Drug interaction prediction using ontology-driven hypothetical assertion framework for pathway generation followed by numerical simulation
© Arikuma et al; licensee BioMed Central Ltd. 2008
Published: 28 May 2008
In 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).
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%.
These 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.
The role of in silico prediction of drug interactions on the pathway level is becoming more and more important for solving real-world problems. Multiple-drug regimens exemplify the need for the computer-assisted prediction of drug interactions. Multiple-drug regimens are commonly prescribed for elderly patients suffering from more than one disease. However, they sometimes cause unexpected severe side effects because of the drug interactions or individual differences concerning response to the drugs . Therefore, the prediction of drug interactions for preventing the side effects is an important issue for these regimens.
On the other hand, information useful for in silico drug interaction prediction has increased very rapidly in recent years. Technological innovations in genomic sciences have produced an enormous amount of biomolecular information including sequences, structures, and pathways. In order to integrate the biomolecular information, ontology is attracting a lot of attention [2, 3]. In addition, pharmacokinetics modeling and simulation are emerging, promising techniques to understand the dynamic behavior of drug metabolic pathways [4, 5].
To develop a practical in silico drug interaction prediction system by integrating the above information and techniques, the following issues must be solved.
Context dependency of drug-metabolic pathways
Drug-metabolic pathways do not exist a priori. They strongly depend on contexts and situations including the administration route, single nucleotide polymorphism (SNP) of drug-response genes, and the administration of multiple drugs and foods. Therefore, a dynamic reconstruction of drug metabolic pathways from primitive molecular events is necessary for drug interaction prediction on the pathway level. Such reconstruction requires the formal definition of molecular events and the relations among them, i.e., the Drug Interaction Ontology (DIO).
Treatment of multiscale events
Pathways triggered by drug administration consist of multiscale events: from the molecular level to the body level, ranging from nanoseconds to hours or days in terms of drug response. For example, drug administration and drug excretion are body-level events, while drug transport and enzymatic reactions are molecular-level events. A comprehensive view from the molecular level to the body level is necessary in order to understand multi-scale events.
Quantitative evaluation of interactions
Quantitative evaluation plays an essential role to estimate the degree of side effects caused by drug interactions. More than one drug interactions may occur in drug-metabolic pathways from the qualitative reasoning view point. However, not all drug interactions cause side effects because of differences in binding affinity and molecular population. Quantitative simulation models with an in silico drug interaction prediction system must be incorporated to discriminate serious drug interactions from negligible ones. It should be also noted that total drug metabolism depends on not only kinetic parameters but also physiological parameters such as organ volumes and blood flows. Incorporation of kinetic parameters and physiological parameters is necessary for a realistic simulation model to recapture experimental data.
In this paper, we propose a new drug-interaction prediction framework called "Ontology-Driven Hypothetic Assertion (OHA)" focusing mainly on automatic generation of drug-metabolic pathways, automatic detection of drug interactions on multiple-drug regimens , and quantitative evaluation of the drug interactions with numerical simulation. The effectiveness of the OHA framework was demonstrated in the prediction of interactions between irinotecan (CPT-11) and ketoconazole.
Ontology-Driven Hypothetical Assertion
The Ontology-Driven Hypothetical Assertion framework maps inferences onto the Drug Interaction Ontology as both machine and human understandable form. The inferences include the results of qualitative reasoning and numerical simulation, i.e. generated pathways and detected drug interactions and generated differential equations and simulation results. The assertions enable to interpret the obtained results such as drug interaction candidates, simulation models, the pharmacokinetic moment values with background knowledge on pharmaceutical science and biochemistry in the ontology.
Causality-based pathway modularization
Modularization is necessary for the dynamic reconstruction of pathways that depend on dose conditions. Careful selection of primitive modules is the key to ensuring the soundness of pathway reconstruction. Molecular events, such as molecular transport and enzymatic reactions, are well-formed primitive modules for this purpose. In this paper, we refer to the primitive modules as "molecular events", and the aggregation of molecular events as "pathways".
To avoid redundant pathway branch constructions, which are non-essential for the target drug interactions, we adopt causality-based modularization in which each molecular event is defined by the unique relationship between key molecules before and after the event. The triadic relationship <trigger, situator, resultant>, proposed by Yoshikawa et al. , is one such causality that can be commonly found in molecular reactions. For example, in case of enzymatic reactions, substrates, enzymes and other products correspond to trigger, situator and resultant, respectively. In the case of molecular transport, extra (intra) cellular molecules, transporters and intra (extra) cellular molecules correspond to the participants of the triadic relation, respectively. The triadic relationship can be applicable to higher level events like drug dosage and drug excretion, as long as its causality is unique and clear.
