scholarly journals Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 509 ◽  
Author(s):  
Xiangzhong Xie ◽  
René Schenkendorf
Keyword(s):  
Process Design ◽  
Measurement Data ◽  
Pharmaceutical Manufacturing ◽  
Chamber Pressure ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Model Based ◽  
Point Estimate Method ◽  
Robust Process

Model-based concepts have been proven to be beneficial in pharmaceutical manufacturing, thus contributing to low costs and high quality standards. However, model parameters are derived from imperfect, noisy measurement data, which result in uncertain parameter estimates and sub-optimal process design concepts. In the last two decades, various methods have been proposed for dealing with parameter uncertainties in model-based process design. Most concepts for robustification, however, ignore the batch-to-batch variations that are common in pharmaceutical manufacturing processes. In this work, a probability-box robust process design concept is proposed. Batch-to-batch variations were considered to be imprecise parameter uncertainties, and modeled as probability-boxes accordingly. The point estimate method was combined with the back-off approach for efficient uncertainty propagation and robust process design. The novel robustification concept was applied to a freeze-drying process. Optimal shelf temperature and chamber pressure profiles are presented for the robust process design under batch-to-batch variation.

scholarly journals Bayesian estimation of parameters in a regional hydrological model

2002 ◽  
Vol 6 (5) ◽  
pp. 883-898 ◽  
Author(s):  
K. Engeland ◽  
L. Gottschalk
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Hydrological Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Daily Streamflow ◽  
Likelihood Model ◽  
Simulation Errors

Abstract. This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC) analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1) process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis

scholarly journals Assessing model-based inferences in decision making with single-trial response time decomposition

2020 ◽  
Author(s):  
Gabriel Weindel ◽  
Royce anders ◽  
F.-Xavier Alario ◽  
Boris BURLE
Keyword(s):  
Decision Making ◽  
Decision Processes ◽  
Emg Activity ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Response Force ◽  
Model Based ◽  
Wide Range ◽  
Response Execution ◽  
Speed Accuracy

Decision-making models based on evidence accumulation processes (the most prolific one being the drift-diffusion model – DDM) are widely used to draw inferences about latent psychological processes from chronometric data. While the observed goodness of fit in a wide range of tasks supports the model’s validity, the derived interpretations have yet to be sufficiently cross-validated with other measures that also reflect cognitive processing. To do so, we recorded electromyographic (EMG) activity along with response times (RT), and used it to decompose every RT into two components: a pre-motor (PMT) and motor time (MT). These measures were mapped to the DDM's parameters, thus allowing a test, beyond quality of fit, of the validity of the model’s assumptions and their usual interpretation. In two perceptual decision tasks, performed within a canonical task setting, we manipulated stimulus contrast, speed-accuracy trade-off, and response force, and assessed their effects on PMT, MT, and RT. Contrary to common assumptions, these three factors consistently affected MT. DDM parameter estimates of non-decision processes are thought to include motor execution processes, and they were globally linked to the recorded response execution MT. However, when the assumption of independence between decision and non-decision processes was not met, in the fastest trials, the link was weaker. Overall, the results show a fair concordance between model-based and EMG-based decompositions of RTs, but also establish some limits on the interpretability of decision model parameters linked to response execution.

scholarly journals Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

Processes ◽  
2018 ◽  
Vol 6 (10) ◽  
pp. 183 ◽  
Author(s):  
Xiangzhong Xie ◽  
René Schenkendorf ◽  
Ulrike Krewer
Keyword(s):  
Robust Optimization ◽  
Process Design ◽  
Optimization Problems ◽  
Equality Constraints ◽  
Uncertain Parameters ◽  
Approximation Accuracy ◽  
Model Based ◽  
Computational Expense ◽  
Robust Process ◽  
Point Estimation Method

Model-based design principles have received considerable attention in biotechnology and the chemical industry over the last two decades. However, parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies have been introduced to solve the robust optimization problem. Most approaches suffer from either unreasonable computational expense or low approximation accuracy. Moreover, they are not rigorous and do not consider robust optimization problems where parameter correlation and equality constraints exist. In this work, we propose a highly efficient framework for solving robust optimization problems with the so-called point estimation method (PEM). The PEM has a fair trade-off between computational expense and approximation accuracy and can be easily extended to problems of parameter correlations. From a statistical point of view, moment-based methods are used to approximate robust inequality and equality constraints for a robust process design. We also apply a global sensitivity analysis to further simplify robust optimization problems with a large number of uncertain parameters. We demonstrate the performance of the proposed framework with two case studies: (1) designing a heating/cooling profile for the essential part of a continuous production process; and (2) optimizing the feeding profile for a fed-batch reactor of the penicillin fermentation process. According to the derived results, the proposed framework of robust process design addresses uncertainties adequately and scales well with the number of uncertain parameters. Thus, the described robustification concept should be an ideal candidate for more complex (bio)chemical problems in model-based design.

