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Type II cytochrome P450 (CYP) ligands cause inhibition by direct coordination to the heme iron atom. This interaction usually leads to high inhibitory potential, which can cause drug-drug interaction. The approach to design compounds with diminished CYP inhibition is different depending on whether the compound binds (type II ligand) or not (type I ligand) to the iron atom of the heme group. In this study, the structural characteristics of nitrogen-containing compounds, which bind to the iron atom in two CYP isoforms (CYP2C9 and CYP3A4), were investigated. The in vitro assays applied were fluorescence inhibition assay, difference spectra measurements, and K s determination. Computational modeling as an alternative method to difference spectra measurements to distinguish between type I and type II ligands was also explored. Since two CYP isoforms were used, information about the isoform specificity of type II ligands was also analyzed. The in silico method developed in this study applied together with the information gained from the experimental measurements may result in better decisions during the drug discovery process.

The cytochrome P450 (CYP) family is composed of a large group of monooxygenases that mediate the metabolism of xenobiotics and endogenous compounds. CYP2C9, one of the major isoforms of the CYP family, is responsible for the phase I metabolism of a variety of drugs. The aim of the present investigation is to use rational design together with MetaSite, a metabolism site prediction program, to synthesize compounds that retain their pharmacological effects but that are metabolically more stable in the presence of CYP2C9. The model compound for the study is the nonsteroidal anti-inflammatory drug celecoxib, a COX-2 selective inhibitor and known CYP2C9 substrate. Thirteen analogs of celecoxib were designed, synthesized, and evaluated with regard to their metabolic properties and pharmacologic effects. The docking solutions and the predictions from MetaSite gave useful information leading to the design of new compounds with improved metabolic properties.

The aim of this work is to present the anchor−GRIND methodology. Anchor-GRIND efficiently combines a priori chemical and biological knowledge about the studied compounds with alignment-independent molecular descriptors derived from molecular interaction fields. Such descriptors are particularly useful for series of ligands sharing a common scaffold but with very diverse substituents. The method uses a specific position of the molecular structure (the “anchor point”) to compare the spatial distribution of the molecular interaction fields of the substituents. The descriptors produced are more detailed and specific than the original GRIND while still avoiding the bias introduced by the alignment. Three data sets have been studied to demonstrate the usefulness of the anchor−GRIND methodology for 3D-QSAR modeling. The two first data sets respectively include congeneric series of the hepatitis C virus NS3 protease and of the acetylcholinesterase inhibitors. The third data set discriminates between factor Xa inh...

A conformer- and alignment-independent three-dimensional structure-activity relationship (3D-QSAR) model has been derived that is based on flexible molecular interaction fields calculated in GRID and the subsequent description of these fields by use of alignment-independent descriptors derived in ALMOND. The training set consisted of 22 diverse and flexible competitive inhibitors of the drug-metabolizing enzyme CYP2C9 and generated a model with r(2) of 0.81 and q(2) of 0.62. The predicitive capacity of the model was externally evaluated with a test set of 12 competitive inhibitors and 11 out of 12 were predicted within 0.5 log unit. The most relevant points of interaction in the model correlated well to the amino acids involved in CYP2C9-substrate/inhibitor binding in the active site of a CYP2C9 homology model, further validating the mechanistic sense of our model. This approach offers the possibility to derive predicitve 3D-QSAR models without the need for an alignment rule for chemically diverse ligands and in the absence of target protein crystal structure information.

The aim of the present study is to develop a method for predicting the site at which molecules will be metabolized by CYP 2C9 (cytochrome P450 2C9) using a previously reported protein homology model of the enzyme. Such a method would be of great help in designing new compounds with a better pharmacokinetic profile, or in designing prodrugs where the compound needs to be metabolized in order to become active. The methodology is based on a comparison between alignment-independent descriptors derived from GRID Molecular Interaction Fields for the CYP 2C9 active site, and a distance-based representation of the substrate. The predicted site of metabolism is reported as a ranking list of all the hydrogen atoms of each substrate molecule. Eighty-seven CYP 2C9-catalyzed oxidative reactions reported in the literature have been analyzed. In more than 90% of these cases, the hydrogen atom ranked at the first, second, or third position was the experimentally reported site of oxidation.

