Biomedical subjects

Sean Ekins

Publications and source records attributed to Sean Ekins.

At least 19 recordsLinked to original sources

PubMed2007

Pathway mapping tools for analysis of high content data.

The complexity of human biology requires a systems approach that uses computational approaches to integrate different data types. Systems biology encompasses the complete biological system of metabolic and signaling pathways, which can be assessed by measuring global gene expression, protein content, metabolic profiles, and individual genetic, clinical, and phenotypic data. High content screening assays can also be used to generate systems biology knowledge. In this review, we will summarize the pathway databases and describe biological network tools used predominantly with this genomics, proteomics, and metabolomics data but which are equally as applicable for high content screening data analysis. We describe in detail the integrated data-mining tools applicable to building biological networks developed by GeneGo, namely, MetaCore and MetaDrug.

Computational Biology↗

PubMed2006

Three-dimensional quantitative structure-activity relationship analysis of human CYP51 inhibitors.

CYP51 fulfills an essential requirement for all cells, by catalyzing three sequential mono-oxidations within the cholesterol biosynthesis cascade. Inhibition of fungal CYP51 is used as a therapy for treating fungal infections, whereas inhibition of human CYP51 has been considered as a pharmacological approach to treat dyslipidemia and some forms of cancer. To predict the interaction of inhibitors with the active site of human CYP51, a three-dimensional quantitative structure-activity relationship model was constructed. This pharmacophore model of the common structural features of CYP51 inhibitors was built using the program Catalyst from multiple inhibitors (n = 26) of recombinant human CYP51-mediated lanosterol 14alpha-demethylation. The pharmacophore, which consisted of one hydrophobe, one hydrogen bond acceptor, and two ring aromatic features, demonstrated a high correlation between observed and predicted IC(50) values (r = 0.92). Validation of this pharmacophore was performed by predicting the IC(50) of a test set of commercially available (n = 19) and CP-320626-related (n = 48) CYP51 inhibitors. Using predictions below 10 microM as a cutoff indicative of active inhibitors, 16 of 19 commercially available inhibitors (84%) and 38 of 48 CP-320626-related inhibitors (79.2%) were predicted correctly. To better understand how inhibitors fit into the enzyme, potent CYP51 inhibitors were used to build a Cerius(2) receptor surface model representing the volume of the active site. This study has demonstrated the potential for ligand-based computational pharmacophore modeling of human CYP51 and enables a high-throughput screening system for drug discovery and data base mining.

Amides↗

PubMed2006

Classification of metabolites with kernel-partial least squares (K-PLS).

Numerous experimental and computational approaches have been developed to predict human drug metabolism. Since databases of human drug metabolism information are widely available, these can be used to train computational algorithms and generate predictive approaches. In turn, they may be used to assist in the identification of possible metabolites from a large number of molecules in drug discovery based on molecular structure alone. In the current study we have used a commercially available database (MetaDrug) and extracted a fraction of the human drug metabolism data. These data were used along with augmented atom descriptors in a predictive machine learning model, kernel-partial least squares (K-PLS). A total of 317 molecules, including parent drugs and their primary and secondary (sequential) metabolites, were used to build these models corresponding to individual metabolism rules, representing the formation of discrete metabolites, e.g., N-dealkylation. Each model was internally validated to assess the capability to classify other molecules that were left out. Using receiver operator curve statistics models for N-dealkylation, O-dealkylation, aromatic hydroxylation, aliphatic hydroxylation, O-glucuronidation, and O-sulfation gave area under the curve values from 0.75 to 0.84 and were able to predict between 61 and 79% active molecules upon leave-one-out testing. This preliminary study indicates that K-PLS and possibly other similar machine learning methods (such as support vector machines) can be applied to predicting human drug metabolite formation in a classification manner. Improvements can be achieved using considerably larger datasets that contain more positive examples for the less frequently occurring metabolite rules, as well as the external evaluation of novel molecules.

Biotransformation↗

PubMed2006

Pharmacophore-based discovery of ligands for drug transporters.

The ability to identify ligands for drug transporters is an important step in drug discovery and development. It can both improve accurate profiling of lead pharmacokinetic properties and assist in the discovery of new chemical entities targeting transporters. In silico approaches, especially pharmacophore-based database screening methods have great potential in improving the throughput of current transporter ligand identification assays, leading to a higher hit rate by focusing in vitro testing to the most promising hits. In this review, the potential of different in silico methods in transporter ligand identification studies are compared and summarized with an emphasis on pharmacophore modeling. Various implementations of pharmacophore model generation, database compilation and flexible screening algorithms are also introduced. Recent successful utilization of database searching with pharmacophores to identify novel ligands for the pharmaceutically significant transporters hPepT1, P-gp, BCRP, MRP1 and DAT are reviewed and the challenges encountered with current approaches are discussed.

