Mechanistic evaluation of the effect of sodium-dependent glucose transporter 2 inhibitors on delayed glucose absorption in patients with type 2 diabetes mellitus using a quantitative systems pharmacology model of human systemic glucose dynamics.

Sodium-dependent glucose transporter (SGLT) 2 is specifically expressed in the kidney, while SGLT1 is present in the kidneys and small intestine. SGLT2 inhibitors are a class of oral antidiabetic drugs that lower elevated plasma glucose levels by promoting the urinary excretion of excess glucose through the inhibition of renal glucose reuptake. The inhibition selectivity for SGLT2 over SGLT1 (SGLT2/1 selectivity) of marketed SGLT2 inhibitors is diverse, while SGLT2/1 selectivity of canagliflozin is relatively low. Although canagliflozin suppresses postprandial glucose levels, the degree of contribution for SGLT1 inhibition to this effect remains unproven. To analyze the effect of SGLT2 inhibitors on postprandial glucose level, we constructed a novel quantitative systems pharmacology (QSP) model, called human systemic glucose dynamics (HSGD) model, integrating intestinal absorption, metabolism, and renal reabsorption of glucose. This HSGD model reproduced the postprandial plasma glucose concentration-time profiles during a meal tolerance test under different clinical trial conditions. Simulations after canagliflozin administration showed a dose-dependent delay of time (Tmax,glc ) to reach maximum concentration of glucose (Cmax,glc ), and the delay of Tmax,glc disappeared when inhibition of SGLT1 was negated. In addition, contribution ratio of intestinal SGLT1 inhibition to the decrease in Cmax,glc was estimated to be 23%-28%, when 100 and 300 mg of canagliflozin are administered. This HSGD model enabled us to provide the partial contribution of intestinal SGLT1 inhibition to the improvement of postprandial hyperglycemia as well as to quantitatively describe the plasma glucose dynamics following SGLT2 inhibitors.

Sophisticated framework between cell cycle arrest and apoptosis induction based on p53 dynamics.

The tumor suppressor, p53, regulates several gene expressions that are related to the DNA repair protein, cell cycle arrest and apoptosis induction, which activates the implementation of both cell cycle arrest and induction of apoptosis. However, it is not clear how p53 specifically regulates the implementation of these functions. By applying several well-known kinetic mathematical models, we constructed a novel model that described the influence that DNA damage has on the implementation of both the G2/M phase cell cycle arrest and the intrinsic apoptosis induction via its activation of the p53 synthesis process. The model, which consisted of 32 dependent variables and 115 kinetic parameters, was used to examine interference by DNA damage in the implementation of both G2/M phase cell cycle arrest and intrinsic apoptosis induction. A low DNA damage promoted slightly the synthesis of p53, which showed a sigmoidal behavior with time. In contrast, in the case of a high DNA damage, the p53 showed an oscillation behavior with time. Regardless of the DNA damage level, there were delays in the G2/M progression. The intrinsic apoptosis was only induced in situations where grave DNA damage produced an oscillation of p53. In addition, to wreck the equilibrium between Bcl-2 and Bax the induction of apoptosis required an extreme activation of p53 produced by the oscillation dynamics, and was only implemented after the release of the G2/M phase arrest. When the p53 oscillation is observed, there is possibility that the cell implements the apoptosis induction. Moreover, in contrast to the cell cycle arrest system, the apoptosis induction system is responsible for safeguarding the system that suppresses malignant transformations. The results of these experiments will be useful in the future for elucidating of the dominant factors that determine the cell fate such as normal cell cycles, cell cycle arrest and apoptosis.

Prediction of key factor controlling G1/S phase in the mammalian cell cycle using system analysis.

Control of the G1/S phase transition of the cell cycle contributes to the maintenance of homeostasis. It is well known that the disruption of the cell cycle is related to cell transformation and carcinogenesis. The G1/S phase transition involves a network of components that includes cyclins, cyclin-dependent kinases, and other proteins. Numerical simulation techniques and system analysis are expected to become powerful tools for investigating complex biological networks. To reach this goal, we designed a mathematical model of the G1/S phase transition. Using our model, we conducted a numerical simulation and comprehensive system analyses of this phase of the cell cycle. In this way, we were able to predict the key factors involved in the control of the G1/S transition.

Mathematical modeling and sensitivity analysis of G1/S phase in the cell cycle including the DNA-damage signal transduction pathway.

The cell cycle has checkpoint systems, which control G1/S, G2/M and G0/G1 phase transitions. When a normal cell suffers from DNA-damage, the signal transduction of DNA-damage causes the cell cycle arrest by using the checkpoint systems. Therefore, the elucidation of interaction between the signal transduction of DNA-damage and the checkpoint systems is an important problem. In this study, we constructed a novel mathematical model (proposed model) which integrated G1/S-checkpoint model with a signal transduction of DNA damage model and performed some numerical simulations. The proposed model realized some biological findings of G1/S phase with or without DNA-damage, which suggested that proposed model is biologically appropriate. Moreover, the results of sensitivity analysis of the proposed model indicated the predominant factors of G1/S phase and some factors concerned with the transformation of cells.

The effect of cholesterol and monosialoganglioside (GM1) on the release and aggregation of amyloid beta-peptide from liposomes prepared from brain membrane-like lipids.

In order to investigate the influence of cholesterol (Ch) and monosialoganglioside (GM1) on the release and subsequent deposition/aggregation of amyloid beta peptide (Abeta)-(1-40) and Abeta-(1-42), we have examined Abeta peptide model membrane interactions by circular dichroism, turbidity measurements, and transmission electron microscopy (TEM). Model liposomes containing Abeta peptide and a lipid mixture composition similar to that found in the cerebral cortex membranes (CCM-lipid) have been prepared. In all, four Abeta-containing liposomes were investigated: CCM-lipid; liposomes with no GM1 (GM1-free lipid); those with no cholesterol (Ch-free lipid); liposomes with neither cholesterol nor GM1 (Ch-GM1-free lipid). In CCM liposomes, Abeta was rapidly released from membranes to form a well defined fibril structure. However, for the GM1-free lipid, Abeta was first released to yield a fibril structure about the membrane surface, then the membrane became disrupted resulting in the formation of small vesicles. In Ch-free lipid, a fibril structure with a phospholipid membrane-like shadow formed, but this differed from the well defined fibril structure seen for CCM-lipid. In Ch-GM1-free lipid, no fibril structure formed, possibly because of membrane solubilization by Abeta. The absence of fibril structure was noted at physiological extracellular pH (7.4) and also at liposomal/endosomal pH (5.5). Our results suggest a possible role for both Ch and GM1 in the membrane release of Abeta from brain lipid bilayers.