Synthesis, Biological Evaluation, and Pharmacophore Generation of

Roberta Barbaro,† Laura Betti,‡ Maurizio Botta,*,§ Federico Corelli,§ Gino Giannaccini,‡ Laura Maccari,§. Fabrizio Manetti,§ Giovannella Str...
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J. Med. Chem. 2001, 44, 2118-2132

Synthesis, Biological Evaluation, and Pharmacophore Generation of New Pyridazinone Derivatives with Affinity toward r1- and r2-Adrenoceptors1 Roberta Barbaro,† Laura Betti,‡ Maurizio Botta,*,§ Federico Corelli,§ Gino Giannaccini,‡ Laura Maccari,§ Fabrizio Manetti,§ Giovannella Strappaghetti,*,† and Stefano Corsano† Istituto di Chimica e Tecnologia del Farmaco, Universita` di Perugia, Via del Liceo 1, 06123 Perugia, Italy, Dipartimento di Psichiatria, Neurobiologia, Farmacologia e Biotecnologie, Universita` di Pisa, Via Bonanno 6, 56126 Pisa, Italy, and Dipartimento Farmaco Chimico Tecnologico, Universita` degli Studi di Siena, Via Aldo Moro, 53100 Siena, Italy Received January 15, 2001

A series of new pyridazin-3(2H)-one derivatives (3 and 4) were evaluated for their in vitro affinity toward both R1- and R2-adrenoceptors by radioligand receptor binding assays. All target compounds showed good affinities for the R1-adrenoceptor, with Ki values in the low nanomolar range. The polymethylene chain constituting the spacer between the furoylpiperazinyl pyridazinone and the arylpiperazine moiety was shown to influence the affinity and selectivity of these compounds. Particularly, a gradual increase in affinity was observed by lengthening the polymethylene chain up to a maximum of seven carbon atoms. In addition, compound 3k, characterized by a very interesting R1-AR affinity (1.9 nM), was also shown to be a highly selective R1-AR antagonist, the affinity ratio for R2- and R1-adrenoceptors being 274. To gain insight into the structural features required for R1 antagonist activity, the pyridazinone derivatives were submitted to a pharmacophore generation procedure using the program Catalyst. The resulting pharmacophore model showed high correlation and predictive power. It also rationalized the relationships between structural properties and biological data of, and external to, the pyridazinone class. Introduction The R1- and R2-adrenoceptors (termed R1-AR and R2AR, respectively, in the text) are members of the seventransmembrane-spanning domain group sharing the common structural motif of seven putative R-helical segments traversing the cell membrane. It is now clear that R1-ARs are comprised of multiple subtypes that have been identified by both pharmacological and binding studies. To date, they are classified into R1A, R1B, and R1D2 and the corresponding cloned counterparts termed R1a-, R1b-, and R1d-AR, respectively.3 In addition, the existence of an additional subtype (R1L), characterized by a low affinity for prazosin, has been postulated. In a similar way, R2-ARs have been classified into four subtypes, called R2A-R2D, respectively.2c In recent years, the search for new selective R1-AR antagonists has intensified, due to their importance in the treatment of hypertension and of benign prostatic hyperplasia (BPH). In fact, R1-AR blockers have been employed in the treatment of BPH for more than two decades, due to the significant improvements in symptoms and flow rates in patients with bladder outflow obstruction.4,5 Nevertheless, the efficacy of R1-AR antagonists in the treatment of BPH is balanced against * To whom correspondence should be addressed. M.B.: Dipartimento Farmaco Chimico Tecnologico, Universita` degli Studi di Siena, Via Aldo Moro, I-53100 Siena, Italy; telephone, +39 0577 234306; fax, +39 0577 234333; e-mail, [email protected]. G.S.: Istituto di Chimica e Tecnologia del Farmaco, Universita` di Perugia, Via del Liceo 1, I-06123 Perugia, Italy; telephone, +39 075 5855136; fax, +39 075 5855129; e-mail, [email protected]. † Universita ` di Perugia. ‡ Universita ` di Pisa. § Universita ` degli Studi di Siena.