Algorithms↗

PubMed2006

Application of data mining approaches to drug delivery.

Computational approaches play a key role in all areas of the pharmaceutical industry from data mining, experimental and clinical data capture to pharmacoeconomics and adverse events monitoring. They will likely continue to be indispensable assets along with a growing library of software applications. This is primarily due to the increasingly massive amount of biology, chemistry and clinical data, which is now entering the public domain mainly as a result of NIH and commercially funded projects. We are therefore in need of new methods for mining this mountain of data in order to enable new hypothesis generation. The computational approaches include, but are not limited to, database compilation, quantitative structure activity relationships (QSAR), pharmacophores, network visualization models, decision trees, machine learning algorithms and multidimensional data visualization software that could be used to improve drug delivery after mining public and/or proprietary data. We will discuss some areas of unmet needs in the area of data mining for drug delivery that can be addressed with new software tools or databases of relevance to future pharmaceutical projects.

Computer Simulation↗

PubMed2006

Rapid identification of P-glycoprotein substrates and inhibitors.

Identifying molecules that interact with P-glycoprotein (P-gp) is important for drug discovery but is also generally reliant on time-consuming in vitro and in vivo studies. As an alternative approach, the current study applied pharmacophore models and database screening to rapidly retrieve molecules that bind as substrates or inhibitors for P-gp from commercial databases and then confirmed their affinity as inhibitors in vitro. Seven molecules (acitretin, cholecalciferol, misoprostol, nafcillin, repaglinide, salmeterol, and telmisartan) with no published details for P-gp affinity, one positive control inhibitor (miconazole), and two negative control molecules (phenelzine and zonisamide) were selected for testing. The MDCK-MDR1 in vitro cell model was used to confirm their inhibitory effect on [3H]digoxin transport, and the ATPase assay was used as an additional in vitro tool to indicate P-gp activation. All seven test drugs were confirmed to have P-gp affinity. Additionally, our experimental results provided plausible explanations for the published pharmacokinetic profiles of the tested drugs and their classification according to the biopharmaceutics and drug disposition classification system. In this study, we showed the successful application of pharmacophore models to accurately predict P-gp binding, which holds promise to anticipate drug-drug interactions from screening drug databases and a priori prediction of novel P-gp inhibitors or substrates.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2006

Insights for human ether-a-go-go-related gene potassium channel inhibition using recursive partitioning and Kohonen and Sammon mapping techniques.

The human ether-a-go-go related gene (hERG) can be inhibited by marketed drugs, and this inhibition may lead to QT prolongation and possibly fatal cardiac arrhythmia. We have collated literature data for 99 diverse hERG inhibitors to generate Kohonen maps, Sammon maps, and recursive partitioning models. Our aim was to investigate whether these computational models could be used either individually or together in a consensus approach to predict the binding of a prospectively selected test set of 35 diverse molecules and at the same time to offer further insights into hERG inhibition. The recursive partitioning model provided a quantitative prediction, which was markedly improved when Tanimoto similarity was included as a filter to remove molecules from the test set that were too dissimilar to the training set (r2 = 0.83, Spearman rho = 0.75, p = 0.0003 for the 18 remaining molecules, >0.77 similarity). This model was also used to screen and prioritize a database of drugs, recovering several hERG inhibitors not used in model building. The mapping approaches used molecular descriptors required for hERG inhibition that were not reported previously and in particular highlighted the importance of molecular shape. The Sammon map model provided the best qualitative classification of the test set (95% correct) compared with the Kohonen map model (81% correct), and this result was also superior to the consensus approach. This study illustrates that patch clamping data from various literature sources can be combined to generate valid models of hERG inhibition for prospective predictions.

Binding, Competitive↗

PubMed2006

Effects of antipsychotic drugs on I(to), I (Na), I (sus), I (K1), and hERG: QT prolongation, structure activity relationship, and network analysis.

