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Results for “In silico modeling”

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At least 19 recordsLinked to original sources

PubMed2003

Genome-scale microbial in silico models: the constraints-based approach.

Genome sequencing and annotation has enabled the reconstruction of genome-scale metabolic networks. The phenotypic functions that these networks allow for can be defined and studied using constraints-based models and in silico simulation. Several useful predictions have been obtained from such in silico models, including substrate preference, consequences of gene deletions, optimal growth patterns, outcomes of adaptive evolution and shifts in expression profiles. The success rate of these predictions is typically in the order of 70-90% depending on the organism studied and the type of prediction being made. These results are useful as a basis for iterative model building and for several practical applications.

Animals↗

PubMed2005

Benzotropolone inhibitors of estradiol methylation: kinetics and in silico modeling studies.

Natural and synthetic benzotropolone compounds were assessed in vitro for their ability to inhibit hydroxyestradiol methylation by catechol-O-methyltransferase (COMT). The compounds were also modeled in silico with a homology model of human COMT. Purpurogallin (1), purpurogallin carboxylic acid (2), and theaflavin-3,3'-digallate (6) were the most potent inhibitors of 2-hydroxy and 4-hydroxyestradiol methylation (IC(50) 0.22-0.50microM). Compounds 1 and 6 decreased the V(max) and increased the K(m) of COMT, indicating a mixed-type inhibition. Compounds 1 and 2 bound to COMT by inserting the six-membered ring of the benzotropolone into the active site. Decreased acidity of the hydroxyl groups on this ring or increased bulkiness reduced potency. Compound 6 bound by inserting the galloyl ester into the active site, which allowed the compound to overcome increased bulkiness and resulted in restored potency. Further studies are needed to determine the impact in vivo of COMT inhibition by these compounds.

Catechol O-Methyltransferase Inhibitors↗

PubMed2004

Patterns of retinal light absorption related to retinitis pigmentosa mutants from in silico model structures of rhodopsin.

Changes induced by mutations in rhodopsin that are associated with the degenerative visual disease retinitis pigmentosa result in an altered pattern of light absorption according to quantum mechanical simulations and reference experimental works. Eleven single-point mutations associated with retinitis pigmentosa at and in the proximity to the retinal binding pocket of rhodopsin have been modeled in silico and their spectra calculated with the NDOL (Neglect of Differential Overlap accounting L azimuthal quantum number) a priori method. The altered pattern of absorption found would lead to cumulative consequences in energy dissipation with aging. Different energy balances in the case of mutants at the very molecular level, compared to native nonmutated rhodopsin, can cause permanent cellular stress and would play a role in the progression of the retine degenerative process. It could explain the worsening of the pathological condition mostly in adults and suggests the probable beneficial effects of using quenching drugs and protection devices against excess of light in the early stages of life for avoiding or reducing potential damage.

Absorption↗

PubMed2005

[High throughput screening of pharmacokinetics and metabolism in drug discovery (III)--investigation on in- silico model for membrane permeability and CYP1A2 inhibition].

Pharmacokinetic and metabolic screening plays an important role in the optimization of a lead compound in drug discovery. Since those screening methods are time-consuming and labor intensive, in silico models would be effective to select compounds and guide derivatization prior to the screening. We investigated in silico models for permeability in Caco-2 cells, brain distribution and cytochrome P450 (CYP) inhibition using molecular weight, lipophilicity (clog D(7.4)), polar surface area (PSA), and number of rotatable bonds (RB). A variety of test compounds was selected from different Caco-2 assay projects. The permeability determined exhibited a good correlation with a combination of PSA and clog D(7.4) rather than with PSA alone. In the brain distribution, PSA, in addition to lipophilicity, was one of the determinant parameters, and compounds were significantly distributed to the brain in rats with the decrease in the PSA value. When this approach was adapted to CYP1A2 inhibition in the fluorometric assay, the inhibitory potential for two plane core structures was successfully predicted by utilizing number of RB, PSA, and clog D(7.4). In particular, an increase in the number of RB weakened the inhibitory potential due to a loss of the plane structures. These results suggest that the PSA and RB are key parameters to design chemical structures in terms of the improvement of both membrane permeability in the brain and gastrointestine and CYP1A2 inhibition, respectively.

