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Results for “Mechanistic”

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PubMed2004

Inotropic effect of digoxin in humans: mechanistic pharmacokinetic/pharmacodynamic model based on slow receptor binding.

PURPOSE: The purpose of this study was to construct a mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for digoxin that describes the relationship between plasma concentration and inotropic response. METHODS: On the basis of results obtained in the isolated perfused rat heart, a PK/PD model for digoxin in humans was developed. In fitting the model to previously published bolus dose and concentration clamp data (shortening of electromechanical systole), the plasma concentration-time curves were used as forcing functions in the computer program ADAPT II. RESULTS: The mechanistic approach allowed a modeling of digoxin pharmacodynamics which is consistent with available inotropic response data. The estimates of the receptor binding parameters were in the same order of magnitude as those measured in vitro for ouabain. The mechanistic model explained the parameters of the empirical link model (EC50, Emax and delay time tau) in terms of the underlying processes, suggesting that the long equilibration half-time of 13 h is due to slow receptor binding. The empirical link model, in contrast, is not compatible with a noninstantaneous receptor binding process and led to estimates of the delay time tau that were dependent on the digoxin administration schedule. CONCLUSIONS: The new, mechanistic model may provide a rationale for better understanding of digoxin pharmacodynamics and could become a tool to bridge the gap between in vitro and in vivo studies.

Animals↗

PubMed2006

Mechanistic home range models capture spatial patterns and dynamics of coyote territories in Yellowstone.

Patterns of space-use by individuals are fundamental to the ecology of animal populations influencing their social organization, mating systems, demography and the spatial distribution of prey and competitors. To date, the principal method used to analyse the underlying determinants of animal home range patterns has been resource selection analysis (RSA), a spatially implicit approach that examines the relative frequencies of animal relocations in relation to landscape attributes. In this analysis, we adopt an alternative approach, using a series of mechanistic home range models to analyse observed patterns of territorial space-use by coyote packs in the heterogeneous landscape of Yellowstone National Park. Unlike RSAs, mechanistic home range models are derived from underlying correlated random walk models of individual movement behaviour, and yield spatially explicit predictions for patterns of space-use by individuals. As we show here, mechanistic home range models can be used to determine the underlying determinants of animal home range patterns, incorporating both movement responses to underlying landscape heterogeneities and the effects of behavioural interactions between individuals. Our analysis indicates that the spatial arrangement of coyote territories in Yellowstone is determined by the spatial distribution of prey resources and an avoidance response to the presence of neighbouring packs. We then show how the fitted mechanistic home range model can be used to correctly predict observed shifts in the patterns of coyote space-use in response to perturbation.

Animals↗

PubMed1998

Prediction of methane production from dairy cows using existing mechanistic models and regression equations.

Ruminants may contribute to global warming through the release of methane gas by enteric fermentation. Until now, methane emissions from ruminants were estimated using simple regression equations. The objective of this study was to compare the capacity of dynamic and mechanistic models to that of regression equations to predict methane production from dairy cows. The updated version of the model of Baldwin et al. and a modified version of the model of Dijkstra et al. and the regression equations of Blaxter and Clapperton and Moe and Tyrrell were challenged with 32 experimental diets selected from 13 publications. The predictive capacity of mechanistic models and regression equations was evaluated by comparing predicted and observed methane production using regression analysis. Results of regression showed better prediction of methane production with mechanistic models than with regression equations. The modified model of Dijkstra et al. predicted methane production with the higher R2 (.71) and the smaller error of prediction (19.87% of the observed mean). The model of Baldwin et al. predicted methane production with a similar R2 (.70) but a higher error of prediction (36.93%). However, a large proportion of this error can be eliminated by a correction factor. Predictions using the equations of Moe and Tyrrell and Blaxter and Clapperton were poor (R2 = .42 and .57; error of prediction = 33.72% and 22.93%, respectively). This study demonstrated that from a large variation in diet composition, mechanistic models allow the prediction of methane production more accurately than simple regression equations.

