved postbolus glucose results? The FDA currently allows BCs to receive approval after undergoing in-silico testing with the University of Virginia’s type 1 diabetes simulator that currently operates with a 300 patient database.(3) Even though this provides a rela…”
Real-World Challenges of Controller Adaptation with the Artificial Pancreas
Bally, L., & Thabit, H. (2017) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0310
“There is no doubt that considerable advances have been achieved, thanks to data and results derived from in silico studies.(22,23) However, real-world studies during free-living conditions are still needed and irreplaceable especially for regulatory approval, due to the unpredictability and variability of human behavi…”
Pathologic changes of pancreatic endothelial cells in Diabetic Rat
Wu, J., Wang, X., Feng, S. D., Li, Q., & Cai, F. (2017) · Journal of Clinical & Experimental Cardiology · doi:10.4172/2155-9880.1000552
Artificial Intelligence and Its Application in Insulin Bolus Calculators
Mansour, A., Amroun, K., & Habbas, Z. (2020) · Springer eBooks · doi:10.1007/978-3-030-57552-6_5
Computer simulation model and automatic control of glucose clamp
Maghoul, P. (2021) · eScholarship@McGill (McGill) · doi:10.82308/47829
(2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815756-5.00023-0
An artificial pancreas system in android phones: A dual app architecture
Chandrasekhar, A., Saini, D., & Padhi, R. (2023) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2023.101767 · Cited by 1 · see citing works
Robust Two-Degree-Of-Freedom Control: A Simulation-Based Approach for Adults Population with T1dPreprint
García-Rodríguez, J. A., Cortes-Solares, G., Diaz-Velazco, R. C., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4784713
Mongini, P. A., Mazzoleni, M., Ferramosca, A., et al. (2024) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc56724.2024.10886116
“It is a software available commercially and largely used in literature [13], but also to design and test the AP control strategies as well as safety modules [14], [15], [16], [17] tested in clinical trials.”
In silico development of a bioelectronic islet-based artificial pancreas
Olçomendy, L. (2021) · theses.fr (ABES) · doi:10.70675/79936eabz2e8az470az8954za69f834cde6d
Maqsood, S., Belousovienė, E., Tamošuitis, T., & Maskeliūnas, R. (2026) · Reviews in Endocrine and Metabolic Disorders · doi:10.1007/s11154-026-10068-z
- Authors
- Visentin, R., Dalla Man, C., Kovatchev, B., & Cobelli, C.
- Affiliations
- University of Padua (IT)
- University of Padova
- University of Virginia (US)
- Corresponding author
- Cobelli, Claudio
- Venue
- Diabetes Technology & Therapeutics
- Publisher
- SAGE Publications
- Published
- 2014-07
- Volume
- 16
- Issue
- 7
- Pages
- 428-434
- ISSN
- 1520-9156 (ISSN-L)
- 1557-8593
- PMC
- PMC4074748
- OpenAlex
- W2322662949
- Semantic Scholar
- 5acb44cd926a9e47ea60993621f01f2ed7c79a9d
- Cited by
- 103
- References
- 11
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 7 · 2023: 12 · 2022: 6 · 2021: 9 · 2020: 9 · 2019: 9 · 2018: 12 · 2017: 15 · 2016: 6 · 2015: 1 · 2014: 2
- FWCI
- 6.44
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Diabetes Technology & Therapeutics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
- Keywords
- type 1 diabetes, artificial pancreas, hypoglycemia, glucose profile, postprandial blood glucose
- MeSH terms
- Adolescent, Adult, Algorithms, Blood Glucose, Clinical Trials as Topic, Computer Simulation, Diabetes Mellitus, Type 1, Female, Humans, Hypoglycemia, Hypoglycemic Agents, Insulin, Insulin Infusion Systems, Male, United States, Blood Glucose Self-Monitoring, Reproducibility of Results
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
