Safety, pharmacokinetics, and immunogenicity of the combination of the broadly neutralizing anti-HIV-1 antibodies 3BNC117 and 10-1074 in healthy adults: A randomized, phase 1 study.
Cohen Yehuda Z,Butler Allison L,Millard Katrina,Witmer-Pack Maggi,Levin Rebeka,Unson-O'Brien Cecilia,Patel Roshni,Shimeliovich Irina,Lorenzi Julio C C,Horowitz Jill,Walsh Stephen R,Lin Shu,Weiner Joshua A,Tse Anna,Sato Alicia,Bennett Chelsey,Mayer Bryan,Seaton Kelly E,Yates Nicole L,Baden Lindsey R,deCamp Allan C,Ackerman Margaret E,Seaman Michael S,Tomaras Georgia D,Nussenzweig Michel C,Caskey Marina
BACKGROUND:Additional forms of pre-exposure prophylaxis are needed to prevent HIV-1 infection. 3BNC117 and 10-1074 are broadly neutralizing anti-HIV-1 antibodies that target non-overlapping epitopes on the HIV-1 envelope. We investigated the safety, tolerability, pharmacokinetics, and immunogenicity of the intravenous administration of the combination of 3BNC117 and 10-1074 in healthy adults. METHODS:This randomized, double-blind, placebo-controlled, single center, phase 1 study enrolled healthy adults aged 18-65 years to receive one infusion of 3BNC117 immediately followed by 10-1074 at 10 mg/kg, three infusions of 3BNC117 followed by 10-1074 at 3 mg/kg or 10 mg/kg every 8 weeks, or placebo infusions. The primary outcomes were safety and pharmacokinetics. This trial is registered with ClinicalTrials.gov, number NCT02824536. FINDINGS:Twenty-four participants were enrolled in a 3:1 ratio to receive the study products or placebo. The combination of 3BNC117 and 10-1074 was safe and generally well tolerated. There were no serious adverse events considered related to the infusions. The mean elimination half-lives of 3BNC117 and 10-1074 were 16.4 ± 4.6 days and 23.0 ± 5.4 days, respectively, similar to what was observed in previous studies in which each antibody was administered alone. Anti-drug antibody responses were rare and without evidence of related adverse events or impact on elimination kinetics. INTERPRETATION:Single and repeated doses of the combination of 3BNC117 and 10-1074 were well tolerated in healthy adults. These data support the further development of the combination of 3BNC117 and 10-1074 as a long-acting injectable form of pre-exposure prophylaxis for the prevention of HIV-1 infection.
Model-based in silico analysis of the PI3K/Akt pathway: the elucidation of cross-talk between diabetes and breast cancer.
Rehman Sammia,Obaid Ayesha,Naz Anam,Ali Amjad,Kanwal Shahzina,Ahmad Jamil
Background:A positive association between diabetes and breast cancer has been identified by various epidemiological and clinical studies. However, the possible molecular interactions between the two heterogeneous diseases have not been fully determined yet. There are several underlying mechanisms which may increase the risk of breast cancer in diabetic patients. Introduction:In this study, we focused on the role of O-GlcNAc transferase (OGT) enzyme in the regulation of phosphatidylinositol-3 kinase (PI3K) pathway through activation/deactivation of Akt protein. The efficiency of insulin signaling in adipocytes is reduced as a result of OGT overexpression which further attenuates Akt signaling; as a result, the efficiency of insulin signaling is reduced by downregulation of insulin-responsive genes. On the other hand, increased expression of OGT results in Akt activation in breast cancer cells, leading to enhanced cell proliferation and inhibition of the apoptosis. However, the interplay amongst these signaling pathways is still under investigation. Methods:In this study, we used Petri nets (PNs) to model and investigate the role of PI3K and OGT pathways, acting as key players in crosstalk between diabetes and breast cancer, resulting in progression of these chronic diseases. Moreover, in silico perturbation experiments were applied on the model to analyze the effects of anti-cancer agents (shRNA and BZX) and anti-diabetic drug (Metformin) on the system. Results:Our PN model reflects the alterations in protein expression and behavior and the correlation between breast cancer and diabetes. The analysis proposed two combination therapies to combat breast cancer progression in diabetic patients including combination of OGTmRNA silencing and OGT inhibitor (BZX) as first combination and BZX and Metformin as the second. Conclusion:The PN model verified that alterations in O-GlcNAc signaling affect both insulin resistance and breast cancer. Moreover, the combination therapy for breast cancer patients consisting of anti-diabetic drugs such as Metformin along with OGT inhibitors, for example BZX, can produce better treatment regimens.
Expansion of the homeostasis model assessment of β-cell function and insulin resistance to enable clinical trial outcome modeling through the interactive adjustment of physiology and treatment effects: iHOMA2.
