Nationwide surveillance for Telmisartan alone or with combination at real world therapy in Indian patients with hypertension (START)

Background: Angiotensin receptor blockers (ARBs) are amongst the most preferred class of antihypertensive as reported at various evidences or guidelines. However, choice amongst ARBs differs between practicing physicians in real-life scenario. This survey aimed to understand the usage preferences of telmisartan therapy alone and in combination for treating hypertension (HT) among practitioners at various clinical settings in real-life scenario in India.Methods: A cross‑sectional survey was conducted with a pre-validated survey questionnaire consisting of 15 questions pertaining to the telmisartan and its combination usage in HT management. Total 498 registered medical practitioners (mostly physicians and cardiologists) had participated in survey. They were approached for seeking their perception, opinions, and prescribing behaviour. Categorical data was summarized by number (n) and percentage (%) in each category. Data were summarised in frequency tables.Results: Key findings from the data analysed were as follows: Around 20-40% of patients been reported to have co-morbid hypertension and diabetes as reported by majority of the physicians. Preferred class of drug in patients with hypertension with diabetes reported to be ARB. Around 90.36% of doctors reported that telmisartan was the most preferred ARB in patients with hypertension associated with high cardiovascular risk. Around 90.76% of doctors reported for their preference for telmisartan in patients with hypertension for 24-hr BP control. Around 82.93% of doctors preferred telmisartan in patients with hypertension and stroke/post-MI status.Conclusions: Indian healthcare practitioners prefer telmisartan as the most preferred ARB either alone or in a combination in patients with hypertension, including those with comorbidities

The sloppy model universality class and the Vandermonde matrix

In a variety of contexts, physicists study complex, nonlinear models with many unknown or tunable parameters to explain experimental data. We explain why such systems so often are sloppy; the system behavior depends only on a few `stiff' combinations of the parameters and is unchanged as other `sloppy' parameter combinations vary by orders of magnitude. We contrast examples of sloppy models (from systems biology, variational quantum Monte Carlo, and common data fitting) with systems which are not sloppy (multidimensional linear regression, random matrix ensembles). We observe that the eigenvalue spectra for the sensitivity of sloppy models have a striking, characteristic form, with a density of logarithms of eigenvalues which is roughly constant over a large range. We suggest that the common features of sloppy models indicate that they may belong to a common universality class. In particular, we motivate focusing on a Vandermonde ensemble of multiparameter nonlinear models and show in one limit that they exhibit the universal features of sloppy models.Comment: New content adde

Methodologies for quantitative systems pharmacology (QSP) models:Design and Estimation

With the increased interest in the application of quantitative systems pharmacology (QSP) models within medicine research and development, there is an increasing need to formalize model development and verification aspects. In February 2016, a workshop was held at Roche Pharma Research and Early Development to focus discussions on two critical methodological aspects of QSP model development: optimal structural granularity and parameter estimation. We here report in a perspective article a summary of presentations and discussions.</p

Methodologies for quantitative systems pharmacology (QSP) models : Design and estimation

With the increased interest in the application of quantitative systems pharmacology (QSP) models within medicine research and development, there is an increasing need to formalize model development and verification aspects. In February 2016, a workshop was held at Roche Pharma Research and Early Development to focus discussions on two critical methodological aspects of QSP model development: optimal structural granularity and parameter estimation. We here report in a perspective article a summary of presentations and discussions

Sloppy Models, Parameter Uncertainty, and the Role of Experimental Design

Computational models are increasingly used to understand and predict complex biological phenomena. These models contain many unknown parameters, at least some of which are difficult to measure directly, and instead are estimated by fitting to time-course data. Previous work has suggested that even with precise data sets, many parameters are unknowable by trajectory measurements. We examined this question in the context of a pathway model of epidermal growth factor (EGF) and neuronal growth factor (NGF) signaling. Computationally, we examined a palette of experimental perturbations that included different doses of EGF and NGF as well as single and multiple gene knockdowns and overexpressions. While no single experiment could accurately estimate all of the parameters, experimental design methodology identified a set of five complementary experiments that could. These results suggest optimism for the prospects for calibrating even large models, that the success of parameter estimation is intimately linked to the experimental perturbations used, and that experimental design methodology is important for parameter fitting of biological models and likely for the accuracy that can be expected from them.National Institutes of Health (U.S.) (U54 CA112967)MIT-Portugal ProgramSingapore-MIT Alliance for Research and Technolog

