Effect of Hydroxyl-Containing Fragments on the Structure and Properties of Membrane-Forming Polyamide-Imides.

The structural features and thermophysical and transport properties of dense nonporous membranes of the casting type from (co)polyamide-imides synthesized by the polycondensation of the diacid chloride of 2-(4-carboxyphenyl)-1,3-dioxoisoindoline-5-carboxylic acid and diamines 5,5'-methylene-bis (2-aminophenol) (DADHyDPhM) and 4,4'-methylenebis(benzeneamine) (DADPhM), taken in molar ratios of 7:3, 1:1, and 3:7, have been studied. The effect of hydroxyl-containing modifying fragments of dihydroxy diphenylmethane introduced in various amounts into the main polymer chain on the pervaporation properties of the formed films is discussed. It has been shown that the presence of the residual solvent N-methyl-2-pyrrolidone in the films not only has a plasticizing effect on the characteristics of film membranes but also promotes the preferential transmembrane transport of polar liquids, primarily methanol (permeation rate over 2 kg for a copolymer with a ratio of DADHyDPhM:DADPhM = 7:3).

Application of different approaches to generate virtual patient populations for the quantitative systems pharmacology model of erythropoiesis.

In a standard situation, a quantitative systems pharmacology model describes a "reference patient," and the model parameters are fixed values allowing only the mean values to be described. However, the results of clinical trials include a description of variability in patients' responses to a drug, which is typically expressed in terms of conventional statistical parameters, such as standard deviations (SDs) from mean values. Therefore, in this study, we propose and compare four different approaches: (1) Monte Carlo Markov Chain (MCMC); (2) model fitting to Monte Carlo sample; (3) population of clones; (4) stochastically bounded selection to generate virtual patient populations based on experimentally measured mean data and SDs.

Heat-Resistant Ferroelectric-Polymer Nanocomposite with High Dielectric Constant.

The high dielectric constant ferroelectric-polymer nanocomposite was developed for producing the heat-resistant and chemical stable planar layers. According to the composite coatings formation conditions, the following value ranges of dielectric constant and loss factor were received: 30⁻400 for dielectric constant and 0.04⁻0.

SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology.

Summary: Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but the software needed to run the experiment needs to be usable by scientists with varying levels of computational expertise, and modellers need to be able to obtain up-to-date experimental data resources easily. We have developed a software suite, the Systems Biology Software Infrastructure (SBSI), to facilitate the parameter-fitting process.

A model of estrogen-related gene expression reveals non-linear effects in transcriptional response to tamoxifen.

Background: Estrogen receptors alpha (ER) are implicated in many types of female cancers, and are the common target for anti-cancer therapy using selective estrogen receptor modulators (SERMs, such as tamoxifen). However, cell-type specific and patient-to-patient variability in response to SERMs (from suppression to stimulation of cancer growth), as well as frequent emergence of drug resistance, represents a serious problem. The molecular processes behind mixed effects of SERMs remain poorly understood, and this strongly motivates application of systems approaches.

Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network.

High levels of variability in cancer-related cellular signalling networks and a lack of parameter identifiability in large-scale network models hamper translation of the results of modelling studies into the process of anti-cancer drug development. Recently global sensitivity analysis (GSA) has been recognised as a useful technique, capable of addressing the uncertainty of the model parameters and generating valid predictions on parametric sensitivities. Here we propose a novel implementation of model-based GSA specially designed to explore how multi-parametric network perturbations affect signal propagation through cancer-related networks.

In Silico Screening of Nonsteroidal Anti-Inflammatory Drugs and Their Combined Action on Prostaglandin H Synthase-1.

The detailed kinetic model of Prostaglandin H Synthase-1 (PGHS-1) was applied to in silico screening of dose-dependencies for the different types of nonsteroidal anti-inflammatory drugs (NSAIDs), such as: reversible/irreversible, nonselective/selective to PGHS-1/PGHS-2 and time dependent/independent inhibitors (aspirin, ibuprofen, celecoxib, etc.) The computational screening has shown a significant variability in the IC50s of the same drug, depending on different in vitro and in vivo experimental conditions. To study this high heterogeneity in the inhibitory effects of NSAIDs, we have developed an in silico approach to evaluate NSAID action on targets under different PGHS-1 microenvironmental conditions, such as arachidonic acid, reducing cofactor, and peroxide concentrations.

Systems biology reveals new strategies for personalizing cancer medicine and confirms the role of PTEN in resistance to trastuzumab.

Resistance to targeted cancer therapies such as trastuzumab is a frequent clinical problem not solely because of insufficient expression of HER2 receptor but also because of the overriding activation states of cell signaling pathways. Systems biology approaches lend themselves to rapid in silico testing of factors, which may confer resistance to targeted therapies. Inthis study, we aimed to develop a new kinetic model that could be interrogated to predict resistance to receptor tyrosine kinase (RTK) inhibitor therapies and directly test predictions in vitro and in clinical samples.

Kinetic modelling of NSAID action on COX-1: focus on in vitro/in vivo aspects and drug combinations.

The detailed kinetic model of Prostaglandin H Synthase-1 (COX-1) was developed to in silico test and predict inhibition effects of nonsteroidal anti-inflammatory drugs (NSAIDs) on target. The model takes into account key features of the complex catalytic mechanism of cyclooxygenase-1, converting arachidonic acid to prostaglandin PGH(2), and includes the description of the enzyme interaction with various types of NSAIDs (reversible/irreversible, non-selective and selective to COX-1/COX-2). Two different versions of the model were designed to simulate the inhibition of COX-1 by NSAIDs in two most popular experimental settings - in vitro studies with purified enzyme, and the experiments with platelets.