Animals↗

PubMed2006

Structure of the oxidized active site of galactose oxidase from realistic in silico models.

A systematic in silico approach is employed to generate an accurate model for the catalytically important oxidized state of galactose oxidase (GO) using spectroscopically calibrated hybrid density-functional theory. GO displays three distinct oxidation states: oxidized [Cu(II)-Y*], semireduced [Cu(II)-Y], and fully reduced [Cu(I)-Y], but only the [Cu(II)-Y*] and the [Cu(I)-Y] states are assumed to be involved in catalysis. We have developed multiple models for the oxidized [Cu(II)-Y*] state, whose structure has not yet been fully characterized. These models were evaluated by comparison of calculated and experimental structural data, singlet-triplet energy gaps, and electronic transitions for the antiferromagnetically coupled oxidized [Cu(II)-Y*] state. An extended model system that includes explicit solvent molecules and second coordination sphere residues (R330, Y405, and W290) is essential to obtain the correct electronic structure of the active site. The model with all the residues that have been shown to affect the radical stability and catalysis resulted in a singlet ground state with the radical centered on the Y272-C228 cofactor. The optimized structure of the oxidized GO [Cu(II)-Y*] reveals a five-coordinated square pyramidal coordination geometry very similar to [Cu(II)-Y] with considerably different Cu-ligand distances. The hydrogen-bonding interactions involving Y495 modulates the spin density distribution and the singlet-triplet energy gaps. The final model as the most reasonable structure of the oxidized [Cu(II)-Y*] state in GO reproduces the spectroscopic signature of oxidized GO.

Binding Sites↗

PubMed2007

3D in silico modeling of the human respiratory system for inhaled drug delivery and imaging analysis.

The efficacies of inhaled pharmacologic drugs could be improved if drugs could be targeted to appropriate sites within the human respiratory system. The spatial deposition patterns of particles can now be detected with a high degree of resolution using advanced techniques of imaging (e.g., SPECT). However, the effectiveness of such laboratory regimens has been limited by the inability to clearly identify airway composition within images. Therefore, we have developed a theoretical protocol to map airways within human lungs that is designed to be used in a complementary manner with laboratory investigations. The in silico model has two components: a mathematical model based on concepts of topology; and, a computer algorithm which tracks the millions of constituent lung airways. The in silico model produces 3D lung structures that are anatomically correct and can be customized to each patient. We have applied the protocol to a SPECT study where the interiors of lungs were partitioned into a series of ten nested shells. Airway composition in the respective shells provides a heretofore unavailable quantification of scintigraphy images. The protocol can be employed in a practical manner in the medical arena to aid in the interpretation of SPECT images, and to provide a platform for the design of human subject tests.

Administration, Inhalation↗

PubMed2003

In silico modeling of asthma.

The incidence of asthma is increasing throughout the world, especially among children, to the extent that it has become a medical issue of serious global concern. Appropriately, numerous pharmacologic drugs and clinical protocols for the treatment and prophylaxis of the disease have been reported. From a scientific perspective, a review of the literature suggests that the targeted delivery of an aerosol would, in a real sense, enhance the efficacy of an inhaled medicine. Therefore, in accordance with published data we have developed a mathematical description of disease-induced effects of disease on airway morphology. A morphological algorithm defining the heterogeneity of asthma has been integrated with a computer code that formulates the behavior and fate of inhaled drugs. In this work, predicted drug particle deposition patterns have been compared with SPECT images from experiments with healthy human subjects (controls) and asthmatic patients. The asthma drug delivery model simulations agree with observations from human testing. The results indicate that in silico modeling provides a technical foundation for addressing effects of disease on the administration of aerosolized drugs, and suggest that modeling should be used in a complementary manner with future inhalation therapy protocols.

