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At least 19 recordsLinked to original sources
PubMed2003

Role of ADME characteristics in drug discovery and their in silico evaluation: in silico screening of chemicals for their metabolic stability.

Drug discovery is a long, arduous process broadly grouped into disease target identification, target validation, high-throughput identification of "hits" and "leads", lead optimization, and pre-clinical and clinical evaluation. Each area is a vast discipline in itself. However, all but the first two stages involve, to varying degrees, the characterization of absorption, distribution, metabolism, excretion, (ADME), and toxicity (T) of the molecules being pursued as potential drug candidates. Clinical failures of about 50% of the Investigational New Drug (IND) filings are attributed to their inadequate ADMET attributes. It is, therefore, no surprise that, in the current climate of social and regulatory pressure on healthcare costs, the pharmaceutical industry is searching for any means to minimize this attrition. Building mathematical models, called in silico screens, to reliably predict ADMET attributes solely from molecular structure is at the heart of this effort in reducing costs as well as development cycle times. This article reviews the emerging field of in silico evaluation of ADME characteristics. For different approaches that have been employed in this area, a critique of the scope and limitations of their descriptors, statistical methods, and reliability are presented. For instance, are geometry-based descriptors absolutely essential or is lower-level structure quantification equally good? What advantages, if any, do we have for methods like artificial neural networks over the least squares optimization methods with rigorous statistical diagnostics? Is any in silico screen worth application, let alone interpretation, if it is not adequately validated? Once deemed acceptable, what good is an in silico screen if it cannot be made available at the workbench of drug discovery teams distributed across the globe throughout multi-national pharmaceutical companies? These are not mere discussion points, rather this article embarks on the stepwise mechanics of developing a successful in silico screen. The process is exemplified by our efforts in developing one such screen for predicting metabolic stability of chemicals in a human S9 liver homogenate assay. A real-life use of this in silico screen in a variety of discovery projects at GlaxoSmithKline is presented, highlighting successes and limitations of such applications. Finally, we project some capabilities of in silico ADME tools for greater impact and contribution to successful, efficient drug discovery.

Computer-Aided Design↗
PubMedDate not supplied

Noise reduction method for molecular interaction energy: application to in silico drug screening and in silico target protein screening.

We developed a new method to improve the accuracy of molecular interaction data using a molecular interaction matrix. This method was applied to enhance the database enrichment of in silico drug screening and in silico target protein screening using a protein-compound affinity matrix calculated by a protein-compound docking software. Our assumption was that the protein-compound binding free energy of a compound could be improved by a linear combination of its docking scores with many different proteins. We proposed two approaches to determine the coefficients of the linear combination. The first approach is based on similarity among the proteins, and the second is a machine-learning approach based on the known active compounds. These methods were applied to in silico screening of the active compounds of several target proteins and in silico target protein screening.

Pharmaceutical Preparations↗
PubMed2002

In Silico and Ex silico ADME approaches for drug discovery.

The high attrition rate of drug candidates during clinical trials for poor pharmacokinetic and metabolic properties has created a need to do these studies as early as it is possible during the drug discovery process. In addition the most successful drug is often not the most potent one but the one that has the suitable level of potency, safety, and pharmacokinetics. Science and technology development during the last few years and the generation of last databases and information has created the basis for doing early experimental PK and ADME studies in addition to eADME. Similarly, testing safety features as early as possible is key to affordable drug discovery and development. Throughput and cost are crucial for early application. In silico methods have by far the highest throughput, followed by the in vitro and in vivo approaches. On the other hand, with regard to relevance and reliability of data the ranking is the opposite. The great challenge for in silico methods is generation of models that correlate more closely with in vivo systems. For the in vitro assays increasing the throughput is an absolute must. Ex silico methods that combine in silico predictions with experimental methods are new additions to the scientific repertoire (e.g. Chromatographic Hydrophobicity Index that is deduced from the reverse phase HPLC data can be used for calculation of lipophilicity). The emerging new approaches have clear impact on the design of early stage screening and combinatorial libraries. In addition to the Lipinski's rules descriptors such as number of rotatable bonds, number of aromatic rings, branching behavior and polar surface area (PSA) are commonly used is the drug design process.