a small, but significant, incidence of side effects, such as orthostatic hypotension, which is considered a critical disadvantage in BPH patients. On the other hand, some of these effects, such as a significant reduction of both systolic and diastolic blood pressure in hypertensive patients,6 and the favorable reduction in the level of LDL cholesterol as well as serum triglyceride profiles,7 could be construed as beneficial. Molecular cloning studies8,9 have shown that the R1and R2-adrenoceptors have many common features which could reflect their similar mechanisms of action. As a consequence of such similarities, synthetic compounds with affinity toward R-AR are expected to potentially bind to both R1 and R2 receptors. On the other hand, many literature reports revealed that the addition of arylpiperazinylalkyl side chains into different heterocycles, like uracils or the pyrimido[5,4-b]indole moiety,10 provides compounds that effectively lower blood pressure by antagonizing the R1-AR. Moreover, great attention has been paid to the compounds containing a pyridazin-3(2H)-one moiety, due to their potential biological activities as antihypertensive agents (compound GYKI-12743 is an example).11 This literature survey led to the suggestion that both the arylpiperazinyl and the pyridazinone moiety are key elements for R1-AR affinity. On the basis of this experimental evidence, in the course of our studies in the field of new and potentially selective R1-AR antagonists containing a pyridazin3(2H)-one ring, we have recently synthesized compounds 1 and 212 (Figure 1 and Table 1) bearing an arylpiperazinylalkyl chain at the 2-position of the pyridazinone moiety. While they all exhibited a good affinity toward R1-AR, with values ranging from 0.6

10.1021/jm010821u CCC: $20.00 © 2001 American Chemical Society Published on Web 05/26/2001

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AR, to investigate the structure-affinity relationships of these ligands, we have used the program Catalyst13 to develop a pharmacophore model for R1-AR antagonists. The calculated model, able to rationalize the relationships between the chemical features of new structures 3 and 4 and their binding affinity data at R1-AR, consists of a positive ionizable portion, three hydrophobic features, and a hydrogen bond acceptor group. It shows a good statistical significance (r ) 0.92, rmsd ) 0.89) and successfully predicts the affinities of the molecules of, and external to, the training set. Figure 1. General structures of compounds 1 and 2.

Chemistry

Table 1. R1- and R2-Adrenergic Receptor Binding Affinities for Pyridazin-3(2H)-one Derivatives 1 and 2

The target compounds 3 and 4 listed in Table 2 were synthesized as outlined in Scheme 1. The first series of compounds 3 were prepared starting from 4-chloro-5[4-(2-furoyl)piperazin-1-yl]pyridazin-3(2H)-one (5), obtained by condensation of 4,5-dichloropyridazin-3(2H)one and 1-(2-furoyl)piperazine in DMF and potassium carbonate or EtOH and Et3N according to the procedure reported by Go`mez-Gil.14 Alkylation of 5 with 1-(2-methoxyphenyl)-4-(3-chloropropyl)piperazine (6a)15 or 1-(2-chlorophenyl)-4-(3chloropropyl)piperazine (6b)15 in dry ethanol in the presence of sodium hydroxide (method A) afforded compounds 3a and 3b, respectively, in moderate yield. Alternatively, 5 was alkylated with 1,2-dibromoethane under phase transfer catalysis, according to the procedure reported by Yamada,16 to give intermediate 7a, which in turn was converted to final compounds 3c and 3d by reaction with 1-(2-methoxyphenyl)piperazine (8) or 1-(2-chlorophenyl)piperazine (9) (Na2CO3/isoamyl alcohol, method B) in 20-30% overall yield. R,ω-Dibromoalkanes having four to seven methylene groups were employed to prepare intermediates 7b-e (K2CO3/ acetone, method C), from which compounds 3e-l were synthesized following method B. Analogously, compound 4d was prepared by direct alkylation of 10,17 according to the procedure previously reported for the synthesis of 4a-c,17 while pyridazinones 4e-l were obtained via intermediates 11a-d, in turn prepared by treatment of 10 with dibromoalkanes (method C). The chemical and physical data of the new compounds are reported in Table 3.

Kia (nM) compd

n

X

1a 1b 1cc 1d 1e 1fd 1g 1hc 1ic 1j 1k 1l 2a 2bc 2c 2d 2ed 2fc 2g 2hc 2id 2jd 2kc 2l 2m 2n prazosin rauwolscine

2 3 4 5 6 7 2 3 4 5 6 7 2 3 4 5 6 7 8 2 3 4 5 6 7 8

OCH3 OCH3 OCH3 OCH3 OCH3 OCH3 Cl Cl Cl Cl Cl Cl OCH3 OCH3 OCH3 OCH3 OCH3 OCH3 OCH3 Cl Cl Cl Cl Cl Cl Cl