Administration, Inhalation↗

PubMed2004

Control of state transitions in an in silico model of epilepsy using small perturbations.

We propose the use of artificial neural networks in an in silico epilepsy model of biological neural networks: 1) to predict the onset of state transitions from higher complexities, possibly chaotic to lower complexity possibly rhythmic activities; and 2) to restore the original higher complexity activity. A coupled nonlinear oscillators model (Bardakjian and Diamant, 1994) was used to represent the spontaneous seizure-like oscillations of CA3 hippocampal neurons (Bardakjian and Aschebrenner-Scheibe, 1995) to illustrate the prediction and control schemes of these state transition onsets. Our prediction scheme consists of a recurrent neural network having Gaussian nonlinearities. When the onset of lower complexity activity is predicted in the in silico model, then our control scheme consists of applying a small perturbation to a system variable (i.e., the transmembrane voltage) when it is sufficiently close to the unstable higher complexity manifold. The system state can be restored back to its higher complexity mode utilizing the forces of the system's vector field.

Action Potentials↗

PubMed2004

Predicting human drug glucuronidation parameters: application of in vitro and in silico modeling approaches.

Cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT), which both exist as enzyme "superfamilies," are together responsible for the metabolism of most hepatically cleared drugs. There is currently intense interest in the development of techniques that permit identification of the CYP and UGT isoform(s) involved in the metabolism of a newly discovered drug, and hence prediction of factors likely to alter elimination in vivo. In addition, the quantitative scaling of kinetic parameters for a metabolic pathway assumes importance for identifying newly discovered drugs with undesirable in vivo pharmacokinetic properties. Although qualitative and quantitative in vitro-in vivo correlation based on data generated using human liver tissue or recombinant enzymes have been applied successfully to many drugs eliminated by CYP, these strategies have proved less definitive for glucuronidated compounds. Computational (in silico) modeling techniques that potentially provide a facile and economic alternative to the in vitro methods are now emerging. This review assesses the utility of in vitro and in silico approaches for the qualitative and quantitative prediction of drug glucuronidation parameters and the challenges facing the development of generalizable models.

Cytochrome P-450 Enzyme System↗

PubMed2003

In silico models for the prediction of dose-dependent human hepatotoxicity.

The liver is extremely vulnerable to the effects of xenobiotics due to its critical role in metabolism. Drug-induced hepatotoxicity may involve any number of different liver injuries, some of which lead to organ failure and, ultimately, patient death. Understandably, liver toxicity is one of the most important dose-limiting considerations in the drug development cycle, yet there remains a serious shortage of methods to predict hepatotoxicity from chemical structure. We discuss our latest findings in this area and present a new, fully general in silico model which is able to predict the occurrence of dose-dependent human hepatotoxicity with greater than 80% accuracy. Utilizing an ensemble recursive partitioning approach, the model classifies compounds as toxic or non-toxic and provides a confidence level to indicate which predictions are most likely to be correct. Only 2D structural information is required and predictions can be made quite rapidly, so this approach is entirely appropriate for data mining applications and for profiling large synthetic and/or virtual libraries.

Computer Simulation↗

PubMed2006

Concepts for developing a collaborative in silico model of the acute inflammatory response using agent-based modeling.

