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.

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.

IKAP: A heuristic framework for inference of kinase activities from Phosphoproteomics data.

MOTIVATION: Phosphoproteomics measurements are widely applied in cellular biology to detect changes in signalling dynamics. However, due to the inherent complexity of phosphorylation patterns and the lack of knowledge on how phosphorylations are related to functions, it is often not possible to directly deduce protein activities from those measurements. Here, we present a heuristic machine learning algorithm that infers the activities of kinases from Phosphoproteomics data using kinase-target information from the PhosphoSitePlus database. By comparing the estimated kinase activity profiles to the measured phosphosite profiles, it is furthermore possible to derive the kinases that are most likely to phosphorylate the respective phosphosite. RESULTS: We apply our approach to published datasets of the human cell cycle generated from HeLaS3 cells, and insulin signalling dynamics in mouse hepatocytes. In the first case, we estimate the activities of 118 at six cell cycle stages and derive 94 new kinase-phosphosite links that can be validated through either database or motif information. In the second case, the activities of 143 kinases at eight time points are estimated and 49 new kinase-target links are derived. AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented in Matlab and be downloaded from github. It makes use of the Optimization and Statistics toolboxes. https://github.com/marcel-mischnik/IKAP.git. CONTACT: marcel.mischnik@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Minimal modeling of insulin secretion in the perfused rat pancreas: a drug effect case study.

The experimental protocol of the perfused rat pancreas is commonly used to evaluate ß-cell function. In this context, mathematical models become useful tools through the determination of indexes that allow the assessment of ß-cell function in different experimental groups and the quantification of the effects of antidiabetic drugs, secretagogues, or treatments. However, a minimal model applicable to the isolated perfused rat pancreas has so far been unavailable. In this work, we adapt the C-peptide minimal model applied previously to the intravenous glucose tolerance test to obtain a specific model for the experimental settings of the perfused pancreas. Using the model, it is possible to estimate indexes describing ß-cell responsivity for first (ΦD) and second phase (ΦS, T) of insulin secretion. The model was initially applied to untreated pancreata and afterward used for the assessment of pharmacologically relevant agents (the gut hormone GLP-1, the potent GLP-1 receptor agonist lixisenatide, and a GPR40/FFAR1 agonist, SAR1) to quantify and differentiate their effect on insulin secretion. Model fit was satisfactory, and parameters were estimated with good precision for both untreated and treated pancreata. Model application showed that lixisenatide reaches improvement of ß-cell function similarly to GLP-1 (11.7- vs. 13.1-fold increase in ΦD and 2.3- vs. 2.8-fold increase in ΦS) and demonstrated that SAR1 leads to an additional improvement of ß-cell function in the presence of postprandial GLP-1 levels.

DvD: An R/Cytoscape pipeline for drug repurposing using public repositories of gene expression data.

SUMMARY: Drug versus Disease (DvD) provides a pipeline, available through R or Cytoscape, for the comparison of drug and disease gene expression profiles from public microarray repositories. Negatively correlated profiles can be used to generate hypotheses of drug-repurposing, whereas positively correlated profiles may be used to infer side effects of drugs. DvD allows users to compare drug and disease signatures with dynamic access to databases Array Express, Gene Expression Omnibus and data from the Connectivity Map. AVAILABILITY AND IMPLEMENTATION: R package (submitted to Bioconductor) under GPL 3 and Cytoscape plug-in freely available for download at www.ebi.ac.uk/saezrodriguez/DVD/.

Understanding drugs and diseases by systems biology?

Systems biology aims to provide a holistic and in many cases dynamic picture of biological function and malfunction, in case of disease. Technology developments in the generation of genome-wide datasets and massive improvements in computer power now allow to obtain new insights into complex biological networks and to copy nature by computing these interactions and their kinetics and by generating in silico models of cells, tissues and organs. The expectations are high that systems biology will pave the way to the identification of novel disease genes, to the selection of successful drug candidates--that do not fail in clinical studies due to toxicity or lack of human efficacy--and finally to a more successful discovery of novel therapeutics. However, further research is necessary to fully unleash the potential of systems biology. Within this review we aim to highlight the most important and promising top-down and bottom-up systems biology applications in drug discovery.

MARS: computing three-dimensional alignments for multiple ligands using pairwise similarities.

The three-dimensional (3D) superimposition of molecules of one biological target reflecting their relative bioactive orientation is key for several ligand-based drug design studies (e.g., QSAR studies, pharmacophore modeling). However, with the lack of sufficient ligand-protein complex structures, an experimental alignment is difficult or often impossible to obtain. Several computational 3D alignment tools have been developed by academic or commercial groups to address this challenge. Here, we present a new approach, MARS (Multiple Alignments by ROCS-based Similarity), that is based on the pairwise alignment of all molecules within the data set using the tool ROCS (Rapid Overlay of Chemical Structures). Each pairwise alignment is scored, and the results are captured in a score matrix. The ideal superimposition of the compounds in the set is then identified by the analysis of the score matrix building stepwise a superimposition of all molecules. The algorithm exploits similarities among all molecules in the data set to compute an optimal 3D alignment. This alignment tool presented here can be used for several applications, including pharmacophore model generation, 3D QSAR modeling, 3D clustering, identification of structural outliers, and addition of compounds to an already existing alignment. Case studies are shown, validating the 3D alignments for six different data sets.

