Computational methods metabolic modeling

C. De Maria, D. Grassini, F. Vozzi, B. Vinci, A. Landi, A. Ahluwalia, and G. Vozzi, Hemet Mathematical model of biochemical pathways for simulation and prediction of hepatocyte metabolism. Comput. Methods Programs Biomed. (2008). 

For a long time in the absence of X-ray crystal structures for many P450s, computational models provided considerable insights. A recent exhaustive review of computational methods for prediction of P450 metabolism and inhibition across all species has documented how these approaches have been used over nearly 20 years, and the reader is encouraged to refer to this [12],... 

A variety of computational methods have recently been applied to the construction of predictive models in the field of ADME/Tox (Absorption, Distribution, Metabolism, Excretion, and Toxicology), and these models can be classified into two types ... 

The most appropriate and widely used method for extracting information from large data sets is QSAR and its relatives, quantitative structure-property relationships (QSPR) for property modeling, and quantitative structure-toxicity relationships (QSTR) for toxicity modeling. QSAR is a simple, well validated, computationally efficient method of modeling first developed by Hansch and Fujita several decades ago (30). QSAR has proven to be very effective for discovery and optimization of drug leads as well as prediction of physical properties, toxicity, and several other important parameters. QSAR is capable of accounting for some transport and metabolic (ADMET) processes and is suitable for analysis of in vivo data. 

The same authors also presented an example of the use of the population balance equation (PBE) (distribution of biomass m) coimected to the multi-zone/CFD model. This example is in several respects relevant for the assessment of the modeling approach. The coupling of the integro-differential equation of the population balance is a numerical challenge, which can nowadays be tackled within the environment of a CFD approach, albeit without consensus on the proper closure assumptions. Still, the computational effort for the numerical solution of the population balance embedded in the multizonal model is extensive, and it is difficult to extend this approach to multiple state variables necessary for dynamic metabolic models. This is an important argument to favor the alternative method of an agent-based Lagrange-Euler approach discussed in Section 3.5. 

Simulation methods have also been developed that include physiologically based pharmacokinetic modeling (PBPK) and methods such as Cloe PK, OMPPPlus, GastroPlus , SimCYP , and others [122] that are described elsewhere in this book. It is likely that the computational metabolism predictions could be integrated with these to assist in deriving more accurate predictions of human pharmacokinetic parameters. 

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