Kinetic modelling of NSAID action on COX-1: Focus on in vitro/in vivo aspects and drug combinations

Goltsov, Alexey; Maryashkin, Anton; Swat, Maciej; Kosinsky, Yuri; Humphery-Smith, Ian; Demin, Oleg; Goryanin, Igor; Lebedeva, Galina

doi:10.1016/j.ejps.2008.10.015

Cited by 19 publications

(30 citation statements)

References 28 publications

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“…In the AA (arachidonic acid) pathway [19], the authors found optimal combinations of AA pathway COX (cyclooxygenase) inhibitor-selective and non-selective NSAIDs (non-steroidal anti-inflammatory drugs). Recently, resistance to inhibition in cost function has been defined in a recursive genetic algorithm [20].…”

Section: Objective Functions In Biomedical Modellingmentioning

confidence: 99%

Computational optimization and biological evolution

Goryanin

2010

Biochemical Society Transactions

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Modelling and optimization principles become a key concept in many biological areas, especially in biochemistry. Definitions of objective function, fitness and co-evolution, although they differ between biology and mathematics, are similar in a general sense. Although successful in fitting models to experimental data, and some biochemical predictions, optimization and evolutionary computations should be developed further to make more accurate real-life predictions, and deal not only with one organism in isolation, but also with communities of symbiotic and competing organisms. One of the future goals will be to explain and predict evolution not only for organisms in shake flasks or fermenters, but for real competitive multispecies environments.

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Section: Objective Functions In Biomedical Modellingmentioning

confidence: 99%

Computational optimization and biological evolution

Goryanin

2010

Biochemical Society Transactions

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“…As in the case of signaling systems, these are mostly formulated as ordinary differential equations and used to evaluate the temporal behavior of the system of interest. Examples are the model of Prostaglandin H Synthase-1 (COX-1) developed to predict inhibition effects of nonsteroidal anti-inflammatory drugs [45] or a model for bacterial methicillin-resistance [46].…”

Section: Target Prediction In Metabolic Networkmentioning

confidence: 99%

Biochemical network-based drug-target prediction

Klipp

Wade

Kummer

2010

Current Opinion in Biotechnology

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“…We have developed a kinetic model of the NSAID inhibition effect on PGHS-1, which includes detailed submodels of PGHS-1 catalysis and the enzyme interaction with NSAID [29]. Below we describe in brief our approach to modeling the complex functioning of PGHS-1 and the basic assumptions used on describing the action of different NSAIDs on PGHS-1.…”

Section: Kinetic Model Of Inhibitory Action Of Nsaids On Pghs-1mentioning

confidence: 99%

“…The bottom panel in Figure 1 shows the detailed catalytic cycle of PGHS-1 [29]. The upper panel presents the kinetic scheme of the interaction of inhibitor with PGHS-1.…”

Section: Kinetic Model Of Inhibitory Action Of Nsaids On Pghs-1mentioning

confidence: 99%

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In Silico Screening of Nonsteroidal Anti-Inflammatory Drugs and Their Combined Action on Prostaglandin H Synthase-1

et al. 2010

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The detailed kinetic model of Prostaglandin H Synthase-1 (PGHS-1) was applied to in silico screening of dose-dependencies for the different types of nonsteroidal anti-inflammatory drugs (NSAIDs), such as: reversible/irreversible, nonselective/selective to PGHS-1/PGHS-2 and time dependent/independent inhibitors (aspirin, ibuprofen, celecoxib, etc.) The computational screening has shown a significant variability in the IC50s of the same drug, depending on different in vitro and in vivo experimental conditions. To study this high heterogeneity in the inhibitory effects of NSAIDs, we have developed an in silico approach to evaluate NSAID action on targets under different PGHS-1 microenvironmental conditions, such as arachidonic acid, reducing cofactor, and peroxide concentrations. The designed technique permits translating the drug IC50, obtained in one experimental setting to another, and predicts in vivo inhibitory effects based on the relevant in vitro data. For the aspirin case, we elucidated the mechanism underlying the enhancement and reduction (aspirin resistance) of its efficacy, depending on PGHS-1 microenvironment in in vitro/in vivo experimental settings. We also present the results of the in silico screening of the combined action of sets of two NSAIDs (aspirin with ibuprofen, aspirin with celecoxib), and study the mechanism of the experimentally observed effect of the suppression of aspirin-mediated PGHS-1 inhibition by selective and nonselective NSAIDs. Furthermore, we discuss the applications of the obtained results to the problems of standardization of NSAID test assay, dependence of the NSAID efficacy on cellular environment of PGHS-1, drug resistance, and NSAID combination therapy.

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Kinetic modelling of NSAID action on COX-1: Focus on in vitro/in vivo aspects and drug combinations

Cited by 19 publications

References 28 publications

Computational optimization and biological evolution

Computational optimization and biological evolution

Biochemical network-based drug-target prediction

In Silico Screening of Nonsteroidal Anti-Inflammatory Drugs and Their Combined Action on Prostaglandin H Synthase-1

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