PURPOSE: To evaluate in vitro and computationally model the effects of selected antipsychotic drugs on several ionic currents that contribute to changes in the action potential in cardiac tissue. METHODS: Fourteen antipsychotic drugs or metabolites were examined to determine whether QT interval prolongation could be accounted for by an effect on one or more myocardial ion channels [I(to), I(Na), I(sus), I(K1), and human ether-a-go-go related gene (hERG)]. Using the patch clamp technique, drug effects on these human cardiac currents were tested. RESULTS: All molecules had little inhibitory effect on ion channels (blocking at concentrations >5 microM) other than hERG. A significant correlation was observed between the estimated hERG blockade and the increase in corrected QT for five of the antipsychotics. Molecular modeling identified hydrophobic features related to the interaction with hERG and correctly rank-ordered the test set molecules olanzapine and its metabolites. A network analysis of ligand and protein interactions around hERG using MetaCore (GeneGo Inc., St. Joseph, MI, USA) was used to visualize antipsychotics with affinity for this channel and their interactions with other proteins in this database. CONCLUSION: The antipsychotics do not inhibit the ion channels I(to), I(Na), I(sus), I(K1) to any appreciable extent; however, blockade of hERG is a likely mechanism for the prolongation of the QT interval.

Action Potentials↗

PubMed2005

A combined approach to drug metabolism and toxicity assessment.

The challenge of predicting the metabolism or toxicity of a drug in humans has been approached using in vivo animal models, in vitro systems, high throughput genomics and proteomics methods, and, more recently, computational approaches. Understanding the complexity of biological systems requires a broader perspective rather than focusing on just one method in isolation for prediction. Multiple methods may therefore be necessary and combined for a more accurate prediction. In the field of drug metabolism and toxicology, we have seen the growth, in recent years, of computational quantitative structure-activity relationships (QSARs), as well as empirical data from microarrays. In the current study we have further developed a novel computational approach, MetaDrug, that 1) predicts metabolites for molecules based on their chemical structure, 2) predicts the activity of the original compound and its metabolites with various absorption, distribution, metabolism, excretion, and toxicity models, 3) incorporates the predictions with human cell signaling and metabolic pathways and networks, and 4) integrates networks and metabolites, with relevant toxicogenomic or other high throughput data. We have demonstrated the utility of such an approach using recently published data from in vitro metabolism and microarray studies for aprepitant, 2(S)-((3,5-bis(trifluoromethyl)benzyl)-oxy)-3(S)phenyl-4-((3-oxo-1,2,4-triazol-5-yl)methyl)morpholine (L-742694), trovofloxacin, 4-hydroxytamoxifen, and artemisinin and other artemisinin analogs to show the predicted interactions with cytochromes P450, pregnane X receptor, and P-glycoprotein, and the metabolites and the networks of genes that are affected. As a comparison, we used a second computational approach, MetaCore, to generate statistically significant gene networks with the available expression data. These case studies demonstrate the combination of QSARs and systems biology methods.

Drug-Related Side Effects and Adverse Reactions↗

PubMed2005

Reengineering the pharmaceutical industry by crash-testing molecules.

The recent decline in drug approvals and the increase in late-stage failures indicate that the ability to generate and screen large numbers of molecules has not improved the drug pipeline. Perhaps the pharmaceutical industry should follow the example of the automotive industry and agree upon a shared modeling language with vendors and academics to enable integration of predictive computational tools across the industry. This will then enable the virtual 'crash-testing' of drugs before synthesis, biological testing and, most importantly, clinical trials. This represents an ambitiously progressive approach using the models for simulating every stage of the drug discovery and development process. Combining the relevant computational algorithms into a grand unified model would enable prioritization of the best ideas before pursuing a discovery program, selecting a target or synthesizing a molecule. The successful application of these virtual crash-testing principles by any of its current proponents could revitalize the pharmaceutical industry so that failure is avoided.

Chemistry, Pharmaceutical↗

PubMed2005

A novel method for generation of signature networks as biomarkers from complex high throughput data.

Traditionally, gene signatures are statistically deduced from large gene expression and proteomics datasets and have been applied as an experimental molecular diagnostic technique that is sensitive to experimental design and statistical treatment. We have developed and applied the approach of "signature networks" which overcomes some of the drawbacks of clustering methods. We have demonstrated signature network assembly, functional analysis and logical operations on the networks that can be generated. In addition, we have used this technique in a proof of concept study to compare the effect of differential drug treatment using 4-hydroxytamoxifen and estrogen on the MCF-7 breast cancer cell line from a previously published study. We have shown that the two compounds can be differentiated by the networks of interacting genes. Both networks consist of a core module of genes including c-Fos as part of c-Fos/c-Jun heterodimer and c-Myc which is clearly visible. Using algorithms in our MetaCore software we are able to subtract the 4-hydroxytamoxifen and estrogen networks to further understand differences between these two treatments and show that the estrogen network is assembled around the core with other modules essential for all phases of the cell cycle. For example, Cyclin D1 is present in networks for the estrogen treated cells from two separate studies. These signature networks represent an approach to identify biomarkers and a general approach for discovering new relationships in complex high throughput toxicology data.

