scholarly journals vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Vi Ngoc-Nha Tran ◽  
Alireza Shams ◽  
Sinan Ascioglu ◽  
Antal Martinecz ◽  
Jingyi Liang ◽  
...  
Keyword(s):  
Drug Concentration ◽  
Computational Models ◽  
Treatment Strategies ◽  
Extended Model ◽  
Concentration Data ◽  
Bacterial Populations ◽  
Web Based ◽  
Antibiotic Target ◽  
Dosing Regimens ◽  
Target Binding

Abstract Background As antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allow non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria. Results In this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis bacteria using our vCOMBAT tool. Conclusions The vCOMBAT online tool is publicly available at https://combat-bacteria.org/.

scholarly journals vCOMBAT: a Novel Tool to Create and Visualize a COmputational Model of Bacterial Antibiotic Target-binding

2020 ◽  
Author(s):  
Vi Ngoc-Nha Tran ◽  
Alireza Shams ◽  
Sinan Ascioglu ◽  
Antal Martinecz ◽  
Jingyi Liang ◽  
...  
Keyword(s):  
Drug Concentration ◽  
Computational Models ◽  
Treatment Strategies ◽  
Extended Model ◽  
Concentration Data ◽  
Bacterial Populations ◽  
Web Based ◽  
Antibiotic Target ◽  
Dosing Regimens ◽  
Target Binding

AbstractMotivationAs antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens to from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allows non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria.ResultsIn this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis (TB) bacteria using our vCOMBAT tool.AvailabilityvCOMBAT online tool is publicly available at https://combat-bacteria.org/.

scholarly journals KiMoSys 2.0: an upgraded database for submitting, storing and accessing experimental data for kinetic modeling

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Hugo Mochão ◽  
Pedro Barahona ◽  
Rafael S Costa
Keyword(s):  
Experimental Data ◽  
Metabolic Networks ◽  
Model Simulation ◽  
Web Interface ◽  
Concentration Data ◽  
Web Based ◽  
Share Data ◽  
Visualization Tools ◽  
Filter Mechanism ◽  
Machine Readable

Abstract The KiMoSys (https://kimosys.org), launched in 2014, is a public repository of published experimental data, which contains concentration data of metabolites, protein abundances and flux data. It offers a web-based interface and upload facility to share data, making it accessible in structured formats, while also integrating associated kinetic models related to the data. In addition, it also supplies tools to simplify the construction process of ODE (Ordinary Differential Equations)-based models of metabolic networks. In this release, we present an update of KiMoSys with new data and several new features, including (i) an improved web interface, (ii) a new multi-filter mechanism, (iii) introduction of data visualization tools, (iv) the addition of downloadable data in machine-readable formats, (v) an improved data submission tool, (vi) the integration of a kinetic model simulation environment and (vii) the introduction of a unique persistent identifier system. We believe that this new version will improve its role as a valuable resource for the systems biology community. Database URL:  www.kimosys.org

scholarly journals Population Pharmacokinetics Modelling and Simulation of Mitotane in Patients with Adrenocortical Carcinoma: An Individualized Dose Regimen to Target All Patients at Three Months?

Pharmaceutics ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 566 ◽  
Author(s):  
Yoann Cazaubon ◽  
Yohann Talineau ◽  
Catherine Feliu ◽  
Céline Konecki ◽  
Jennifer Russello ◽  
...  
Keyword(s):  
Adrenocortical Carcinoma ◽  
Plasma Concentrations ◽  
Therapeutic Index ◽  
Compartment Model ◽  
Volume Of Distribution ◽  
Pharmacokinetic Analysis ◽  
Concentration Data ◽  
Starting Dose ◽  
Dosing Regimens ◽  
Data Files

Mitotane is the most effective agent in post-operative treatment of adrenocortical carcinoma. In adults, the starting dose is 2–3 g/day and should be slightly increased to reach the therapeutic index of 14–20 mg/L. This study developed a population PK model for mitotane and to simulate recommended/high dosing regimens. We retrospectively analyzed the data files of 38 patients with 503 plasma concentrations for the pharmacokinetic analysis. Monolix version 2019R1 was used for non-linear mixed-effects modelling. Monte Carlo simulations were performed to evaluate the probability of target attainment (PTA ≥ 14 mg/L) at one month and at three months. Mitotane concentration data were best described by a linear one-compartment model. The estimated PK parameters (between-subject variability) were: 8900 L (90.4%) for central volume of distribution (V) and 70 L·h−1 (29.3%) for clearance (Cl). HDL, Triglyceride (Tg) and a latent covariate were found to influence Cl. The PTA at three months for 3, 6, 9, and 12 g per day was 10%, 55%, 76%, and 85%, respectively. For a loading dose of 15 g/day for one month then 5 g/day, the PTA in the first and third months was 57 and 69%, respectively. This is the first PKpop model of mitotane highlighting the effect of HDL and Tg covariates on the clearance as well as a subpopulation of ultrafast metabolizer. The simulations suggest that recommended dose regimens are not enough to target the therapeutic threshold in the third month.