R1

-ARb

R2-AR

R2/R1

38.0 ( 5.0 1261.0 ( 210.0 33.2 7.3 ( 0.6 254.0 ( 40.3 34.8 0.6 ( 0.1 (1.8) 62.0 ( 8.0 103.3 12.9 ( 1.5 69.8 ( 9.2 5.4 7.0 ( 0.8 71.8 ( 6.3 10.2 2.3 ( 0.2 (19) 45.8 ( 6.3 20.0 18.0 ( 2.0 370.0 ( 50.2 20.5 6.8 ( 0.6 (7.5) 316.0 ( 40.3 46.5 0.8 ( 0.1 (3.3) 69.4 ( 6.0 86.7 7.0 ( 0.9 138.0 ( 25.0 20.0 8.4 ( 1.2 138.8 ( 20.3 16.5 15.0 ( 2.5 139.0 ( 15.3 9.3 16.0 ( 1.7 409.0 ( 35.7 25.5 14.5 ( 1.3 (4.9) 245.0 ( 25.3 17.0 4.3 ( 0.3 230.0 ( 20.0 53.0 3.9 ( 0.2 15.0 ( 1.9 3.8 1.5 ( 0.1 (14) 3.5 ( 0.4 2.3 1.4 ( 0.1 (9.7) 4.6 ( 0.5 3.2 3.5 ( 0.4 22.7 ( 3.0 6.5 58.8 ( 3.7 (39) 292.3 ( 30.2 5.0 27.8 ( 3.0 (5.6) 219.0 ( 15.8 7.8 10.0 ( 1.5 (2.8) 39.3 ( 5.3 4.0 4.5 ( 0.2 (8.6) 29.0 ( 4.5 6.4 4.2 ( 0.6 25.2 ( 3.8 6.0 2.7 ( 0.3 7.4 ( 0.5 2.7 5.6 ( 0.7 22.8 ( 2.5 4.0 0.24 ( 0.05 4.0 ( 0.3

a The K binding data were calculated as described in the i Experimental Section. The Ki values are means ( standard deviation (SD) of separate series assays, each performed in triplicate. Inhibition constants (Ki) were calculated according to the equation of Cheng and Prusoff:36 Ki ) IC50/[1 + ([L]/Kd)], where [L] is the ligand concentration and Kd its dissociation constant. The Kd of [3H]prazosin binding to rat cortex membranes was 0.24 nM (R1), and the Kd of [3H]rauwolscine binding to rat cortex membranes was 4 nM (R2). b In parentheses are estimated and predicted affinity values calculated by Catalyst for the training set and test set, respectively. c Compounds used to build the training set. d Compounds used to build the test set.

(compound 1c) to 58.8 nM (compound 2h), a good selectivity was found for compound 1i [R2/R1 ratio of 86.7 (Table 1)] and 1c [R2/R1 ratio of 103.3 (Table 1)]. The goal of this work was the synthesis of new pyridazinone derivatives possibly characterized by high affinity and selectivity toward the R1-adrenoceptors. In this paper, we report the synthesis and biological evaluation of pyridazinone derivatives 3 and 4 (Scheme 1 and Table 2) bearing at the 2-position an orthosubstituted arylpiperazinylalkyl side chain and, at the 5- or 6-position, a furoylpiperazinyl moiety. In addition, taking into account the fact that the new pyridazinones showed a higher affinity toward R1-AR than toward R2-

Molecular Modeling Studies Pharmacophore Generation. Our goal is to gain insight into the structural factors responsible for R1 affinity, and to design new ligands with possibly higher selectivity for the R1 receptor, with respect to the R2AR. In particular, here we report a study that applies a ligand-based drug design (pharmacophore development) method to rationalize the relationships between new pyridazinone-arylpiperazine structures and their affinity data at the R1 receptor. Experimentally determined affinities are used to derive a pharmacophore model that describes the three-dimensional structural properties required to have profitable interactions with R1 receptor sites. Since we were primarily interested in the design of new selective ligands for R1-AR, based on pyridazinonearylpiperazine lead compounds 1-4, and various other molecules collected from the literature (Figure 2 and Table 4), we decided to employ a computational ap-

