Pharmacophore, Drug Metabolism, and Pharmacokinetics Models on

J. Med. Chem. 2005, 48, 4389-4399

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Pharmacophore, Drug Metabolism, and Pharmacokinetics Models on Non-Peptide AT1, AT2, and AT1/AT2 Angiotensin II Receptor Antagonists Giuliano Berellini,† Gabriele Cruciani,*,† and Raimund Mannhold‡ Laboratory for Chemometrics and Cheminformatics, Department of Chemistry, University of Perugia, Via Elce di sotto 10, I-06123 Perugia, Italy, and Department of Laser Medicine, Molecular Drug Research Group, Heinrich-Heine-Universita¨ t, Universita¨ tsstrasse 1, 40225 Du¨ sseldorf, Germany Received December 1, 2004

About 20 non-peptide angiotensin II receptor antagonists are in various stages of clinical development. Different modeling approaches were used to predict the pharmacophoric requirements for AT1 (angiotensin II receptor subtype 1) affinity. However, to our knowledge, none was used to predict both the selectivity toward AT1 and AT2 (angiotensin II receptor subtype 2) receptor subtypes. In this paper, partial least squares discriminant analysis is applied to derive the chemical features guiding AT1 and AT2 selectivity or mixed AT1/AT2 receptor binding. The method can be used to modulate AT1 versus AT2 selectivity. Concerns that unopposed stimulation of the AT2 receptor might produce adverse effects initiated a search for new balanced antagonists. Moreover, it can serve as a fast filtering procedure in database searches. Finally, some relevant pharmacokinetics and metabolic properties of the database of 53 compounds are calculated using the VolSurf and MetaSite software to allow the simultaneous characterization of pharmacodynamic and pharmacokinetics properties of the chemical space of angiotensin II receptor antagonists. Introduction

Table 1. The Most Interesting Sartans

The renin angiotensin system (RAS) plays a key role in blood pressure regulation and electrolyte homeostasis.1 The RAS constitutes a proteolytic cascade in which angiotensinogen from the liver is cleaved by the aspartyl protease renin to produce the decapeptide angiotensin I. Biologically inactive angiotensin I is cleaved by the metalloprotease angiotensin-converting enzyme (ACE) to produce the endogenous octapeptide hormone angiotensin II. Drugs interfering with the RAS represent potent antihypertensives; ACE inhibitors were the first class of compounds shown to lower blood pressure via the RAS. Some side effects of ACE inhibitors such as coughing were attributed to a lack of specifity of the ACE. This has initiated an intense search for alternative approaches for interfering with the RAS. Whereas renin inhibitors have not reached the market, inhibition of the final step of the RAS, i.e., angiotensin II receptor blockade, is successfully applied in antihypertensive therapy (for some interesting examples, see Table 1).2 Attempts to develop therapeutically useful angiotensin II receptor antagonists date back to the early 1970s and were initially focused on peptidic angiotensin analogues. Whereas early efforts to develop non-peptide angiotensin receptor antagonists failed, the detection of a series of imidazole-5-acetic acid derivatives3 in the early 1980s led to the development of new interesting compounds. Some were found to be highly specific, albeit weak, non-peptide angiotensin II receptor antagonists.4 By use of drug design and molecular modeling tech* To whom correspondence should be addressed. Phone: +39-0755855550. Fax: +39-075-45646. E-mail: [email protected]. † University of Perugia. ‡ Heinrich-Heine-Universita ¨ t.

compd candesartan losartan EXP3174 valsartan irbesartan eprosartan tasosartan enoltasosartan telmisartan L-158809 EXP7711

brand name Atacand Cozaar (active metabolite of losartan) Diovan Avapro Teveten Verdia (active metabolite of tasosartan) Micardis

company AstraZeneca Merck Novartis Sanofi Synthelabo Solvay Pharma Wyeth-Ayerst Lab Boehringer Inc.

