New 2,6,9-trisubstituted adenines as adenosine receptor antagonists: a preliminary SAR profile

A new series of 2,6,9-trisubstituted adenines (5–14) have been prepared and evaluated in radioligand binding studies for their affinity at the human A1, A2A and A3 adenosine receptors and in adenylyl cyclase experiments for their potency at the human A2B subtype. From this preliminary study the conclusion can be drawn that introduction of bulky chains at the N6 position of 9-propyladenine significantly increased binding affinity at the human A1 and A3 adenosine receptors, while the presence of a chlorine atom at the 2 position resulted in a not univocal effect, depending on the receptor subtype and/or on the substituent present in the N6 position. However, in all cases, the presence in the 2 position of a chlorine atom favoured the interaction with the A2A subtype. These results demonstrated that, although the synthesized compounds were found to be quite inactive at the human A2B subtype, adenine is a useful template for further development of simplified adenosine receptor antagonists with distinct receptor selectivity profiles.
Introduction
Adenosine, a naturally occurring nucleoside, is involved in a wide variety of physiological and pathophysiological processes [1]. Adenosine mediates these effects through the activation of at least four human receptor subtypes (P1), belonging to the superfamily of G protein-coupled receptors, which have been recently cloned [2] and classified as A1, A2A, A2B and A3 [3]. The subtypes are classified on the bases of coupling to second messengers and pharmacological profiles for agonists and antagonists. In fact, A1 and A3 adenosine receptor subtypes are linked to inhibition of adenylyl cyclase and A2A and A2B subtypes are linked to stimulation of the same enzyme [4].
In particular, A2B receptors have been implicated in several physiological functions such as the regulation of mast cell secretion [5, 6], gene expression [5, 7, 8], cell growth [9] and intestinal functions. A2B receptors may also play a role in asthma, since they mediate mast cell degranulation from human mast cells and are present in high density in human blood eosinophils [10, 11]. For this reason A2B antagonists could be considered potential antiasthmatic agents [10–12]. While the A1, A2A and A3 adenosine receptors have been pharmacologically characterized through the use of highly potent and selective agonists and/or antagonists, an accurate investigation of the pathophysiological role of A2B receptors is precluded due to the lack of very selective ligands [13]. On the other hand, only recently radiolabelled adenosine antagonists have been used for binding assays at the A2B receptor subtype [1]. Recently, xanthine derivatives, such as compounds 1 and 2 in Fig. 1, have been proposed as potent and selective adenosine receptor antagonists [14, 15]. On the other hand, in the non-xanthine family poor results have been obtained in recent years. However, mention should be made of the pyrazolo-triazolo-pyrimidine derivative 3, which showed promising binding affinity at the A2B adenosine receptor although the level of selectivity vs the human (h) A3 subtype was still poor [16]. Very recently, a bipyrimidyl derivative 4 has been proposed as an A2B adenosine receptor antagonist, with affinity in the same range of compound 3, while the selectivity vs the other receptor subtypes was found to be significantly better [17] (Fig. 1).
Fig. 1Structures and binding profile (Ki nM) of some A2B adenosine receptor antagonists
A structural analysis of the derivatives 1 and 3 clearly shows the presence of bulky substituents such as aryloxyacetylamino-phenyl groups at the 8 position (compound 1) and arylacetyl moiety at the N5 position (compound 3).
On the other hand, in recent years a number of substituted adenines have been synthesized and tested at the four adenosine receptor subtypes, demonstrating that the introduction of different substituents at the 2, 8 and 9 positions of the adenine core resulted in high-affinity antagonists with distinct receptor selectivity profile [18–22]. At the A2B receptor the derivatives bearing an ethyl in the 9 position and linear chains in the 2 position showed potency in the μM range, while the presence of sterically hindered substituents in the same positions was detrimental for the potency. Furthermore, substitution of the 9-ethyl group with a propyl chain seems to favour the interaction with human A2B receptors [21].
Hence, on the basis of the results obtained with compound 3, introduction of a bulky substituent on the N6 amino group of the adenine and 2-chloroadenine moiety could increase potency and selectivity for the human A2B adenosine receptor subtypes.
Hence, bulky substituents such as arylacetyl or aryloxyphenylacetyl moieties were introduced on the N6 amino group of compounds 5 and 6 to obtain derivatives 7–14, with the aim of finding a new class of A2B antagonists (Fig. 2).
