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Sean Ekins

Publications and source records attributed to Sean Ekins.

At least 37 records · Page 2Linked to original sources
PubMed2004

A novel method for visualizing nuclear hormone receptor networks relevant to drug metabolism.

The increasing generation of biological data represents a challenge to understanding the complexity of systems, resulting in scientists increasingly focused on a relatively narrow area of study, thereby limiting insight that can be gained from a broader perspective. In the field of drug metabolism and toxicology we are witnessing the characterization of many proteins. Most of the key enzymes and transporters are recognized as transcriptionally regulated by the nuclear hormone receptors such as pregnane X receptor, constitutive androstane receptor, vitamin D receptor, glucocorticoid receptor, and others. There is apparent cross talk in regulation, since multiple receptors may modulate expression of a single enzyme or transporter, representing one of many areas of active research interest. We have used published data on nuclear hormone receptors, enzymes, ligands, and other biological information to manually annotate an Oracle database, forming the basis of a platform for querying (MetaDrug). Using algorithms, we have demonstrated how nuclear hormone receptors alone can form a network of direct interactions, and when expanded, this network increases in complexity to describe the interactions with target genes as well as small molecules known to bind a receptor, enzyme, or transporter. We have also described how the database can be used for visualizing high-throughput microarray data derived from a published study of MCF-7 cells treated with 4-hydroxytamoxifen, to highlight potential downstream effects of molecule treatment. The database represents a novel knowledge mining and analytical tool that, to be relevant, requires continual updating to evolve alongside other key storage systems and sources of biological knowledge.

Databases, Factual↗
PubMed2004

Quantitative structure-metabolism relationship modeling of metabolic N-dealkylation reaction rates.

It is widely recognized that preclinical drug discovery can be improved via the parallel assessment of bioactivity, absorption, distribution, metabolism, excretion, and toxicity properties of molecules. High-throughput computational methods may enable such assessment at the earliest, least expensive discovery stages, such as during screening compound libraries and the hit-to-lead process. As an attempt to predict drug metabolism and toxicity, we have developed an approach for evaluation of the rate of N-dealkylation mediated by two of the most important human cytochrome P450s (P450), namely CYP3A4 and CYP2D6. We have taken a novel approach by using descriptors generated for the whole molecule, the reaction centroid, and the leaving group, and then applying neural network computations and sensitivity analysis to generate quantitative structure-metabolism relationship models. The quality of these models was assessed by using the cross-validated correlation coefficients of 0.82 for CYP3A4 and 0.79 for CYP2D6 as well as external test molecules for each enzyme. The relative performance of different neural networks was also compared, and modular neural networks with two hidden layers provided the best predictive ability. Functional dependencies between the neural network input and output variables, generalization ability, and limitations of the described approach are also discussed. These models represent an initial approach to predicting the rate of P450-mediated metabolism and may be applied and integrated with other models for P450 binding to produce a systems-based approach for predicting drug metabolism.

Cytochrome P-450 CYP2D6↗
PubMed2004

Kohonen maps for prediction of binding to human cytochrome P450 3A4.

The drug development process utilizes the parallel assessment of activity at a therapeutic target as well as absorption, distribution, metabolism, excretion, and toxicity properties of molecules. The development of novel, reliable, and inexpensive computational methods for the early assessment of metabolism and toxicity is becoming increasingly an important part of this process. We have used a computational approach for the assessment of drugs and drug-like compounds which bind to the cytochromes P450 (P450s) with experimentally determined Km values. The physicochemical properties of these compounds were calculated using molecular descriptor software and then analyzed using Kohonen self-organizing maps. This approach was applied to generate a P450-specific classification of nearly 500 drug compounds. We observed statistically significant differences in the molecular properties of low Km molecules for various P450s and suggest a relationship between 33 of these compounds and their CYP3A4-inhibitory activity. A test set of additional CYP3A4 inhibitors was used, and 13 of 15 of these molecules were colocated in the regions of low Km values. This computational approach represents a novel method for use in the generation of metabolism models, enabling the scoring of libraries of compounds for their Km values to numerous P450s.

Cytochrome P-450 CYP3A↗
PubMed2004

Predicting undesirable drug interactions with promiscuous proteins in silico.

Although computational tools have been used to predict toxic responses resulting from molecules binding either as substrates or inhibitors to proteins, there are complications to be resolved. Some proteins appear promiscuous in their ability to bind a diverse array of hydrophobic molecules. This promiscuity arises from the binding site simultaneously accommodating more than one molecule, multiple separate binding sites, protein flexibility, or a combination of all these properties. With the availability of more crystal structures for these non-target proteins, we should be able to predict binding in silico with a greater accuracy, thus avoiding or managing toxic side effects, therefore ultimately improving the success of drug discovery.