The complexity of the acute inflammatory response (AIR) is, by now, generally recognized. The primary manifestation of this property has been the difficulty in translating the information derived from reductionist, basic science research into effective clinical treatment regimens for sepsis. However, the recognition of the "complexity" of the AIR is not without its pitfalls. Despite its limitations, reductionism remains the primary means of obtaining scientific information. Furthermore, a functional shortcoming of use of the term complex has been to make it equivalent to "essentially unsolvable." Therefore, a mechanism is needed to integrate the apparatus of reductionist analysis into a complex synthetic methodology that overcomes the current limitations of both. Toward this end, I propose a structure for a class of collaborative, community-wide in silico models that use the framework of agent-based modeling. Agent-based modeling is a type of mathematical modeling that focuses on the behaviors of the components of complex systems and is well suited for translating the results of basic science experiments. I will also introduce a preliminary version of a syntactical "grammar" that can potentially be used to facilitate the transfer of basic science data into computer code. It is hoped that when a mature version of this framework is implemented, the resulting models will provide a functional, synthetic data base on the AIR that could be used for directing research, testing hypotheses, teaching and training, and drug discovery/testing.

Acute Disease↗

PubMed2025

In vivo and in silico models of Drosophila for Parkinson's disease.

The fruit fly Drosophila melanogaster has emerged as an important model organism to shed light on neurodegeneration. Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder, the cause of which is still mostly unclear. The long-term use of available PD drugs may have major side effects, and they only target the symptoms without providing any effective cure for the disease. Therefore, in vivo and in silico approaches are extensively used to model PD-like phenotypes in Drosophila and investigate cellular alterations underlying PD pathogenesis. In vivo models are particularly crucial to provide insight into the PD-related molecular processes. It has been a preferred approach to investigate these models by collecting omics datasets, which can be further analysed using in silico modeling such as genome-scale metabolic models and artificial intelligence applications. This review aims to summarise in vivo and in silico modeling studies in the literature to illustrate the potential of the Drosophila in the characterisation of PD-related biological mechanisms towards providing early biomarkers and novel treatment options for PD.

Humans↗

PubMed2007

An in silico model for HIF-alpha regulation and hypoxia response in tumor cells.

The dependency of the growth and metastasis of tumors on the new blood vessel formation, or angiogenesis, has opened up new potentials to tumor therapy, nevertheless understanding the molecular mechanisms involved in angiogenesis is crucial in the bioengineering of novel anti-angiogenic drugs. The key component in hypoxia sensing in tumor cells is the hypoxia-inducible factor, HIF-1alpha, which is inactivated through proteosome-mediated degradation under normoxic conditions. Two enzymes have been reported to hydroxylate HIF-1alpha, namely prolyl hydroxylase (PH), recruiting the proetasome complex and degrading cytoplasmic HIF-1alpha, and asparaginyl hydroxylase/factor inhibiting HIF-1alpha (FIH-1), downregulating the recruitment of p300 to the promoter, thereby reducing the transcriptional activity of HIF-1alpha. In this study, we have constructed an in silico model of a tumor cell using the GEPASI 3.30 biochemical simulation software (http://www.gepasi.org) and studied the performances of PH and FIH-1 on HIF-1alpha degradation and inactivation, respectively, as monitored by expression of the vascular endothelial growth factor, VEGF, during hypoxia. In our biochemical models, FIH-1 can successfully increase hypoxic transcription of VEGF, however FIH-1 on its own is not sufficient to inactivate HIF-1 completely, leading to background VEGF transcription under normoxic conditions. On the other hand, PH is necessary to increase the hypoxic transcriptional response, and can effectively shut off normoxic transcription. We therefore propose that regulating PH activity can be a primary target for anti-angiogenic bioengineering research.

Computational Biology↗

PubMed2004

The PDB-Preview database: a repository of in-silico models of 'on-hold' PDB entries.

UNLABELLED: The PDB-Preview database is a dynamic web repository of in-silico predicted three-dimensional (3D) models of experimentally determined structures that are deposited into the PDB but are not yet publicly released, and are kept 'on-hold'. The PDB-Preview database is automatically generated on a weekly basis by the bioinfo.pl meta-server, which uses top-of-the-line fold-recognition methods. The PDB-Preview provides biologists with preliminary fold assignments well before the experimentally determined 3D structures are released. AVAILABILITY: http://bioinfo.pl/PDB-Preview/.