How Do We Treat Pregnancy-Related Venous Thromboembolism?

2019 ◽  
Vol 40 (01) ◽  
pp. 054-063 ◽  
Author(s):  
Birgit Linnemann ◽  
Birgit Seelbach-Goebel ◽  
Susanne Heimerl ◽  
Christina Hart
Keyword(s):  
Venous Thromboembolism ◽  
Vitamin K Antagonists ◽  
Treatment Strategies ◽  
Factor Xa ◽  
Study Data ◽  
Time Interval ◽  
Uncomplicated Pregnancy ◽  
Delivery Date ◽  
Maternal Morbidity And Mortality ◽  
Dosing Regimens

AbstractVenous thromboembolism (VTE) is a major cause of maternal morbidity and mortality during pregnancy and the postpartum period. Due to a lack of adequate study data, therapeutic strategies for pregnancy-related VTE are deduced from observational studies and extrapolated from recommendations for nonpregnant patients. Because heparins do not cross the placenta, weight-adjusted therapeutic-dose low-molecular-weight heparins (LMWHs) are the anticoagulant treatment of choice in cases of VTE during pregnancy. Once- and twice-daily dosing regimens are suitable. There is no evidence that measurement of factor Xa activities and consecutive LMWH dose adjustments improve clinical outcomes. There is no support for the routine use of vitamin K antagonists, direct oral thrombin or factor Xa inhibitors, fondaparinux, or danaparoid in uncomplicated pregnancy-related VTE. Management of delivery deserves special attention, and treatment strategies depend on the time interval between the diagnosis of acute VTE and the expected delivery date. In lactating women, an overlapping switch from LMWH to warfarin is possible. Anticoagulation should be continued for at least 6 weeks postpartum or for a minimum period of 3 months.

scholarly journals Population Pharmacokinetics of Amikacin in Adult Patients with Cystic Fibrosis

2018 ◽  
Vol 62 (10) ◽  
Author(s):  
Sílvia M. Illamola ◽  
Hoa Q. Huynh ◽  
Xiaoxi Liu ◽  
Zubin N. Bhakta ◽  
Catherine M. Sherwin ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Population Pharmacokinetics ◽  
Drug Disposition ◽  
Adult Patients ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Concentration Data ◽  
First Order ◽  
Dosing Regimens ◽  
Pulmonary Exacerbations

ABSTRACTPractitioners commonly use amikacin in patients with cystic fibrosis. Establishment of the pharmacokinetics of amikacin in adults with cystic fibrosis may increase the efficacy and safety of therapy. This study was aimed to establish the population pharmacokinetics of amikacin in adults with cystic fibrosis. We used serum concentration data obtained during routine therapeutic drug monitoring and explored the influence of patient covariates on drug disposition. We performed a retrospective chart review to collect the amikacin dosing regimens, serum amikacin concentrations, blood sampling times, and patient characteristics for adults with cystic fibrosis admitted for treatment of acute pulmonary exacerbations. Amikacin concentrations were retrospectively collected for 49 adults with cystic fibrosis, and 192 serum concentrations were available for analysis. A population pharmacokinetic model was developed using nonlinear mixed-effects modeling with the first-order conditional estimation method. A two-compartment model with first-order elimination best described amikacin pharmacokinetics. Creatinine clearance and weight were identified as significant covariates for clearance and the volume of distribution, respectively, in the final model. Residual variability was modeled using a proportional error model. Typical estimates for clearance, central and peripheral volumes of distribution, and intercompartmental clearance were 3.06 liters/h, 14.4 liters, 17.1 liters, and 0.925 liters/h, respectively. The pharmacokinetics of amikacin in individuals with cystic fibrosis seems to differ from those in individuals without cystic fibrosis. However, further investigations are needed to confirm these results and, thus, the need for variations in amikacin dosing. Future pharmacodynamic studies will potentially establish the optimal amikacin dosing regimens for the treatment of acute pulmonary exacerbations in adult patients with CF.