ATP Binding Cassette Transporter, Subfamily B, Mem↗
PubMed2004

PXR and the regulation of apoA1 and HDL-cholesterol in rodents.

Orphan nuclear receptors (ONRs) have been implicated in the regulation of lipids. Several clinical studies conducted either prospectively or epidemiologically have pointed to a link between the regulation of hepatic CYP enzymes and HDL-cholesterol (HDL-C) and/or apolipoprotein A1 (apoA1). The treatment of rats with a series of imidazole inducers of CYP3A yielded correlations between in vitro CYP3A activity measured as erythromycin demethylase activity and plasma HDL-C and hepatic apoA1 mRNA. Similarly, a correlation was established between in vivo CYP3A activity, measured as ethosuximide clearance, and plasma HDL-C and hepatic apoA1 mRNA. The treatment of wild-type (WT) mice with PXR agonists elicited increases in serum HDL-C and serum apoA1 levels. On the other hand, the treatment of PXR-knockout mice (PXR-KOs) with the same PXR agonists failed to elicit increases in either serum HDL-C or serum apoA1 levels. Superposition of the structures of three imidazoles known to be active CYP3A inducers in rats with the human PXR pharmacophore demonstrated a partial fit and predicted EC(50) values typical of weak-moderate hPXR inducers in humans. These imidazoles have been shown to increase apoA1 and HDL-C in rats and mice. Taken together, these data suggest that PXR plays an important role in the regulation of apoA1 and HDL-C in rodents.

Animals↗
PubMed2003

In vitro and pharmacophore insights into CYP3A enzymes.

The cytochrome P450 3A (CYP3A) enzymes have a major role in the metabolism of drugs in humans. Their wide substrate specificity and induction by a vast array of structurally diverse compounds presents the possibility of metabolic drug-drug interactions. Understanding the enzymes themselves is crucial. Over the past decade, this has occurred mostly with in vitro studies, although more recent approaches incorporate computational models to predict CYP inhibition and substrate potential. The three-dimensional displacement, or pharmacophore, of chemical features in space that are derived from inhibition data have produced pharmacophores for CYP3A4, CYP3A5 and CYP3A7, and provide new insights into ligand binding for each enzyme.

Animals↗
PubMed2003

Generation and validation of rapid computational filters for cyp2d6 and cyp3a4.

CYP2D6 and CYP3A4 represent two particularly important members of the cytochrome p450 enzyme family due to their involvement in the metabolism of many commercially available drugs. Avoiding potent inhibitory interactions with both of these enzymes is highly desirable in early drug discovery, long before entering clinical trials. Computational prediction of this liability as early as possible is desired. Using a commercially available data set of over 1750 molecules to train computer models that were generated with commercially available software enabled predictions of inhibition for CYP2D6 and CYP3A4, which were compared with empirical data. The results suggest that using a recursive partitioning (tree) technique with augmented atom descriptors enables a statistically significant rank ordering of test-set molecules (Spearman's rho of 0.61 and 0.48 for CYP2D6 and CYP3A4, respectively), which represents an increased rate of identifying the best compounds when compared with the random rate. This approach represents a valuable computational filter in early drug discovery to identify compounds that may have p450 inhibition liabilities prior to molecule synthesis. Such computational filters offer a new approach in which lead optimization in silico can occur with virtual molecules simultaneously tested against multiple enzymes implicated in drug-drug interactions, with a resultant cost savings from a decreased level of molecule synthesis and in vitro screening.

Computational Biology↗
PubMed2003

Influence of molecular structure on substrate binding to the human organic cation transporter, hOCT1.

Organic cation transporters play a critical role in the elimination of therapeutic compounds in the liver and the kidney. We used computational quantitative structure activity approaches to predict molecular features that influence interaction with the human ortholog of the organic cation transporter (hOCT1). [(3)H]tetraethylammonium uptake in HeLa cells stably expressing hOCT1 was inhibited to varying extents by a diverse set of 30 molecules. A subset of 22 of these was used to produce, using Catalyst, a pharmacophore that consisted of three hydrophobic features and a positive ionizable feature. The correlation coefficient of observed versus predicted IC(50) was 0.86 for this training set, which was superior to calculated logP alone (r = 0.73) as a predictor of hOCT1 inhibition. A descriptor-based quantitative structure-activity relationship study using Cerius(2) resulted in an equation relating five molecular descriptors to log IC(50) with a correlation coefficient of 0.95. Furthermore, a group of phenylpyridinium and quinolinium compounds were used to investigate the spatial limitations of the hOCT1 binding site. The affinity for hOCT was higher for 4-phenylpyridiniums > 3-phenylpyridiniums > quinolinium, indicating that substrate affinity was influenced by the distribution of hydrophobic mass. In addition, supraplanar hydrophobic mass was found to increase the affinity for binding hOCT1. These results indicate how a combination of computational and in vitro approaches may yield insight into the binding affinity of transporters and may be applicable to predicting these properties for new therapeutics.