Ontology-based knowledge base construction
The ontological approach in knowledge base design is adopted for resource sharing and the semantic description of molecular events and pathways. Ontology is necessary to define molecular events and pathways in a form that can be shared among computers and human beings. This enables the full use of powerful computational intelligence for dynamic pathway reconstruction in a way that human intelligence can follow and understand. Ontology is also important for establishing interoperability among web resources and thereby to make use of the latest drug reaction information published in the semantic web [2, 8]. Public biological ontologies, especially in the field of chemical biology, are now dramatically increasing, and have a great potential to develop sustainable knowledge bases for molecular reaction and pathways.
Dynamic pathway reconstruction and drug interaction detection
The drug metabolic pathway, due to its dynamic nature, is difficult to define a priori in the manner seen in biomolecular metabolic pathways in the Kyoto Encyclopedia of Genes and Genomics (KEGG) . Therefore, the OHA framework provides the Pathway Object Constructor (POC) and Drug Interaction Detector (DID). Pathway Object Constructor dynamically deduces pathways from the DIO, depending on contexts or situations. Drug Interaction Detector detects drug interaction candidates finding intersections from those generated pathways.
These inferences are mapped onto the Drug Interaction Ontology as hypothetical assertions. The generated pathways and detected drug interactions are asserted as aggregation of molecular events. The detected drug interactions can be segmentalized such as competitive inhibition, noncompetitive inhibition, and uncompetitive inhibition when binding site information is available. This is effective to select an appropriate differential equation depending on the inhibition model.
In order to incorporate a quantitative simulation system into the OHA framework, the following two aspects were considered: a methodology for automatic conversion from a generated pathway to a quantitative simulation model, and a methodology for the validation of numerical simulation models by analysing the dependences of kinetic parameters and physiological parameters. By solving these two issues, the OHA framework can can be applied to in silico prediction of individual drug interactions for multiple-drug regimens, assuming that kinetic parameters and the initial enzyme concentration are roughly estimated by individual genetic variations and health indices of bio-markers.
A simulation model is automatically generated from a pathway generated by the Pathway Object Constructor and Drug Interaction Detector. The pathway is converted to intermediate model by merging organs and molecular events, respectively, to fit a given simulation model framework such as compartment model. Differential equations for the simulation model are generated from the intermediate model by converting the merged events to mathematical expression.
We used the parameter-parameter dependency analysis system (PPD Viewer) designed by Konagaya et al. , with high-throughput numerical simulation engine and interactive visualization tools developed on OBIGrid [3, 13–16]. The system can predict the concentration/time profiles and those moment parameters such as area under the curve (AUC), area under the moment curve (AUMC), and mean resident time (MRT) when changing some kinetic parameters in the range of one thousandth to thousands of physiological conditions. The system visualizes dependencies among kinetic parameters and molecular concentrations in terms of moment parameters of a compound in target organs.
The generated simulation models and pharmacokinetic moment parameter values are mapped onto the Drug Interaction Ontology as hypothetical assertions. The simulation models are asserted as aggregations of objects representing terms and parameters in differential equations. Those objects have references to the components of the pathway objects from which the simulation models were generated. The moment parameter values are asserted with the drug interaction objects and the corresponding simulation model for the further analysis.
Interaction between Irinotecan and Ketoconazole
Irinotecan (CPT-11) is an anti cancer drug which is commonly used for colon and breast cancers . Irinotecan is a prodrug of SN-38, anti neoplastic topoisomerase I inhibitor, and is bioactivatied by carboxyl esterase (CE) . About 60% of irinotecan is excreated as unchanged drug from bile and kidney . Irinotecan is also metabolised by CYP3A4 to form APC and NPC . NPC is further metabolized by CE to form SN-38. SN-38 undergoes glucronidation by UGT1A1 to form the inactive glucronide, SN-38G . In addition, it is known that the mutations on UGT1A1, UGT1A1*28 which decreases the expression of UGT1A1 enzyme down to 30%, has strong relationship with some side effects of irinotecan [19, 20]. Ketoconazole (KCZ) is an anti fungal drug and a well known inhibitor of CYP3A4. Ketoconazole undergoes extensive metabolism in the liver to form several metabolites [21, 22]. About 2 to 4% of urinary radioactivity represents unchanged drug .
It has been reported that the inhibition of CYP3A4 by ketoconazole influences the metabolism of irinotecan, which results in 6% SN-38 increase .
Results and discussion
Pathway generation and drug interaction detection
AUC and Cmax of blood concentration/time profile obtained by numerical simulations.
AUC UGT [nmol·min/ml]
Cmax UGT [μ M]
AUC UGT /AUCKCZ-[%]
Cmax UGT /CmaxKCZ-[%]
Pathway reconstruction and drug interaction detection
The OHA framework automatically generated pathways for co-administration of irinotecan and ketoconazole, and detected drug interactions. The generated pathways and two of the drug interactions involving CYP3A4 were confirmed by published papers. The detected interactions with plasma albumin (ddi2 and ddi3) have not been reported in any literature as far as we know. One possible reason is that the effects of drug interactions may be negligible because of the high plasma albumin concentration compared to drug concentration. To distinguish serious drug interactions from those negligible ones, a qualitative evaluation, such as numerical simulation, is necessary.
