Modeling mechanism-based

In an elegant approach, Comba and co-workers initiated molecular-mechanics-based models that allow the rational design of ligand systems which are able to stabilize copper-dioxygen compounds. As a part of this investigation, complexes (241) (r = 0.12),223 (242) (r = 0.31),224 and (243) (r = 0.85)224 were synthesized and the reactivity of copper(I) complexes (Section 6.6.4.2.2(iv)) with dioxygen was investigated. 

Given the complexity, some form of mechanism-based modelling is required to understand or predict emissions in given circumstances. Approaches to modelling emissions from rice are reviewed by van der Gon et al. (2000). A complete... 

Furthermore biological mechanism-based models which are built on the basis of human physiological parameters provide the opportunity to translate in vitro and/or in silico data into knowledge which is relevant for the situation in man. This approach allows optimization and selection of compounds based on the expected human profile rather than mice or rat data which might be irrelevant for human [2]. 

In silico methods differ depending on various criteria, two major ones being (i) the way they are constructed (mechanism-based models versus statistical models) (ii) the chemical space they cover (global models built for non-congeneric sets of chemicals versus local models built for specific chemical classes with a common mechanism of action). 

A statistical-mechanics based model for mixtures of molecules of disparate sizes has been presented in this paper. Results obtained to date demonstrate that the lattice EOS is probably more suited for modelling polymer-SCF equilibria than a modified cubic EOS, while for the other systems, outside the critical region, it performs as well as classically employed techniques. 

Let us try to illustrate the mechanism-based modeling approach through the process of formulating a model of the ultradian oscillations in human insulin secretion [9], A better understanding of the role and underlying mechanisms of these oscillations is clearly of interest in the design of an optimal treatment of diabetes. 

Fig. 2.2 Simulation of a mechanism-based model of ultradian insulin-glucose oscillations. Using independently determined parameters and nonlinear relations, the model displays self-sustained oscillations of the correct period with proper amplitudes and phase relationships. The model also responds correctly to a meal as well as to changes in the rate of glucose infusion.

Figure 2.2 presents the results obtained with our mechanism-based model of the ultradian insulin-glucose oscillations [9], Although clearly only a preliminary model of the phenomenon, the applied model passes all of the above tests. The model produces self-sustained oscillations of the correct period and proper amplitudes, and the model also responds correctly both to a meal and to changes in the rate of glucose infusion. The next step is to use the model to predict the outcome of experiments that have not previously been performed. To the extent that the model is successful in such predictions, the hypothesis underlying the model structure gains additional support. 

The previous section presented a simplified (and perhaps somewhat idealized) picture of the mechanism-based modeling approach. To provide a more concrete example let us consider the problem of modeling the absorption kinetics of subcutaneously injected soluble insulin [11]. 

The above description provides a coherent explanation to the overall picture. There are clearly details in Fig. 2.5 that are not accounted for. In panel F, for instance, only one of the curves displays the tail phenomenon. In panel H, none of the absorption curves show a tail, even though one would expect such tails on basis of the systematic aspects of the figure. At the end of this section, we make a few comments on the role of such deviations in connection with mechanism-based modeling. 

The full model can reproduce all the systematic features of the absorption curves in Fig. 2.5 [11]. In the present form, the model does not account for the interpatient variability observed in Fig. 2.4 or for the nonsystemahc phenomena discussed in connection with Fig. 2.5. Ideally, in our mechanism-based modeling approach, any form of unsystematic variation in the experimental results should be given a separate explanation, i.e. specific studies should be undertaken to explain the observed variability in absorption rates and binding capacities. Statistical outliers should be considered as potential sources of new information. 

For further progress towards mechanisms based models, such phenomenological descriptions shall also be examined in context with disease-related disturbances of autonomous functions. This mainly concerns disturbances of sleep-wake cycles and cortisol release which are the most reliable biological markers of mental diseases, especially major depression, and can provide objective and quantifiable parameters (e.g. EEG frequency components, cortisol blood level) for the estimation of an otherwise mainly subjective and only behaviorally manifested illness. Moreover, there is a manifold of data which interlink the alterations of the autonomous system parameters (sleep states, cortisol release) with alterations of neural dynamics. Therefore, the most promising approach also to understand the interrelations between neural dynamics and affective disorders probably goes via the analysis of mood related disturbances of autonomous functions. 

Cleton, A. et al. Mechanism-based modeling of functional adaptation upon chronic treatment with midazolam. Pharm Res 2000,17 321-327. 

Table 17.1 Empirical versus mechanism-based models.

Another challenge in the development of mechanism-based models is data ownership and information distribution. As the pharmaceutical industry is a highly competitive area, the information-sharing philosophy of the pharmaceutical companies is improvable. Hopefully, future liaisons and partnerships will improve the data-sharing between various institutions like companies, regulatory agencies, and academia. 

Overall, the implementation of complex mechanism-based models in drug development progresses slowly. There might be several reasons for this. One is that the number of success stories is small and, consequently, the investment in this technology is low. Second, the complexity of these models requires a new type of modeling expert which is currently very rare. Third, currently most mechanism-based models do not consider variability in their model parameters. 

All of the CMC properties that govern structural utility and life depend upon the constituent properties (fibers, matrix, interfaces), as well as the fiber architecture. Since the constituents are variables, optimization of the property profiles needed for design and lifing become prohibitively expensive if traditional empirical procedures are used. The philosophy of this article is based on the recognition that mechanism-based models are needed, which allow efficient interpolation between a well-conceived experimental matrix. The emphasis is on the creation of a framework which allows models to be inserted, as they are developed, and which can also be validated by carefully chosen experiments. 

Vlassak JJ. A contact-mechanics based model for dishing and erosion in chemical-mechanical polishing. Mater Res Soc S5unp Proc 2001 671 M4.6.1-M4.6.6. 

The most common approach in mechanism-based modeling is to model the system as a continuum. The underlying assumption is that the state variables of the system (e.g., species concentrations) vary continuously in time and space and that, therefore,... 

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