niques and through a clever series of stepwise modifications, these lead compounds later gave rise to orally active, potent, and selective non-peptide angiotensin II receptor antagonists. Losartan was the first developed drug, but after its discovery many additional nonpeptide angiotensin II receptor antagonists have been developed by numerous pharmaceutical companies, and at least 20 compounds are now in various stages of clinical development.5 Several angiotensin II receptor subtypes are known. However, most of the known effects of angiotensin II such as vasoconstriction can be attributed to the angiotensin II receptor subtype 1. The relevance of the other receptors is poorly understood, and data regarding their properties are now emerging from binding studies. The two major types of angiotensin binding sites are designated as AT1 (angiotensin II receptor subtype 1) and AT2 (angiotensin II receptor subtype 2). AT1 receptors selectively bind biphenylimidazoles typified, for example, by losartan, irbesartan, and candesartan (Table 2), whereas AT2 binding sites preferentially bind 1,4-

10.1021/jm049024x CCC: $30.25 © 2005 American Chemical Society Published on Web 06/03/2005

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Table 2. Data Set Used in This Study: AT1 Antagonists

a

Reference 7. b Reference 8. c Reference 9.

d

Reference 10. e Reference 11.

Table 3. Data Set Used in This Study: AT2 Antagonists

a


piperazinic groups as present in compounds like AT2S2_1 and AT2-S2_5 (Table 3).

The great majority of non-peptide angiotensing II receptor antagonists can be divided into three sub-

Models on Receptor Antagonists

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Table 4. Data Set Used in This Study: AT1/AT2 Antagonists

a


groups: selective AT1 receptor antagonists, selective AT2 receptor antagonists, and mixed AT1/AT2 receptor antagonists. Most compounds in clinical development are selective AT1 antagonists. However, theoretical concerns that unopposed stimulation of the AT2 receptor might produce adverse effects have initiated a search for balanced or mixed antagonists.6 Moreover, very little is known to discriminate between different subtypes, despite the availability of many non-peptide AT1 antagonists. There are also indications that the mechanism of action of AT1 antagonists is more complicated than just to prevent angiotensin II from binding to the receptor site. Finally, a lot of space is available to minimize undesirable side effects in the therapeutic use of the compounds. Correspondingly, the above summarized information on angiotensin II receptor antagonists underlines the urgent need for detailed chemometric studies that allow us to define the pharmacophoric requirements for subtype selectivity toward AT1 or AT2 receptors or for a balanced binding to both receptors. A comprehensive study should also involve approaches for rationalizing pharmacokinetics profiling. Thus, in this study, 27 selective AT1 receptor antagonists (Table 2), 14 selective AT2 receptor antagonists (Table 3), and 12 balanced AT1/AT2 (Table 4) from different literature sources were included. Partial least squares (PLS) discriminant analysis was used to highlight the chemical features responsible for the selectivity of AT1 and AT2 receptor antagonists. For the most interesting sartans (Table 1), 3D-QSAR (three-dimensional quantitative structure-activity relationship) and ADME (absorption, distribution, metabolism, and excretion) models were developed that allow both pharmacodynamic and pharmacokinetics profiling. Results and Discussion A three-dimensional model of angiotensin II in complex with the transmembrane (TM) region of the AT1 receptor is not available. Several attempts were made to derive it by molecular modeling procedures employing structural homology to the X-ray structure of rhodopsin.16 Alignments between the human angiotensin II (type 1), human β2 adrenergic, human neurokinin-1, and