Algorithms↗

PubMed2005

Systems-ADME/Tox: resources and network approaches.

The increasing cost of drug development is partially due to our failure to identify undesirable compounds at an early enough stage of development. The application of higher throughput screening methods have resulted in the generation of very large datasets from cells in vitro or from in vivo experiments following the treatment with drugs or known toxins. In recent years the development of systems biology, databases and pathway software has enabled the analysis of the high-throughput data in the context of the whole cell. One of the latest technology paradigms to be applied alongside the existing in vitro and computational models for absorption, distribution, metabolism, excretion and toxicology (ADME/Tox) involves the integration of complex multidimensional datasets, termed toxicogenomics. The goal is to provide a more complete understanding of the effects a molecule might have on the entire biological system. However, due to the sheer complexity of this data it may be necessary to apply one or more different types of computational approaches that have as yet not been fully utilized in this field. The present review describes the data generated currently and introduces computational approaches as a component of ADME/Tox. These methods include network algorithms and manually curated databases of interactions that have been separately classified under systems biology methods. The integration of these disparate tools will result in systems-ADME/Tox and it is important to understand exactly what data resources and technologies are available and applicable. Examples of networks derived with important drug transporters and drug metabolizing enzymes are provided to demonstrate the network technologies.

ATP Binding Cassette Transporter 1↗

PubMed2005

Comparative pharmacophore modeling of organic anion transporting polypeptides: a meta-analysis of rat Oatp1a1 and human OATP1B1.

The organic anion transporting polypeptides OATPs are key membrane transporters for which crystal structures are not currently available. They transport a diverse array of xenobiotics and are expressed at the interface of hepatocytes, renal tubular cells, enterocytes, and the choroid plexus. To aid the understanding of the key molecular features for substrate-transporter interactions, pharmacophore models were produced for the two OATPs that have been most extensively studied, namely rat Oatp1a1 and human OATP1B1. Literature data from Chinese hamster ovary, HeLa, human embryonic kidney 293 cells, and Xenopus laevis oocytes were used to construct pharmacophores for each individual transporter which were later merged to show similarities across cell lines for the same transporter. Additionally, meta-pharmacophores were generated from the combined datasets of each cell system used with the same transporter. The pharmacophores for each transporter consisted of hydrogen bond acceptor and hydrophobic features. There was good agreement between the merged and meta-pharmacophores containing two hydrogen bond acceptors and two or three hydrophobic features for Oatp1a1 and OATP1B1. External test sets were used to validate the individual pharmacophores. The meta-pharmacophores were also used to make predictions for molecules not included in the models and provided new molecular insight into the key features for these OATP transporters. This approach can be extended to other transporters for which limited data are available.

Animals↗

PubMed2005

In vitro and pharmacophore-based discovery of novel hPEPT1 inhibitors.

PURPOSE: The human proton-coupled small peptide carrier (hPEPT1) is a low-affinity, high-capacity transporter with broad substrate specificity. We have taken an iterative in vitro and in silico approach to the discovery of molecules with hPEPT1 affinity. METHODS: A pharmacophore-based approach was taken to identifying hPEPT1 inhibitors. The well-characterized and relatively high affinity ligands Gly-Sar, bestatin, and enalapril were used to generate a common features (HIPHOP) pharmacophore. This consisted of two hydrophobic features, a hydrogen bond donor, acceptor, and a negative ionizable feature. RESULTS: The pharmacophore was used to search the Comprehensive Medicinal Chemistry (CMC) database of more than 8000 drug-like molecules and retrieved 145 virtual hits mapping to the pharmacophore features. The highest scoring compounds within this set were selected and tested in a stably transfected CHO-hPepT1 cell model. The antidiabetic repaglinide and HMG CoA reductase inhibitor fluvastatin were found to inhibit hPEPT1 with sub-millimolar potency (IC(50) 178 +/- 1.0 and 337 +/- 4 microM, respectively). The pharmacophore was also able to identify known hPEPT1 substrates and inhibitors in further database mining of more than 500 commonly prescribed drugs. CONCLUSIONS: This study demonstrates the potential of combining computational and in vitro approaches to determine the affinity of compounds for hPEPT1 and, in turn, provides insights into key molecular interactions with this transporter.

Animals↗

PubMed2005

Molecular determinants of substrate/inhibitor binding to the human and rabbit renal organic cation transporters hOCT2 and rbOCT2.