Fig. 2Structures of designed compounds
Chemistry
The designed compounds (5–14) have been synthesized as summarized in Schemes 1 and 2. The starting 9-propyladenine (5) [23] was obtained by alkylation of commercially available adenine (15) with propyliodide in the presence of potassium carbonate. Flash chromatography led to the desired 9-substituted isomer 5 as the major product (yield 79%) together with the 7-isomer 5a (yield 9%). Isomeric structure of compounds 5 and 5a was assigned on the bases of 1D-1H-NOE difference spectra. In fact, irradiation of both CH2 groups of the propylic chain in compound 5a gave a NOE at both H-C(8) and 6-NH2 groups, demonstrating the 7 position as the alkylation site. On the contrary, a NOE at both H-C(8) and H-C(2), and not at the 6-NH2 group in compound 5, upon saturation of CH2 groups of the propylic chain, confirmed the 9 position as the alkylation site.
Scheme 1a Synthesis of 9-propyladenine; b Synthesis of 2-chloro-9-propyladenine. Reagents: i: DMF, K2 CO3, propyliodide, RT; ii: liq. NH3, sealed tube, RTScheme 2Reagents: i: CDI, dry THF, reflux
The 2-chloro derivative 6 was obtained by alkylation of commercially available 2,6-dichloropurine (16) with propyliodide, using the same procedure utilized for compound 5, to afford the 9-substituted isomer 17 as the major product (yield 75%) along with the 7-isomer 17a (yield 10%) [24]. [1H]-NMR spectra of 17 and 17a in CDCl3 are in agreement with those reported in the literature [24]; in the experimental part [1H]-NMR spectra of the same compounds are reported using dimethyl sulfoxide (DMSO) as the solvent. Compound 17 was reacted with liquid ammonia in a sealed tube at room temperature (RT) overnight to give the 2-chloro-9-propyladenine (6) [24] (Scheme 1).
Final compounds 7–14 were obtained by condensation of amino compounds 5 or 6 with the appropriate acid 18–22 [25] in the presence of carbonyldiimidazole in tetrahydrofurane (THF) at reflux for 18 h (Scheme 2).
Results and discussion
All the compounds were evaluated at the human recombinant adenosine receptors, stably transfected into Chinese hamster ovary (CHO) cells, utilizing radioligand binding studies (A1, A2A, A3) or adenylyl cyclase activity assay (A2B). Receptor binding affinity was determined using [3H]CCPA (2-chloro-N6-cyclopentyladenosine) as the radioligand for A1 receptors, whereas [3H]NECA (5’-N-ethylcarboxamidoadenosine) was used for the A2A and A3 subtypes. In the case of A2B receptors Ki values were calculated from IC50 values determined by inhibition of NECA-stimulated adenylyl cyclase activity. Ki values are in μM, with 95% confidence intervals in parentheses [26]. The results of binding and cyclase activity studies are reported in Table 1.
Table 1Biological profile of synthesized compounds 5–14aDisplacement of specific [3H]-CCPA binding at human A1 receptors expressed in CHO cells. bDisplacement of specific [3H]-NECA binding at human A2A receptors expressed in CHO cells. cKi values of the inhibition of NECA-stimulated adenylyl cyclase activity in CHO cells expressing human A2B receptors. dDisplacement of specific [3H]-NECA binding at human A3 receptors expressed in CHO cells.
All the tested compounds 5–14 showed affinities at the human A1, A2A and A3 adenosine receptors in the μM range without significant levels of selectivity. At A2B receptors most compounds were found to be inactive when tested at a concentration up to 100 μM (Ki values > 30 μM). It is quite evident that the introduction of phenylacetic or aryloxyphenylacetic moieties at the N6 position of 9-propyladenine (5) or of 2-chloro-9-propyladenine (6) to give compounds 7–14 modifies the binding profile of the derivatives, although without significantly increasing binding affinity (Table 1). On the other hand, the same substitutions were found to be detrimental for the activity at the A2B receptor subtype. In fact, the N6-unsubstituted derivative 6 proved to be the most potent of the series with Ki A2B = 11 μM.
The effect of the chlorine at the 2 position on binding affinity at the adenosine receptors it is not univocal, depending on the receptor subtype and/or on the substituent in N6. Analysis of the binding profile of the N6-unsubstituted derivatives in more detail revealed that the presence of a chlorine atom at the 2 position (compound 6) increased the affinity (A1, A2A and A3) and potency (A2B) at adenosine receptors two- to threefold compared with the unsubstituted analogue 5.