human bradykinin receptors were used to generate a 3D model of the AT1 receptor.17 Although these models may answer some of the structural questions about the human AT1 receptor, experimental validation was never completely satisfactory. In the absence of detailed information on the 3D structure of angiotensin II receptor subtypes 1 and 2, the use of pharmacophoric models based on molecular interaction fields (MIFs) is one of the few methods to produce a discriminant model for human angiotensin II receptor antagonists. Recently we have developed a method that allows us to generate three-dimensional virtual receptor sites (VRS), starting only from ligand structures (in our case the AT ligands). This method is based on the grid independent descriptors (GRIND) technology.18 It is described in detail in the Experimental Section as well as in Figure 1. Only a few other methods do a similar job, and some were in fact applied to a similar problem,19 although only one receptor subtype was considered. However, we prefer the GRIND approach because no initial hypothesis is required. GRIND descriptors are highly relevant with respect to biological properties, easy to obtain, and easy to interpret. Moreover, the most important characteristic of these descriptors is that they are insensitive to the position and orientation of the molecular structures in the space. This is extremely important in this case study, where little is known on the pharmacophoric chemical features discriminating between different receptor subtypes. Angiotensin II Receptor Subtype Selectivity. PLS Discriminant Analysis. The 53 data set compounds were generated using 2D-3D conversion via CONFORT20 (see Experimental Section). In turn, GRIND descriptors were generated for all structures using standard conditions in Almond, version 3.30,21 using hydrophobic (DRY), hydrogen-bond acceptor (carbonyl oxygen), and donor (amide nitrogen) probes. Threehundred descriptor variables were generated consistently for all molecules, leading to the X-matrix used as input for statistical analysis. Partial least squares discriminant analysis was used to extract information from the data. For this purpose, the scores 1 0 0, 0 1 0,

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Figure 1. (a) MIF of hydrophobic regions for candesartan. (b) GRIND variables for candesartan obtained from MIF. The arrow shows the energy for the correlation at 14 Å distance of two regions of candesartan. The two regions are connected in (a) by a line.

Figure 2. Scores plot of PLS discriminant analysis separating selective AT1, selective AT2, and mixed AT1/AT2 non-peptide angiotensin II receptor antagonists. The ellipses identify the global conformational space occupied by the subtype series of AT1, AT2, and AT1/AT2 angiotensin II receptor antagonists, respectively.

and 0 0 1 were assigned to AT1, mixed AT1/AT2, and AT2 antagonists, respectively. The PLS score plot in Figure 2 reports the positions of the 2120 conformers in the PLS space. Only the “best energy conformer” for each compound is reported for clarity. However, the global conformational space spanned by every subtype is highlighted. Figure 2 shows a clear separation between AT1 and AT2 antagonists, highlighted by the second component, while the first component shows the separation between selective compounds (AT1 or AT2) and those with a mixed binding behavior (AT1/AT2). Correspondingly, the model is able to detect the chemical moieties that are important for the biological discrimination of the ligands, irrespective of the multiple conformations used to represent a single compound. Since all of the most diverse conformations were retrieved (see Experimental Section), the model results are implicitly independent of conformational sampling. Figure 3 reports the coefficients of the model. Positive coefficients are related to chemical moieties that tend to favor AT1 selectivity, while negative coefficients are