Organic cation transporters are important for the elimination of many drugs and toxins from the body. In the present study, substrate-transporter interactions were investigated in Chinese hamster ovary cells stably transfected with either the human or rabbit orthologs of the principal organic cation transporter in the kidney, OCT2. IC(50) values, ranging from 0.04 muM to >3 mM, for inhibition of [(14)C]tetraethylammonium transport were determined for more than 30 structurally diverse compounds. Although the two OCT orthologs displayed similar IC(50) values for some of these compounds, the majority varied by as much as 20-fold. Marked differences in substrate affinity were also noted when comparing hOCT2 to the closely related homolog hOCT1. These data suggest the molecular determinants of substrate binding differ markedly among both homologous and orthologous OCT transporters. The software package Cerius(2) (Accelrys, San Diego, CA) was used to generate a descriptor-based, two-dimensional, quantitative structure-activity relationship (QSAR) to produce a model relating the affinity of hOCT2 to particular physicochemical features of substrate/inhibitor molecules (r(2) = 0.81). Comparative molecular field analysis (Tripos, St. Louis, MO) was used to generate three-dimensional QSARs describing the structural basis of substrate binding to hOCT2 and rbOCT2 (q(2) = 0.60 and 0.53, respectively, and each with r(2) = 0.97). The quality of the models was assessed by their ability to successfully predict the inhibition of a set of test compounds. The current models enabled prediction of OCT2 affinity and may prove useful in the prediction of unwanted drug interactions at the level of the renal secretory process.

Animals↗

PubMed2005

Techniques: application of systems biology to absorption, distribution, metabolism, excretion and toxicity.

It is widely recognized that either predicting or determining the absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties of molecules helps to prevent the failure of some compounds before they reach the clinic. Consequently, there has been considerable research into developing better in silico, in vitro and in vivo methods and models. Toxicogenomics, proteomics, metabonomics and pharmacogenomics represent the latest experimental approaches that can be combined with high-throughput molecular screening of targets to provide a view of the complete biological system that is modulated by a compound. The functional interpretation and relevance of these complex multidimensional data to the phenotype observed in humans is the focus of current research in toxicology. Multiple content databases, data mining and predictive modeling algorithms, visualization tools, and high-throughput data-analysis solutions are being integrated to form systems-ADME/Tox. In this review, we focus on the most recent advances and applications in this area.

Animals↗

PubMed2005

Computational prediction of human drug metabolism.

There is an urgent requirement within the pharmaceutical and biotechnology industries, regulatory authorities and academia to improve the success of molecules that are selected for clinical trials. Although absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties are some of the many components that contribute to successful drug discovery and development, they represent factors for which we currently have in vitro and in vivo data that can be modelled computationally. Understanding the possible toxicity and the metabolic fate of xenobiotics in the human body is particularly important in early drug discovery. There is, therefore, a need for computational methodologies for uncovering the relationships between the structure and the biological activity of novel molecules. The convergence of numerous technologies, including high-throughput techniques, databases, ADME/Tox modelling and systems biology modelling, is leading to the foundation of systems-ADME/Tox. Results from experiments can be integrated with predictions to globally simulate and understand the likely complete effects of a molecule in humans. The development and early application of major components of MetaDrug (GeneGo, Inc.) software will be described, which includes rule-based metabolite prediction, quantitative structure-activity relationship models for major drug metabolising enzymes, and an extensive database of human protein-xenobiotic interactions. This represents a combined approach to predicting drug metabolism. MetaDrug can be readily used for visualising Phase I and II metabolic pathways, as well as interpreting high-throughput data derived from microarrays as networks of interacting objects. This will ultimately aid in hypothesis generation and the early triaging of molecules likely to have undesirable predicted properties or measured effects on key proteins and cellular functions.

Computational Biology↗

PubMed2005

Comprehensive computational assessment of ADME properties using mapping techniques.

One strategy to potentially improve the success of drug discovery is to apply computational approaches early in the process to select molecules and scaffolds with ideal binding and physicochemical properties. Numerous algorithms and different molecular descriptors have been used for modeling ligand-protein interactions as well as absorption, distribution, metabolism and excretion (ADME) properties. In most cases a single data set has been evaluated with one approach or multiple algorithms that have been compared for a single dataset. These models have been primarily evaluated by leave-one out analysis or boot strapping with groups representing 25-50% of the training set left out of the final model. In a very few examples a test set of molecules not included in the model has been used for an external evaluation. In the present study we have applied Sammon non-linear maps, Support Vector Machines and Kohonen Self Organizing Maps to modeling numerous datasets for ADME properties including human intestinal absorption, blood brain barrier permeability, cytochrome P450 binding, plasma protein binding, P-gp inhibition, volume of distribution and plasma half life.

ATP Binding Cassette Transporter, Subfamily B, Mem↗