Hill Nathan R,Levy Jonathan C,Matthews David R
OBJECTIVE:To describe and make available an interactive, 24-variable homeostasis model assessment (iHOMA2) that extends the HOMA2 model, enabling the modeling of physiology and treatment effects, to present equations of the HOMA2 and iHOMA2 models, and to exemplify iHOMA2 in two widely differing scenarios: changes in insulin sensitivity with thiazolidinediones and changes in renal threshold with sodium glucose transporter 2 (SGLT2) inhibition. RESEARCH DESIGN AND METHODS:iHOMA2 enables a user of the available software to examine and modify the mathematical functions describing the organs and tissues involved in the glucose and hormonal compartments. We exemplify this with SGLT2 inhibition modeling (by changing the renal threshold parameters) using published data of renal effect, showing that the modeled effect is concordant with the effects on fasting glucose from independent data. RESULTS:iHOMA2 modeling of thiazolidinediones effect suggested that changes in insulin sensitivity in the fasting state are predominantly hepatic. SGLT2 inhibition modeled by iHOMA2 resulted in a decrease in mean glucose of 1.1 mmol/L. Observed data showed a decrease in glucose of 0.9 mmol/L. There was no significant difference between the model and the independent data. Manipulation of iHOMA2's renal excretion threshold variable suggested that a decrease of 17% was required to obtain a 0.9 mmol/L decrease in mean glucose. CONCLUSIONS:iHOMA2 is an extended mathematical model for the assessment of insulin resistance and β-cell function. The model can be used to evaluate therapeutic agents and predict effects on fasting glucose and insulin and on β-cell function and insulin sensitivity.
A new mathematical approach for qualitative modeling of the insulin-TOR-MAPK network.
Nijhout H Frederik,Callier Viviane
Frontiers in physiology
In this paper we develop a novel mathematical model of the insulin-TOR-MAPK signaling network that controls growth. Most data on the properties of the insulin and MAPK signaling networks are static and the responses to experimental interventions, such as knockouts, overexpression, and hormonal input are typically reported as scaled quantities. The modeling paradigm we develop here uses scaled variables and is ideally suited to simulate systems in which much of the available data are scaled. Our mathematical representation of signaling networks provides a way to reconcile theory and experiments, thus leading to a better understanding of the properties and function of these signaling networks. We test the performance of the model against a broad diversity of experimental data. The model correctly reproduces experimental insulin dose-response relationships. We study the interaction between insulin and MAPK signaling in the control of protein synthesis, and the interactions between amino acids, insulin and TOR signaling. We study the effects of variation in FOXO expression on protein synthesis and glucose transport capacity, and show that a FOXO knockout can partially rescue protein synthesis capacity of an insulin receptor (INR) knockout. We conclude that the modeling paradigm we develop provides a simple tool to investigate the qualitative properties of signaling networks.
A mathematical model of metabolic insulin signaling pathways.
Sedaghat Ahmad R,Sherman Arthur,Quon Michael J
American journal of physiology. Endocrinology and metabolism
We develop a mathematical model that explicitly represents many of the known signaling components mediating translocation of the insulin-responsive glucose transporter GLUT4 to gain insight into the complexities of metabolic insulin signaling pathways. A novel mechanistic model of postreceptor events including phosphorylation of insulin receptor substrate-1, activation of phosphatidylinositol 3-kinase, and subsequent activation of downstream kinases Akt and protein kinase C-zeta is coupled with previously validated subsystem models of insulin receptor binding, receptor recycling, and GLUT4 translocation. A system of differential equations is defined by the structure of the model. Rate constants and model parameters are constrained by published experimental data. Model simulations of insulin dose-response experiments agree with published experimental data and also generate expected qualitative behaviors such as sequential signal amplification and increased sensitivity of downstream components. We examined the consequences of incorporating feedback pathways as well as representing pathological conditions, such as increased levels of protein tyrosine phosphatases, to illustrate the utility of our model for exploring molecular mechanisms. We conclude that mathematical modeling of signal transduction pathways is a useful approach for gaining insight into the complexities of metabolic insulin signaling.
The impact of mathematical modeling on the understanding of diabetes and related complications.
Ajmera I,Swat M,Laibe C,Le Novère N,Chelliah V
CPT: pharmacometrics & systems pharmacology
Diabetes is a chronic and complex multifactorial disease caused by persistent hyperglycemia and for which underlying pathogenesis is still not completely understood. The mathematical modeling of glucose homeostasis, diabetic condition, and its associated complications is rapidly growing and provides new insights into the underlying mechanisms involved. Here, we discuss contributions to the diabetes modeling field over the past five decades, highlighting the areas where more focused research is required.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e54; doi:10.1038/psp.2013.30; advance online publication 10 July 2013.
Insulin sensitivity predictions in individuals with obesity and type II diabetes mellitus using mathematical model of the insulin signal transduction pathway.
Ho Clark K,Sriram Ganesh,Dipple Katrina M
Molecular genetics and metabolism
Mathematical modeling approaches have been commonly used in complex signaling pathway studies such as the insulin signal transduction pathway. Our expanded mathematical model of the insulin signal transduction pathway was previously shown to effectively predict glucose clearance rates using mRNA levels of key components of the pathway in a mouse model. In this study, we re-optimized and applied our expanded model to study insulin sensitivity in other species and tissues (human skeletal muscle) with altered protein activities of insulin signal transduction pathway components. The model has now been optimized to predict the effect of short term exercise on insulin sensitivity for human test subjects with obesity or type II diabetes mellitus. A comparison between our extended model and the original model showed that our model better simulates the GLUT4 translocation events of the insulin signal transduction pathway and glucose uptake as a clinically relevant model output. Results from our extended model correlate with O'Gorman's published in-vivo results. This study demonstrates the ability to adapt this model to study insulin sensitivity to many biological systems (human skeletal muscle and mouse liver) with minimal changes in the model parameters.