A quite similar profile could be observed when a 4-bromophenylacetic group was introduced at the N6 position (compare compound 8 with 7).
An opposite effect of the chlorine atom was detected at A1, A2B and A3 receptors when a bulkier substituent, such as the 4-aryloxyphenylacetic chain, was introduced at the N6 position. In fact, this kind of combination significantly reduced or did not modify the A1 and A3 affinity and the A2B potency (9: Ki A1 = 1.4 μM, Ki A2B > 30 μM, Ki A3 = 5.3 μM vs 10: Ki A1 = 26 μM, Ki A2B > 30 μM, Ki A3 = 4.9 μM and 12: Ki A1 = 13 μM, Ki A2B = 22 μM, Ki A3 = 10 μM vs 13: Ki A1 = 22 μM, Ki A2B > 30 μM, Ki A3 = 19 μM).
However, in all cases, the presence of a chlorine atom in the 2 position favoured the interaction with the A2A subtype (compare A2A affinity of 5, 7, 9 and 12 with 6, 8, 10 and 13, respectively), while the presence of any substituent on the N6 position seems to somewhat reduce the affinity. In fact the compound endowed with the highest A2A affinity proved to be the 2-chloro-9-propyladenine (6: Ki A2A = 2.2 μM). These findings are in agreement with previous observations related to adenosine analogues strongly suggesting that the introduction of substituents in the N6 position dramatically reduces the A2A affinity [27–32].
Nevertheless, it should be underlined that the presence of a bulky chain at the N6 position significantly increased (20- to 70-fold) the affinity at the A1 and A3 subtypes in comparison with 9-propyladenine (9: Ki A1 = 1.4 μM and 11: Ki A3 = 1.4 μM vs 5: Ki A1 = 24 μM and Ki A3 > 100 μM).
This increase of affinity seems also to be modulated by the substituent on the aryloxyphenylacetic group; in fact, substitution with a lipophilic bromine (9) or methyl group (11) at the para position is responsible for the increased A1 and A3 receptor affinity, respectively, while the presence of a hydrogen (12) or a methoxy group (14) did not positively influence the binding profile.
Conclusions
In conclusion the study herein presented, although it did not reach the proposed goal of obtaining A2B adenosine receptor antagonists, increased knowledge of the structure-activity relationships in adenine derivatives.
Moreover, it was demonstrated that the introduction of bulky substituents at the N6 position of adenine derivatives significantly increased the affinity at the A1 and A3 adenosine receptors, while the presence of a chlorine atom in the 2 position favoured the interaction with the A2A subtype. These results demonstrated that, although the synthesized compounds were found to be quite inactive at the human A2B subtype, adenine is a useful template for further development of simplified adenosine receptor antagonists with distinct receptor selectivity profiles, opening up new chances to design structurally simplified A1 and A3 adenosine receptor antagonists.
Experimental section
Chemistry
General: melting points were determined with a Büchi apparatus and are uncorrected. 1H NMR spectra were obtained with Varian VXR 300 MHz spectrometer; δ in ppm, J in Hz. All exchangeable protons were confirmed by addition of D2O. Thin layer chromatography (TLC) was carried out on precoated TLC plates with silica gel 60 F-254 (Merck). For column chromatography, silica gel 60 (Merck) was used. Elemental analyses were determined on Fisons Instruments Model EA 1108 CHNS-O model analyser and are within ± 0.4% of theoretical values.
9-Propyladenine (5) and 7-propyladenine (5a)
To a solution of adenine (15) (0.5 g, 3.7 mmol) in dry DMF (10 ml), under nitrogen, K2CO3 (0.83 g, 5.97 mmol) and propyliodide (0.433 ml, 4.44 mmol) were added. The mixture was stirred at RT for 16 h, then the solvent was removed under reduced pressure and the crude purified by flash chromatography (CHCl3-MeOH 98:2) to afford 5 [23] and 5a (yield 79 and 9%, respectively) as white solids, after crystallization from CH3OH.
5: m.p. 173–175°C. 1H-NMR (DMSO-d6) δ 0.85 (t, 3H, J = 7.4, CH3); 1.82 (m, 2H, CH2-CH3); 4.11 (t, 2H, J = 7.0, CH2-N); 7.21 (bs, 2H, NH2); 8.15 (s, 2H, H-2 and H-8). Anal. Calcd. for C5H5N5 (177.2) C, 54.22; H, 6.26; N, 39.52; found: C, 54.54; H, 6.71; N, 39.33.