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.

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.

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.

Glucose-regulated and drug-perturbed phosphoproteome reveals molecular mechanisms controlling insulin secretion.

Insulin-secreting beta cells play an essential role in maintaining physiological blood glucose levels, and their dysfunction leads to the development of diabetes. To elucidate the signalling events regulating insulin secretion, we applied a recently developed phosphoproteomics workflow. We quantified the time-resolved phosphoproteome of murine pancreatic cells following their exposure to glucose and in combination with small molecule compounds that promote insulin secretion. The quantitative phosphoproteome of 30,000 sites clustered into three main groups in concordance with the modulation of the three key kinases: PKA, PKC and CK2A. A high-resolution time course revealed key novel regulatory sites, revealing the importance of methyltransferase DNMT3A phosphorylation in the glucose response. Remarkably a significant proportion of these novel regulatory sites is significantly downregulated in diabetic islets. Control of insulin secretion is embedded in an unexpectedly broad and complex range of cellular functions, which are perturbed by drugs in multiple ways.

Improving Efficacy of Inhaled Technosphere Insulin (Afrezza) by Postmeal Dosing: In-silico Clinical Trial with the University of Virginia/Padova Type 1 Diabetes Simulator.

BACKGROUND: Technosphere(®) insulin (TI), an inhaled human insulin with a fast onset of action, provides a novel option for the control of prandial glucose. We used the University of Virginia (UVA)/Padova simulator to explore in-silico the potential benefit of different dosing regimens on postprandial glucose (PPG) control to support the design of further clinical trials. Tested dosing regimens included at-meal or postmeal dosing, or dosing before and after a meal (split dosing). METHODS: Various dosing regimens of TI were compared among one another and to insulin lispro in 100 virtual type-1 patients. Individual doses were identified for each regimen following different titration rules. The resulting postprandial glucose profiles were analyzed to quantify efficacy and the risk for hypoglycemic events. RESULTS: This approach allowed us to assess the benefit/risk for each TI dosing regimen and to compare results with simulations of insulin lispro. We identified a new titration rule for TI that could significantly improve the efficacy of treatment with TI. CONCLUSION: In-silico clinical trials comparing the treatment effect of different dosing regimens with TI and of insulin lispro suggest that postmeal dosing or split dosing of TI, in combination with an appropriate titration rule, can achieve a superior postprandial glucose control while providing a lower risk for hypoglycemic events than conventional treatment with subcutaneously administered rapid-acting insulin products.

Crystallographic studies on acyl ureas, a new class of glycogen phosphorylase inhibitors, as potential antidiabetic drugs.

Acyl ureas were discovered as a novel class of inhibitors for glycogen phosphorylase, a molecular target to control hyperglycemia in type 2 diabetics. This series is exemplified by 6-{2,6-Dichloro- 4-[3-(2-chloro-benzoyl)-ureido]-phenoxy}-hexanoic acid, which inhibits human liver glycogen phosphorylase a with an IC(50) of 2.0 microM. Here we analyze four crystal structures of acyl urea derivatives in complex with rabbit muscle glycogen phosphorylase b to elucidate the mechanism of inhibition of these inhibitors. The structures were determined and refined to 2.26 Angstroms resolution and demonstrate that the inhibitors bind at the allosteric activator site, where the physiological activator AMP binds. Acyl ureas induce conformational changes in the vicinity of the allosteric site. Our findings suggest that acyl ureas inhibit glycogen phosphorylase by direct inhibition of AMP binding and by indirect inhibition of substrate binding through stabilization of the T' state.

Structure-based drug discovery using GPCR homology modeling: successful virtual screening for antagonists of the alpha1A adrenergic receptor.

In this paper, we describe homology modeling of the alpha1A receptor based on the X-ray structure of bovine rhodopsin. The protein model has been generated by applying ligand-supported homology modeling, using mutational and ligand SAR data to guide the protein modeling procedure. We performed a virtual screening of the company's compound collection to test how well this model is suited to identify alpha1A antagonists. We applied a hierarchical virtual screening procedure guided by 2D filters and three-dimensional pharmacophore models. The ca. 23,000 filtered compounds were docked into the alpha1A homology model with GOLD and scored with PMF. From the top-ranked compounds, 80 diverse compounds were tested in a radioligand displacement assay. 37 compounds revealed K(i) values better than 10 microM; the most active compound binds with 1.4 nM to the alpha1A receptor. Our findings suggest that rhodopsin-based homology models may be used as the structural basis for GPCR lead finding and compound optimization.