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Scheme 1a

a Compounds: 3a, n ) 3, X ) OCH ; 3b, n ) 3, X ) Cl; 3c, n ) 2, X ) OCH ; 3d, n ) 2, X ) Cl; 3e, n ) 4, X ) OCH ; 3f, n ) 4, X ) 3 3 3 Cl; 3g, n ) 5, X ) OCH3; 3h, n ) 5, X ) Cl; 3i, n ) 6, X ) OCH3; 3j, n ) 6, X ) Cl; 3k, n ) 7, X ) OCH3; 3l, n ) 7, X ) Cl; 4a, n ) 2, X ) OCH3; 4b, n ) 2, X ) Cl; 4c, n ) 3, X ) OCH3; 4d, n ) 3, X ) Cl; 4e, n ) 4, X ) OCH3; 4f, n ) 4, X ) Cl; 4g, n ) 5, X ) OCH3; 4h, n ) 5, X ) Cl; 4i, n ) 6, X ) OCH3; 4j, n ) 6, X ) Cl; 4k, n ) 7, X ) OCH3; 4l, n ) 7, X ) Cl; 7a, n ) 2; 7b, n ) 4; 7c, n ) 5; 7d, n ) 6; 7e, n ) 7; 11a, n ) 4; 11b, n ) 5; 11c, n ) 6; 11d, n ) 7. Reagents: (a) 6a or 6b, dry EtOH, NaOH; (b) BrCH2CH2Br, benzene, TBAB, KOH; (c) 8 or 9, Na2CO3, isoamyl alcohol; (d) Br(CH2)nBr (n ) 4-7), K2CO3, acetone.

proach to build an “inclusive” pharmacophore for R1AR antagonists, without considering the additional variable of the selectivity between the R1-AR subtypes. In fact, we provided the program with the structure of the compounds to be analyzed and their affinity data, without any information about the selectivity of the R1AR subtypes. The training set for the pharmacophore development has been chosen according to the Catalyst guidelines.18 Fourteen molecules of the whole set of pyridazinones (namely, compounds 1c, 1h, 1i, 2b, 2f, 2h, 2k, 3e, 3g, 3l, 4a, 4e, 4h, and 4l) have been selected as a part of the training set. They are characterized by affinity values spanning ∼2.5 orders of magnitude, the minimum value of 0.6 nM being associated with compound 1c and the maximum value of 184 nM being associated with compound 4e. In addition, with the aim of covering the optimal value of 4 orders of magnitude in affinity required by the program, we investigated literature reports to find some additional R1-AR antagonists with the appropriate biological properties. Within the large set of compounds that were found, to ensure the highest degree of homogeneity of biological data with respect to those of the pyridazinone derivatives, we have focused our attention on such compounds whose antagonist activity on R1-AR was tested on rat cortex homogenates and expressed as Ki. Moreover, because we are mainly interested in compounds sharing common chemical features with pyridazinone derivatives 1-4, we have decided to select only some of the arylpiperazine-bearing molecules found in the literature. Between them, parasubstituted derivatives have not been arbitrarily in-

cluded in the training set, taking into account the findings showing that para substituents (particularly, nitro and amino groups) are unfavorable to ligandreceptor binding. As an example, 12 [Ki ) 0.21 nM (Figure 2)] has been reported to be much more active than its p-methoxy counterpart (Ki > 5000 nM).10 Moreover, a CoMFA study19 on hydantoin-phenylpiperazine derivatives has shown that the affinity of the para-substituted compounds is modulated mainly by steric factors. On the basis of this hypothesis, one may account for the dramatic drop in affinity observed when a nitro or amino group is introduced in the para position with respect to a fluoro substituent that leads to a moderate increase in affinity. In contrast, a different CoMFA study recently published20 led to the conclusion that the para position is forbidden to electronegative substituents. As a consequence of these choices, 10 additional compounds [namely, 12-15, 18, and 20-24 (Figure 2 and Table 4)] have been added to the training set to obtain a total number of 24 structures to be used for pharmacophore generation. Biological data associated with the training set, expressed as Ki, were in the range between 0.21 (compound 12) and 2396 nM (compound 13). While the biological activities of compounds 1 and 2 (Table 1) and 3 and 4 (Table 2) were experimentally determined (see Experimental Section), data associated with the remaining compounds were assembled from the literature (Table 4) under the assumption that all these substances are acting through the same binding site. Because no experimental data on the biologically

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Table 2. R1- and R2-Adrenergic Receptor Binding Affinities for Pyridazin-3(2H)-one Derivatives 3 and 4 Kia (nM) compd

R1-ARb

R2-AR

R2/R1

3a 3bc 3cc 3d 3ed 3f 3gd 3h 3i 3j 3k 3ld 4ad,e 4be 4ce 4d 4ed 4fc 4g 4hd 4ic 4j 4k 4ld prazosin rauwolscine