The simulation model succeeded to reproduce Slatter's experimental data of irinotecan dosage to a large extent except for the Cmax of irinotecan blood concentration, cyclic fluctuation of irinotecan concentration and a bile cancer patient data.
The Cmax of drug concentrations in the simulation model strongly depend on renal clearance, bile clearance, and kinetic parameters of enzymes. Because these parameters were estimated from the recovery amount data in urine and feces by Slatter et al.  and in vitro experiments data reported as published papers [25–29], the excess of irinotecan's Cmax in the simulation model might be explained by the false peak in the experimental data which results from time lag of sampling
Another possibility is the effect of reabsorption in intestine. The time course of the experimental data indicate slight increase of SN-38G blood concentration in the period of 12 hours. This may result from the reabsorption of SN-38 thorough small intestine. In order to include the reabsorption process into the simulation, the kinetic parameters in bacterial flora in the intestine and amount/time profile data of bile excretion are required.
Lastlym Slatter et al. reported that one patient with a bile cancer showed different bile and urine recovery pattern from other patients . Virtual population  may be helpful to reconstruct this behaviour.
Deduction of hypothetic drug interactions from the Drug Interaction Ontology was demonstrated for an irinotecan plus ketoconazole regimen. The prototype system detected four drug interactions. Two of them concerned cytochrome p450 (CYP3A4) and were consistent with known drug interactions. The other two concerned albumin, the effects of the interactions would be negligible as long as the drug concentration is low. We then quantitatively examined the effect of the drug interactions concerned CYP3A4 and the effect of genetic variation UGT1A1*28 using numerical simulation. The numerical simulation indicates that the drug interaction has only a limited effect on the pharmacokinetics of SN-38 for an irinotecan plus ketoconazole regimen. However, the genetic variation on UGT1A1 showed two-fold increase of SN-38's AUC.
Finally, in order to realize in silico prediction of drug interactions, the following future works are remained: expansion of the Drug Interaction Ontology for other drugs, incorporation of virtual population model for generic variation analysis, and refinements of the pathway generation rules, drug interaction detection rules, and the numerical simulation models.
The OHA framework
DIO, shown in Figure 2, was written in OWL-DL; the controlled vocabularies of process and continuant were implemented as OWL class hierarchy, and extension and part of attributes, including molecules and organs, were implemented as OWL instances. The molecular event objects in extension were represented by OWL instances and OWL properties. The ontology referred to other taxonomies and ontologies for well-established vocabularies of biochemical terms, anatomical entities and properties. This enabled the reduction of our ontology construction cost and to concentrate our efforts on the information specific to drug interaction.
The major properties of molecular event objects in OWL-DL.
The inference programs of the Pathway Object Constructor and Drug Interaction Detector were implemented with SWI-Prolog  and the Semweb library. The Semweb library enables the system to access OWL and RDF files as Prolog clauses. All logical inferences, including pathway generation and drug interaction detection, were implemented in Prolog.
Systems for simulation model generation, and pathway visualization were implemented in JAVA. These systems access DIO through SWI-Prolog semweb library to get detailed information of the generated pathways. The simulation models were generated in the form of SBML, then translated in the form of Octave. Graphviz , a graph visualization program designed by Gansner et al., was used to visualize pathways generated by the inference programs. The PPD Viewer designed by Azuma et al.  was used for numerical simulation.
Numerical simulation model for co-administration of Irinotecan and Ketoconazole
In order to increase predictive performance, a simplified pathway was used for the generation of simulation models from the viewpoint of the trade off between model complexity and data availability. Reabsorption through small intestine and reactions concerning albumin were omitted due to the lack of information. Furthermore, enzymatic reactions involved metabolism of ketoconazole were integrated into a single enzymatic reaction based on the work of Chien et al. .
Here k 0 I is a constant for the velocity of drip infusion.
Kinetic parameters for the protein binding, tissue distribution, and urinary and biliary excretion of ketoconazole, irinotecan, and their metabolites.
Kinetic parameters for metabolic enzymes.
K m [μ M]
V max [pmol/min/mg protein]
α[mg protein/g tissue]
V max ·α/K m [mL/min/g tissue]
Blood flow rate [mL/min/kg]
Parameters for drug interactions and drug administrations.
1.40 * 10-3
5.94 * 10-4
ka KCZ [min-1]
1.83 * 10-2
dose Irinotecan [nmol/kg]
4.86 * 103
dose KCZ [nmol/kg]
5.16 * 103
The authors give special thanks to Dr. Fumikazu Konishi, Mr. Shingo Ohki and Mr. Ryo Umetsu in RIKEN GSC for their contributions in providing the CONDOR pool and visualization tools on the Open Bioinformatics Grid (OBIGrid).
The computations in this work were performed using the Open Bioinformatics Grid (OBIGrid) .
This article has been published as part of BMC Bioinformatics Volume 9 Supplement 6, 2008: Symposium of Computations in Bioinformatics and Bioscience (SCBB07). The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/9?issue=S6.
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