Animal Feed↗

PubMed2004

Mechanistic studies on acyl-transferase ribozymes and beyond.

In vitro selection has allowed the isolation of many new ribozymes that are able to catalyze an ever-widening array of chemical transformations. Mechanistic studies on these selected ribozymes have provided valuable insight into the methods that RNA can invoke to overcome different catalytic tasks. We focus on the methods employed in these mechanistic studies using the acyl-transferase family of selected ribozymes as well-studied reference systems. Chemical and biochemical techniques have been used in tandem in order to draw conclusions on the various modes of catalysis employed by the different family members. In turn, this type of mechanistic information may provide a means for the redesign and optimization of existing ribozymes or the basis for new selection systems for more powerful RNA catalysts.

Acyltransferases↗

PubMed2025

BMDx2: A Tool for Integrating Toxicogenomics-Based Dose-Dependency Analysis and AOP-Based Mechanistic Insights.

Despite the advent of mechanistic toxicology using omics data to link molecular perturbations with systemic outcomes, regulatory toxicology still lacks the application of mechanism-anchored metrics from such data. This is partially because traditional gene-centric analysis often falls short of linking molecular changes to adverse outcomes. To address this gap, BMDx2, an open-source tool that transforms multi-dose toxicogenomics datasets into quantitative, mechanistic evidence for human chemical safety assessment is developed. BMDx2 couples benchmark-dose modeling with Adverse Outcome Pathway (AOP) enrichment to derive transcriptomic-based points of departure, enabling potency ranking, chemical prioritization, and mechanistically anchored explanations of the effect of chemical exposures. BMDx2 can process a broad range of data, including DNA microarray and RNA sequencing studies. Here, case studies are used to illustrate the versatility of BMDx2 in characterizing the mechanism of action of chemicals. An initial case study on carbon nanotubes exposure applies integrative analysis of transcriptomics and genome-wide DNA methylation data, uncovering cellular reprogramming processes underlying fibrosis. A second case study on bleomycin exposure demonstrate how transcriptomic data alone can be mapped to fibrosis-related AOPs in a standardized, regulatory appropriate manner. Together, these examples show how BMDx2 supports the regulatory application of toxicogenomics and accelerates mechanism-based chemical safety evaluation.

Toxicogenetics↗

PubMed2002

Modeling host-parasite coevolution: a nested approach based on mechanistic models.

In this study we introduce a mechanistic framework for modeling host-parasite coevolution using a nested modeling approach. The first step in this approach is to construct a mechanistic model of the parasite population dynamics within a host. The second step is to define an epidemiological model which is used to derive the fitness functions for both the host and the parasite. The within-host model is then nested within the epidemiological model by linking the epidemiological parameters such as the transmission rate of the infection or the additional host mortality rate to the dynamics of the within-host model. Nesting the within-host model into an epidemiological model allows us to evaluate the fitness functions for each interactor which in turn allows us to determine the coevolutionary dynamics of the system. This nested approach has the advantage over other approaches in that mechanistic descriptions of the host-parasite biology are used to derive, rather than impose, life-history trade-offs. We illustrate this framework by analysing a simple host-parasite system. In this particular system we find that the coevolutionary equilibrium is always stable and that host survivorship and parasite fitness vary greatly with the cost of the immune response and parasite growth.

Animals↗

PubMed1995

Specialists without spirit: limitations of the mechanistic biomedical model.

This paper examines the origin and the development of the mechanistic model of the human body and health in terms of Max Weber's theory of rationalization. It is argued that the development of Western scientific medicine is a part of the broad process of rationalization that began in sixteenth century Europe as a result of the Reformation. The development of the mechanistic view of the human body in Western medicine is consistent with the ideas of calculability, predictability, and control-the major tenets of the process of rationalization as described by Weber. In recent years, however, the limitations of the mechanistic model have been the topic of many discussions. George Engel, a leading advocate of general systems theory, is one of the leading proponents of a new medical model which includes the general quality of life, clean environment, and psychological, or spiritual stability of life. The paper concludes with consideration of the potential of Engel's proposed new model in the context of the current state of rationalization in modern industrialized society.