Purpose. The purpose of this study was to investigate the pH-dependent passive and active transport of weakly basic drugs across the human intestinal epithelium. Methods. The bidirectional pH-dependent transport of weak bases was studied in Caco-2 cell monolayers in the physiologic pH range of the gastrointestinal tract. Results. A net secretion of atenolol and metoprolol was observed when a pH gradient was applied. However, the bidirectional transport of both compounds was equal in the nongradient system. Hence, at lower apical than basolateral pH a change in passive transport caused by an imbalance in the concentration of the uncharged drug species resulted in a “false” asymmetry (efflux ratio). Furthermore, a mixture of pH-dependent passive and active efflux was found for the P-glycoprotein (P-gp, MDR1, ABCB1) substrates, talinolol and quinidine, but not for the neutral drug, digoxin. However, the clinically important digoxin-quinidine interaction depended on the presence of a pH gradient. Hence, the degree of interaction depends on the amount of quinidine available at the binding site of the P-gp. Conclusions. Active efflux of weak bases can only be accounted for when the fraction of unionized drug species is equal in all compartments because the transport is biased by a pH-dependent passive component. However, this component may take part in vivo and contribute to drug-drug interactions involving P-gp.

This study demonstrates a selectivity analysis using the GRID/CPCA strategy on four human cytochrome P450 2C homology models (CYP2C8, 2C9, 2C18, and 2C19). Although the four enzymes share more than 80% amino acid sequence identity, the substrate specificity differs. To investigate the selectivity of the enzymes and the amino acids that determine the specificity of each CYP2C enzyme, a selectivity analysis was made using GRID/CPCA. In the GRID calculations 10 probes were used covering hydrophobic, steric, and hydrogen bond acceptor and donor interactions. The selectivity analysis showed that the most important determinants of selectivity among the CYP2C models are the geometrical features of the active sites and the hydrophobic interactions. The selectivity analysis singled out CYP2C8 as the most different of the four CYP2C enzymes with amino acids with distinct properties in positions 114, 205, and 476 (Ser, Phe, and Ile, respectively) compared to the other enzymes. An inverse pharmacophore model for CYP2C9 was constructed from the selective regions, and the model agreed with the docking of diclofenac where the properties of the ligand overlapped with the pharmacophoric points in the model.

One of the key factors in drug discovery is related to the metabolic properties of the lead compound, which may influence the bioavailability of the drug, its therapeutic window, and unwanted side-effects of its metabolites. Therefore, it is of critical importance to enable the fast translation of the experimentally determined metabolic information into design knowledge. The elucidation of the metabolite structure is the most structurally rich and informative end-point in the available range of metabolic assays. A methodology is presented to partially automate the analysis of this experimental information, making the process more efficient. The computer assisted method helps in the chromatographic peak selection and the metabolite structure assignment, enabling automatic data comparison for qualitative applications (kinetic analysis, cross species comparison).

A new method for fragment and scaffold replacement is presented that generates new families of compounds with biological activity, using GRID molecular interaction fields (MIFs) and the crystal structure of the targets. In contrast to virtual screening strategies, this methodology aims only to replace a fragment of the original molecule, maintaining the other structural elements that are known or suspected to have a critical role in ligand binding. First, we report a validation of the method, recovering up to 95% of the original fragments searched among the top-five proposed solutions, using 164 fragment queries from 11 diverse targets. Second, six key customizable parameters are investigated, concluding that filtering the receptor MIF using the co-crystallized ligand atom type has the greatest impact on the ranking of the proposed solutions. Finally, 11 examples using more realistic scenarios have been performed; diverse chemotypes are returned, including some that are similar to compounds that are known to bind to similar targets.

The cytochrome P450 (CYP) family is composed of monooxygenases, which mediate the metabolism of xenobiotics and endogenous compounds. The characterization of the interactions between these enzymes and candidate drugs is an important part of the drug discovery process. CYP2C9, one isoform of the CYPs, mediates the oxidation of several important drugs. The aim of this work is to investigate the possibility to study inhibition and substrate interactions with CYP2C9, using docking and the site of metabolism predictions. The model compounds used for the study were the COX-2 inhibitor celecoxib and a series of 13 analogues known to be metabolized by CYP2C9. The results obtained using the two methods gave valuable information about important interactions of inhibitors and substrates with CYP2C9. The two methods could be used to predict the site of metabolism and to determine the productive docking pose for each compound. These predictions were verified by metabolite identification using LC/MS/MS after incubation with recombinant CYP2C9.

A new GRID-based method for scaffold hopping (SHOP) is presented. In a fully automatic manner, scaffolds were identified in a database based on three types of 3D-descriptors. SHOP's ability to recover scaffolds was assessed and validated by searching a database spiked with fragments of known ligands of three different protein targets relevant for drug discovery using a rational approach based on statistical experimental design. Five out of eight and seven out of eight thrombin scaffolds and all seven HIV protease scaffolds were recovered within the top 10 and 31 out of 31 neuraminidase scaffolds were in the 31 top-ranked scaffolds. SHOP also identified new scaffolds with substantially different chemotypes from the queries. Docking analysis indicated that the new scaffolds would have similar binding modes to those of the respective query scaffolds observed in X-ray structures. The databases contained scaffolds from published combinatorial libraries to ensure that identified scaffolds could be feasibly synthesized.