Absorption↗
PubMedDate not supplied

Neighborhood behavior of in silico structural spaces with respect to in vitro activity spaces--a benchmark for neighborhood behavior assessment of different in silico similarity metrics.

In a previous work, we have introduced Neighborhood Behavior (NB) criteria for calculated molecular similarity metrics, based on the analysis of in vitro activity spaces that simultaneously monitor the responses of a compound with respect to an entire panel of biologically relevant receptors and enzymes. Now, these novel NB criteria will be used as a benchmark for the comparison of different in silico molecular similarity metrics, addressing the following topics: (1) the relative performance of 2D vs 3D descriptors, (2) the importance of the similarity scoring function for a given descriptor set, and (3) binary or Fuzzy Pharmacophore Fingerprints-can they capture the similarity of the spatial distribution of pharmacophoric groups despite different molecular connectivity? It was found that fuzzy pharmacophore descriptors (FBPA) displayed an optimal NB and, unlike their binary counterparts, were successful in evidencing pharmacophore pattern similarity independently of topological similarity. Topological FBPA, identical to the former except for the use of topological instead of 3D atom pair distances, display a somehow weaker, but significant NB. Metrics based on "classical" global 2D and 3D molecular descriptors and a Dice scoring function also performed well. The choice of the similarity scoring function is therefore as important as the choice of the appropriate molecular descriptors.

Journal Article↗
PubMed2004

G protein-coupled receptors: in silico drug discovery in 3D.

The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the predict method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 "virtual hit" compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12-21% (full dose-response curves, Ki < 5 microM). In most cases, the best hit was a novel compound (New Chemical Entity) in the 1- to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery.

Algorithms↗
PubMed2003

Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae.

A large-scale in silico evaluation of gene deletions in Saccharomyces cerevisiae was conducted using a genome-scale reconstructed metabolic model. The effect of 599 single gene deletions on cell viability was simulated in silico and compared to published experimental results. In 526 cases (87.8%), the in silico results were in agreement with experimental observations when growth on synthetic complete medium was simulated. Viable phenotypes were correctly predicted in 89.4% (496 out of 555) and lethal phenotypes were correctly predicted in 68.2% (30 out of 44) of the cases considered. The in silico evaluation was solely based on the topological properties of the metabolic network which is based on well-established reaction stoichiometry. No interaction or regulatory information was accounted for in the in silico model. False predictions were analyzed on a case-by-case basis for four possible inadequacies of the in silico model: (1) incomplete media composition, (2) substitutable biomass components, (3) incomplete biochemical information, and (4) missing regulation. This analysis eliminated a number of false predictions and suggested a number of experimentally testable hypotheses. A genome-scale in silico model can thus be used to systematically reconcile existing data and fill in our knowledge gaps about an organism.

Computational Biology↗
PubMed2004

A quantitative in silico analysis calculates the angiotensin I converting enzyme (ACE) inhibitory activity in pea and whey protein digests.

Angiotensin I converting enzyme (ACE) inhibitory peptides can induce antihypertensive effects after oral administration. By means of an ACE inhibitory peptide database, containing about 500 reported sequences and their IC(50) values, the different proteins in pea and whey were quantitatively evaluated as precursors for ACE inhibitory peptides. This analysis was combined with experimental data from the evolution in ACE inhibitory activity and protein degradation during in vitro gastrointestinal digestion. Pea proteins produced similar in silico scores and were degraded early in the in vitro digestion. High ACE inhibitory activity was observed after the simulated stomach phase and augmented slightly in the simulated small intestine phase. The major whey protein beta-lactoglobulin obtained the highest in silico scores, which corresponded with the fact that degradation of this protein in vitro only occurred from the simulated small intestine phase on and resulted in a 10-fold increase in the ACE inhibitory activity. Whey protein obtained total in silico scores of about 124 ml/mg, compared to 46 ml/mg for pea protein, indicating that whey protein would be a richer source of ACE inhibitory peptides than pea protein. Although beta-lactoglobulin is only partially digested, a higher ACE inhibitory activity was indeed found in the whey (IC(50) = 0.048 mg/ml) compared to the pea digest (IC(50) = 0.076 mg/ml). In silico gastrointestinal digestion of the highest scoring proteins in pea and whey, vicilin and albumin PA2, and beta-lactoglobulin, respectively, directly released a number of potent ACE inhibitory peptides. Several other ACE inhibitory sequences resisted in silico digestion by gastrointestinal proteases. Briefly, the quantitative in silico analysis will facilitate the study of precursor proteins on a large scale and the specific release of bioactive peptides.