Europe↗

PubMed1981

Screening for toxic effects on interspecies interactions: a mechanistic or an empirical approach?

The use of empirical and mechanistic approaches are possible in the development of tests to screen for a substance's potential to affect interspecies interactions. The advantages and disadvantages of the two approaches are discussed. An experimental study is presented, in which an empirical and a mechanistic screening test for effects on exploitative competition between bacterial species were established and perturbed with nalidixic acid. Comparison of test results indicates that the mechanistic test was faster, cheaper, more sensitive, and more quantitative. The empirical test attained similar sensitivity and quantification only if the dynamics of the competition event was continuously monitored; requiring even greater cost and time.

Bacteria↗

PubMed2002

Generalisation: mechanistic and functional explanations.

An overview of mechanistic and functional accounts of stimulus generalisation is given. Mechanistic accounts rely on the process of spreading activation across units representing stimuli. Different models implement the spread in different ways, ranging from diffusion to connectionist networks. A functional account proposed by Shepard analyses the probabilistic structure of the world for invariants. A universal law based on one such invariant claims that under a suitable scaling of the stimulus dimension, generalisation gradients should be approximately exponential in shape. Data from both vertebrates and invertebrates so far uphold Shepard's law. Some data on spatial generalisation in honeybees are presented to illustrate how Shepard's law can be used to determine the metric for combining discrepancies in different stimulus dimensions. The phenomenon of peak shift is discussed. Comments on mechanistic and functional approaches to generalisation are given.

Adaptation, Physiological↗

PubMed2005

Bridging the pharmacokinetics and pharmacodynamics of UK-279,276 across healthy volunteers and stroke patients using a mechanistically based model for target-mediated disposition.

PURPOSE: UK-279,276 is a recombinant glycoprotein and is a selective antagonist of CD11b, which in preclinical models of acute stroke blocks the infiltration of activated neutrophils into the site of infarction. Binding of UK-279,276 to the CD11b receptors is hypothesized to facilitate its elimination. The event of an acute stroke leads to proliferation of neutrophils and an up-regulation of CD11b, which results in different pharmacokinetics/pharmacodynamics (PK/PD) in patients than in healthy volunteers. The aim of this current analysis was to develop a mechanistically based model to bridge the differences between healthy volunteers and patients. METHODS: PK samples, neutrophil counts, and total number and number of free CD11b receptors per neutrophils from three healthy volunteer studies (n=98) and one patient study (n=169) were modeled using the mixed effects modeling software NONMEM version VI (beta). Three mechanistic submodels were developed based on underlying physiology and pharmacology: (1) neutrophil maturation and proliferation, (2) CD11b up-regulation, and (3) three clearance pathways for UK-279-276 including CD11b-mediated elimination. RESULTS: The model accurately described the time course of CD11b expression, CD11b binding, and the measured PK of UK-279,276 and accounted for the PK/PD differences between healthy volunteers and patients. CONCLUSIONS: A complex mechanistic model that closely resembled the "true" underlying system provided an effective bridge between healthy volunteers and patients by appropriately accounting for the underlying disease-dependent target mediated disposition.

CD11b Antigen↗

PubMed2006

On the mechanistic derivation of various discrete-time population models.

We present a derivation of various discrete-time population models within a single unifying mechanistic context. By systematically varying the within-year patterns of reproduction and aggression between individuals we can derive various discrete-time population models. These models include classical examples such as the Ricker model, the Beverton-Holt model, the Skellam model, the Hassell model, and others. Some of these models until now lacked a good mechanistic interpretation or have been derived in a different context. By using this mechanistic approach, the model parameters can be interpreted in terms of individual behavior.

Animals↗

PubMed2006

A new mechanistic Parachor model to predict dynamic interfacial tension and miscibility in multicomponent hydrocarbon systems.

