Ryuzo Azuma

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

BackgroundIn accordance with the increasing amount of information concerning individual differences in drug response and molecular interaction, the role of in silico 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).ResultsThe 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%.ConclusionThese results demonstrate that the Ontology-Driven Hypothetic Assertion framework is a promising approach for in silico prediction of drug interactions. The following future researches for the in silico 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.

Expression of excess receptors and negative feedback control of signal pathways are required for rapid activation and prompt cessation of signal transduction.

BackgroundCellular signal transduction is initiated by the binding of extracellular ligands to membrane receptors. Receptors are often expressed in excess, and cells are activated when a small number of receptors bind ligands. Intracellular signal proteins are activated at a high level soon after ligand binding, and the activation level decreases in a negative feedback manner without ligand clearance. Why are excess receptors required? What is the physiological significance of the negative feedback regulation?ResultsTo answer these questions, we developed a Monte Carlo simulation program to kinetically analyze signal pathways using the model in which ligands are bound to receptors and then membrane complexes with other membrane proteins are formed. Our simulation results showed that excess receptors are not required for cell activation when the dissociation constant (Kd) of the ligand-receptor complex is 10-10 M or less. However, such low Kd values cause delayed signal shutdown after ligand clearance from the extracellular space. In contrast, when the Kd was 10-8 M and the ligand level was less than 1 μM, excess receptors were required for prompt signal propagation and rapid signal cessation after ligand clearance. An initial increase in active cytosolic signal proteins to a high level is required for rapid activation of cellular signal pathways, and a low level of active signal proteins is essential for the rapid shutdown of signal pathways after ligand clearance.ConclusionThe present kinetic analysis revealed that excess receptors and negative feedback regulation promote activation and cessation of signal transduction with a low amount of extracellular ligand.

Discovering Dynamic Characteristics of Biochemical Pathways using Geometric Patterns among Parameter-Parameter Dependencies in Differential Equations

AbstractThis paper proposes a novel approach to the analysis and validation of mathematical models using two-dimensional geometrical patterns representing parameter-parameter dependencies (PPD) in dynamic systems. A geometrical pattern is obtained by calculating moment values, such as the area under the curve (AUC), area under the moment curve (AUMC), and mean residence time (MRT), for a series of simulations with a wide range of parameter values. In a mathematical model of the metabolic pathways of the cancer drug irinotecan (CPT11), geometrical patterns can be classified into three major categories: “independent,” “hyperbolic,” and “complex.” These categories characterize substructures arising in differential equations, and are helpful for understanding the behavior of large-scale mathematical models. The Open Bioinformatics Grid (OBIGrid) provides a cyber-infrastructure for users to share these data as well as computational resources.

Kinetic Analysis of Ligand-Receptor Complex Formation with the Aid of Computer Simulation

Cellular signal transduction is initiated by the binding of extracellular ligands to membrane receptors. However, the concentration of ligand required for cellular activation is often lower than that required for receptor binding; receptors are often expressed in excess. To elucidate the physiological significance of this phenomenon, we have developed a Monte Carlo simulation program for kinetic analysis of ligand-receptor formation. Our present simulation showed that an excess amount of the receptors was not required for signal activation when the dissociation constant (Kd) of the ligand-receptor complex (LR) was low (10~9 or less). However, a low Kd value caused delayed LR dissociation after clearance of ligand from the extracellular space; no signal shutdown took place. These data indicate that an excess amount of receptors with high Kd (10~8 or more) was required for prompt signal propagation at physiological ligand concentrations and rapid signal cessation following ligand clearance. Our simulations were conducted using a conventional personal computer with a CPU running at 2.6 GHz under Windows XP or 2000 operating systems, and single simulation runs typically took less than two hours. Our simulation program could be readily implemented for kinetic analysis of any signal transduction system with various parameters, and could be used by any investigator because special computing hardware and training are not required.

Particle Simulation Approach for Sub-cellular Dynamics and Interactions of Biological Molecules

Spatio-temporal dynamics within cells can now be recorded on film at appropriate resolutions thanks to advances made in florescence microscopy technologies. Even the single-particle tracking technique is now being applied to observations of biological molecules. Conversely, little is known about how reaction diffusion at the molecular level affects properties at the cellular level. Therefore, we propose an algorithm designed for the three-dimensional simulation of the reaction-diffusion dynamics of molecules, based on a particle model. Chemical reactions proceed through the interactions of particles in space. The activation energy determines the rate of these chemical reactions at each interaction. This energy-based model allows incorporating of the cellular membrane, membranes of other organelles, and cytoskeletons. The simulation algorithm was tested for a reversible enzyme reaction model and its validity verified . A snapshot image taken from simulated molecular interactions on the cellular membrane was shown to yield a clustering pattern associated with raft.