5a: m.p. >250°C. 1H-NMR (DMSO-d6) δ 0.86 (t, 3H, J = 7.3, CH3); 1.91 (m, 2H, CH2-CH3); 4.26 (t, 2H, J = 7.0, CH2-N); 7.75 (s, 1H, H-2); 7.85 (bs, 2H, NH2); 8.34 (s, 1H, H-8). Anal. Calcd. for C5H5N5 (177.2) C, 54.22; H, 6.26; N, 39.52; found: C, 54.45; H, 6.45; N, 39.45.
2,6-Dichloro-9-propyl-9H-purine (17) and 2,6-dichloro-7-propyl-9H-purine (17a)
To a solution of 2,6-dichloropurine (16) (1 g, 5.29 mmol) in dry DMF (14 ml), under nitrogen, K2CO3 (1.18 g, 6.61 mmol) and propyliodide (0.59 ml, 6.08 mmol) were added. The mixture was stirred at RT overnight, then the solvent was removed under reduced pressure and the crude purified by flash chromatography (cC6H12-EtOAc 75:25) to afford 17 and 17a as white solids (yield 75 and 10%, respectively) [24].
17: m.p. 58–59°C; 1H-NMR (DMSO- d6) δ 0.86 (t, 3H, J = 7.5 Hz, CH2CH3), 1.85 (m, 2H, CH2CH3), 4.21 (t, 2H, J = 7.0 Hz, N-CH2), 8.76 (s, 1H, H-8). Anal. Calcd. for C8H8Cl2N4 (231.1) C, 41.58; H, 3.49; N, 24.25. Found: C, 41.85; H, 3.70; N, 24.10.
17a: m.p. 103–105°C; 1H-NMR (DMSO-d6) δ 0.87 (t, 3H, J = 7.4 Hz, CH2CH3), 1.84 (m, 2H, CH2CH3), 4.40 (t, 2H, J = 7.2 Hz, N-CH2), 8.89 (s, 1H, H-8). Anal. Calcd. for C8H8Cl2N4 (231.1) C, 41.58; H, 3.49; N, 24.25. Found: C, 41.75; H, 3.55; N, 24.19.
2-Chloro-9-propyladenine (6)
Liquid ammonia (5 ml) and compound 17 (0.46 g, 1.97 mmol) were poured into a sealed tube and the resulting mixture was stirred at RT overnight. Ammonia was evaporated and the crude purified by flash chromatography (CHCl3-MeOH 99:1) to give 6 [24] as a white solid (yield 75%) m.p. 224–226°C. 1H-NMR (DMSO-d6) δ 0.84 (t, 3H, J = 7.3 Hz, CH2CH3), 1.79 (m, 2H, CH2CH3), 4.05 (t, 2H, J = 7.2 Hz, N-CH2), 7.72 (s, 2H, NH2), 8.15 (s, 1H, H-8). Anal. Calcd. for C8H10ClN5 (211.7) C, 45.40; H, 4.76; N, 33.09. Found: C, 45.75; H, 4.80; N, 32.87.
General procedure for the preparation of the N6-acylaminoadenine (7–14)
A solution in dry THF (4 ml) of the appropriate acid (18–22) (0.46 mmol) and carbonyldiimidazole (83 mg, 0.51 mmol) was poured at reflux under nitrogen for 1 h. Then the amino compound 5 or 6 (0.46 mmol) was added and the resulting mixture was refluxed overnight. The solvent was removed under reduced pressure and the crude purified by flash chromatography to afford the desired final compounds 7–14.
6-[(4-Bromophenyl)acetyl]amino-9-propyladenine (7)
Eluent for chromatography CHCl3-MeOH 95:5; yield 59%, white solid; m.p. 149–151°C (dec.); 1H-NMR (DMSO-d6): δ 0.83 (t, 3H, J = 7.2 Hz, CH2CH3), 1.84 (m, 2H, CH2CH3), 3.89 (s, 2H, CH2-CO), 4.19 (t, 2H, J = 7.1 Hz, N-CH2), 7.30 (d, 2H, J = 8.4 Hz, H-Ph), 7.51 (d, 2H, J = 8.4 Hz, H-Ph), 8.47 (s, 1H, H-8), 8.62 (s, 1H, H-2), 10.91 (s, 1H, NH). Anal. Calcd. for C16H16BrN5O (374.2) C, 51.35; H, 4.31; N, 18.71. Found: C, 51.65; H, 4.80; N, 18.50.