Amino Acid Sequence↗
PubMed1990

Strength of bond with Comspan Opaque to three silicoated alloys and titanium.

In Sweden high-gold alloys or cobalt-chromium alloys are used for resin-bonded prostheses. The bond strength between a resin cement and different sandblasted or silicoated metals were measured before and after thermocycling; in connection with this some rapid thermocycling methods were studied. The effect of different storage times and different protection coatings on bond strength were tested. Finally, the influence of rubbing and contamination with saliva on bond strength were investigated. Silicoating increased the bond strength significantly. The highest bond strengths were these of silicoated Wirobond and titanium, unsusceptible to thermal stress; the bond strengths of the sandblasted metals were the weakest, and sensitive to thermocycling as well. The influence on bond strength for silicoated gold alloys, protected with an unpolymerized composite resin coating, stored in sealed plastic bags up to 7 days, was negligible. Rubbing and contamination with saliva did not influence bond strength. Preferably, silicoated Wirobond and titanium should be used for resin-bonded prostheses, but gold alloys may still be adequate for clinical use. The experimental method described for storing, sealing, and cleaning the silicoated metal surfaces in this article can be recommended for laboratory and clinical use.

Composite Resins↗
PubMed2002

In silico metabolic pathway analysis and design: succinic acid production by metabolically engineered Escherichia coli as an example.

The intracellular metabolic fluxes can be calculated by metabolic flux analysis, which uses a stoichiometric model for the intracellular reactions along with mass balances around the intracellular metabolites. In this study, we have constructed in silico metabolic pathway network of Escherichia coli consisting of 301 reactions and 294 metabolites. Metabolic flux analyses were carried out to estimate flux distributions to achieve the maximum in silico yield of succinic acid in E. coli. The maximum in silico yield of succinic acid was only 83% of its theoretical yield. The lower in silico yield of succinic acid was found to be due to the insufficient reducing power, which could be increased to its theoretical yield by supplying more reducing power. Furthermore, the optimal metabolic pathways for the production of succinic acid could be proposed based on the results of metabolic flux analyses. In the case of succinic acid production, it was found that pyruvate carboxylation pathway should be used rather than phosphoenolpyruvate carboxylation pathway for its optimal production in E. coli. Then, the in silico optimal succinic acid pathway was compared with conventional succinic acid pathway through minimum set of wet experiments. The results of wet experiments indicate that the pathway predicted by in silico analysis is more efficient than conventional pathway.

Computational Biology↗
PubMed2005

Power of in silico QTL mapping from phenotypic, pedigree, and marker data in a hybrid breeding program.

Most quantitative trait locus (QTL) mapping studies in plants have used designed mapping populations. As an alternative to traditional QTL mapping, in silico mapping via a mixed-model approach simultaneously exploits phenotypic, genotypic, and pedigree data already available in breeding programs. The statistical power of this in silico mapping method, however, remains unknown. Our objective was to evaluate the power of in silico mapping via a mixed-model approach in hybrid crops. We used maize (Zea mays L.) as a model species to study, by computer simulation, the influence of number of QTLs (20 or 80), heritability (0.40 or 0.70), number of markers (200 or 400), and sample size (600 or 2,400 hybrids). We found that the average power to detect QTLs ranged from 0.11 to 0.59 for a significance level of alpha=0.01, and from 0.01 to 0.47 for alpha=0.0001. The false discovery rate ranged from 0.22 to 0.74 for alpha=0.01, and from 0.05 to 0.46 for alpha=0.0001. As with designed mapping experiments, a large sample size, high marker density, high heritability, and small number of QTLs led to the highest power for in silico mapping via a mixed-model approach. The power to detect QTLs with large effects was greater than the power to detect QTL with small effects. We conclude that gene discovery in hybrid crops can be initiated by in silico mapping. Finding an acceptable compromise, however, between the power to detect QTL and the proportion of false QTL would be necessary.

Breeding↗
PubMed2005

In silico assessment of chemical mutagenesis in comparison with results of Salmonella microsome assay on 909 chemicals.