scholarly journals Web-gLV: A Web Based Platform for Lotka-Volterra Based Modeling and Simulation of Microbial Populations

2019 ◽  
Vol 10 ◽  
Author(s):  
Bhusan K. Kuntal ◽  
Chetan Gadgil ◽  
Sharmila S. Mande
Keyword(s):  
Modeling And Simulation ◽  
Time Series Data ◽  
Microbial Populations ◽  
Series Data ◽  
Estimation Of Parameters ◽  
Valuable Contribution ◽  
Bacterial Populations ◽  
Web Based ◽  
Automatic Estimation ◽  
High Throughput Dna Sequencing

The affordability of high throughput DNA sequencing has allowed us to explore the dynamics of microbial populations in various ecosystems. Mathematical modeling and simulation of such microbiome time series data can help in getting better understanding of bacterial communities. In this paper, we present Web-gLV—a GUI based interactive platform for generalized Lotka-Volterra (gLV) based modeling and simulation of microbial populations. The tool can be used to generate the mathematical models with automatic estimation of parameters and use them to predict future trajectories using numerical simulations. We also demonstrate the utility of our tool on few publicly available datasets. The case studies demonstrate the ease with which the current tool can be used by biologists to model bacterial populations and simulate their dynamics to get biological insights. We expect Web-gLV to be a valuable contribution in the field of ecological modeling and metagenomic systems biology.

scholarly journals Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells

2020 ◽  
Vol 117 (32) ◽  
pp. 19455-19464 ◽  
Author(s):  
Helen K. Alexander ◽  
R. Craig MacLean
Keyword(s):  
De Novo ◽  
Single Cells ◽  
Treatment Strategies ◽  
Bacterial Populations ◽  
Resistance Plasmids ◽  
Evolution Of Resistance ◽  
Resistant Strains ◽  
Low Probability ◽  
Emergence Of Resistance ◽  
Resistant Cells

A better understanding of how antibiotic exposure impacts the evolution of resistance in bacterial populations is crucial for designing more sustainable treatment strategies. The conventional approach to this question is to measure the range of concentrations over which resistant strain(s) are selectively favored over a sensitive strain. Here, we instead investigate how antibiotic concentration impacts the initial establishment of resistance from single cells, mimicking the clonal expansion of a resistant lineage following mutation or horizontal gene transfer. Using twoPseudomonas aeruginosastrains carrying resistance plasmids, we show that single resistant cells have <5% probability of detectable outgrowth at antibiotic concentrations as low as one-eighth of the resistant strain’s minimum inhibitory concentration (MIC). This low probability of establishment is due to detrimental effects of antibiotics on resistant cells, coupled with the inherently stochastic nature of cell division and death on the single-cell level, which leads to loss of many nascent resistant lineages. Our findings suggest that moderate doses of antibiotics, well below the MIC of resistant strains, may effectively restrict de novo emergence of resistance even though they cannot clear already-large resistant populations.

scholarly journals Phase transition behaviour in yeast and bacterial populations under stress

2020 ◽  
Vol 7 (7) ◽  
pp. 192211
Author(s):  
Stephen W. Ordway ◽  
Dawn M. King ◽  
David Friend ◽  
Christine Noto ◽  
Snowlee Phu ◽  
...  
Keyword(s):  
Phase Transition ◽  
Computational Models ◽  
Evolutionary Dynamics ◽  
Population Decline ◽  
Equilibrium Phase ◽  
Sharp Change ◽  
Population Response ◽  
Bacterial Populations ◽  
Population Collapse ◽  
Similar Range

Non-equilibrium phase transitions from survival to extinction have recently been observed in computational models of evolutionary dynamics. Dynamical signatures predictive of population collapse have been observed in yeast populations under stress. We experimentally investigate the population response of the budding yeast Saccharomyces cerevisiae to biological stressors (temperature and salt concentration) in order to investigate the system's behaviour in the vicinity of population collapse. While both conditions lead to population decline, the dynamical characteristics of the population response differ significantly depending on the stressor. Under temperature stress, the population undergoes a sharp change with significant fluctuations within a critical temperature range, indicative of a continuous absorbing phase transition. In the case of salt stress, the response is more gradual. A similar range of response is observed with the application of various antibiotics to Escherichia coli , with a variety of patterns of decreased growth in response to antibiotic stress both within and across antibiotic classes and mechanisms of action. These findings have implications for the identification of critical tipping points for populations under environmental stress.

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