A Test of Highly Optimized Tolerance Reveals Fragile Cell-Cycle Mechanisms Are Molecular Targets in Clinical Cancer Trials

Robustness, a long-recognized property of living systems, allows function in the face of uncertainty while fragility, i.e., extreme sensitivity, can potentially lead to catastrophic failure following seemingly innocuous perturbations. Carlson and Doyle hypothesized that highly-evolved networks, e.g., those involved in cell-cycle regulation, can be resistant to some perturbations while highly sensitive to others. The “robust yet fragile” duality of networks has been termed Highly Optimized Tolerance (HOT) and has been the basis of new lines of inquiry in computational and experimental biology. In this study, we tested the working hypothesis that cell-cycle control architectures obey the HOT paradigm. Three cell-cycle models were analyzed using monte-carlo sensitivity analysis. Overall state sensitivity coefficients, which quantify the robustness or fragility of a given mechanism, were calculated using a monte-carlo strategy with three different numerical techniques along with multiple parameter perturbation strategies to control for possible numerical and sampling artifacts. Approximately 65% of the mechanisms in the G1/S restriction point were responsible for 95% of the sensitivity, conversely, the G2-DNA damage checkpoint showed a much stronger dependence on a few mechanisms; ∼32% or 13 of 40 mechanisms accounted for 95% of the sensitivity. Our analysis predicted that CDC25 and cyclin E mechanisms were strongly implicated in G1/S malfunctions, while fragility in the G2/M checkpoint was predicted to be associated with the regulation of the cyclin B-CDK1 complex. Analysis of a third model containing both G1/S and G2/M checkpoint logic, predicted in addition to mechanisms already mentioned, that translation and programmed proteolysis were also key fragile subsystems. Comparison of the predicted fragile mechanisms with literature and current preclinical and clinical trials suggested a strong correlation between efficacy and fragility. Thus, when taken together, these results support the working hypothesis that cell-cycle control architectures are HOT networks and establish the mathematical estimation and subsequent therapeutic exploitation of fragile mechanisms as a novel strategy for anti-cancer lead generation

An iterative identification procedure for dynamic modeling of biochemical networks

<p>Abstract</p> <p>Background</p> <p>Mathematical models provide abstract representations of the information gained from experimental observations on the structure and function of a particular biological system. Conferring a predictive character on a given mathematical formulation often relies on determining a number of non-measurable parameters that largely condition the model's response. These parameters can be identified by fitting the model to experimental data. However, this fit can only be accomplished when identifiability can be guaranteed.</p> <p>Results</p> <p>We propose a novel iterative identification procedure for detecting and dealing with the lack of identifiability. The procedure involves the following steps: 1) performing a structural identifiability analysis to detect identifiable parameters; 2) globally ranking the parameters to assist in the selection of the most relevant parameters; 3) calibrating the model using global optimization methods; 4) conducting a practical identifiability analysis consisting of two (<it>a priori </it>and <it>a posteriori</it>) phases aimed at evaluating the quality of given experimental designs and of the parameter estimates, respectively and 5) optimal experimental design so as to compute the scheme of experiments that maximizes the quality and quantity of information for fitting the model.</p> <p>Conclusions</p> <p>The presented procedure was used to iteratively identify a mathematical model that describes the NF-<it>κ</it>B regulatory module involving several unknown parameters. We demonstrated the lack of identifiability of the model under typical experimental conditions and computed optimal dynamic experiments that largely improved identifiability properties.</p

Incorporation of enzyme concentrations into FBA and identification of optimal metabolic pathways