related to the chemical moieties that tend to favor AT2 selectivity. The coefficients of Figure 3 indicated with an arrow are the most important in the model, and they are projected into the chemical structures in Figures 4 and 6. Colors refer to the same chemical moieties. Features Guiding AT1 Selectivity. Candesartan (see Figure 4) shows two H-bond donor regions (darkgray regions) at 10 Å distance that are important for AT1 pharmacophore recognition. These pharmacophoric regions are present in all selective AT1 antagonists. However, one of the two regions is not present in irbesartan. In addition, candesartan shows two H-bond acceptor regions (black regions) separated by 18 Å, which are due to the tetrazolyl and benzoimidazoyl groups. Also, these interactions are observed in all important AT1 selective compounds. Finally, a third requisite with high importance for AT1 recognition is generated by a hydrophobic region (lightgray) and a H-bond donor region (dark-gray) exhibiting a mutual distance of 3-4 Å. The latter pattern is present in all AT1 selective compounds, although the magnitude of the region energy changes with the modification of the chemical structure. Summing up, recognition of the AT1 binding site requires the following ligand features: (1) H-bond donor T H-bond donor regions at 10 Å distance; (2) H-bond acceptor T H-bond acceptor regions at 18 Å distance; (3) hydrophobic T H-bond donor regions at 3-4 Å distance. Plotting all these features onto the candesartan molecule elucidates the pharmacophore pattern that is required for AT1 selectivity (see Figure 5). Features Guiding AT2 Selectivity. Figure 6 displays hydrophobic regions that correspond to the lightgray coefficients in Figure 3 projected onto the chemical structure of some AT2 antagonists. These compounds lack all of the pharmacophore moieties characteristic for AT1 antagonists. Moreover, they show two hydrophobic regions at 18 Å distance. These regions are never present in AT1 compounds and appear to be an important prerequisite for the selective recognition of AT2 receptors. Features Guiding Mixed AT1/AT2 Receptor Binding. Additionally, Figure 6 indicates the most characteristic regions in the chemical structure of one AT1/ AT2 antagonist. As expected, this compound shows a mixture of the patterns that are typical for AT1 or AT2



Figure 3. PLS coefficients profile. Positive coefficients characterize AT1, and negative ones characterize AT2 receptor antagonists.

Figure 4. Important H-bond donor, H-bond acceptor, and hydrophobic regions for AT1 selectivity of some the most interesting sartan analogues.

recognition. Thus, these molecules possess the H-bond acceptor T H-bond acceptor regions at 18 Å distance (typical of AT1 ligands) and at the same time the hydrophobic T hydrophobic regions at 18 Å distance typical of AT2 compounds, and also H-donor T H-donor regions as typical for AT1 compounds but with a greater distance (17 Å for the best energy conformer). Therefore, they exhibit possibilities for interacting with both receptors. The complete descriptor matrix for the AT1, AT2, AT1/ AT2 receptor antagonists is reported in the Supporting Information. 3D-QSAR Modeling of AT1 Receptor Affinity. A 3D-QSAR study was carried out on 11 AT1 antagonists for which binding affinities to the AT1 receptor, determined under similar conditions, were available (for details, see Table 5). Losartan was chosen as structural template for alignment. The aligned structures are shown in Figure 7a. The 3D-QSAR correlation obtained with the GOLPE22 procedure provided a PLS model with very good predictive power for the AT1 receptor affinity of all compounds.

Figure 5. 3D pharmacophore for AT1 selectivity superposed on candesartan.

The GOLPE analysis, using the SRD algorithm and the FFD variable selection procedure,23 identified the

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Figure 6. Important hydrophobic regions for AT2 selectivity of AT2-S2_1, PD123319, and AT2-S1_5; and hydrophobic (typical for AT2 selectivity), H-donor/H-donor, and H-bond acceptor/H-bond acceptor regions (typical for AT1 selectivity) of the AT1/AT2 receptor antagonist 7b. Table 5. Pharmacokinetic Dataa for the AT1 Antagonists Reported in Table 1

compd candesartan losartan EXP3174 valsartan irbesartan eprosartan tasosartan enoltasosartan telmisartan L-158809 EXP7711 a

modification of adsorbed drug (%)

bioavailability (%)

exptl BBB

VolSurf BBB prediction

exptl VD (L/kg)

15-40 33

-

-

0.38c

pKa

τ1/2 (h)

3.9CN4H 4.9 2.8 3.1CO2H 4.7CN4H 4.7 4-5 4-5

3-11 1.5-3.2 4.4-6.4 6.1

3-4 1 3 3-4

∼0.1 6.4 1.3 1.7

20 75 9

25

10-21 5-7 11-15 40 20-24

2-2.5 13 0.5 1 0.5-1

1.3 9.2 1.2 0.4 ∼1 0.5b 230b

20 20 2

70 13 60

(

16

40-58

+

4.5

Emax (h)

AT1 affinity (nM)


d

0.22d

( ( ( -

Reference 28. e Reference 29. f Reference 30. g Reference 31.