Computational Biology↗
PubMed2003

Prediction of human drug metabolizing enzyme induction.

New chemical entities are routinely screened in vitro and in vivo for their ability to induce cytochrome P450s (CYP), other drug-metabolizing enzymes and possibly transporters in an attempt to more accurately predict clinical parameters such as drug-drug interactions and clearance in humans. Some of these potential therapeutic agents can cause induction of the metabolism of another molecule or auto-induction thereby increasing their own metabolism and elimination, as well as potentially any molecules metabolized by the same enzyme(s). Key CYPs in the 1A, 2B, 2C, and 3A families have all been shown to be inducible. It would be clearly advantageous to know the potential for a compound to induce drug metabolizing enzymes or transporters prior to clinical development, and many in vitro systems have been developed for this purpose. Newer computational technologies are also being applied in order to attempt to predict induction from the molecular structure alone before a molecule is even synthesized or tested. This review will cover the various in vitro and in silico methods developed for prediction of key inducers of CYPs and other proteins, as well as the limitations of such technologies and applications in the future.

Animals↗
PubMed2002

Modeling of active transport systems.

Transport proteins have critical physiological roles in nutrient transport and may be utilized as a mechanism to increase drug absorption. However, we have little understanding of these proteins at the molecular level due to the absence of high-resolution crystal structures. Numerous efforts have been made to characterize the P-glycoprotein efflux pump, the peptide transporter (PepT1) and the apical sodium-dependent transporter (ASBT) which are important not only for their native transporter function but also as drug targets to increase absorption and bioactivity. In vitro and computational approaches have been applied to gain some insight into these transporters with some success. This represents an opportunity for optimizing molecules as substrates for the solute transporters and providing a further screening system for drug discovery. Clearly the future growth in knowledge of transporter function will be led by integrated in vitro and in silico approaches.

ATP-Binding Cassette Transporters↗
PubMed2002

Pharmacophore modeling of cytochromes P450.

Understanding the binding of ligands in the active site of a membrane-bound protein is difficult in the absence of a crystal structure. When these proteins are the enzymes involved in drug metabolism, it leaves little option but to use site-directed mutagenesis and in vitro studies to provide critical information relating to determinants of binding affinity. Pharmacophore models and three-dimensional quantitative structure-activity relationships have been used either alone or in combination with protein homology models to provide this information for cytochrome P450s. At present, their application has been directed to the major enzymes but this may escalate in future as more in vitro data are generated for other P450s. The following review outlines the methodologies and models as well as future prospects for applying these technologies to P450s in the hope that future drugs will be selected with increased metabolic stability and fewer incidences of undesirable drug-drug interactions.

Animals↗
PubMed2002

In silico ADME/Tox: the state of the art.

The field of computational (in silico) ADME/Tox (absorption, distribution, metabolism, excretion, and toxicity) is receiving increased attention due a better appreciation that these molecular properties should be considered earlier in the drug discovery process. This report briefly reviews selected papers presented at the 220th National Meeting of the American Chemical Society, Washington, DC, 20-24 August, 2000, and describes the types of ADME/Tox computational models presented, the results obtained, and relevant recent publications that coincide with the work reported.

Biological Availability↗
PubMed2002

Towards a new age of virtual ADME/TOX and multidimensional drug discovery.

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.

Biological Availability↗
PubMed2002

A ligand-based approach to understanding selectivity of nuclear hormone receptors PXR, CAR, FXR, LXRalpha, and LXRbeta.