Widely used traditional Parachor model fails to provide reliable interfacial tension predictions in multicomponent hydrocarbon systems due to the inability of this model to account for mass transfer effects between the fluid phases. In this paper, we therefore proposed a new mass transfer enhanced mechanistic Parachor model to predict interfacial tension and to identify the governing mass transfer mechanism responsible for attaining the thermodynamic fluid phase equilibria in multicomponent hydrocarbon systems. The proposed model has been evaluated against experimental data for two gas-oil systems of Rainbow Keg River and Terra Nova reservoirs. The results from the proposed model indicated good IFT predictions and that the vaporization of light hydrocarbon components from crude oil to gas phase is the governing mass transfer mechanism for the attainment of fluid phase equilibria in both the gas-oil systems used. A multiple linear regression model has also been developed for a priori prediction of exponent in the mechanistic model by using only the reservoir fluid compositions, without the need for experimental measurements. The dynamic nature of interfacial tensions observed in the experiments justifies the use of diffusivities in the mechanistic model, thus enabling the proposed model predictions to determine dynamic gas-oil miscibility conditions in multicomponent hydrocarbon systems.

Journal Article↗

PubMed2005

Molecular responses to xenoestrogens: mechanistic insights from toxicogenomics.

The xenoestrogen group of endocrine disruptors has the potential to cause reproductive and developmental effects through stimulation or disruption of sex steroid nuclear receptor signalling pathways. A more detailed understanding of the ways in which xenoestrogens interact with biological systems at the molecular level will provide a mechanistic basis for improved safety assessment. The recent sequencing of mammalian genomes has driven the development of toxicogenomic technologies, including microarray based gene expression profiling, which allow the expression levels of thousands of genes to be measured simultaneously. Since the cellular responses to xenoestrogens are predominantly mediated by estrogen receptors, which function as ligand-activated transcription factors to regulate gene expression, the application of toxicogenomics has great potential for providing insights into the molecular mechanisms of xenoestrogen action. A major challenge in applying toxicogenomics to the field of endocrine disruption is the need to define how xenoestrogen-induced changes in gene expression relate to conventional physiological and toxicological endpoints. Gene Ontology Mapping, Pathway Mapping and Phenotypic Anchoring of xenoestrogen-induced gene expression changes to cellular pathways and processes represent key steps in defining these relationships. Mechanistic insights into how xenoestrogens target specific genes and into the functional significance of xenoestrogen-induced alterations in gene expression can be further enhanced by combining transcript profiling with transgenic animal models or cell-based systems in which the estrogen receptor signalling pathways have been modified experimentally. This review illustrates how these toxicogenomic approaches are providing an unprecedented amount of mechanistic information on the molecular responses to xenoestrogens and how they are likely to impact on hazard and risk assessment.

Animals↗

PubMed1996

Structure-activity and mechanistic relationships: the effect of chemical overlap on structural overlap in data bases of varying size and composition.

Similarities between structures in SAR models derived from different data bases, termed 'structural overlap', can be used for determining mechanistic similarities, e.g., mutagenicity vs. carcinogenicity. However, the structural overlap may be affected by the proportion of common chemicals in the data bases, termed 'chemical overlap'. In order to refine our ability to determine mechanistic similarities, we investigated the relationship between chemical and structural overlap as well as the effect of the ratio of active to inactive chemicals and data base size on that relationship, using a data base of Salmonella mutagenicity. A linear relationship between chemical overlap and structural overlap with a slope of 0.332 was found. For data bases of 210, 300 and 390 chemicals, this relationship was consistent. Differences in ratios of active to inactive chemicals, i.e., 1:2, 1:1 and 2:1, did not appear to affect the linear model. We can use this relationship to adjust for chemical overlap when examining the structural overlap of data bases developed for different endpoints to determine the extent of mechanistic similarities.

Databases, Factual↗

PubMed2003

Mechanistic and empirical modeling of skin permeation of drugs.

