Computational metabolically structured model

Chang, R. L., Xie, L., Bourne, P. E., and Palsson, B. O. 2010. Drug off-tai et effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput. Biol. 6 el000938. 

In conclusion, it is likely that computational approaches for metabolism prediction will continue to be developed and integrated with other algorithms for pharmaceutical research and development, which may in turn ultimately aid in their more widespread use in both industry and academia. Such models may already be having some impact when integrated with bioanalytical approaches to narrow the search for possible metabolites that are experimentally observed. Software that can be updated by the user as new metabolism information becomes available would also be of further potential value. The held of metabolism prediction has therefore advanced rapidly over the past decade, and it will be important to maintain this momentum in the future as the hndings from crystal structures for many discrete metabolic enzymes are integrated with the diverse types of computational models already derived. 

Figure 26. The proposed workflow of structural kinetic modeling Rather than constructing a single kinetic model, an ensemble of possible models is evaluated, such that the ensemble is consistent with available biological information and additional constraints of interest. The analysis is based upon a (thermodynamically consistent) metabolic state, characterized by a vector S° and the associated flux v° v(S°). Since based only on the an evaluation of the eigenvalues of the Jacobian matrix are evaluated, the approach is (computationally) applicable to large scale system. Redrawn and adapted from Ref. 296.

A number of reports also describe the prediction of mechanism-based inhibition (MBI) [17,18]. In this type of model, MBI is determined in part by spectral shift and inactivation kinetics. Jones et al. applied computational pharmacophores, recursive partitioning and logistic regression in attempts to predict metabolic intermediate complex (MIC) formation from structural inputs [17]. The development of models that accurately predict MIC formation will provide another tool to help reduce the overall risk of DDI [19]. 

In this workflow, chemists want to draw or import a set of molecules, profile their molecular properties, such as computed ADME T (absorption, distribution, metabolism, excretion, and toxicity) properties, estimated activities against specific protein targets based on existing SAR models, and make selections based on the analysis of structural features and computed molecular properties of those singleton molecules. 

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