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

Arikuma, Takeshi; Sumi, Y.; Azuma, Ryuzo; Watanabe, Kentaro; Matsumura, Kazumi; Konagaya, Akihiko

doi:10.1186/1471-2105-9-s6-s11

Cited by 18 publications

(29 citation statements)

References 33 publications

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“…In this particular context it is reasonable to assume that the standard deviations of the Downloaded 11/19/14 to 193.0.65.67. Redistribution subject to SIAM license or copyright; see http://www.siam.org/journals/ojsa.php B19 measured values are proportional to the quantities measured [2]. In general, other error models can also be considered.…”

Section: Problem Statementmentioning

confidence: 98%

“…Such roughness can appear in the coefficient identification problem of a system of ODEs when the system is solved numerically, hence robustness against Downloaded 11/19/14 to 193.0.65.67. Redistribution subject to SIAM license or copyright; see http://www.siam.org/journals/ojsa.php In order to investigate further whether these obtained values are specific to the choice of the initial iterate or similar for most of the solutions of this underdetermined inverse problem, we have computed multiple solutions (multiple sets of model parameters) using the LM method with different initial iterates close to the "typical" values listed in [2]. Figure 1.2 shows the concentrations of CPT-11 and SN-38 in blood simulated by the pharmacokinetics model using 1,000 sets of model parameters found by the LM method with 1,000 different initial iterates.…”

Section: Introductionmentioning

confidence: 99%

“…Our interest in underdetermined inverse problems of this kind was initiated by the parameter identification problem of a pharmacokinetics model for the anticancer drug CPT-11 (also known as Irinotecan) [2]. This pharmacokinetics model is an ODE-based mathematical model for the transportation, metabolization, and excretion of the drug in a human body.…”

Section: Introductionmentioning

confidence: 99%

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Cluster Newton Method for Sampling Multiple Solutions of Underdetermined Inverse Problems: Application to a Parameter Identification Problem in Pharmacokinetics

Aoki

Hayami

Sterck

et al. 2014

SIAM J. Sci. Comput.

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A new algorithm is proposed for simultaneously finding multiple solutions of an underdetermined inverse problem. The algorithm was developed for an ODE parameter identification problem in pharmacokinetics for which multiple solutions are of interest. The algorithm proceeds by computing a cluster of solutions simultaneously, and is more efficient than algorithms that compute multiple solutions one-by-one because it fits the Jacobian in a collective way using a least squares approach. It is demonstrated numerically that the algorithm finds accurate solutions that are suitably distributed, guided by a priori information on which part of the solution set is of interest, and that it does so much more efficiently than a baseline Levenberg-Marquardt method that computes solutions one-by-one. It is also demonstrated that the algorithm benefits from improved robustness due to an inherent smoothing provided by the least-squares fitting. Introduction.Since the information we can obtain clinically from a live patient going through treatment is often much less extensive than the complexity of the internal activity in a patient's body, underdetermined inverse problems naturally appear in the field of mathematical medicine. Our interest in underdetermined inverse problems of this kind was initiated by the parameter identification problem of a pharmacokinetics model for the anticancer drug CPT-11 (also known as Irinotecan) [2]. This pharmacokinetics model is an ODE-based mathematical model for the transportation, metabolization, and excretion of the drug in a human body. In this problem, a large set of parameters needs to be estimated from a very small number of measurements that correspond to integrated (accumulated) quantities (area-underthe-curve) at a single final time Konagaya has proposed a framework called "virtual patient population convergence" [12] (see [13] for the English translation), whose essential idea is to estimate the parameters of a whole body pharmacokinetics model from the clinically observed patient data. The essential difference of this framework with other approaches is that instead of finding a single set of parameters that is suitable for the pharmacokinetics model to reproduce the clinically observed data, its aim is to find multiple sets of such parameters, because the ranges and extremal values of

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Section: Problem Statementmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cluster Newton Method for Sampling Multiple Solutions of Underdetermined Inverse Problems: Application to a Parameter Identification Problem in Pharmacokinetics

Aoki

Hayami

Sterck

et al. 2014

SIAM J. Sci. Comput.