Genotoxicity is one of the important endpoints for risk assessment of environmental chemicals. Many short-term assays to evaluate genotoxicity have been developed and some of them are being used routinely. Although these assays can generally be completed within a short period, their throughput is not sufficient to assess the huge number of chemicals, which exist in our living environment without information on their safety. We have evaluated three commercially available in silico systems, i.e., DEREK, MultiCASE, and ADMEWorks, to assess chemical genotoxicity. We applied these systems to the 703 chemicals that had been evaluated by the Salmonella/microsome assay from CGX database published by Kirkland et al. We also applied these systems to the 206 existing chemicals in Japan that were recently evaluated using the Salmonella/microsome assay under GLP compliance (ECJ database). Sensitivity (the proportion of the positive in Salmonella/microsome assay correctly identified by the in silico system), specificity (the proportion of the negative in Salmonella/microsome assay correctly identified) and concordance (the proportion of correct identifications of the positive and the negative in Salmonella/microsome assay) were increased when we combined the three in silico systems to make a final decision in mutagenicity, and accordingly we concluded that in silico evaluation could be optimized by combining the evaluations from different systems. We also investigated whether there was any correlation between the Salmonella/microsome assay result and the molecular weight of the chemicals: high molecular weight (>3000) chemicals tended to give negative results. We propose a decision tree to assess chemical genotoxicity using a combination of the three in silico systems after pre-selection according to their molecular weight.

Biological Assay↗
PubMed2006

An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression.

We report the discovery of a novel, potent, and selective amidosulfonamide nonazapirone 5-HT1A agonist for the treatment of anxiety and depression, which is now in Phase III clinical trials for generalized anxiety disorder (GAD). The discovery of 20m (PRX-00023), N-{3-[4-(4-cyclohexylmethanesulfonylaminobutyl)piperazin-1-yl]phenyl}acetamide, and its backup compounds, followed a new paradigm, driving the entire discovery process with in silico methods and seamlessly integrating computational chemistry with medicinal chemistry, which led to a very rapid discovery timeline. The program reached clinical trials within less than 2 years from initiation, spending less than 6 months in lead optimization with only 31 compounds synthesized. In this paper we detail the entire discovery process, which started with modeling the 3D structure of 5-HT1A using the PREDICT methodology, and then performing in silico screening on that structure leading to the discovery of a 1 nM lead compound (8). The lead compound was optimized following a strategy devised based on in silico 3D models and realized through an in silico-driven optimization process, rapidly overcoming selectivity issues (affinity to 5-HT1A vs alpha1-adrenergic receptor) and potential cardiovascular issues (hERG binding), leading to a clinical compound. Finally we report key in vivo preclinical and Phase I clinical data for 20m tolerability, pharmacokinetics, and pharmacodynamics and show that these favorable results are a direct outcome of the properties that were ascribed to the compound during the rational structure-based discovery process. We believe that this is one of the first examples for a Phase III drug candidate that was discovered and optimized, from start to finish, using in silico model-based methods as the primary tool.

Animals↗
PubMed2004

In silico mapping of quantitative trait loci in maize.

Quantitative trait loci (QTL) are most often detected through designed mapping experiments. An alternative approach is in silico mapping, whereby genes are detected using existing phenotypic and genomic databases. We explored the usefulness of in silico mapping via a mixed-model approach in maize (Zea mays L.). Specifically, our objective was to determine if the procedure gave results that were repeatable across populations. Multilocation data were obtained from the 1995-2002 hybrid testing program of Limagrain Genetics in Europe. Nine heterotic patterns comprised 22,774 single crosses. These single crosses were made from 1,266 inbreds that had data for 96 simple sequence repeat (SSR) markers. By a mixed-model approach, we estimated the general combining ability effects associated with marker alleles in each heterotic pattern. The numbers of marker loci with significant effects--37 for plant height, 24 for smut [Ustilago maydis (DC.) Cda.] resistance, and 44 for grain moisture--were consistent with previous results from designed mapping experiments. Each trait had many loci with small effects and few loci with large effects. For smut resistance, a marker in bin 8.05 on chromosome 8 had a significant effect in seven (out of a maximum of 18) instances. For this major QTL, the maximum effect of an allele substitution ranged from 5.4% to 41.9%, with an average of 22.0%. We conclude that in silico mapping via a mixed-model approach can detect associations that are repeatable across different populations. We speculate that in silico mapping will be more useful for gene discovery than for selection in plant breeding programs.

