Individual-based models submodels

The statistical submodel characterizes the pharmacokinetic variability of the mAb and includes the influence of random - that is, not quantifiable or uncontrollable factors. If multiple doses of the antibody are administered, then three hierarchical components of random variability can be defined inter-individual variability inter-occasional variability and residual variability. Inter-individual variability quantifies the unexplained difference of the pharmacokinetic parameters between individuals. If data are available from different administrations to one patient, inter-occasional variability can be estimated as random variation of a pharmacokinetic parameter (for example, CL) between the different administration periods. For mAbs, this was first introduced in sibrotuzumab data analysis. In order to individualize therapy based on concentration measurements, it is a prerequisite that inter-occasional variability (variability within one patient at multiple administrations) is lower than inter-individual variability (variability between patients). Residual variability accounts for model misspecification, errors in documentation of the dosage regimen or blood sampling time points, assay variability, and other sources of error. 

DEMETRA models are based on hybrid techniques. Each model for trout, daphnia, quail, or bee is composed of a number of submodels. A hybrid model integrates, in an intelligent way, the results of the submodels to achieve better prediction values capable of reducing false negatives. The regulator can see the minimal and maximal values of the individual models, should the regulator decide to use the lowest predicted value. Another innovative feature of DEMETRA is that its models are freely available (http //www.demetra-tox. net) for wide use, and the user has only to calculate 2D descriptors. 

Within the framework of component development, CFD is used for scientific modeling and model validation in addition to the classical engineering parameter studies and optimization processes. Both approaches are based on the use of HPC calculation capacity. Within the framework of modeling and vahdation, HPC facilitates a complex representation of the physical phenomena with fine space and time discretization. With the aid of such submodels and appropriate laboratory experiments, models for nozzles, heat transfer phenomena, two-phase flow, and so on can be derived and vahdated. CFD models thus selected and validated form the basis for the CFD-based design and optimization of flow systems. The classical engineering problem of parameter variation and optimization requires a large number of simulation calculations and therefore leads to an extremely high cost of computation. HPC allows the parallelization of individual simulations, which in turn makes it possible to calculate several simulations simultaneously and thus enables comprehensive parameter studies and flow optimizations to be completed in an acceptable time frame. In the ATR 10 development process, CFD simulations were conducted on up to 16 cores of the JuRoPA supercomputer simultaneously. This meant that when two simulation... 

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