Simulation correction methods

The mechanistic simulation ACAT model was modified to account automatically for the change in small intestinal and colon k as a function of the local (pH-dependent) log D of the drug molecule. The rank order of %HIA from GastroPlus was directly compared with rank order experimental %HIA with this correction for the log D of each molecule in each of the pH environments of the small intestine. A significant Spearman rank correlation coefficient for the mechanistic simulation-based method of 0.58 (p < 0.001) was found. The mechanistic simulation produced 71% of %HIA predictions within 25% of the experimental values. 

A different approach to mention here because it has some similarity to QM/MM is called RISM-SCF [5], It is based on a QM description of the solute, and makes use of some expressions of the integral equation of liquids (a physical approach that for reasons of space we cannot present here) to obtain in a simpler way the information encoded in the solvent distribution function used by MM and QM/MM methods. Both RISM-SCF and QM/MM use this information to define an effective Hamiltonian for the solute and both proceed step by step in improving the description of the solute electronic distribution and solvent distribution function, which in both methods are two coupled quantities. There is in this book a contribution by Sato dedicated to RISM-SCF to which the reader is referred. Sato also includes a mention of the 3D-RISM approach [6] which introduces important features in the physics of the model. In fact the simulation-based methods we have thus far mentioned use a spherically averaged radial distribution function, p(r) instead of a full position dependent function p(r) expression. For molecules of irregular shape and with groups of different polarity on the molecular periphery the examination of the averaged p(r) may lead to erroneous conclusions which have to be corrected in some way [7], The 3D version we have mentioned partly eliminates these artifacts. 

The efficiency of the presented correction method was verified on model frac-tograms for different conditions. A very good correlation between the original distribution and the corrected fractogram was found for simulated data. The ne-... 

The computational approach described here, based on the combination of the Kalman filter algorithm and iterative optimization by the simulated annealing method, was able to find the optimal alignment of the pure component peaks with respect to the shifted components in the overlapped spectra, and hence, to correctly estimate the contributions of each component in the mixture. The simulated annealing demonstrated superior ability over the other optimization methods, simplex and steepest descent, in yielding more reliable convergences at the expense of not much more computer time, at least for resolving ternary shifted overlapped spectra. 

Buvat I., et al., Comparative assessment of nine scatter correction methods based on spectral analysis using Monte Carlo simulations, /. Nucl. Med., 1995 36 1476-1488. 

Prougenes, P. Berek, D. Meira, G. Size exclusion chromatography of polymers with molar mass detection. Computer simulation study on instrumental broadening biases and proposed correction method. Polymer 1998, 40, 117-124. 

In the following, we will present jellium-related theoretical approaches (specifically the shell-correction method (SCM) and variants thereof) appropriate for describing shell effects, energetics and decay pathways of metal-cluster fragmentation processes (both the monomer/dimer dissociation and fission), which were inspired by the many similarities with the physics of shell effects in atomic nuclei (Section 4.2). In Section 4.3, we will compare the experimental trends with the resulting theoretical SCM interpretations, and in addition we will discuss theoretical results from first-principles MD simulations (Section 4.3.3.1). Section 4.4 will discuss some of the latest insights concerning the importance of electronic-entropy and finite-temperature effects. Finally, Section 4.5 will provide a summary. 

This simplified description has assumed that the exact physical probabilities are utilized to determine the outcome of every decision when this is done, the resulting simulation is termed an analog simulation. More sophisticated statistical treatments are included in modern computer codes that utilize nonphysical distributions with corrections (in a defined parficle weight) to keep the results of the simulation unbiased these can be shown to improve the efficiency of the simulation. These methods are called "variance reduction" methods, although this is somewhat of a misnomer because many of these methods increase efficiency by saving computer time, not by reducing variance. The exact theory and technique for doing this is beyond the scope of this handbook but is well described in Monte Carlo descriptions such as in Lewis and Miller (1993). 

At the risk of labouring the point, a glance at Feldberg (1980) may make it clear that the box method carries serious problems into the dme simulation. By not assuming the known diffusion equation, 8.8 or 8.13, and working with time-varying shell-elements, one requires much ingenuity to do the simulation correctly. 

A further possibility for the design of a profile die exists in computation by the finite element mefliod (FEM), thus a numerical simulation. This method requires a high level of skill on the part of these employees, to ensure that the boundary conditions are correctly assessed and interpreted. Additionally, high costs caused by hardware equipment, software licenses, employee training etc. have to be expected. 

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