Smart Algorithms for Efficient Insulin Therapy Initiation in Individuals With Type 2 Diabetes: An in Silico Study.

BACKGROUND: Insulin-naive subjects with type 2 diabetes (T2D) start basal insulin titration from a low initial insulin dose (IID), which is adjusted weekly or twice per week based on fasting plasma glucose (FPG) measurement as recommended by the American Diabetes Association (ADA). The procedure to reach the optimal insulin dose (OID) is time-consuming, especially in subjects with high insulin needs (HIN). The aim of this study is to provide a fast and effective, but still safe, insulin titration algorithm in insulin-naive T2D subjects with HIN. METHOD: To do that, we in silico cloned 300 subjects, matching a real population of insulin-naive T2D and used a logistic regression model to classify them as subjects with HIN or subjects with low insulin needs (LIN). Then, we applied to the subjects with HIN both a more aggressive insulin dose initiation (SMART-IID) and two newly developed titration algorithms (continuous glucose monitoring [CGM]-BASED and SMART-CGM-BASED) in which CGM was used to guide the decision-making process. RESULTS: The new titration algorithm applied to HIN-classified individuals guaranteed a faster reaching of OID, with significant improvements in time in range (TIR) and reduction in time above range (TAR) in the first months of the trial, without any clinically significant increase in the risk of hypoglycemia. CONCLUSIONS: Smart basal insulin titration algorithms enable insulin-naive T2D individuals to achieve OID and improve their glycemic control faster than standard guidelines, without jeopardizing patient safety.

Short- and Long-Term Effects on Glucose Control of Nonadherence to Insulin Therapy in People With Type 2 Diabetes An In Silico Study.

BACKGROUND: Strict adherence to multiple daily insulin (MDI) therapy is a cornerstone for the achievement of good glucose control in people with advanced type 2 diabetes (T2D). Here, we aim to in silico assess glucose control in T2D subjects with poor adherence to MDI therapy. METHODS: We tuned the Padova T2D Simulator, originally describing early-stage T2D physiology, around advanced T2D people. One hundred in silico advanced T2D subjects were generated and equipped with optimal MDI therapy: specifically, basal and bolus insulin amounts and injection times were individualized for each subject by applying titration algorithms that iteratively update insulin dose based on glucose deviation from its target. Then, the effect of nonadhering to MDI therapy was assessed using standard glucose control metrics calculated in two 6-month 3-meal/day in silico scenarios: in Scenario 1, subjects received the optimal basal and prandial insulin bolus at each meal; in Scenario 2, subjects received optimal basal insulin and randomly delayed or skipped the prandial insulin bolus in 3 lunches during working days and 1 dinner during weekends. RESULTS: A statistically significant degradation was found in all glucose control outcome metrics in Scenario 2 versus Scenario 1: e.g., percent time above 180 mg/dL increased by 22.2% and glucose management index by 0.2%. CONCLUSIONS: Impaired adherence to MDI therapy in T2D leads to glucose control deteriorations in both short and long terms. Interestingly, short-term hyperglycemia seems being contrasted by residual endogenous insulin secretion, which statistically increased by 3-fold after delayed/skipped insulin boluses compared with optimal ones.

Tailoring the Padova Type 2 Diabetes Simulator for Treatment Guidance in Target Populations.

OBJECTIVE: The Padova type 2 diabetes (T2D) simulator (T2DS) has been recently proposed to optimize T2D treatments including novel long-acting insulins. It consists of a physiological model and an in silico population describing glucose dynamics, derived from early-stage T2D subjects studied with sophisticated tracer-based experimental techniques. This limits T2DS domain of validity to this specific sub-population. Conversely, running simulations in insulin-naïve or advanced T2D subjects, would be more valuable. However, it is rarely possible or cost-effective to run complex experiments in such populations. Therefore, we propose a method for tuning the T2DS to any desired T2D sub-population using published clinical data. As case study, we extended the T2DS to insulin-naïve T2D subjects, who need to start insulin therapy to compensate the reduced insulin function. METHODS: T2DS model was identified based on literature data of the target population. The estimated parameters were used to generate a virtual cohort of insulin-naïve T2D subjects (inC1). A model of basal insulin degludec (IDeg) was also incorporated into the T2DS to enable basal insulin therapy. The resulting tailored T2DS was assessed by simulating IDeg therapy initiation and comparing simulated vs. clinical trial outcomes. For further validation, this procedure was reiterated to generate a new cohort of insulin-naïve T2D (inC2) assuming inC1 as target population. RESULTS: No statistically significant differences were found when comparing fasting plasma glucose and IDeg dose, neither in clinical data vs. inC1, nor inC1 vs. inC2. CONCLUSIONS: The tuned T2DS allowed reproducing the main findings of clinical studies in insulin-naïve T2D subjects. SIGNIFICANCE: The proposed methodology makes the Padova T2DS usable for supporting treatment guidance in target T2D populations.