6-[(4-Bromophenyl)acetyl]amino-2-chloro-9-propyladenine (8)
Eluent for chromatography CHCl3-cC6H12 80:20; yield 26%, white solid; m.p. 164–166°C; 1H-NMR (DMSO-d6): δ 0.84 (t, 3H, J = 7.5 Hz, CH2CH3), 1.83 (m, 2H, CH2CH3), 3.88 (s, 2H, CH2-CO), 4.15 (t, 2H, J = 6.9 Hz, N-CH2), 7.30 (d, 2H, J = 8.4 Hz, H-Ph), 7.53 (d, 2H, J = 8.4 Hz, H-Ph), 8.50 (s, 1H, H-8), 11.25 (s, 1H, NH). Anal. Calcd. for C16H15BrClN5O (408.7) C, 47.02; H, 3.70; N, 17.14. Found: C, 47.49; H, 3.83; N, 17.40.
6-[(4-(4-Bromobenzyloxy)phenyl)acetyl]amino-9-propyladenine (9)
Eluent for chromatography CHCl3-MeOH 95:5; yield 58%, white solid; m.p. 154–156°C; 1H-NMR (DMSO-d6): δ 0.85 (t, 3H, J = 7.5 Hz, CH2CH3), 1.85 (m, 2H, CH2CH3), 3.82 (s, 2H, CH2-CO), 4.21 (t, 1H, J = 7.0 Hz, N-CH2), 5.07 (s, 2H, CH2-O), 6.95 (d, 2H, J = 8.8 Hz, H-Ph), 7.27 (d, 2H, J = 8.4 Hz, H-Ph), 7.40 (d, 2H, J = 8.4 Hz, H-Ph), 7.58 (d, 2H, J = 8.4 Hz, H-Ph), 8.48 (s, 1H, H-8), 8.62 (s, 1H, H-2), 10.81 (s, 1H, NH). Anal. Calcd. for C23H22BrN5O2 (480.4) C, 57.51; H, 4.62; N, 14.58. Found: C, 57.99; H, 4.66; N, 14.55.
6-[(4-(4-Bromobenzyloxy)phenyl)acetyl]amino-2-chloro-9-propyladenine (10)
Eluent for chromatography CHCl3-cC6H12-MeOH 50:48:2; yield 14%, white solid; m.p. 176–178°C; 1H-NMR (DMSO-d6): δ 0.81 (t, 3H, J = 7.5 Hz, CH2CH3), 1.79 (m, 2H, CH2CH3), 3.76 (s, 2H, CH2-CO), 4.12 (t, 2H, J = 7.4 Hz, N-CH2), 5.03 (s, 2H, CH2-O), 6.92 (d, 2H, J = 8.6 Hz, H-Ph), 7.22 (d, 2H, J = 8.4 Hz, H-Ph), 7.36 (d, 2H, J = 8.4 Hz, H-Ph), 7.54 (d, 2H, J = 8.4 Hz, H-Ph), 8.46 (s, 1H, H-8), 11.11 (s, 1H, NH). Anal. Calcd. for C23H21BrClN5O2 (514.8) C, 53.66; H, 4.11; N, 13.60. Found: C, 53.75; H, 4.25; N, 13.29.
6-[(4-(4-Methylbenzyloxy)phenyl)acetyl]amino-9-propyladenine (11)
Eluent for chromatography CHCl3-cC6H12-MeOH 70:28:2; yield 35%, white solid; m.p.131–132°C; 1H-NMR (DMSO-d6): δ 0.83 (t, 3H, J = 7.3 Hz, CH2CH3), 1.84 (m, 2H, CH2CH3), 2.28 (s, 3H, CH3-Ph), 3.80 (s, 2H, CH2-CO), 4.20 (t, 2H, J = 7.1 Hz, N-CH2), 5.02 (s, 2H, CH2-O), 6.93 (d, 2H, J = 8.4 Hz, H-Ph), 7.24 (m, 6H, H-Ph), 8.47 (s, 1H, H-8), 8.62 (s, 1H, H-2), 10.83 (s, 1H, NH). Anal. Calcd. for C24H25N5O2 (415.5) C, 69.38; H, 6.06; N, 16.86. Found: C, 69.74; H, 6.35; N, 16.54.