scholarly journals The Impact of Global Sensitivities and Design Measures in Model-Based Optimal Experimental Design

Processes ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 27 ◽  
Author(s):  
René Schenkendorf ◽  
Xiangzhong Xie ◽  
Moritz Rehbein ◽  
Stephan Scholl ◽  
Ulrike Krewer
Keyword(s):  
Experimental Design ◽  
Mathematical Models ◽  
Chemical Engineering ◽  
Protein Kinase Inhibitors ◽  
Optimal Experimental Design ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Global Parameter ◽  
Model Based ◽  
The Impact

In the field of chemical engineering, mathematical models have been proven to be an indispensable tool for process analysis, process design, and condition monitoring. To gain the most benefit from model-based approaches, the implemented mathematical models have to be based on sound principles, and they need to be calibrated to the process under study with suitable model parameter estimates. Often, the model parameters identified by experimental data, however, pose severe uncertainties leading to incorrect or biased inferences. This applies in particular in the field of pharmaceutical manufacturing, where usually the measurement data are limited in quantity and quality when analyzing novel active pharmaceutical ingredients. Optimally designed experiments, in turn, aim to increase the quality of the gathered data in the most efficient way. Any improvement in data quality results in more precise parameter estimates and more reliable model candidates. The applied methods for parameter sensitivity analyses and design criteria are crucial for the effectiveness of the optimal experimental design. In this work, different design measures based on global parameter sensitivities are critically compared with state-of-the-art concepts that follow simplifying linearization principles. The efficient implementation of the proposed sensitivity measures is explicitly addressed to be applicable to complex chemical engineering problems of practical relevance. As a case study, the homogeneous synthesis of 3,4-dihydro-1H-1-benzazepine-2,5-dione, a scaffold for the preparation of various protein kinase inhibitors, is analyzed followed by a more complex model of biochemical reactions. In both studies, the model-based optimal experimental design benefits from global parameter sensitivities combined with proper design measures.

Simultaneous Determination of Skeletal Model Parameter Values, Motions, and Controls From Noisy Measurement Data

2008 ◽  
Author(s):  
Michael G. Fattey ◽  
Benjamin J. Fregly
Keyword(s):  
Inverse Dynamics ◽  
Optimal Parameter ◽  
Measurement Data ◽  
Biomechanical Model ◽  
Optimization Techniques ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Model Parameter ◽  
Multi Level ◽  
Cadaver Studies

Accurate model parameter value and motion determination is important for obtaining reliable results from inverse dynamics analyses of gait. If the model parameters do not properly match their true values, the predicted motions and loads may lose their clinical significance [1]. Typical approaches to biomechanical model parameter estimation have included the use of scaling rules based on cadaver studies [2] and the use of multi-level optimization routines [3,4]. However, scaling rules do not provide optimal parameter estimates, and multi-level optimization techniques are computationally expensive.

scholarly journals Sodar Measurements of Wing Vortex Strength and Position

2007 ◽  
Vol 24 (2) ◽  
pp. 141-155 ◽  
Author(s):  
Stuart Bradley ◽  
Erich Mursch-Radlgruber ◽  
Sabine von Hünerbein
Keyword(s):  
Real Time ◽  
Measurement Data ◽  
Ground Surface ◽  
Vortex Pair ◽  
Performance Criteria ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
And Performance ◽  
Sodar Measurements ◽  
Flow Vortex

Abstract A method is developed for robust real-time visualization of aircraft vortex spatial and temporal development based on measurement data from a line array of sodars. The method relies on using a potential-flow vortex model, with spatial averaging according to the along-beam and transverse spatial resolution of the sodar. The model comprises the wing vortex pair, together with two image vortices below ground such that there is no flow through the ground surface. An analytic solution for the temporal–spatial evolution of this four-vortex system is obtained as an aid to establishing relevant scales and performance criteria for any sodar. Field results from an array of four sodars are used on an individual profile basis (every 2 s of real time) to fit the model parameters of vortex circulation, position, and spacing. This method gives vortex trajectories and strength as a function of real time without dependence on assumptions regarding interactions with the atmosphere. Estimates of parameter uncertainties are also produced in real time, and it is found that estimates of position and spacing can be obtained to around ±4 m and of vortex circulation to ±50 m2 s−1. Recommendations are given for optimizing sodars for vortex measurements using practical technology.