Modeling Doxorubicin Pharmacokinetics in Multiple Myeloma Suggests Mechanism of Drug Resistance.

OBJECTIVE: Multiple myeloma (MM) is a plasma cell malignancy often treated with chemotherapy drugs. Among these, doxorubicin (DOXO) is commonly employed, sometimes in combined-drug therapies, but it has to be optimally administered in order to maximize its efficacy and reduce possible side effects. To support DOXO studies and treatment optimization, here we propose an experimental/modeling approach to establish a model describing DOXO pharmacokinetics (PK) in MM cells. METHODS: A series of in vitro experiments were performed in MM1R and MOLP-2 cells. DOXO was administered at two dosages (200 nM, 450 nM) at [Formula: see text] = 0 and removed at [Formula: see text] = 3 hrs. Intracellular DOXO concentration was measured via fluorescence microscopy during both drug uptake ([Formula: see text] = 0-3 hrs) and release phases ([Formula: see text] = 3-8 hrs). Four PK candidate models were identified, and were compared and selected based on their ability to describe DOXO data and numerical parameter identification. RESULTS: The most parsimonious model consists of three compartments describing DOXO distribution between the extracellular space, the cell cytoplasm and the nucleus, and defines the intracellular DOXO efflux rate through a Hill function, simulating a threshold/saturation drug resistance mechanism. This model predicted DOXO data well in all the experiments and provided precise parameter estimates (mean ± standard deviation coefficient of variation: 15.8 ± 12.2%). CONCLUSIONS: A reliable PK model describing DOXO uptake and release in MM cells has been successfully developed. SIGNIFICANCE: The proposed PK model, once integrated with DOXO pharmacodynamics, has the potential of allowing the study and the optimization of DOXO treatment strategies in MM.

Increased Insulin Secretion and Glucose Effectiveness in Obese Patients with Type 2 Diabetes following Bariatric Surgery.

Background: ß-cell dysfunction and insulin resistance are the main mechanisms causing glucose intolerance in type 2 diabetes (T2D). Bariatric surgeries, i.e., sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB), are procedures both known to induce weight loss, increase insulin action, and enhance ß-cell function, but hepatic insulin extraction and glucose effectiveness may also play a role. Methods: To determine the contribution of these regulators on glucose tolerance after bariatric surgery, an oral glucose tolerance test (OGTT) was performed before and 2 months after surgery in 9 RYGB and 7 SG subjects. Eight healthy subjects served as metabolic controls. Plasma glucose, insulin, C-peptide, GLP-1, and GIP were measured during each OGTT. Insulin sensitivity and secretion, glucose effectiveness, and glucose rate of appearance were determined via oral minimal models. Results: RYGB and SG resulted in similar weight reductions (13%, RYGB (p < 0.01); 14%, SG (p < 0.05)). Two months after surgery, insulin secretion (p < 0.05) and glucose effectiveness both improved equally in the two groups (11%, RYGB (p < 0.01); 8%, SG (p > 0.05)), whereas insulin sensitivity remained virtually unaltered. Bariatric surgery resulted in a comparable increase in the GLP-1 response during the OGTT, whereas GIP concentrations remained unaltered. Following surgery, oral glucose intake resulted in a comparable increase in hepatic insulin extraction, the response in both RYGB and SG patients significantly exceeding the response observed in the control subjects. Conclusions: These results demonstrate that the early improvement in glucose tolerance in obese T2D after RYGB and SG surgeries is attributable mainly to increased insulin secretion and glucose effectiveness, while insulin sensitivity seems to play only a minor role. This trial is registered with NCT02713555.

A software interface for in silico testing of type 2 diabetes treatments.

