A Model‐Based Approach to Quantify the Time‐Course of Anti‐Drug Antibodies for Therapeutic Proteins

2018 ◽  
Vol 105 (4) ◽  
pp. 970-978 ◽  
Author(s):  
Yupeng Ren ◽  
Liang Li ◽  
Susan Kirshner ◽  
Yaning Wang ◽  
Chandrahas Sahajwalla ◽  
...  
Keyword(s):  
Time Course ◽  
Therapeutic Proteins ◽  
Model Based

scholarly journals Quantitative assessment of near-infrared spectroscopy time course under hypercapnia using an a priori model-based fitting

2020 ◽  
Vol 118 ◽  
pp. 103638
Author(s):  
Victor Vagné ◽  
Emmanuelle Le Bars ◽  
Jérémy Deverdun ◽  
Olivier Rossel ◽  
Stéphane Perrey ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Quantitative Assessment ◽  
Time Course ◽  
Near Infrared ◽  
A Priori ◽  
Model Based

scholarly journals Pharmacokinetic/Pharmacodynamic (PK/PD) Indices of Antibiotics Predicted by a Semimechanistic PKPD Model: a Step toward Model-Based Dose Optimization

2011 ◽  
Vol 55 (10) ◽  
pp. 4619-4630 ◽  
Author(s):  
Elisabet I. Nielsen ◽  
Otto Cars ◽  
Lena E. Friberg
Keyword(s):  
Drug Concentration ◽  
Time Course ◽  
Drug Exposure ◽  
Full Time ◽  
Antibacterial Drugs ◽  
Model Based ◽  
Pkpd Model ◽  
Dosing Intervals ◽  
Time Kill

ABSTRACTA pharmacokinetic-pharmacodynamic (PKPD) model that characterizes the full time course ofin vitrotime-kill curve experiments of antibacterial drugs was here evaluated in its capacity to predict the previously determined PK/PD indices. Six drugs (benzylpenicillin, cefuroxime, erythromycin, gentamicin, moxifloxacin, and vancomycin), representing a broad selection of mechanisms of action and PK and PD characteristics, were investigated. For each drug, a dose fractionation study was simulated, using a wide range of total daily doses given as intermittent doses (dosing intervals of 4, 8, 12, or 24 h) or as a constant drug exposure. The time course of the drug concentration (PK model) as well as the bacterial response to drug exposure (in vitroPKPD model) was predicted. Nonlinear least-squares regression analyses determined the PK/PD index (the maximal unbound drug concentration [fCmax]/MIC, the area under the unbound drug concentration-time curve [fAUC]/MIC, or the percentage of a 24-h time period that the unbound drug concentration exceeds the MIC [fT>MIC]) that was most predictive of the effect. Thein silicopredictions based on thein vitroPKPD model identified the previously determined PK/PD indices, withfT>MICbeing the best predictor of the effect for β-lactams andfAUC/MIC being the best predictor for the four remaining evaluated drugs. The selection and magnitude of the PK/PD index were, however, shown to be sensitive to differences in PK in subpopulations, uncertainty in MICs, and investigated dosing intervals. In comparison with the use of the PK/PD indices, a model-based approach, where the full time course of effect can be predicted, has a lower sensitivity to study design and allows for PK differences in subpopulations to be considered directly. This study supports the use of PKPD models built fromin vitrotime-kill curves in the development of optimal dosing regimens for antibacterial drugs.

scholarly journals A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data

2007 ◽  
Vol 8 (1) ◽  
pp. 228 ◽  
Author(s):  
Reuben Thomas ◽  
Carlos J Paredes ◽  
Sanjay Mehrotra ◽  
Vassily Hatzimanikatis ◽  
Eleftherios T Papoutsakis
Keyword(s):  
Dna Microarray ◽  
Time Course ◽  
Expression Data ◽  
Regulatory Interactions ◽  
Microarray Expression Data ◽  
Optimization Framework ◽  
Model Based ◽  
Microarray Expression

scholarly journals Differential Expression Analysis Using A Model-Based Gene Clustering Algorithm for RNA-Seq Data

2020 ◽  
Author(s):  
Takayuki Osabe ◽  
Kentaro Shimizu ◽  
Koji Kadota
Keyword(s):  
Differential Expression ◽  
Time Course ◽  
Clustering Algorithm ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Real Data ◽  
Gene Clustering ◽  
Rna Seq ◽  
Model Based ◽  
Group Data