20.4 ( 2.1 3.5 ( 0.4 (11) 33.0 ( 5.3 (36) 3.9 ( 0.2 33.5 ( 4.7 (15) 19.0 ( 1.7 1.9 ( 0.1 (10) 3.6 ( 0.3 4.1 ( 0.6 5.7 ( 0.5 1.9 ( 0.1 11.5 ( 1.7 (19) 94.5 ( 8.5 (31) 34.0 ( 5.3 64.5 ( 5.7 12.8 ( 1.8 180.0 ( 15.3 (88) 7.9 ( 0.5 (10) 43.7 ( 5.2 13.3 ( 1.2 (34) 17.8 ( 2.5 (20) 5.9 ( 0.6 54.7 ( 6.7 5.6 ( 0.5 (6.3) 0.24 ( 0.05

680.0 ( 120 85.0 ( 18.3 440.0 ( 36 150.0 ( 15.5 870 ( 165 280.0 ( 38.5 23.3 ( 3.2 18.0 ( 2.5 24.5 ( 3.6 66.0 ( 9.2 520.0 ( 4.2 36.2 ( 5.3 NDf 52.6 ( 3.7 631.0 ( 40.5 260.0 ( 24.5 790.0 ( 54.3 24.0 ( 3.8 255.0 ( 25.3 105.0 ( 20.3 100.0 ( 15.2 23.7 ( 2.5 96.0 ( 8.4 23.5 ( 3.0

33.3 24.3 13.3 38.4 26.0 14.7 12.3 5.0 6.0 11.6 273.7 3.1 1.5 9.8 20.3 4.4 3.0 5.8 7.9 5.6 4.0 1.7 4.2

4.0 ( 0.3

a

The Ki binding data were calculated as described in the Experimental Section. The Ki values are means ( SD of separate series assays, each performed in triplicate. Inhibition constants (Ki) were calculated according to the equation of Cheng and Prusoff:36 Ki ) IC50/[1 + ([L]/Kd)], where [L] is the ligand concentration and Kd its dissociation constant. The Kd of [3H]prazosin binding to rat cortex membranes was 0.24 nM (R1), and the Kd of [3H]rauwolscine binding to rat cortex membranes was 4 nM (R2). b In parentheses are estimated and predicted affinity values calculated by Catalyst for the training set and test set, respectively. c Compounds used to build the test set. d Compounds used to build the training set. e Compounds described elsewhere by our research group.17 f ND, not determined.

relevant conformations of the selected compounds (for example, atomic coordinates derived from X-ray crystallographic studies of their complexes with the putative receptor) are available, we resorted to a molecular mechanics approach to build the conformational models to be used for pharmacophore generation. All the conformers of each compound, within a range of 20 kcal/ mol with respect to the global minimum, have been employed to derive a set of pharmacophore hypotheses. Pharmacophore Description. As a result of pharmacophore generation, 100.9 bits as the total fixed cost (ideal hypothesis) and 154.9 bits as the cost of the null hypothesis have been found. The cost range over the 10 best generated hypotheses was 20.4 bits, suggesting that there was a homogeneous set of hypotheses and that the signal generated by this training set was strong. Moreover, the fact that the hypotheses were much closer to the fixed cost (i.e., the cost of hypothesis 1 is 112.9) meant that the signal could be interpreted. The composition (in terms of chemical features that are necessary for activity), ranking score, and statistical parameters associated with the pharmacophore hypotheses are reported in Table 5. Even if the highest scoring hypothesis is usually the most likely to yield relevant information about the pharmacophore elements of a set of compounds, with the aim of extracting the maximum amount of information from the computational run, we

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Table 3. Chemical and Physical Data of the New Compounds compd 3a 3b 3c 3d 3e 3f 3g 3h 3i 3j 3k 3l 4d 4e 4f 4g 4h 4i 4j 4k 4l 5 7a 7b 7c 7d 7e 9a 9b 9c 9d

X OCH3 Cl OCH3 Cl OCH3 Cl OCH3 Cl OCH3 Cl OCH3 Cl Cl OCH3 Cl OCH3 Cl OCH3 Cl OCH3 Cl -

n 3 3 2 2 4 4 5 5 6 6 7 7 3 4 4 5 5 6 6 7 7 2 4 5 6 7 4 5 6 7

formula

mp (°C)

yield (%)