BACKGROUND AND OBJECTIVE: The increasing incidence of diabetes continuously stimulates the research on new antidiabetic drugs. Computer simulation can save time and costs, alleviating the need of animal trials and providing useful information for optimal experiment design and drug dosing. We recently presented a type 2 diabetes (T2D) simulator as tool for in silico testing of new molecules and guiding treatment optimization. Here we present a user-friendly interface aimed to increase the usability of the simulator. METHOD: The simulator, based on a large-scale glucose, insulin, and C-peptide model and equipped with 100 virtual subjects well describing system dynamics in a real T2D population, is extended to incorporate pharmacokinetics/pharmacodynamics (PK/PD) of a drug of interest. A graphical interface is developed on top of the simulator, allowing an easy design of in silico experiments: specifically, it is possible to select the population size to test, design the experiment (crossover or parallel), its duration and the sampling grid, choose glucose and insulin doses, and define treatment PK/PD and dose administered. The simulator also provides the outcome metrics requested by the user, and performs statistical comparisons among treatments and/or placebo. RESULTS: To illustrate the potential of the simulator, we provided a case study using metformin and liraglutide. Literature-based PK/PD models of metformin and liraglutide have been incorporated in the simulator, by modulating key drug-sensitive model parameters. An in silico placebo-controlled trial has been done by simulating a three-arm meal tolerance test with subjects receiving placebo, metformin 850 mg, liraglutide 1.80 mg, respectively. The obtained results are in agreement with the clinical evidences, in terms of main glucose, insulin, and C-peptide outcome metrics. CONCLUSIONS: We developed a user-friendly software interface for the T2D simulator to support the design and test of new antidiabetic drugs and treatments. This increases the simulator usability, making it suitable also for users who have low experience with computer programming.

A Novel Method for Generation of In Silico Subjects with Type 2 Diabetes.

A type 2 diabetes (T2D) simulator has been recently proposed for supporting drug development and treatment optimization. This tool consists of a physiological model of glucose/insulin/C-peptide dynamics and a virtual cohort of T2D subjects (i.e., random extractions of model parameterizations from a joint parameter distribution) well describing both average and variability realistic T2D dynamics . However, the state-of-art procedure to get a reliable virtual population requires some post-processing after subject extraction, in order to discard implausible behaviors. We propose an improved method for virtual subjects' generation to overcome this burdensome task. To do so, we first assessed a refined joint parameter distribution, from which extracting a number of subjects, greater than the target population size. Then, target-size subsets are undersampled from the large cohort. The final virtual population is selected among the subsets as the one maximizing the similarity with T2D data and model parameter distribution, by means of measurement' outcome metrics and Euclidian distance (Δ), respectively. In the final population, almost all the outcome metrics are statistically identical to the clinical counterparts (p-value>0.05) and model parameters' distribution differs by ~5-10% from that derived from data. The methodology described here is flexible, thus resulting suitable for different T2D stages and type 1 diabetes, as well.Clinical Relevance- A straightforward subjects' generation would ease the availability of tailored in silico trials for testing diabetes treatment in a specific population.

Modeling Pharmacokinetics of Doxorubicin in Multiple Myeloma Cells.

Doxorubicin (DOXO) is a well-established chemotherapy drug for treatment of different tumors, ranging from breast cancer, melanoma to multiple myeloma (MM). Here, we present a coupled experimental/modeling approach to study DOXO pharmacokinetics in MM cells, investigate its distribution among the extracellular and intracellular compartments during time. Three model candidates are considered and identified. Model selection is performed based on its ability to describe the data both qualitatively and in terms of quantitative indexes. The most parsimonious model consists of a nonlinear structure with a saturation-threshold control of intracellular DOXO efflux by the DOXO bound to the cellular DNA. This structure could explain the hypothesis that MM cells are drug-resistant, likely due to the involvement of P-glycoproteins.The proposed model is able to predict the intracellular (free and bound) DOXO and suggests the presence of a saturation-threshold drug-resistant mechanism.Clinical Relevance- The model can be used to properly understand and guide further experimental setup, e.g., to investigate multiple myeloma cell variability among different cell lines.

Determinants of hepatic insulin clearance - Results from a Mendelian Randomization study.