Abstract Background RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report a model-based clustering algorithm, MBCluster.Seq, that can be implemented using an R package for DE analysis.Results The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm.Conclusions MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

scholarly journals A mathematical model-based approach to optimize loading schemes of isometric resistance training sessions

2020 ◽  
Author(s):  
Johannes L. Herold ◽  
Andreas Sommer
Keyword(s):  
Optimal Control ◽  
Resistance Training ◽  
Time Course ◽  
Optimal Control Problems ◽  
Real Life ◽  
Parameter Estimates ◽  
Control Problems ◽  
Maximum Voluntary Isometric Contraction ◽  
Physiological Models ◽  
Model Based

AbstractIndividualized resistance training (RT) is necessary to optimize training results. A model-based optimization of loading schemes could provide valuable impulses for practitioners and complement the predominant manual program design by customizing the loading schemes to the trainee and the training goals. We compile a literature overview of model-based approaches used to simulate or optimize the response to single RT sessions or to longterm RT plans in terms of strength, power, muscle mass, or local muscular endurance by varying the loading scheme. To the best of our knowledge, contributions employing a predictive model to algorithmically optimize loading schemes for different training goals are nonexistent in the literature. Thus, we propose to set up optimal control problems as follows. For the underlying dynamics, we use a phenomenological model of the time course of maximum voluntary isometric contraction force. Then, we provide mathematical formulations of key performance indicators for loading schemes identified in sport science and use those as objective functionals or constraints. We then solve those optimal control problems using previously obtained parameter estimates for the elbow flexors. We discuss our choice of training goals, analyze the structure of the computed solutions, and give evidence of their real-life feasibility. The proposed optimization methodology is independent from the underlying model and can be transferred to more elaborate physiological models once suitable ones become available.

scholarly journals Simulating the time course of MT neuron responses with a model based on V1 neuron properties

2010 ◽  
Vol 2 (7) ◽  
pp. 38-38 ◽  
Author(s):  
J. A. Perrone ◽  
R. J. Krauzlis
Keyword(s):  
Time Course ◽  
Model Based

scholarly journals Rapid acquisition and model-based analysis of cell-free transcription–translation reactions from nonmodel bacteria

2018 ◽  
Vol 115 (19) ◽  
pp. E4340-E4349 ◽  
Author(s):  
Simon J. Moore ◽  
James T. MacDonald ◽  
Sarah Wienecke ◽  
Alka Ishwarbhai ◽  
Argyro Tsipa ◽  
...  
Keyword(s):  
Time Course ◽  
Resource Competition ◽  
Regulatory Elements ◽  
Limiting Factors ◽  
Model Parameters ◽  
Modeling Framework ◽  
Experimental Conditions ◽  
Rapid Acquisition ◽  
Model Based ◽  
Model Based Analysis

Native cell-free transcription–translation systems offer a rapid route to characterize the regulatory elements (promoters, transcription factors) for gene expression from nonmodel microbial hosts, which can be difficult to assess through traditional in vivo approaches. One such host,Bacillus megaterium, is a giant Gram-positive bacterium with potential biotechnology applications, although many of its regulatory elements remain uncharacterized. Here, we have developed a rapid automated platform for measuring and modeling in vitro cell-free reactions and have applied this toB. megateriumto quantify a range of ribosome binding site variants and previously uncharacterized endogenous constitutive and inducible promoters. To provide quantitative models for cell-free systems, we have also applied a Bayesian approach to infer ordinary differential equation model parameters by simultaneously using time-course data from multiple experimental conditions. Using this modeling framework, we were able to infer previously unknown transcription factor binding affinities and quantify the sharing of cell-free transcription–translation resources (energy, ribosomes, RNA polymerases, nucleotides, and amino acids) using a promoter competition experiment. This allows insights into resource limiting-factors in batch cell-free synthesis mode. Our combined automated and modeling platform allows for the rapid acquisition and model-based analysis of cell-free transcription–translation data from uncharacterized microbial cell hosts, as well as resource competition within cell-free systems, which potentially can be applied to a range of cell-free synthetic biology and biotechnology applications.

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