Population pharmacokinetics (Pop PK) of MYL-1402O (a proposed biosimilar to bevacizumab) and reference product (Avastin) in patients with nonsquamous non-small cell lung cancer (nsNSCLC).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21087-e21087
Author(s):  
Joel Owen ◽  
Russell J. Rackley ◽  
Matthew A. Hummel ◽  
Stefan Roepcke ◽  
Hannah Huang ◽  
...  
Keyword(s):  
Reference Product ◽  
Phase 1 ◽  
Model Development ◽  
Stage Iv ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Test Model ◽  
Phase 3 ◽  
Double Blind ◽  
Model Based

e21087 Background: MYL-1402O (MYL) is a proposed biosimilar to bevacizumab reference product. A multicenter, double blind randomized, phase 3 study compared the efficacy, safety, PK, and immunogenicity of MYL and Avastin in patients with Stage IV metastatic nsNSCLC. Patients received either MYL or reference product, in combination with carboplatin-paclitaxel up to 18 weeks (6 cycles) followed by monotherapy for up to an additional 24 weeks (8 cycles). The objective was to develop a Pop PK model based on data from a phase 3 study pooled with a single dose healthy volunteer phase 1 study data; to assess PK linearity across the dose levels of 1 mg/kg to 15 mg/kg in 2 clinical studies; to assess the PK similarity of MYL and reference product in patients with nsNSCLC; and to explore potential covariates to account for variability in Pop PK model parameters. Methods: A Pop PK model was developed based on preliminary analyses of MYL phase 1 data and published population analyses of reference product using a 2-compartment linear model (Han K et al., 2016). Individual empiric Bayesian parameter estimates of nsNSCLC patients were used to predict PK measures reflecting exposure to drug and were compared qualitatively between treatments. Results: The data subset used for model development consisted of 8724 records from 771 subjects. Population PK analyses indicated no differences between PK profiles of patients in the MYL and reference product arms. Importantly, treatment was not a significant covariate of clearance ( P = 0.453) or volume of the central compartment ( P = 0.161) using the likelihood ratio χ2 test. Model-based steady state exposure measures, predicted based on the final model for all patients, were also similar between treatment arms (Table). Conclusions: The model supported linear PK at clinical doses in patients with nsNSCLC; there were no clinically relevant/significant differences between the PK of MYL and reference product; and the findings were consistent with the PK study in normal, healthy volunteers. Bayesian Parameter Clinical trial information: 2015-005141-32. [Table: see text]

Bayesian closed-loop robust process design considering model uncertainty and data quality

2019 ◽  
Vol 52 (3) ◽  
pp. 288-300 ◽  
Author(s):  
Linhan Ouyang ◽  
Jianxiong Chen ◽  
Yizhong Ma ◽  
Chanseok Park ◽  
Jionghua (Judy) Jin
Keyword(s):  
Data Quality ◽  
Process Design ◽  
Model Uncertainty ◽  
Closed Loop ◽  
Robust Process

scholarly journals Modeling of German Low Voltage Cables with Ground Return Path

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1265 ◽  
Author(s):  
Johanna Geis-Schroer ◽  
Sebastian Hubschneider ◽  
Lukas Held ◽  
Frederik Gielnik ◽  
Michael Armbruster ◽  
...  
Keyword(s):  
Low Voltage ◽  
Measurement Data ◽  
Analytical Calculation ◽  
Laboratory Measurement ◽  
Model Parameters ◽  
Calculation Methods ◽  
Modeling Approach ◽  
Measurement Results ◽  
Simulation Results ◽  
Return Path

In this contribution, measurement data of phase, neutral, and ground currents from real low voltage (LV) feeders in Germany is presented and analyzed. The data obtained is used to review and evaluate common modeling approaches for LV systems. An alternative modeling approach for detailed cable and ground modeling, which allows for the consideration of typical German LV earthing conditions and asymmetrical cable design, is proposed. Further, analytical calculation methods for model parameters are described and compared to laboratory measurement results of real LV cables. The models are then evaluated in terms of parameter sensitivity and parameter relevance, focusing on the influence of conventionally performed simplifications, such as neglecting house junction cables, shunt admittances, or temperature dependencies. By comparing measurement data from a real LV feeder to simulation results, the proposed modeling approach is validated.

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