AIMS/HYPOTHESIS: Besides insulin resistance, type 2 diabetes associates with decreased hepatic insulin clearance (HIC). We now tested for causal relationship of HIC to liver fat accumulation or features of the metabolic syndrome. METHODS: HIC was derived from oral glucose tolerance tests with the "Oral C-peptide and Insulin Minimal Models" (n = 3311). Liver fat was quantified by magnetic resonance spectroscopy (n = 1211). Mendelian Randomization was performed using established single nucleotide polymorphisms (SNPs; 115 for liver fat, 155 alanine-aminotransferase, 37 insulin sensitivity, 37 insulin secretion, 72 fasting insulin, 5285 BMI, 163 visceral fat, 270 waist circumference, 442 triglycerides, 620 HDL-Cholesterol, 193 C-reactive protein, 53 lipodystrophy-like phenotypes). RESULTS: HIC associated inversely with liver fat (p < 0.003) and insulin sensitivity (p < 0.0001). Both liver fat and HIC were independently associated with insulin sensitivity (p < 0.0001). Neither liver fat nor alanine-aminotransferase were causally linked to HIC, as indicated by Mendelian Randomization (Nliver fat = 1054, NHIC = 2254; Nalanineaminotranferase = 1985, NHIC = 2251). BMI-related SNPs were causally associated with HIC (NBMI = 2772, NHIC = 2259, p < 0.001) but not waist circumference-SNPs (NSNPs-waist circumference = 2751, NHIC = 2280). Genetically determined insulin sensitivity was not causally related to HIC (Ninsulin sensitivity = 2752, NHIC = 2286). C-reactive protein and HDL were causally associated with HIC, with higher C-reactive protein and lower HDL leading to higher HIC (NC-reactive protein = 2660, NHIC = 2240; NHDL = 2694, NHIC = 2275). CONCLUSIONS: This Mendelian Randomization analysis does not support a causal link between hepatic steatosis and HIC. Other components of the metabolic syndrome seem to compensate peripheral hyperinsulinemia by increasing hepatic insulin extraction.

Improved postprandial glucose metabolism in type 2 diabetes by the dual glucagon-like peptide-1/glucagon receptor agonist SAR425899 in comparison with liraglutide.

AIM: To gain further insights into the efficacy of SAR425899, a dual glucagon-like peptide-1/glucagon receptor agonist, by providing direct comparison with the glucagon-like peptide-1 receptor agonist, liraglutide, in terms of key outcomes of glucose metabolism. RESEARCH DESIGN AND METHODS: Seventy overweight to obese subjects with type 2 diabetes (T2D) were randomized to receive once-daily subcutaneous administrations of SAR425899 (0.12, 0.16 or 0.20 mg), liraglutide (1.80 mg) or placebo for 26 weeks. Mixed meal tolerance tests were conducted at baseline (BSL) and at the end of treatment (EOT). Metabolic indices of insulin action and secretion were assessed via Homeostasis Model Assessment (HOMA2) and oral minimal model (OMM) methods. RESULTS: From BSL to EOT (median [25th, 75th] percentile), HOMA2 quantified a significant improvement in basal insulin action in liraglutide (35% [21%, 74%]), while secretion enhanced both in SAR425899 (125% [63%, 228%]) and liraglutide (73% [43%, 147%]). OMM quantified, both in SAR425899 and liraglutide, a significant improvement in insulin sensitivity (203% [58%, 440%] and 36% [21%, 197%]), basal beta-cell responsiveness (67% [34%, 112%] and 40% [16%, 59%]), and above-basal beta-cell responsiveness (139% [64%, 261%] and 69% [-15%, 120%]). A significant delay in glucose absorption was highlighted in SAR425899 (37% [52%,18%]). CONCLUSIONS: SAR425899 and liraglutide improved postprandial glucose control in overweight to obese subjects with T2D. A significantly higher enhancement in beta-cell function was shown by SAR425899 than liraglutide.

In Silico Cloning of Target Type 2 Diabetes Population for Treatments Development and Decision Support.

Therapies for treatment of type 2 diabetes (T2D) involve a variety of medications, depending on the stage of T2D progression. It is now an accepted knowledge that in silico trials can help to accelerate drug development and support treatment optimization. A T2D simulator (T2DS), consisting of a model of the glucose-insulin system and an in silico population describing glucose-insulin dynamics in T2D subjects, has been recently developed based on early-stage T2D data, studied with sophisticated experimental techniques. This limits the domain of validity of the simulator to this specific sub-population of T2D. Here we proposed a method for tuning the T2DS to any desired T2D target population, e.g. insulin-naïve (i.e., not experienced with insulin) patients, without the need to resort to complex and expensive clinical studies. This will allow to use the T2DS for testing treatments in the target population. To illustrate the methodology, we used a case study: extending the T2DS to reproduce the behavior of insulin-naïve T2D subjects. The methodology described here can be extended to other stages of T2D, allowing an extensive in silico testing phase of different treatments before human trials.