Virtual screening of biogenic amine-binding G-protein coupled receptors: comparative evaluation of protein- and ligand-based virtual screening protocols.

In this paper, we compare protein- and ligand-based virtual screening techniques for identifying the ligands of four biogenic amine-binding G-protein coupled receptors (GPCRs). For the screening of the virtual compound libraries, we used (1) molecular docking into GPCR homology models, (2) ligand-based pharmacophore and Feature Tree models, (3) three-dimensional (3D)-similarity searches, and (4) statistical methods [partial least squares (PLS) and partial least squares discriminant analysis (PLS-DA) models] based on two-dimensional (2D) molecular descriptors. The comparison of the different methods in retrieving known antagonists from virtual libraries shows that in our study the ligand-based pharmacophore-, Feature Tree-, and 2D quantitative structure-activity relationship (QSAR)-based screening techniques provide enrichment factors that are higher than those provided by molecular docking into the GPCR homology models. Nevertheless, the hit rates achieved when docking with GOLD and ranking the ligands with GoldScore (up to 60% among the top-ranked 1% of the screened databases) are still satisfying. These results suggest that docking into GPCR homology models can be a useful approach for lead finding by virtual screening when either little or no information about the active ligands is available.

Acyl ureas as human liver glycogen phosphorylase inhibitors for the treatment of type 2 diabetes.

Using a focused screening approach, acyl ureas have been discovered as a new class of inhibitors of human liver glycogen phosphorylase (hlGPa). The X-ray structure of screening hit 1 (IC50 = 2 microM) in a complex with rabbit muscle glycogen phosphorylase b reveals that 1 binds at the AMP site, the main allosteric effector site of the dimeric enzyme. A first cycle of chemical optimization supported by X-ray structural data yielded derivative 21, which inhibited hlGPa with an IC50 of 23 +/- 1 nM, but showed only moderate cellular activity in isolated rat hepatocytes (IC50 = 6.2 microM). Further optimization was guided by (i) a 3D pharmacophore model that was derived from a training set of 24 compounds and revealed the key chemical features for the biological activity and (ii) the 1.9 angstroms crystal structure of 21 in complex with hlGPa. A second set of compounds was synthesized and led to 42 with improved cellular activity (hlGPa IC50 = 53 +/- 1 nM; hepatocyte IC50 = 380 nM). Administration of 42 to anaesthetized Wistar rats caused a significant reduction of the glucagon-induced hyperglycemic peak. These findings are consistent with the inhibition of hepatic glycogenolysis and support the use of acyl ureas for the treatment of type 2 diabetes.

Incorporation of inhaled insulin into the FDA accepted University of Virginia/Padova Type 1 Diabetes Simulator.

The University of Virginia/Padova Type 1 Diabetes (T1DM) Simulator has been extensively used in artificial pancreas research mostly for testing and design of control algorithms. However, it also offers the possibility of testing new insulin analogs and alternative routes of delivery given that subcutaneous insulin administration present significant delays & variability. Inhaled insulin appears an important candidate to improve post-prandial glucose control given its rapid appearance in plasma. In this contribution, we present the results of incorporating a pharmacokinetic model of inhaled Technosphere(®) Insulin (TI) into the T1DM simulator. In particular, we successfully reproduced in silico the post-prandial glucose control observed in T1DM subjects treated with TI given at meal time, and the post-prandial glucose dynamics in response to different timing of TI dose.

Identification of nonpeptidic urotensin II receptor antagonists by virtual screening based on a pharmacophore model derived from structure-activity relationships and nuclear magnetic resonance studies on urotensin II.

The vasoactive cyclic 11-amino acid peptide urotensin II (U-II) has recently been discovered as the endogenous ligand of the orphan G-protein-coupled receptor GPR14. As U-II might be involved in the regulation of cardiovascular homeostasis and pathology, a nonpeptidic GPR14/U-II antagonist is of considerable basic and therapeutic interest. We have performed structure-activity relationship studies on U-II by investigating 25 peptide analogues to mobilize intracellular calcium in GPR14-transfected CHO cells, demonstrating that only the side chains of the residues Trp-7, Lys-8, and Tyr-9 are required for receptor recognition and activation. The solution structure of U-II derived by nuclear magnetic resonance has served as a structural template for a three-dimensional three point pharmacophore query for the virtual screening of the Aventis compound repository for nonpeptidic U-II receptor antagonists. Highly active lead compounds of six different scaffold classes could be identified, antagonizing the biological activity of U-II in vitro. The most potent compound identified by the virtual screening approach, 1-(3-carbamimidoyl-benzyl)-4-methyl-1H-indole-2-carboxylic acid (naphthalen-1-ylmethyl)amide, reveals an IC(50) of 400 nM in a functional fluorometric imaging plate reader assay and constitutes a promising lead.