Analysis of Variance↗
PubMed2005

In-silico screening using flexible ligand binding pockets: a molecular dynamics-based approach.

In-silico screening of flexible ligands against flexible ligand binding pockets (LBP) is an emerging approach in structure-based drug discovery. Here, we describe a molecular dynamics (MD) based docking approach to investigate the influence on the high-throughput in-silico screening of small molecules against flexible ligand binding pockets. In our approach, an ensemble of 51 energetically favorable structures of the LBP of human estrogen receptor alpha (hERalpha) were collected from 3 ns MD simulations. In-silico screening of 3500 endocrine disrupting compounds against these flexible ligand binding pockets resulted in thousands of ER-ligand complexes of which 582 compounds were unique. Detailed analysis of MD generated structures showed that only 17 of the LBP residues significantly contribute to the overall binding pocket flexibility. Using the flexible LBP conformations generated, we have identified 32 compounds that bind better to the flexible ligand-binding pockets compared to the crystal structure. These compounds, though chemically divergent, are structurally similar to the natural hormone. Our MD-based approach in conjunction with grid-based distributed computing could be applied routinely for in-silico screening of large databases against any given target.

Amino Acid Sequence↗
PubMed1999

Effect of sandblasting and silicoating on bond strength of polymer-glass composite to cast titanium.

STATEMENT OF PROBLEM: There is little information regarding the mechanical and chemical retention of polymer-glass composite to cast titanium. PURPOSE: This study examined whether sandblasting in conjunction with silicoating improves the bond strength of the polymer-glass composite to cast titanium. MATERIALS AND METHODS: Disk patterns (10 mm in diameter, 2.5 mm thick) were cast with commercially pure titanium (CP Ti) and Type IV gold alloy. Three pretreatments were applied: 50 microm Al(2)O(3) sandblasting (50 SB), 250 microm Al(2)O(3) sandblasting (250 SB), and 600-grit SiC paper polishing (600 SiC). After surface preparation, the Siloc system (silicoating) was applied on the disks. The 50 SB specimens without Siloc system were also prepared as controls. Then sticky tape with a circular hole (4.76 mm diameter) was placed onto the disk to define the bonding area. Artglass (polymer-glass) opaque, dentin, and enamel composites were applied using Teflon matrices and then light-polymerized. Shear bond strength (n = 8) was determined at a crosshead speed of 5 mm/min. Results were analyzed statistically with 2-way ANOVA and the Tukey-Kramer test (alpha=.05). RESULTS: The Siloc system significantly (P <.05) improved the mean shear bond strength of Artglass to both metals in the 50 SB specimens. Statistical differences (P <.05) in shear bond strength were found among surface treatments for the silicoated CP Ti specimens, in which 250 SB specimens yielded the greatest bond strength. The Type IV specimens treated with Siloc system showed no significant differences in shear bond strength between the 50 SB and the 250 SB specimens. CONCLUSION: Sandblasting with coarser alumina particles in conjunction with silicoating significantly enhanced bond strength of polymer-glass composite to cast titanium.

Analysis of Variance↗
PubMed2002

Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.

Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.

Acetic Acid↗
PubMed2000

The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities.

The Escherichia coli MG1655 genome has been completely sequenced. The annotated sequence, biochemical information, and other information were used to reconstruct the E. coli metabolic map. The stoichiometric coefficients for each metabolic enzyme in the E. coli metabolic map were assembled to construct a genome-specific stoichiometric matrix. The E. coli stoichiometric matrix was used to define the system's characteristics and the capabilities of E. coli metabolism. The effects of gene deletions in the central metabolic pathways on the ability of the in silico metabolic network to support growth were assessed, and the in silico predictions were compared with experimental observations. It was shown that based on stoichiometric and capacity constraints the in silico analysis was able to qualitatively predict the growth potential of mutant strains in 86% of the cases examined. Herein, it is demonstrated that the synthesis of in silico metabolic genotypes based on genomic, biochemical, and strain-specific information is possible, and that systems analysis methods are available to analyze and interpret the metabolic phenotype.

Bacterial Proteins↗