Biomedical subjects

Sean Ekins

Publications and source records attributed to Sean Ekins.

41 records · Page 3Linked to original sources

PubMed2002

Three-dimensional quantitative structure-activity relationship for inhibition of human ether-a-go-go-related gene potassium channel.

The protein product of the human ether-a-go-go gene (hERG) is a potassium channel that when inhibited by some drugs may lead to cardiac arrhythmia. Previously, a three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophore model was constructed using Catalyst with in vitro inhibition data for antipsychotic agents. The rationale of the current study was to use a combination of in vitro and in silico technologies to further test the pharmacophore model and qualitatively predict whether molecules are likely to inhibit this potassium channel. These predictions were assessed with the experimental data using the Spearman's rho rank correlation. The antipsychotic-based hERG inhibitor model produced a statistically significant Spearman's rho of 0.71 for 11 molecules. In addition, 15 molecules from the literature were used as a further test set and were also well ranked by the same model with a statistically significant Spearman's rho value of 0.76. A Catalyst General hERG pharmacophore model was generated with these literature molecules, which contained four hydrophobic features and one positive ionizable feature. Linear regression of log-transformed observed versus predicted IC(50) values for this training set resulted in an r(2) value of 0.90. The model based on literature data was evaluated with the in vitro data generated for the original 22 molecules (including the antipsychotics) and illustrated a significant Spearman's rho of 0.77. Thus, the Catalyst 3D-QSAR approach provides useful qualitative predictions for test set molecules. The model based on literature data therefore provides a potentially valuable tool for discovery chemistry as future molecules may be synthesized that are less likely to inhibit hERG based on information provided by a pharmacophore for the inhibition of this potassium channel.

Antipsychotic Agents↗

PubMed2002

Three-dimensional quantitative structure-activity relationships of inhibitors of P-glycoprotein.

P-glycoprotein (P-gp) is an efflux transporter involved in limiting the oral bioavailability and tissue penetration of a variety of structurally divergent molecules. A better understanding of the structural requirements of modulators of P-gp function will aid in the design of therapeutic agents. Toward this goal, three-dimensional quantitative structure-activity relationship (3D-QSAR) models were generated using in vitro data associated with inhibition of P-gp function. Several approaches were undertaken with multiple iterations, yielding Catalyst 3D-QSAR models being able to qualitatively rank-order and predict IC(50) values for P-gp inhibitors excluded from the model in question. The success of these validations suggests that a P-gp pharmacophore for 27 inhibitors of digoxin transport in Caco-2 cells consisted of four hydrophobes and one hydrogen bond acceptor. A second pharmacophore generated with 21 inhibitors of vinblastine binding to plasma membrane vesicles derived from CEM/VLB(100) cells contained three ring aromatic features and one hydrophobic feature. A third pharmacophore generated with 17 inhibitors of vinblastine accumulation in P-gp expressing LLC-PK1 cells contained four hydrophobes and one hydrogen bond acceptor. A final pharmacophore was generated for inhibition of calcein accumulation in P-gp expressing LLC-PK1 cells and found to contain two hydrophobes, a ring aromatic feature, and a hydrogen bond donor. The similarity of features for the pharmacophores of P-gp inhibitors of digoxin transport and vinblastine binding suggest some commonality in their binding sites. Utilization of such models may prove to be of value for prediction of molecules that may modulate one or more P-gp binding sites.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2002

Application of three-dimensional quantitative structure-activity relationships of P-glycoprotein inhibitors and substrates.

Using in vitro data, we previously built Catalyst 3-dimensional quantitative structure activity relationship (3D-QSAR) models that qualitatively rank and predict IC(50) values for P-glycoprotein (P-gp) inhibitors. These models were derived and tested with data for inhibition of digoxin transport, calcein accumulation, vinblastine accumulation, and vinblastine binding. In the present study, 16 inhibitors of verapamil binding to P-gp were predicted using these models. These inhibition results were then used to generate a new pharmacophore that consisted of one hydrogen bond acceptor, one ring aromatic feature, and two hydrophobes. This model predicted the rank order of the four data sets described previously and correctly ranked the inhibitory potency of a further four verapamil metabolites identified in the literature. The degree of similarity in rank ordering prediction by these inhibitor pharmacophore models generated to date confirms a likely overlap in the sites to which the three P-gp substrates used in these studies (verapamil, vinblastine, and digoxin) bind. Alignment of the three substrate probes indicated that they are likely to bind the same or overlapping sites within P-gp. Important features on these substrates include multiple hydrophobic and hydrogen bond acceptor features, which are widely dispersed and in agreement among most of the five inhibitor pharmacophores we have described so far. These 3D-QSAR models will be useful for future prediction of likely substrates and inhibitors of P-gp.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2002

Optimizing higher throughput methods to assess drug-drug interactions for CYP1A2, CYP2C9, CYP2C19, CYP2D6, rCYP2D6, and CYP3A4 in vitro using a single point IC(50).

Drug-drug interactions involving cytochrome P(450) (CYP) are an important factor in whether a new chemical entity will survive through to the development stage. Therefore, the identification of this potential as early as possible in vitro could save considerable future unnecessary investment. In vitro CYP interaction screening data generated for CYP2C9, CYP2D6, and CYP3A4 were initially analyzed to determine the correlation of IC(50) from 10- and 3-point determinations. A high correlation (r = 0.99) prompted the further assessment of predicting the IC(50) by a single value of percent inhibition at either 10, 3, or 1 microM. Statistical analysis of the initial proprietary compounds showed that there was a strong linear relationship between log IC(50) and percent inhibition at 3 microM, and that it was possible to predict a compound's IC(50) by the percent inhibition value obtained at 3 microM. Additional data for CYP1A2, CYP2C19, and the recombinant CYP2D6 were later obtained and used together with the initial data to demonstrate that a single statistical model could be applicable across different CYPs and different in vitro microsomal systems. Ultimately, the data for all five CYPs and the recombinant CYP2D6 were used to build a statistical model for predicting the IC(50) with a single point. The 95% prediction boundary for the region of interest was about +/- 0.37 on log(10) scale, comparable to the variability of in vitro determinations for positive control IC(50) data. The use of a single inhibitor concentration would enable determination of more IC(50) values on a 96-well plate and result in more economical use of compounds, human liver or expressed enzyme microsomes, substrates, and reagents. This approach would offer the opportunity to increase screening for CYP-mediated drug-drug interactions, which may be important given the challenges provided by the generation of orders of magnitude more new chemical entities in the field of combinatorial chemistry. In addition, the algorithmic approach we propose would obviously be applicable for other in vitro bioactivity and therapeutic target enzyme and receptor screens.

Algorithms↗

PubMedDate not supplied

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↗