6-[(4-Benzyloxyphenyl)acetyl]amino-9-propyladenine (12)
Eluent for chromatography CHCl3-MeOH 97:3; yield 52%, white solid; m.p. 121–123°C; 1H-NMR (DMSO-d6): δ 0.83(t, 3H, J = 7.5 Hz, CH2CH3), 1.84 (m, 2H, CH2CH3), 3.80 (s, 2H, CH2-CO), 4.19 (t, 2H, J = 7.1 Hz, N-CH2), 5.07 (s, 2H, CH2-O), 6.95 (d, 2H, J = 8.4 Hz, H-Ph), 7.25 (d, 2H, J = 8.8 Hz, H-Ph), 7.37 (m, 5H, H-Ph), 8.47 (s, 1H, H-8), 8.62 (s, 1H, H-2), 10.82 (s, 1H, NH). Anal. Calcd. for C23H23N5O2 (401.5) C, 68.81; H, 5.77; N, 17.44. Found: C, 68.97; H, 5.89; N, 17.35.
6-[(4-Benzyloxyphenyl)acetyl]amino-2-chloro-9-propyladenine (13)
Eluent for chromatography CHCl3-MeOH 99:1; yield 17%, white solid; m.p. 150–152°C; 1H-NMR (DMSO-d6): δ 0.84 (t, 3H, J = 7.3 Hz, CH2CH3), 1.82 (m, 2H, CH2CH3), 3.79 (s, 2H, CH2-CO), 4.14 (t, 2H, J = 7.0 Hz, N-CH2), 5.07 (s, 2H, CH2-O), 6.95 (d, 2H, J = 8.6 Hz, H-Ph), 7.25 (d, 2H, J = 8.4 Hz, H-Ph), 7.37 (m, 5H, H-Ph), 8.49 (s, 1H, H-8), 11.16 (s, 1H, NH). Anal. Calcd. for C23H22ClN5O2 (435.9) C, 63.37; H, 5.09; N, 16.07. Found: C, 63.56; H, 5.14; N, 15.76.
6-{[(4-Methoxybenzyloxy)phenyl]acetyl}amino-2-chloro-9-propyladenine (14)
Eluent for chromatography CHCl3-MeOH 99:1; yield 29%, white solid; m.p. 174–176°C; 1H-NMR (DMSO-d6): δ 0.84 (t, 3H, J = 7.4 Hz, CH2CH3), 1.82 (m, 2H, CH2CH3), 3.73 (s, 3H, CH3-O), 3.79 (s, 3H, CH2-CO), 4.14 (t, 2H, J = 7.0 Hz, N-CH2), 4.98 (s, 2H, CH2-O), 6.92 (d, 2H, J = 8.0 Hz, H-Ph), 6.93 (d, 2H, J = 8.4 Hz, H-Ph), 7.24 (d, 2H, J = 8.4 Hz, H-Ph), 7.35 (d, 2H, J = 8.4 Hz, H-Ph), 8.49 (s, 1H, H-8), 11.15 (s, 1H, NH). Anal. Calcd. for C24H24ClN5O3 (465.9) C, 61.87; H, 5.19; N, 15.03. Found: C, 61.99; H, 5.33; N, 14.91.
Biology
All pharmacological methods followed the procedures as described earlier [26]. In brief, membranes for radioligand binding were prepared from CHO cells stably transfected with human adenosine receptor subtypes in a two-step procedure. In a first low-speed step (1,000 g) cell fragments and nuclei were removed. The crude membrane fraction was sedimented from the supernatant at 100,000 g. The membrane pellet was resuspended in the buffer used for the respective binding experiments, frozen in liquid nitrogen and stored at −80°C. For the measurement of adenylyl cyclase activity only one high speed centrifugation of the homogenate was used. The resulting crude membrane pellet was resuspended in 50 mM Tris/HCl, pH 7.4 and immediately used for the cyclase assay.
For radioligand binding at A1 adenosine receptors 1 nM [3H]CCPA was used, whereas 30 and 10 nM [3H]NECA were used for A2A and A3 receptors, respectively. Non-specific binding of [3H]CCPA was determined in the presence of 1 mM theophylline; in the case of [3H]NECA 100 pM R-PIA was used. Ki values from competition experiments were calculated with the program SCTFIT [33]. At A2B adenosine receptors inhibition of NECA-stimulated adenylyl cyclase activity was used as a measurement of potency of the new compounds. IC50 values from these experiments were converted to Ki values with the Cheng and Prusoff equation [34].