0.9e 0.16f 4.54g

h

VolSurf VD predictionh (L/kg) 0.1-0.3 0.4-1.1 0.1-0.3 0.1-0.4 0.4-0.9 0.2-0.4 0.2-0.5 0.4-0.8 1.1-1.8 0.5-1.0 0.5-1.1

Conformational range.

Figure 7. (a) Alignment of the 11 structures used in the 3D-QSAR model. (b) Correlation between calculated -log(IC50) vs exsperimental -log(IC50).

significant GRID variables corresponding to the regions of the molecules involved in the binding to the AT1 receptor. The PLS model, derived on the 7000 variables selected from initially 275 000, is optimal with only one PLS component. The first PLS component already explains 95% of the variance in the AT1 receptor binding affinity and is highly predictive. Figure 7b reports a graphical representation of the model fitting ability.

The GRID plot of the partial weights (see Figure 8) identifies at least four areas in space, labeled as A-D, that highly contribute to the model for AT1 receptor binding affinity. The superimposed structures of candesartan, losartan, and tasosartan are included in this figure to facilitate interpretation. The light-green regions of Figure 8A highlight areas in close contact with a virtual hydrophobic group (e.g., phenyl, naphthyl,


Figure 8. The four most important areas identified by partial weights.

benzimidazoyl, etc. in the ligand structure), which causes an increase in the binding affinity. Candesartan (see Figure 9a), the most potent AT1 ligand, shows a huge hydrophobic area superposed to the light-green coefficient regions of Figure 8. Such strong hydrophobic interactions are an important component of the high affinity of candesartan. Tasosartan (see Figure 9b) shows relatively large hydrophobic interactions in the same region, although less pronounced than those of candesartan. Losartan (see Figure 9c) shows the smallest hydrophobic area, and it is in fact the least potent of the three template compounds. The dark-green region in Figure 8B represents another statistically positive coefficient region. Candesartan (see Figure 9a) shows a hydrophilic area superposed to the dark-green coefficient region of Figure 8. The effect of such hydrophilic interactions is the second component of candesartan activity. Tasosartan and losartan (see Figure 9b,c) do not show any similar interaction pattern. Finally, darkred areas in Figure 8C,D represent regions with a negative effect to the binding energy. Losartan shows relatively large hydrophilic interactions in this area (see Figure 9c), while candesartan does not report any corresponding interaction. In summary, the regions reported in Figure 8 are able to modulate the binding energies of the modeled ligands. Candesartan shows high complementarity with the positive regions. The


complementarity is less for tasosartan and even less for losartan, nicely reflecting the decreasing AT1 affinity. However, the high-affinity of candesartan, superior to all the other AT1 antagonists, may be due principally to this large hydrophobic region indicated by the green color in Figure 9a. It is likely that this region is adequately located to interact with some of the amino acids that are known to be linked to the binding process in the AT1 receptor, such as L112, W253, Y292, and H256. Modeling ADME Properties. The necessity of the pharmaceutical industry to limit the time and expense of drug development and the realization that quite often failures in drug development are due to nonoptimal absorption, distribution, metabolism, and excretion properties have initiated interest in high-throughput screening methods and their in silico counterparts for rapid estimation of ADME properties at the early stages of drug development. In the following sections we describe the application of the software packages VolSurf and MetaSite to model ADME properties of AT1, AT2, and AT1/AT2 receptor antagonists. Although VolSurf shows only a marginal dependence on conformational variation, we have used all conformers to guarantee homogeneity of results. In particular we wanted to focus on aspects of the blood-brain barrier penetration, volume of distribution, and metabolism. Table 5 collects some pharmacokinetics data for the 11 AT1 ligands listed in Table 1. Blood-Brain Barrier (BBB) Penetration. Information on the likelihood of crossing the BBB is important for central nervous system (CNS) targets and also for predicting the possibility of CNS side effects of nonCNS drugs. Unfortunately, experimental BBB values are not available for all 11 compounds. Therefore, the BBB model included in VolSurf24 was used to predict their BBB profile. The model correctly assigned the experimental BBB profile for the compounds for which the BBB value was available (see Table 5). Table 5 indicates that the majority of the sartans does not cross the BBB. However, some of them show a certain potential for penetration, thus inducing a possible side effect at the CNS level. Tasosartan, candesartan, losartan, valsartan, and eprosartan are predicted as being poorly penetrating, while irbesartan and telmisartan seem to cross the BBB to a larger extent, as confirmed by experimental data.