The skin forms a barrier to the external environment, maintaining body fluids within our system and excluding harmful substances, while the skin is a site of administration of drugs for topical and systemic chemotherapy. It is an important issue to predict the rate at which drugs or other xenobiotics penetrate the skin. In this article, we review modeling approaches for predicting skin permeation of compounds, including both mechanistic and empirical approaches. Mechanistic approaches can give us much information on understanding of skin permeation of the compounds, such as structure-permeability relationship, contribution of each barrier step, mechanism of penetration enhancers, and in vivo-in vitro relationship. On the other hand, empirical modeling can overcome any inaccuracies of mechanistic models caused by the existence of uncertainties and, therefore, give us better predictions from the practical point of view. Artificial neural networks are being available for empirical modeling of complex skin transport phenomenon.

Administration, Cutaneous↗

PubMed2002

Incorporating mechanistic data into risk assessment.

Incorporating mechanistic data into risk assessment is intended to improve the overall scientific validity, and thereby reduce the uncertainty, in human risk analysis. Relevant mechanistic data provides a link between the molecular and cellular event(s) and adverse outcome, and insight into mechanism of action is critical for accurate evaluation of dose-response relationships and inter-species extrapolation. This paper provides an example of applying mechanistic data to human risk assessment using the frameworks outlined by the International Programme on Chemical Safety (IPCS) and the U.S. Environmental Protection Agency (USEPA). Key components of the data are used to address the strength and consistency of the mechanism and to apply these data to human risk characterization.

Animals↗

PubMed2006

Mechanistic investigations of the pseudouridine synthase RluA using RNA containing 5-fluorouridine.

The pseuoduridine synthases (psi synthases) isomerize uridine (U) to pseudouridine (psi) in RNA, and they fall into five families that share very limited sequence similarity but have the same overall fold and active-site architecture, including an essential Asp. The mechanism by which the psi synthases operate remains unknown, and mechanistic work has largely made use of RNA containing 5-fluorouridine (f5U) in place of U. The psi synthase TruA forms a covalent adduct with such RNA, and heat disruption of the adduct generates a hydrated product of f5U, which was reasonably concluded to result from the hydrolysis of an ester linkage between the essential Asp and f5U. In contrast, the psi synthase TruB, which is a member of a different family, does not form an adduct with f5U in RNA but catalyzes the rearrangement and hydration of the f5U, which labeling studies with [18O]water showed does not result from ester hydrolysis. To extend the line of mechanistic investigation to another family of psi synthases and an enzyme that makes an adduct with f5U in RNA, the behavior of RluA toward RNA containing f5U was examined. Stem-loop RNAs are shown to be good substrates for RluA. Heat denaturation of the adduct between RluA and RNA containing f5U produces a hydrated nucleoside product, and labeling studies show that hydration does not occur by ester hydrolysis. These results are interpreted in light of a consistent mechanistic scheme for the handling of f5U by psi synthases.

Escherichia coli↗

PubMed2002

Mechanistic information on the reversible binding of NO to selected iron(II) chelates from activation parameters.

Mechanistic insight on the reversible binding of NO to Fe(II) chelate complexes as potential catalysts for the removal of NO from effluent gas streams has been obtained from the temperature and pressure parameters for the "on" and "off" reactions determined using a combination of flash photolysis and stopped-flow techniques. These parameters are correlated with those for water exchange reactions on the corresponding Fe(II) and Fe(III) chelate complexes, from which mechanistic conclusions are drawn. Small and positive Delta V(++) values are found for NO binding to and release from all the selected complexes, consistent with a dissociative interchange (I(d)) mechanism. The only exception in the series of studied complexes is the binding of NO to [Fe(II)(nta)(H(2)O)(2)](-). The negative volume of activation observed for this reaction supports the operation of an I(a) ligand substitution mechanism. The apparent mechanistic differences can be accounted for in terms of the electronic and structural features of the studied complexes. The results indicate that the aminocarboxylate chelates affect the rate and overall equilibrium constants, as well as the nature of the substitution mechanism by which NO coordinates to the selected complexes. There is, however, no simple correlation between the rate and activation parameters and the selected donor groups or overall charge on the iron(II) complexes.

Journal Article↗