C27H33ClN6O4 C26H30Cl2N6O3 C26H31ClN6O4 C25H28Cl2N6O3 C28H35ClN6O4 C27H32Cl2N6O3 C29H37ClN6O4 C28H34Cl2N6O3 C30H39ClN6O4 C29H36Cl2N6O3 C31H41ClN6O4 C30H38Cl2N6O3 C26H31ClN6O3 C28H36N6O4 C27H33ClN6O3 C29H38N6O4 C28H35ClN6O3 C30H40N6O4 C29H37ClN6O3 C31H42N6O4 C30H39ClN6O3 C13H13ClN4O3 C16H18BrClN4O3 C18H22BrClN4O3 C15H24BrClN4O3 C16H26BrClN4O3 C17H28BrClN4O3 C17H21BrN4O3 C18H23BrN4O3 C18H25BrN4O3 C19H27BrN4O3

183-185a

35 55 70 35 30 25 60 60 50 35 70 65 40 20 70 45 40 65 60 50 35 40 40 65 50 40 30 50 30 30 35

95-100b 214-216a 162-165a 106-110b 228-230b 62-65a 40-45a 96-98b 70-75b 128-130b 35-40b 102-105b 68-70c 35-40b 148-150d 48-52d 55-58b 70-74c 140-147d 75-78b 214-216 130-135 oil oil oil oil oil oil oil oil

a As dihydrochloride monohydrate. b As dihydrochloride. c As trihydrochloride. d As trihydrochloride monohydrate.

have chosen to analyze the three highest-scoring hypotheses among the 10 pharmacophore models generated by the program. All these three pharmacophores exhibit five chemical features and are characterized by either similar composition or spatial location of the features (Table 5). The major difference in composition arises from the replacement of one of the hydrophobic features found in both hypotheses 1 and 2 with a more specific aromatic ring feature (see Table 5 for the composition of hypothesis 3). Nevertheless, the RA-HY1 assembly (these two pharmacophore features were found at a 3.0 Å distance) of hypothesis 3 constitutes a constraint for the matching of the ligands with the pharmacophore. In fact, when the program tries to fit the substituted phenyl ring of the arylpiperazine moiety into the RA-HY1 system, the only possible orientation is that where RA is mapped by the phenyl group and HY1 by the methoxy substituent on the same ring. Thus, hypothesis 3 seems to be too restrictive with respect to the orientation of the substituted phenyl ring. Moreover, it was found to be characterized by a higher cost and rmsd, and by a lower correlation coefficient with respect to the first two hypotheses (Table 5). These findings, combined with the aim of broadening the possible orientations of the substituted phenyl moiety into the above-mentioned portion of the pharmacophore, led us to prefer hypotheses 1 and 2 over hypothesis 3. Hence, only hypotheses 1 and 2, bearing the HY1 and HY2 features (instead of RA and HY1 found in hypothesis 3) potentially mapped by either the phenyl ring or its substituent(s) in alternate conformations, were further investigated. The vector describing HBA makes the difference between hypotheses 1 and 2. The three-dimensional

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Figure 2. Structures of R1-adrenoceptor antagonists collected from the literature. Table 4. Actual and Calculated (Estimated or Predicted) R1-Adrenergic Receptor Binding Affinities for Arylpiperazines Collected from the Literature, As Calculated by Catalyst Ki (nM) compd 12a,b 13a,c 14a,c 15a,c 16c,d 17c,d 18a,c 19c,d 20a,c

actual 0.21 2395 24.6 46.7 42.7 15.6 147 7.9 2308

Ki (nM)

calcd

compd

actual

calcd

0.11 1200 15 11 11 9.2 68 0.86 1000

21a,b

0.34 374 258 516 0.8 11.9 17.5 0.74

0.36 140 380 620 0.46 190 6.7 9.8 (1.1)e

22a,b 23a,b 24a,c 26b,d 27b,d 28b,d 33d

a Compounds belonging to the training set. b Compounds taken from ref 22. c Compounds taken from ref 24. d Compounds belonging to the test set. e In parentheses is the affinity calculated using the manipulated pharmacophore model (see ref 31).

coordinates of the HBA projecting point (corresponding to the position of the hydrogen atom involved in hydrogen bonding with the hydrogen bond acceptor of the ligand) are in fact slightly different between the two hypotheses. On the basis of the very similar composition of the two hypotheses, hypothesis 1, characterized by the best statistical parameters (Table 5), has been chosen to represent “the pharmacophore model”. The regression line (Figure 3) of “true” versus “estimated” R1-AR antagonist affinity for the training set, based on the lowest cost Catalyst-generated hypothesis, exhibited a correlation coefficient r of 0.92, and a rootmean-square deviation (rmsd) of 0.89. Comparison between the estimated affinity of the compounds in the training set relative to their experimentally measured values shows, in the worst case, a