Figure 9. Hydrophobic (light-green), H-bond acceptor (light-blue), and H-bond donor (red) regions for the AT1 antagonists (a) candesartan, (b) tasosartan, and (c) losartan.

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Figure 10. Filled points represent BBB Volsurf prediction of 20 diverse selected conformers of all (a) AT1, (b) AT2, and (c) AT1/ AT2 antagonists of the data set used. Unfilled points represent the compounds of the BBB VolSurf model.

Figure 11. Filled points represent VD Volsurf prediction of 20 diverse selected conformers of all (a) AT1, (b) AT2, and (c) AT1/AT2 antagonists of the data set used. Unfilled points represent the compounds of the VD VolSurf model.

In the second step, the BBB prediction was extended to the entire data set. Parts a, b, and c of Figure 10 respectively report the projection of AT1, AT2 and AT1/ AT2 compounds on the VolSurf BBB model. It is important to point out that such a model is based on passive transport. The projection shows that the most chemicals report either poor BBB penetration or no penetration at all. However, while the distribution of selective AT1 and selective AT2 compounds is similar, with a minority of chemicals predicted to have certain BBB penetration, the projection for mixed AT1/AT2 compounds is rather different. The latter seems to penetrate the BBB to a much lesser extent. At least, for new balanced AT1/AT2 antagonists, the BBB permeation does not seem to be a problem. Volume of Distibution (VD). Table 5 reports the experimentally available half-life for some compounds. The half-life of a drug is a major factor in the dosing regimen, and it is a function of the clearance and the apparent volume of distribution.25 This volume is very difficult to predict because it represents a complex combination of multiple chemical and biochemical phenomena such as tissue binding.26 Moreover, measurements of tissue binding in humans is not practical. The VD model in VolSurf was used to characterize the AT antagonists. Parts a, b, and c of Figure 11 respectively report the projection of AT1, AT2, and AT1/AT2 compounds on the VolSurf VD model. Interestingly, the AT1 compounds are well-separated from AT2 compounds, while the mixed AT1/AT2 compounds show intermediate

VD properties. Most AT1 compounds exhibit small VD values in a range from 0.1 to 1 L/kg, whereas higher VD values ranging from 0.3 to 6 L/kg are reported for AT2 antagonists. Drug lipophilicity and dipole-dipole interaction seem to be relevant to the nonspecific tissue binding properties. VD values are reported in the Supporting Information. Metabolic Sites of Oxidation. In general, metabolic transformations tend to reduce the bioavailability of a compound and, in turn, the pharmacological profile.5 The metabolic transformations of losartan to EXP3174 and tasosartan to enoltasosartan induced by human cytochromes are rare cases of metabolites exceeding their parent drug in activity. Therefore, we have computed the prediction of the site of metabolism using the MetaSite32 software. MetaSite can be used to evaluate the metabolic stability from the 3D structure of a drug candidate prior to experimental measurements. Figure 12 shows the known experimental and the main predicted sites of metabolism for the compounds reported in Table 1. A literature search (Table 6) shows that mainly the cytochromes 2C9 and 3A4 are involved. Metasite correctly predicts the hydroxylation of losartan resulting in its main metabolite EXP3174 as tasosartan resulting in its metabolite enoltasosartan. Moreover, MetaSite predicts other sites of metabolism that in turn produce other metabolites. All the main metabolites of candesartan, valsartan, and tasosartan are also wellpredicted.



