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. We applied these approaches to generate virtual patient populations in the QSP model of erythropoiesis. According to the results of our research, stochastically bounded selection showed slightly better results than the other three methods as it allowed the description of any number of patients from clinical trials and could be applied in the case of complex models with a large number of variable parameters.

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. SBSI is a modular software suite composed of three major components: SBSINumerics, a high-performance library containing parallelized algorithms for performing parameter fitting; SBSIDispatcher, a middleware application to track experiments and submit jobs to back-end servers; and SBSIVisual, an extensible client application used to configure optimization experiments and view results. Furthermore, we have created a plugin infrastructure to enable project-specific modules to be easily installed. Plugin developers can take advantage of the existing user-interface and application framework to customize SBSI for their own uses, facilitated by SBSI's use of standard data formats. AVAILABILITY AND IMPLEMENTATION: All SBSI binaries and source-code are freely available from http://sourceforge.net/projects/sbsi under an Apache 2 open-source license. The server-side SBSINumerics runs on any Unix-based operating system; both SBSIVisual and SBSIDispatcher are written in Java and are platform independent, allowing use on Windows, Linux and Mac OS X. The SBSI project website at http://www.sbsi.ed.ac.uk provides documentation and tutorials.

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). The removal of the residual solvent from the polymer film, both thermally (heating to 200 °C) and by displacement with another solvent as a result of sequential pervaporation, led to a significant decrease in the rate of transfer of polar liquids and a decrease in the selectivity of the membrane. However, the dehydrocyclization reaction resulted in more brittle films with low permeability to penetrants of different polarities. The results of our comprehensive study made it possible to assume the decisive influence of structural changes in membranes occurring in connection with the competitive formation of intra- and intermolecular hydrogen bonds.

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 0.04⁻0.1 for loss tangent, accordingly. Unlike of composite components, the obtained composite material is characterized by thermo-stability of electrical parameters up to 250 °C. The dielectric frequency spectra of the composite exhibit two clearly visible peaks in contrast to the spectra of the polymer and ferroelectric ceramics. The developed composite material can be used as a built-in film capacitors material in microelectronic devices.

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. In this work, we aimed to establish a mathematical model of ER-dependent gene expression to explore potential mechanisms underlying the variable actions of SERMs. RESULTS: We developed an equilibrium model of ER binding with 17ß-estradiol, tamoxifen and DNA, and linked it to a simple ODE model of ER-induced gene expression. The model was parameterised on the broad range of literature available experimental data, and provided a plausible mechanistic explanation for the dual agonism/antagonism action of tamoxifen in the reference cell line used for model calibration. To extend our conclusions to other cell types we ran global sensitivity analysis and explored model behaviour in the wide range of biologically plausible parameter values, including those found in cancer cells. Our findings suggest that transcriptional response to tamoxifen is controlled in a complex non-linear way by several key parameters, including ER expression level, hormone concentration, amount of ER-responsive genes and the capacity of ER-tamoxifen complexes to stimulate transcription (e.g. by recruiting co-regulators of transcription). The model revealed non-monotonic dependence of ER-induced transcriptional response on the expression level of ER, that was confirmed experimentally in four variants of the MCF-7 breast cancer cell line. CONCLUSIONS: We established a minimal mechanistic model of ER-dependent gene expression, that predicts complex non-linear effects in transcriptional response to tamoxifen in the broad range of biologically plausible parameter values. Our findings suggest that the outcome of a SERM's action is defined by several key components of cellular micro-environment, that may contribute to cell-type-specific effects of SERMs and justify the need for the development of combinatorial biomarkers for more accurate prediction of the efficacy of SERMs in specific cell types.

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. We use area-under-the-curve for time course of changes in phosphorylation of proteins as a characteristic for sensitivity analysis and rank network parameters with regard to their impact on the level of key cancer-related outputs, separating strong inhibitory from stimulatory effects. This allows interpretation of the results in terms which can incorporate the effects of potential anti-cancer drugs on targets and the associated biological markers of cancer. To illustrate the method we applied it to an ErbB signalling network model and explored the sensitivity profile of its key model readout, phosphorylated Akt, in the absence and presence of the ErbB2 inhibitor pertuzumab. The method successfully identified the parameters associated with elevation or suppression of Akt phosphorylation in the ErbB2/3 network. From analysis and comparison of the sensitivity profiles of pAkt in the absence and presence of targeted drugs we derived predictions of drug targets, cancer-related biomarkers and generated hypotheses for combinatorial therapy. Several key predictions have been confirmed in experiments using human ovarian carcinoma cell lines. We also compared GSA-derived predictions with the results of local sensitivity analysis and discuss the applicability of both methods. We propose that the developed GSA procedure can serve as a refining tool in combinatorial anti-cancer drug discovery.

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. The designed technique permits translating the drug IC50, obtained in one experimental setting to another, and predicts in vivo inhibitory effects based on the relevant in vitro data. For the aspirin case, we elucidated the mechanism underlying the enhancement and reduction (aspirin resistance) of its efficacy, depending on PGHS-1 microenvironment in in vitro/in vivo experimental settings. We also present the results of the in silico screening of the combined action of sets of two NSAIDs (aspirin with ibuprofen, aspirin with celecoxib), and study the mechanism of the experimentally observed effect of the suppression of aspirin-mediated PGHS-1 inhibition by selective and nonselective NSAIDs. Furthermore, we discuss the applications of the obtained results to the problems of standardization of NSAID test assay, dependence of the NSAID efficacy on cellular environment of PGHS-1, drug resistance, and NSAID combination therapy.

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. The developed models were applied to calculate the dose-dependence of aspirin and celecoxib action on COX- 1 in vitro and in vivo conditions. The mechanism of the enhancement of aspirin efficiency in platelet as compared to its action on purified COX-1 was elucidated. The dose-dependence of celecoxib simulated with the use of the "in vivo" version of the model predicted potentially strong inhibitory effect of celecoxib on thromboxan production in platelets. Simulation of the combined effect of two NSAIDs, aspirin and celecoxib, on COX-1 allowed us to reveal the mechanism underlying the suppression of aspirin-mediated COX-1 inhibition by celecoxib. We discuss our modelling results in the context of the on-going debates on the potential cardio-vascular risks associated with co-administration of various types of NSAIDs.

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. The new mathematical model included RTK inhibitor antibody binding, HER2/HER3 dimerization and inhibition, AKT/mitogen-activated protein kinase cross-talk, and the regulatory properties of PTEN. The model was parameterized using quantitative phosphoprotein expression data from cancer cell lines using reverse-phase protein microarrays. Quantitative PTEN protein expression was found to be the key determinant of resistance to anti-HER2 therapy in silico, which was predictive of unseen experiments in vitro using the PTEN inhibitor bp(V). When measured in cancer cell lines, PTEN expression predicts sensitivity to anti-HER2 therapy; furthermore, this quantitative measurement is more predictive of response (relative risk, 3.0; 95% confidence interval, 1.6-5.5; P < 0.0001) than other pathway components taken in isolation and when tested by multivariate analysis in a cohort of 122 breast cancers treated with trastuzumab. For the first time, a systems biology approach has successfully been used to stratify patients for personalized therapy in cancer and is further compelling evidence that PTEN, appropriately measured in the clinical setting, refines clinical decision making in patients treated with anti-HER2 therapies.