In recent years discussion of nuclear hormone receptors, transporters, and drug-metabolizing enzymes has begun to take place as our knowledge of the overlapping ligand specificity of each of these proteins has deepened. This ligand specificity is potentially valuable information for influencing future drug design, as it is important to avoid certain enzymes or transporters in order to circumvent potential drug-drug interactions. Similarly, it is critical that the induction of these same proteins via nuclear hormone receptors is avoided, as this can result in further toxicities. Using a ligand-based approach in this review we describe new and previously published computational models for PXR, CAR, FXR, LXRalpha, and LXRbeta that may help in understanding the complexity of interactions between transporters and enzymes. The value of these types of models is that they may enable us to design molecules to selectively modulate pathways for therapeutic effect and in addition predict the potential for drug interactions more reliably. Simultaneously, we might learn which came first: the transporter, the enzyme, or the nuclear hormone receptor?

Amino Acid Sequence↗
PubMed2002

Structural biology and function of solute transporters: implications for identifying and designing substrates.

Solute carrier (SLC) proteins have critical physiological roles in nutrient transport and may be utilized as a mechanism to increase drug absorption. However, we have little understanding of these proteins at the molecular level due to the absence of high-resolution crystal structures. Numerous efforts have been made in characterizing the peptide transporter (PepT1) and the apical sodium dependent bile acid transporter (ASBT) that are important for both their native transporter function as well as targets to increase absorption and act as therapeutic targets. In vitro and computational approaches have been applied to gain some insight into these transporters with some success. This represents an opportunity for optimizing molecules as substrates for the solute transporters and providing a further screening system for drug discovery. Clearly the future growth in knowledge of SLC function will be led by integrated in vitro and in silico approaches.

Amino Acid Sequence↗
PubMed2002

A pharmacophore for human pregnane X receptor ligands.

The pregnane X receptor (PXR) is involved in transcriptional regulation of multiple cytochromes P450 and multidrug resistance-associated protein (MDR1), which encodes for the drug transporter P-glycoprotein. Crystal structure analyses suggest that the ligand binding domain is highly hydrophobic and flexible, allowing molecules of differing sizes to bind in multiple orientations. Using literature data for EC(50) (half-maximal inhibitory concentration) values for PXR activation derived for 12 human PXR ligands, a pharmacophore was developed. This pharmacophore supports the hydrophobic nature of the ligand binding domain recently deduced from the X-ray crystal structure because it contains four hydrophobic regions and one hydrogen bond acceptor. These features are consistent with at least one of the three experimentally determined orientations in which SR12813 binds to PXR, as determined by overlay studies. SR12813 fulfills all of the five pharmacophore features, as does the potent ligand hyperforin. The pharmacophore was also used to predict the binding affinity for 28 molecules not in the model but known to be PXR ligands of differing potencies. The pharmacophore distinguished the most potent activators of PXR (that display >5-fold activation/deactivation), like ecteinascidin, troglitazone, nifedipine, and dexamethasone-t-butylacetate, from poor activators, such as scopoletin and kaempferol. The model could be useful in drug development, potentially acting as a high-throughput filter for identifying compounds that may bind to PXR before in vitro determination. Ultimately, this will aid in the selection of molecules with a lesser capacity to be potent PXR ligands and thus avoid induction of numerous drug-metabolizing enzymes and MDR1.

Binding Sites↗
PubMed2002

Quantitative structure activity relationships for the glucuronidation of simple phenols by expressed human UGT1A6 and UGT1A9.

UGT1A6 and UGT1A9 have both been demonstrated to rapidly glucuronidate simple phenolic compounds. A series of simple phenols were selected and screened with both isoforms and then used as model substrates for the generation of V(max) and K(m) values. UGT1A6 showed a more restricted acceptance of phenolic substrates compared with UGT1A9. However, the affinity of UGT1A6 for these compounds exhibited higher K(m) values than UGT1A9, although rates of turnover were similar. Molecular surface-weighted holistic invariant molecular descriptors were generated for each substrate and used to produce the first quantitative structure activity relationship models generated for expressed human UGTs. Models relating log of the K(m) value to the generated descriptors correlated well with the experimental data r(2) value of 0.996 for UGT1A6 and r(2) value of 0.83 for UGT1A9. Cross validation by a leave-one-out method also showed good predictive capability within the subset with a q(2) value of 0.98 for UGT1A6 and q(2) value of 0.73 for UGT1A9. Empirically, UGT1A6 V(max) decreased as the 4-substituent increased in size, and a trend was observed when UGT1A6 V(max) was plotted against molecular volume. The larger UGT1A6 substrates were typified by low activity and lower K(m) values than their smaller counterparts. Extrapolating from this, it was demonstrated that phenols with large 4-substituents, which were not UGT1A6 substrates, could inhibit 4-ethylphenol glucuronidation. The K(m) values for UGT1A9 showed a similar relationship to UGT1A6 but with much lower K(m) values and greater variability in range of this value.

Cell Line↗