Figure 12. Main sites of metabolism predicted by MetaSite. Hydroxylation via cytochromes 2C9 or 3A4 is indicated by darkgray or light-gray arrows, respectively. The black circles indicate the known experimental sites of metabolism (ref 5). Table 6. Literature Information on Metabolism of Some Sartan Analogues Reported in Table 1

e

name

CYP

type

candesartan losartan

valsartan irbesartan

2C9 3A4 3A5 2C9 2C9 2C9 2C9

eprosartan tasosartan

2C9 2C9 3A4

substratea-c substrate (EXP3174 metabolite)a-f substrate (EXP3174 metabolite)a-c substrate oxidation (EXP3174 metabolite)a-f sub. hydroxyl.d-f shows also weak inhibitiona-c substrate (hydroxyl. C, 4 metabolites, major enzyme)g substrate (hydroxyl. C, 2 metabolites)a-c,g shows also weak inhibitiona-c substrate (enoltasosartan metabolite)

a Reference 33. b Reference 34. c Reference 35. Reference 37. f Reference 38. g Reference 39.

d

Reference 36.

This detailed information can be used by chemists in an early ADME phase to increase the metabolic stability of drug candidates or to select appropriate chemical modifications leading to a better pharmacokinetics and metabolic profile. Predictions of the metabolic reaction for all compounds of the data set are reported in the Supporting Information. Conclusion This study represents the first successful attempt to obtain a unique model that defines the pharmacophoric requirements for subtype selectivity toward AT1 and AT2 receptors or for a balanced binding to both receptors. The model is obtained from a conformational space of the studied ligands and is thus neither alignmentnor conformation-dependent. The regions guiding AT1 and AT2 selectivity or mixed AT1/AT2 receptor binding were identified and explained. For 11 AT1 antagonists, a 3D-QSAR model was developed that quantifies the chemical determinants of their AT1 receptor affinity. In the second part of the study, the pharmacokinetics variability of the entire data set was addressed using VolSurf software for modeling blood-brain barrier penetration and volume of distribution. Finally, the metabolic profile was computed for the 11 AT1 antagonists with MetaSite and compared with experimental data, when available.

Experimental Section A. Data Set. The data set comprises a total of 53 compounds including 27 AT1 selective receptor antagonists (Table 2), 14 balanced AT2 (Table 3), and 12 AT1/AT2 selective antagonists (Table 4). The data set compounds were generated using 2D3D conversion via CONFORT. CONFORT was also used to perform a conformational analysis to trace the most diverse local minima. An energy window of 10 kcal was retained with a maximum of 40 local minima for each molecule, although some molecules did not cover all the allowed conformational sampling. B. GRIND Descriptors. Grid-independent descriptors, GRIND,18 were generated, analyzed, and interpreted using the software ALMOND, version 3.30.21 GRIND have been designed mainly to represent pharmacodynamic properties; they start from molecular interaction fields (MIF) computed with the program GRID.40,41 When MIF are computed for the database molecules, the regions showing favorable energies of interaction represent positions where groups of a receptor would interact favorably with a database molecule. Using different probes, one can obtain a set of such positions that defines a virtual receptor site (VRS). GRIND are based on the concept of VRS. Basically, GRIND are a small set of variables representing the geometrical relationships between relevant regions of the VRS. The procedure for obtaining GRIND involves three steps: (a) computing a set of MIF, (b) filtering the MIF to extract the most relevant regions that define the VRS, and (c) encoding the VRS into the GRIND variables. These variables represent the product of the field energy of node pairs that are separated by certain distances in the 3D space of each compound. GRIND variables are organized in correlograms either representing node pairs of the same field (autocorrelograms) or node pairs of different fields (crosscorrelograms). The alignment-independent GRIND obtained this way can be used directly for the chemometric analysis and can be interpreted with the appropriate software, using graphical representations of the pharmacophoric regions and their interactions, together with the molecular structures, in interactive 3D plots (see also Figure 1). C. VolSurf Descriptors. VolSurf24 is a computational procedure to produce 2D molecular descriptors from 3D molecular interaction energy grid maps. The basic idea of VolSurf is to compress the information present in 3D maps into a few 2D numerical descriptors that are very simple to understand and to interpret. VolSurf working examples are extensively reported.42-45 By use of multivariate statistics coupled with interactive 2D and 3D plots, valuable insights for drug design, pharmacokinetics profiling, and screening are obtained.

