Cell-based simulation

One of the problems of most prediction programs is that the algorithm is only as good as the training set upon which it was built.

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ANN ARBOR, Mich.—One of the problems of most prediction programs is that the algorithm is only as good as the training set upon which it was built. In looking at pharmacokinetics, researchers at the University of Michigan addressed by focusing their efforts on a program that operated from the cell's perspective.
 
As described in Molecular Pharmaceutics, the researchers used fundamental biophysical properties of cell membranes, cytoplasms, and compartments to develop a simulator that could predict passive transcellular permeability of small molecules. They then relied on three basic properties of druglike small molecules as determinants of transport and accumulation: the lipid/water partition coefficient of a neutral (logPn) and ionized (logPd) molecule and the dissociation constant of the protonated functional group (pKa).
 
Using tools like MATLAB and SRC PhysProp Database, the researchers predicted the permeability properties of a number of well characterized drugs and druglike small molecules, comparing the predicted results with the literature results. They noted a good correlation between the predicted values and those identified in PAMPA or Caco-2 assays, but even better correlation with human intestinal permeability results, where available.
 
The researchers acknowledge that their simulator only accommodates drugs distributed in high concentration via passive permeability, but argue that this represents the majority of existing drugs. They are looking to expand their model system to accommodate a wider spectrum of compounds.


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