Ligand-based virtual screening approaches were applied to search for new chemotype KCOs activating Kir6.2/SUR1 KATP channels. A total of 65 208 commercially available compounds, extracted from the ZINC archive, served as database for screening. In a first step, pharmacokinetic filtering via VolSurf reduced the initial database to 1913 compounds. Afterward, six molecules were selected as templates for similarity searches: similarity scores, obtained toward these templates, were calculated with the GRIND, FLAP, and TOPP approaches, which differently encode structural information into potential pharmacophores. In this way, we obtained 32 hit candidates, 16 via GRIND and eight each via FLAP and TOPP. For biological testing of the hit candidates, their effects on membrane potentials in HEK 293 cells expressing Kir6.2/SUR1 were studied. GRIND, FLAP, and TOPP all yielded hits, but no method top-ranked all the actives. Thus, parallel application of different approaches probably improves hit detection.

The objective of the study is to evaluate the mechanism behind alterations in passive and active transport of drugs in the presence of basolaterally applied extracellular protein using the Caco-2 cell model. The permeation across Caco-2 monolayers of two groups of compounds, transported either solely by passive diffusion or partly also by active transport in the secretory direction, was studied at two donor concentrations in the absence or presence of bovine serum albumin (BSA, 0–4%). Each group contained compounds with high or low protein binding (PB) capabilities and high or low absorption in humans (fraction absorbed, f a). The unbound fraction (f u) of each compound was determined by ultrafiltration. The transport rate of the passively permeating compounds was the same in both apical-to-basolateral (absorptive) and basolateral-to-apical (secretory) directions in the absence of BSA. Basolaterally applied BSA increased the transport rate in the absorptive direction and decreased it in the secretory direction for all compounds, in direct proportion to the extent of PB. The efflux ratios for the actively transported compounds were reduced in the presence of BSA. Basolaterally applied BSA, which mimics in vivo PB, alters both passive and active drug transport in the Caco-2 cell model. Using C u in the calculations of transport rate allowed elucidation of the different mechanisms behind these alterations. Our data also suggest that active secretory transport for highly protein-bound compounds might have less effect in vivo than predicted from traditional Caco-2 cell models (without BSA).

Molecular descriptors calculated by the VolSurf program have been extensively used to model pharmacokinetic properties, e.g., passive permeability through the gastrointestinal tract or through the blood-brain barrier. These descriptors quantify steric, hydrophobic, and hydrogen bond interactions between model compounds and different environments. Since these interactions are the same as those involved in the ligand-receptor binding, VolSurf descriptors could potentially be relevant in modeling this process as well. We obtained a significant model (r(2) = 0.85, q(2) = 0.75) using VolSurf descriptors derived from the ligand, the protein, and the ligand-protein complex for a diverse set of 38 structures previously used in the VALIDATE (ref 23) training set. Furthermore, a statistically significant model (r(2) = 0.94, q(2) = 0.89) was obtained using the same type of descriptors for a homogeneous set of glycogen phosphorylase inhibitors (ref 25). Using the VolSurf computational framework, both ligand-receptor binding and the ligand's pharmacokinetic behavior can be modeled simultaneously during the preclinical aspects of drug discovery.

This study describes the generation of a three-dimensional quantitative structure activity relationship (3D-QSAR) model for 29 structurally diverse, competitive CYP2C9 inhibitors defined experimentally from an initial data set of 73 compounds. In parallel, a homology model for CYP2C9 using the rabbit CYP2C5 coordinates was built. For molecules with a known interaction mode with CYP2C9, this homology model, in combination with the docking program GOLD, was used to select conformers to use in the 3D-QSAR analysis. The remaining molecules were docked, and the GRID interaction energies for all conformers proposed by GOLD were calculated. This was followed by a principal component analysis (PCA) of the GRID energies for all conformers of all compounds. Based on the similarity in the PCA plot to the inhibitors with a known interaction mode, the conformer to be used in the 3D-QSAR analysis was selected. The compounds were randomly divided into two groups, the training data set (n = 21) to build the model and the external validation set (n = 8). The PLS (partial least-squares) analysis of the interaction energies against the K(i) values generated a model with r(2) = 0.947 and a cross-validation of q(2) = 0.730. The model was able to predict the entire external data set within 0.5 log units of the experimental K(i) values. The amino acids in the active site showed complementary features to the grid interaction energies in the 3D-QSAR model and were also in agreement with mutagenesis studies.