<p>Abstract</p> <p>Background</p> <p>In the present article, we propose a method for determining optimal metabolic pathways in terms of the level of concentration of the enzymes catalyzing various reactions in the entire metabolic network. The method, first of all, generates data on reaction fluxes in a pathway based on steady state condition. A set of constraints is formulated incorporating weighting coefficients corresponding to concentration of enzymes catalyzing reactions in the pathway. Finally, the rate of yield of the target metabolite, starting with a given substrate, is maximized in order to identify an optimal pathway through these weighting coefficients.</p> <p>Results</p> <p>The effectiveness of the present method is demonstrated on two synthetic systems existing in the literature, two pentose phosphate, two glycolytic pathways, core carbon metabolism and a large network of carotenoid biosynthesis pathway of various organisms belonging to different phylogeny. A comparative study with the existing extreme pathway analysis also forms a part of this investigation. Biological relevance and validation of the results are provided. Finally, the impact of the method on metabolic engineering is explained with a few examples.</p> <p>Conclusions</p> <p>The method may be viewed as determining an optimal set of enzymes that is required to get an optimal metabolic pathway. Although it is a simple one, it has been able to identify a carotenoid biosynthesis pathway and the optimal pathway of core carbon metabolic network that is closer to some earlier investigations than that obtained by the extreme pathway analysis. Moreover, the present method has identified correctly optimal pathways for pentose phosphate and glycolytic pathways. It has been mentioned using some examples how the method can suitably be used in the context of metabolic engineering.</p

Global parameter estimation methods for stochastic biochemical systems

<p>Abstract</p> <p>Background</p> <p>The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness) or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data.</p> <p>Results</p> <p>Three parameter estimation methods are proposed based on the maximum likelihood and density function distance, including probability and cumulative density functions. Since stochastic models such as chemical master equations are typically solved using a Monte Carlo approach in which only a finite number of Monte Carlo realizations are computationally practical, specific considerations are given to account for the effect of finite sampling in the histogram binning of the state density functions. Applications to three practical case studies showed that while maximum likelihood method can effectively handle low replicate measurements, the density function distance methods, particularly the cumulative density function distance estimation, are more robust in estimating the parameters with consistently higher accuracy, even for systems showing multimodality.</p> <p>Conclusions</p> <p>The parameter estimation methodologies described in this work have provided an effective and practical approach in the estimation of kinetic parameters of stochastic systems from either sparse or dense cell population data. Nevertheless, similar to kinetic parameter estimation in other modelling frameworks, not all parameters can be estimated accurately, which is a common problem arising from the lack of complete parameter identifiability from the available data.</p

Reduced levels of intracellular calcium releasing in spermatozoa from asthenozoospermic patients

<p>Abstract</p> <p>Background</p> <p>Asthenozoospermia is one of the most common findings present in infertile males characterized by reduced or absent sperm motility, but its aetiology remains unknown in most cases. In addition, calcium is one of the most important ions regulating sperm motility. In this study we have investigated the progesterone-evoked intracellular calcium signal in ejaculated spermatozoa from men with normospermia or asthenozoospermia.</p> <p>Methods</p> <p>Human ejaculates were obtained from healthy volunteers and asthenospermic men by masturbation after 4–5 days of abstinence. For determination of cytosolic free calcium concentration, spermatozoa were loaded with the fluorescent ratiometric calcium indicator Fura-2.</p> <p>Results</p> <p>Treatment of spermatozoa from normospermic men with 20 micromolar progesterone plus 1 micromolar thapsigargin in a calcium free medium induced a typical transient increase in cytosolic free calcium concentration due to calcium release from internal stores. Similar results were obtained when spermatozoa were stimulated with progesterone alone. Subsequent addition of calcium to the external medium evoked a sustained elevation in cytosolic free calcium concentration indicative of capacitative calcium entry. However, when progesterone plus thapsigargin were administered to spermatozoa from patients with asthenozoospermia, calcium signal and subsequent calcium entry was much smaller compared to normospermic patients. As expected, pretreatment of normospermic spermatozoa with both the anti-progesterone receptor c262 antibody and with progesterone receptor antagonist RU-38486 decreased the calcium release induced by progesterone. Treatment of spermatozoa with cytochalasin D or jasplakinolide decreased the calcium entry evoked by depletion of internal calcium stores in normospermic patients, whereas these treatments proved to be ineffective at modifying the calcium entry in patients with asthenozoospermia.</p> <p>Conclusion</p> <p>Our results suggest that spermatozoa from asthenozoospermic patients present a reduced responsiveness to progesterone.</p