The Padova Type 2 Diabetes Simulator from Triple-Tracer Single-Meal Studies: In Silico Trials Also Possible in Rare but Not-So-Rare Individuals.

Background:In silico trials in type 2 diabetes (T2D) would be useful for testing diabetes treatments and accelerating the development of new antidiabetic drugs. In this study, we present a T2D simulator able to reproduce the variability observed in a T2D population. The simulator also allows to safely experiment on virtual subjects with severe (and possibly rare) pathological conditions. Methods: A meal simulation model of glucose, insulin, and C-peptide systems, made of 15 differential equations and 39 parameters, has been identified using a system decomposition and forcing function Bayesian strategy on data of 51 T2D subjects undergoing a single triple-tracer mixed meal. One hundred T2D in silico subjects have been generated from the joint distribution of estimated model parameters. A case study is presented to illustrate the simulator use for testing a virtual drug (improving insulin action and secretion) in a subpopulation of rare, extremely impaired, T2D subjects. Results: The model well fitted T2D data and parameters were estimated with precision. Simulated plasma glucose, insulin, and C-peptide well matched the data (e.g., median [25th-75th percentile] glucose area under the curves of 6.9 [6.1-8.5] 104 mg/dL·min in silico vs. 7.0 [5.6-8.2] 104 mg/dL·min in vivo). The potential use of the simulator was shown in a case study, in which the (virtual) antidiabetic drug dose was optimized for very insulin-resistant T2D subjects. Conclusions: We have developed a T2D simulator that captures the behavior of T2D population during a meal, both in terms of average and intersubject variability. The simulator represents a cost-effective way to test new antidiabetic drugs, before moving to human trials.

In Silico Head-to-Head Comparison of Insulin Glargine 300 U/mL and Insulin Degludec 100 U/mL in Type 1 Diabetes.

Background: Second-generation long-acting insulin glargine 300 U/mL (Gla-300) and degludec 100 U/mL (Deg-100) provide novel basal insulin therapies for the treatment of type 1 diabetes (T1D). Both offer a flatter pharmacokinetic (PK) profile than the previous generation of long-acting insulins, thus improving glycemic control while reducing hypoglycemic events. This work describes an in silico head-to-head comparison of the two basal insulins on 24-h glucose profiles and was used to guide the design of a clinical trial. Materials and Methods: The Universities of Virginia (UVA)/Padova T1D simulator describes the intra-/interday variability of glucose-insulin dynamics and thus provides a robust bench-test for assessing glucose control for basal insulin therapies. A PK model describing subcutaneous absorption of Deg-100, in addition to the one already available for Gla-300, has been developed based on T1D clinical data and incorporated into the simulator. One hundred in silico T1D subjects received a basal insulin dose (Gla-300 or Deg-100) for 12 weeks (8 weeks uptitration, 4 weeks stable dosing) by morning or evening administration in a basal/bolus regimen. The virtual patients were uptitrated to their individual doses with two different titration rules. Results: The last 2-week simulated continuous glucose monitoring data were used to calculate various outcome metrics for both basal insulin treatments, with primary outcome being the percent time in glucose target (70-140 mg/dL). The simulations show no statistically significant difference for Gla-300 versus Deg-100 in the main endpoints. Conclusions: This work suggests comparable glucose control using either Gla-300 or Deg-100 and was used to guide the design of a clinical trial intended to compare second-generation long-acting insulin analogues.

Modeling Subcutaneous Absorption of Long-Acting Insulin Glargine in Type 1 Diabetes.