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D. Metasite. Metasite21,32 involves the calculation of different sets of descriptors, one for the CYP enzymes and one set for the potential substrates. The set of descriptors used to characterize CYP enzymes is based on GRID flexible molecular interaction fields. For the 2C9 and 3A4 enzymes, the crystal structures of human isoforms are used. For the other CYPs, homology models were used. The 3D structures of the substrates were obtained using conformational search followed by energy minimization. Each of the substrates was submitted to a conformational search followed by energy minimization by means of an in-house developed software. The runs were performed under the constraints to obtain a population of conformers with a 3D structure induced by the interaction fields and shape of the CYP-active site. The CYP-MIFs were subsequently transformed. The selected interaction points were used to calculate a set of descriptors using the GRIND technology developed by Pastor et al.18 For each CYP-probe interaction, this approach transforms the interaction energies at a certain spatial position (MIF descriptors) into a number of histograms that capture the 3D pharmacophoric interaction patterns of the flexible protein (correlograms). Substrate MIFs were transformed in a similar way, and both sets of descriptors were compared. The hydrophobic complementarity, the charge complementarity (negative in the protein and positive in the ligands and viceversa), the H-bond donor descriptors for the protein and the H-bond acceptor for the substrate, and finally the H-bond acceptor for the protein and the donor complementarities for the ligand are computed using the Carbo´ similarity index.46 Finally, the different substrate atoms are ranked according to the computed total similarity index. The reactivity component was added by considering the site of metabolism for the most common reactions described by a probability function PSM that is correlated to, and can be considered, the free energy of the overall process:

PSM,i ) Ei Ri where P is the probability of atom i to be the site of metabolism because of the cyp-heme group, E is the exposition of the atom i to the heme, and R is the reactivity of atom i in the actual mechanism of reaction. Ei is the recognition score between the cyp-protein and the ligand, when the ligand is positioned in the cyp-protein and exposes atom i toward the heme. It depends on the ligand 3D structure, conformation, chirality, and 3D cyp-protein structure. The Ei score is proportional to the exposure of atom i to the heme group. Similarly, Ri is the reactivity of atom i in the appropriate reaction mechanism and represents the activation energy involved to produce the reactive intermediate. It depends on the ligand 3D structure and on the considered mechanism of reaction. For the same ligand, the PSM function assumes different values for different atoms according to the Ei and Ri components. E. Statistical Analysis. Partial least squares (PLS) analysis was performed within the software ALMOND.21 No scaling was applied. The optimal dimensionality of the PLS model was chosen according to the results of crossvalidation. All computations were run on Silicon Graphics SGI O2 R12000 workstations and on ×86 workstations with RedHat Enterprise Linux (RHEL).

Acknowledgment. We thank AstraZeneca, Italy, for financial support of this work. Supporting Information Available: Tables of X-matrix GRIND descriptors of the selectivity model for all data sets and a complete list of molecules with BBB prediction,VD predictions, and metabolic site prediction. This material is available free of charge via the Internet at http://pubs.acs.org.

Berellini et al.

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