Drug interaction prediction using ontology-driven hypothetical assertion framework for pathway generation followed by numerical simulation

<p>Abstract</p> <p>Background</p> <p>In accordance with the increasing amount of information concerning individual differences in drug response and molecular interaction, the role of <it>in silico </it>prediction of drug interaction on the pathway level is becoming more and more important. However, in view of the interferences for the identification of new drug interactions, most conventional information models of a biological pathway would have limitations. As a reflection of real world biological events triggered by a stimulus, it is important to facilitate the incorporation of known molecular events for inferring (unknown) possible pathways and hypothetic drug interactions. Here, we propose a new Ontology-Driven Hypothetic Assertion (OHA) framework including pathway generation, drug interaction detection, simulation model generation, numerical simulation, and hypothetic assertion. Potential drug interactions are detected from drug metabolic pathways dynamically generated by molecular events triggered after the administration of certain drugs. Numerical simulation enables to estimate the degree of side effects caused by the predicted drug interactions. New hypothetic assertions of the potential drug interactions and simulation are deduced from the Drug Interaction Ontology (DIO) written in Web Ontology Language (OWL).</p> <p>Results</p> <p>The concept of the Ontology-Driven Hypothetic Assertion (OHA) framework was demonstrated with known interactions between irinotecan (CPT-11) and ketoconazole. Four drug interactions that involved cytochrome p450 (CYP3A4) and albumin as potential drug interaction proteins were automatically detected from Drug Interaction Ontology (DIO). The effect of the two interactions involving CYP3A4 were quantitatively evaluated with numerical simulation. The co-administration of ketoconazole may increase AUC and Cmax of SN-38(active metabolite of irinotecan) to 108% and 105%, respectively. We also estimates the potential effects of genetic variations: the AUC and Cmax of SN-38 may increase to 208% and 165% respectively with the genetic variation UGT1A1*28/*28 which reduces the expression of UGT1A1 down to 30%.</p> <p>Conclusion</p> <p>These results demonstrate that the Ontology-Driven Hypothetic Assertion framework is a promising approach for <it>in silico </it>prediction of drug interactions. The following future researches for the <it>in silico </it>prediction of individual differences in the response to the drug and drug interactions after the administration of multiple drugs: expansion of the Drug Interaction Ontology for other drugs, and incorporation of virtual population model for genetic variation analysis, as well as refinement of the pathway generation rules, the drug interaction detection rules, and the numerical simulation models.</p

Proline as a charge stabilizing amino acid in peptide radical cations

Long distance electron transfer in proteins requires relay stations that can be transitorily oxidized or reduced. Although individual prolines cannot assume this function, because of their high ionization energy, it has been shown that polyprolines have the ability to transfer charges. In order to determine the role of the proline in the hole distribution and transport within a PheProPhe tripeptide, the radical cation of a model compound where the phenylalanines carry two or three methoxy groups, respectively, was generated by flash photolysis. Surprisingly, after equilibration, about two thirds of the holes were found to reside on the phen(OMe)2 instead of the more easily oxidizable phen(OMe)3 moiety. DFT calculations showed that, in most of the accessible conformations, the phen(OMe)2• +-moiety profits more from stabilization by N- and/or O-lone pairs of neighboring amide groups than the phen(OMe)3• + moiety can, which explains the apparently counterthermodynamic hole distribution. Similar calculations showed that, in several conformers of the natural PheProPhe radical cation, the unpaired electron is delocalized over two amide groups, by residing in a σ MO which links the N-lone pair of the central proline unit with the O-lone pair of a proximate amino acid, through hyperconjugation via the intervening C―Cα σ-bond. The same pattern is found in a model compound, N-acetylproline dimethylamide. It seems that prolines favor conformers which foster hyperconjugation of two amide groups, which lowers the ionization energy of peptides. One should thus consider such interacting amide groups as potential relay stations in the course of electron transfer in polyprolines