Computer Simulation↗

PubMed2005

In silico modeling and simulation of bone biology: a proposal.

Contemporary, computer-based mathematical modeling techniques make it possible to represent complex biological mechanisms in a manner that permits hypothesis testing in silico. This perspective shows how such approaches might be applied to bone remodeling and therapeutic research. Currently, the dominant conceptual model applied in bone research involves the dynamic balance between the continual build-up and breakdown of bone matrix by two cell types, the osteoblasts and osteoclasts, acting together as a coordinated, remodeling unit. This conceptualization has served extraordinarily well as a focal point for understanding how mutations, chemical mediators, and mechanical force, as well as external influences (e.g., drugs, diet) affect bone structure and function. However, the need remains to better understand and predict the consequences of manipulating any single factor, or combination of factors, within the context of this complex system's multiple interacting pathways. Mathematical models are a natural extension of conceptual models, providing dynamic, quantitative descriptions of the relationships among interacting components. This formalization creates the ability to simulate the natural behavior of a system, as well as its modulation by therapeutic or dietetic interventions. A number of mathematical models have been developed to study complex bone functions, but most include only a limited set of biological components needed to address a few specific questions. However, it is possible to develop larger, multiscale models that capture the dynamic interactions of many biological components and relate them to important physiological or pathological outcomes that allow broader study. Examples of such models include entelos' physiolab platforms. These models simulate the dynamic, quantitative interactions among a biological system's biochemicals, cells, tissues, and organs and how they give rise to key physiologic and pathophysiologic outcomes. We propose that a similar predictive, dynamical, multiscale mathematical model of bone remodeling and metabolism would provide a better understanding of the mechanisms governing these phenomena as well as serve as an in silico platform for testing pharmaceutical and clinical interventions on metabolic bone disease.

Bone Diseases↗

PubMedDate not supplied

In silico modelling of ADMET-a minireview of work from 2000 to 2004.

This article represents a minireview of work published so far in the 21st century in the in silico ADMET field of research related to investigations in the areas of solubility, hERG and cytochrome P450 3A4. Various approaches including 2D- and 3D-QSARs and pharmacophore modelling are discussed. The pros and cons of the methods used and models derived are examined. More general remarks on model development and validation are also reported.

Computer Simulation↗

PubMed2006

In silico modelling of the interaction of flavonoids with human P-glycoprotein nucleotide-binding domain.

A three-dimensional model of human ABCB1 nucleotide-binding domain (NBD) was developed by homology modelling using the high-resolution human TAP1 transporter structure as template. Interactions between NBD and flavonoids were investigated using in silico docking studies. Ring-A of unmodified flavonoid was located within the NBD P-loop with the 5-hydroxyl group involved in hydrogen bonding with Lys1076. Ring-B was stabilised by hydrophobic stacking interactions with Tyr1044. The 3-hydroxyl group and carbonyl oxygen were extensively involved in hydrogen bonding interactions with amino acids within the NBD. Addition of prenyl, benzyl or geranyl moieties to ring-A (position-6) and hydrocarbon substituents (O-n-butyl to O-n-decyl) to ring-B (position-4) resulted in a size-dependent decrease in predicted docking energy which reflected the increased binding affinities reported in vitro.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2003

ADMET in silico modelling: towards prediction paradise?

Following studies in the late 1990s that indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development, it has become widely appreciated that these areas should be considered as early as possible in the drug discovery process. However, in recent years, combinatorial chemistry and high-throughput screening have significantly increased the number of compounds for which early data on absorption, distribution, metabolism, excretion (ADME) and toxicity (T) are needed, which has in turn driven the development of a variety of medium and high-throughput in vitro ADMET screens. Here, we describe how in silico approaches will further increase our ability to predict and model the most relevant pharmacokinetic, metabolic and toxicity endpoints, thereby accelerating the drug discovery process.

Animals↗