OBJECTIVE: Subcutaneous (sc) administration of long-acting insulin analogs is often employed in multiple daily injection (MDI) therapy of type 1 diabetes (T1D) to cover patient's basal insulin needs. Among these, insulin glargine 100 U/mL (Gla-100) and 300 U/mL (Gla-300) are formulations indicated for once daily sc administration in MDI therapy of T1D. A few semi-mechanistic models of sc absorption of insulin glargine have been proposed in the literature, but were not quantitatively assessed on a large dataset. The aim of this paper is to propose a model of sc absorption of insulin glargine able to describe the data and provide precise model parameters estimates with a clear physiological interpretation. METHODS: Three candidate models were identified on a total of 47 and 77 insulin profiles of T1D subjects receiving a single or repeated sc administration of Gla-100 or Gla-300, respectively. Model comparison and selection were performed on the basis of their ability to describe the data and numerical identifiability. RESULTS: The most parsimonious model is linear two-compartment and accounts for the insulin distribution between the two compartments after sc administration through parameter k. Between the two formulations, we report a lower fraction of insulin in the first versus second compartment (k = 86% versus 94% in Gla-100 versus Gla-300, p < 0.05), a lower dissolution rate from the first to the second compartment ([Formula: see text] versus 0.0008 min-1 in Gla-100 versus Gla-300, p << 0.001), and a similar rate of insulin absorption from the second compartment to plasma ([Formula: see text] versus 0.0016 min-1 in Gla-100 versus Gla-300, p = NS), in accordance with the mechanisms of insulin glargine protraction. CONCLUSIONS: The proposed model is able to both accurately describe plasma insulin data after sc administration and precisely estimate physiologically plausible parameters. SIGNIFICANCE: The model can be incorporated in simulation platforms potentially usable for optimizing basal insulin treatment strategies.

Dual glucagon-like peptide-1 receptor/glucagon receptor agonist SAR425899 improves beta-cell function in type 2 diabetes.

AIM: To evaluate the change in insulin sensitivity, ß-cell function and glucose absorption after 28 days of treatment with high and low doses of SAR425899, a novel dual glucagon-like peptide-1 receptor/glucagon receptor agonist, versus placebo. MATERIALS AND METHODS: Thirty-six overweight to obese subjects with type 2 diabetes were randomized to receive daily subcutaneous administrations of low-dose SAR425899 (0.03, 0.06 and 0.09 mg) and high-dose SAR425899 (0.06, 0.12 and 0.18 mg) or placebo for 28 days; dose escalation occurred after days 7 and 14. Mixed meal tolerance tests were conducted before treatment (day -1) and on days 1 and 28. Oral glucose and C-peptide minimal models were used to quantify metabolic indices of insulin sensitivity, ß-cell responsiveness and glucose absorption. RESULTS: With low-dose SAR425899, high-dose SAR425899 and placebo, ß-cell function from day -1 to day 28 increased by 163%, 95% and 23%, respectively. The change in area under the curve for the rate of meal glucose appearance between 0 and 120 minutes was -32%, -20% and 8%, respectively. CONCLUSIONS: After 28 days of treatment, SAR425899 improved postprandial glucose control by significantly enhancing ß-cell function and slowing glucose absorption rate.

Mechanisms of hyperinsulinaemia in apparently healthy non-obese young adults: role of insulin secretion, clearance and action and associations with plasma amino acids.

AIMS/HYPOTHESIS: This study aimed to examine the metabolic health of young apparently healthy non-obese adults to better understand mechanisms of hyperinsulinaemia. METHODS: Non-obese (BMI < 30 kg/m2) adults aged 18-35 years (N = 254) underwent a stable isotope-labelled OGTT. Insulin sensitivity, glucose effectiveness and beta cell function were determined using oral minimal models. Individuals were stratified into quartiles based on their insulin response during the OGTT, with quartile 1 having the lowest and quartile 4 the highest responses. RESULTS: Thirteen per cent of individuals had impaired fasting glucose (IFG; n = 14) or impaired glucose tolerance (IGT; n = 19), allowing comparisons across the continuum of insulin responses within the spectrum of normoglycaemia and prediabetes. BMI (~24 kg/m2) was similar across insulin quartiles and in those with IFG and IGT. Despite similar glycaemic excursions, fasting insulin, triacylglycerols and cholesterol were elevated in quartile 4. Insulin sensitivity was lowest in quartile 4, and accompanied by increased insulin secretion and reduced insulin clearance. Individuals with IFG had similar insulin sensitivity and beta cell function to those in quartiles 2 and 3, but were more insulin sensitive than individuals in quartile 4. While individuals with IGT had a similar degree of insulin resistance to quartile 4, they exhibited a more severe defect in beta cell function. Plasma branched-chain amino acids were not elevated in quartile 4, IFG or IGT. CONCLUSIONS/INTERPRETATION: Hyperinsulinaemia within normoglycaemic young, non-obese adults manifests due to increased insulin secretion and reduced insulin clearance. Individual phenotypic characterisation revealed that the most hyperinsulinaemic were more similar to individuals with IGT than IFG, suggesting that hyperinsulinaemic individuals may be on the continuum toward IGT. Furthermore, plasma branched-chain amino acids may not be an effective biomarker in identifying hyperinsulinaemia and insulin resistance in young non-obese adults.