Self Cite

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“…8,14,15 For example, Rubrichi and Quaglini 11 used a representation of the DDI domain to annotate a set of documents for the training and testing of an information extraction (IE) system, while the Pharmacodynamics Ontology representation framework was used to predict DDIs occurring via a pharmacodynamic mechanism. 8 Both NLP of pharmacological texts and prediction of DDIs are relevant and promising research areas for the management of DDIs and have attracted a great deal of attention in recent years.…”

Section: ■ Introductionmentioning

confidence: 99%

DINTO: Using OWL Ontologies and SWRL Rules to Infer Drug–Drug Interactions and Their Mechanisms

Herrero-Zazo

Segura-Bedmar

Hastings

et al. 2015

J. Chem. Inf. Model.

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The early detection of drug-drug interactions (DDIs) is limited by the diffuse spread of DDI information in heterogeneous sources. Computational methods promise to play a key role in the identification and explanation of DDIs on a large scale. However, such methods rely on the availability of computable representations describing the relevant domain knowledge. Current modeling efforts have focused on partial and shallow representations of the DDI domain, failing to adequately support computational inference and discovery applications. In this paper, we describe a comprehensive ontology for DDI knowledge (DINTO), which is the first formal representation of different types of DDIs and their mechanisms and its application in the prediction of DDIs. This project has been developed using currently available semantic web technologies, standards, and tools, and we have demonstrated that the combination of drug-related facts in DINTO and Semantic Web Rule Language (SWRL) rules can be used to infer DDIs and their different mechanisms on a large scale. The ontology is available from https://code.google.com/p/dinto/.

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“…It also retrieves the information from the spontaneous reporting systems such as US Food and Drug Administration's Adverse Event Reporting System (AERS) [2][11] [13] by using the technologies like the semantic web [15] and linked data.…”

Section: Knowledge Based Approachesmentioning

confidence: 99%

Predicting Drug-Drug Interactions for Premarket Drug Development Process: A Network Based Approach

Gunawardena

Weerasinghe

Wijesinghe

2018

Int J on Adv. in ICT for Emerging Countries

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Abstract-Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for the safety of the patient but also it is very challenging. In recent years, several drugs have been withdrawn from the market due to interaction related Adverse Events (AEs).This study describes a model which can be used to predict novel DDIs based on the similarity of drug interaction candidates to drugs involved in established DDIs which can be used in a large scale to discover novel DDIs. This model is mainly based on the assumption that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. We have created a drug network using the 2011 snapshot of a widely used drug safety database which utilizes 352 distinct drugs and contains 3 700 interactions. Then, it was used to develop the proposed model for predicting future DDIs. The target similarities and side effect similarities (P-score) were calculated for all selected pairs of drugs. Then, it was used to develop the proposed model for predicting future DDIs. The proposed model mainly follows two distinct approaches: 'Which forces the preservation of existing (known) DDIs' and 'Without forced to preserve existing DDIs.' Underneath each of these approaches, three different techniques: target similarity score, side effect similarity (P-score) and resulting score were used to retrieve novel DDIs.The proposed model was evaluated using the Drugbank 2014 snapshot as a gold standard for the same set of drugs which produce novel DDIs with an average accuracy of 95% and 92%, average AUC (Area Under the Curve) of 0.9834 and 0.8651 under each of these two approaches respectively.The results presented in this study demonstrate the usefulness of the proposed network based drug-drug interaction methodology as a promising approach. The method described in this article is very simple, efficient, and biologically sound.

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

Cited by 18 publications

References 33 publications

Cluster Newton Method for Sampling Multiple Solutions of Underdetermined Inverse Problems: Application to a Parameter Identification Problem in Pharmacokinetics

Cluster Newton Method for Sampling Multiple Solutions of Underdetermined Inverse Problems: Application to a Parameter Identification Problem in Pharmacokinetics

DINTO: Using OWL Ontologies and SWRL Rules to Infer Drug–Drug Interactions and Their Mechanisms

Predicting Drug-Drug Interactions for Premarket Drug Development Process: A Network Based Approach

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