Incorporating Long-Acting Insulin Glargine Into the UVA/Padova Type 1 Diabetes Simulator for In Silico Testing of MDI Therapies.

OBJECTIVE: Glargine 100 U/mL (Gla-100) and 300 U/mL (Gla-300) are long-acting insulin analogs providing basal insulin supply in multiple daily injection (MDI) therapy of type 1 diabetes (T1D). Both insulins require extensive testing to arrive at the optimal dosing regimen, e.g., timing and amount. Here we aim at a simulation tool for evaluating benefits/risks of different dosing schemes and up-titration rules for both Gla-100 and Gla-300 before clinical testing. METHODS: A new pharmacokinetic (PK) model of both Gla-100 and Gla-300 was incorporated into the FDA-accepted University of Virginia/Padova T1D simulator: Specifically, a joint parameter distribution, built from PK parameter estimates, was used to generate individual PK parametrizations for each in silico subject. A virtual trial comparing Gla-100 vs. Gla-300 was performed and assessed against a clinical study to validate the glargine simulator. RESULTS: Like in vivo, in silico both insulins performed similarly with respect to glucose control: percent time of glucose between [80-140] mg/dL with Gla-100 vs. Gla-300 (primary endpoint) were 41.5 ± 1.1% vs. 39.0 ± 1.2% (P = 0.11) in silico, 31.0 ± 1.6% vs. 31.8 ± 1.5% (P = 0.73) in vivo. CONCLUSIONS: The glargine simulator reproduced the main findings of the clinical trial, proving its validity for testing MDI therapies. SIGNIFICANCE: In silico testing of MDI therapies can help designing clinical trials. Due to the more standardized settings in silico (e.g., standardized meals and strict adherence to titration rule), any potential treatment effect is reaching statistical significance in simulation vs. clinical trial.

Long-acting Insulin in Diabetes Therapy: In Silico Clinical Trials with the UVA/Padova Type 1 Diabetes Simulator.

The University of Virginia /Padova Type 1 Diabetes (TID) simulator has been widely used for testing artificial pancreas controllers, and, recently, novel insulin formulations and glucose sensors. However, a module describing the pharmacokinetics of the new long-acting insulin analogues is not available. The aim of this contribution is to reproduce multiple daily insulin injection (MDI) therapy, with insulin glargine 100 U/mL (Gla-100) as basal insulin, using the TID simulator. This was achieved by developing a model of Gla-100 and by incorporating it into the simulator. The methodology described here can be extended to other insulins, allowing an extensive in silico testing of different long-acting insulin analogues under various settings before starting human trials.

A Model of Acetaminophen Pharmacokinetics and its Effect on Continuous Glucose Monitoring Sensor Measurements.

Some of commercial continuous glucose monitoring (CGM) devices, i.e., minimally-invasive sensors able to measure almost continuously glucose concentration in the subcutaneous tissue, recently received the regulatory approval to be used for making therapeutic decisions in diabetes management. A fundamental requirement for its safe and effective use is represented by the accuracy of CGM measurements. However, despite recent advances in sensors accuracy and reliability, CGM still suffers from inaccuracy problems in presence of pharmacologic interferences, e.g., the common orally administered acetaminophen (APAP), which artificially raises CGM glucose readings for several hours. A model of the artifact induced by APAP on CGM measurements would be useful to design algorithms to compensate such a distortion. The aim of this work is to exploit the data published by previous literature studies to design a model of oral APAP pharmacokinetics and its effect on glucose concentration measured by CGM sensors. Specifically, the developed model was identified on average data of both plasma APAP concentration and the APAP effect on CGM profiles after an oral administration of 1000 mg of APAP. The APAP effect on CGM readings was estimated from the difference observed, in the same study, between the glucose profile measured by a Dexcom G4 Platinum sensor and the plasma glucose concentration. The model was validated by comparing the simulated effect of mealtime APAP administration in CGM measurements of 100 virtual subjects generated by the UVA/Padova Type 1 Diabetes (TID) Simulator vs. the effect observed in a clinical study by Maahs et al. (Diabetes Care, 2015) in 40 TID subjects taking APAP at breakfast. Results suggest that the proposed model is able to reliably describe the mean APAP effect on CGM measurements.