adenosine receptors
adenosine receptor antagonists
g protein-coupled receptors
adenine derivatives
adenosine receptor ligands


Architecture for Knowledge-Based and Federated Search of Online Clinical Evidence

Background It is increasingly difficult for clinicians to keep up-to-date with the rapidly growing biomedical literature. Online evidence retrieval methods are now seen as a core tool to support evidence-based health practice. However, standard search engine technology is not designed to manage the many different types of evidence sources that are available or to handle the very different information needs of various clinical groups, who often work in widely different settings.
Introduction
Clinicians need to keep up-to-date with the biomedical literature in order to practice according to the best available evidence. However, this has become increasingly difficult as the amount of medical literature a clinician needs to consider grows exponentially [1,2]. As a result, the effort required to find a specific piece of evidence increases year after year [3]. Clinicians typically work under time pressure, which compounds the problem. The need to develop robust methods and tools to support evidence access is now widely recognized. Online evidence retrieval methods are increasingly seen as a core tool in support of evidence-based health care [4]. In the traditional model of online evidence services, clinicians have access to a number of online information sources, such as journals, databases, and Medline, each with its own idiosyncrasies and search interfaces. This means users need to know which resources are most suitable for their current question and how the search query must be formulated for a given resource. Interoperability standards for the efficient dissemination of content are being developed (eg, the Open Archive Initiative [5]), but until the majority of content adheres to such standards, there is still a need to search through heterogenous data sources.
The meta-search engine approach [6,7] addresses many of the limitations of these models by providing a mechanism to search all the available resources at one time and by translating user queries into the respective query languages of each resource. This typically uses a least-common-denominator approach, directly passing on user keywords to different information sources without regard for the specific capabilities or limitations of these resources. For example, a meta-search engine often disregards the rich query language available with some resources in order to simplify the overall meta-search process. Consequently, while the user expects the meta-search to return an integrated set of search results, the reality is that some resources would have been able to perform much better had they been queried individually; the user is unaware of the variations in search quality across the different resources that have been queried for them. Variants of the standard meta-search engine approach have been shown [8] to provide search capabilities beyond the least common denominator but still require users to select the resources they wish to search. One solution to this problem is to “federate” the different resources so that they more genuinely behave as one uniform data source. A federated search system may perform a syntactic reformulation of a user query, translating it into queries that have been optimized for the native query language of individual evidence sources. Semantic reformulation is also possible [9]. For example, user keywords may be translated into equivalent keywords or phrases using a terminological system.
However, a federated search can still produce an excessive number of candidate documents, or hits, many of them failing reasonable tests of relevance. One way to improve the chance of retrieving clinically relevant information is to pre-program a search system with specialist bibliographic knowledge using search filters. Search filters capture expert strategies for searching that are known to improve the precision of searches. For example, Medline offers a small set of “clinical queries,” which are pre-defined and validated search filters optimized to retrieve documents that are most likely to be clinically relevant, emphasizing disease etiology, diagnosis, therapy, or prognosis [10,11]. Such search filters are necessarily highly customized to the capabilities of individual information sources and their native search engines. For a federated search system to consistently use search filters, it would need to develop a generalized approach to search filters, or meta-search filters. Quick Clinical (QC) [4] is a federated evidence retrieval system designed to meet the specific needs of clinicians. Its design incorporates the novel use of meta-search filters to optimize search strategies, and it is based upon a wrapper-mediator architecture built around a universal query language. This paper describes the system architecture of QC and the technical challenges to the design of online evidence retrieval systems, and it reports on the technical performance of the system from a clinical trial with primary care physicians.
Methods
The Quick Clinical System
User Interface
In the QC user model, a user is presented with a single query interface, which connects to an arbitrarily large number of federated knowledge sources and incorporates query specific meta-search filters called “profiles.” QC guides users to first consider the purpose of their search through selection of a profile, and it then asks them to provide specific keywords related to that search task. As a consequence, users are guided through a process that structures their query for them and improves the chances that they will ask a well-formed query and receive an appropriate answer. Figure 1 depicts the QC search interface. On the left hand is a list of search filters that describe typical search tasks and that are customized to the specific information needs of different user groups. Figure 1 shows filters specifically designed for use by primary care physicians.
Figure 1
The QC search query user interface
In QC, individual profiles are able to define different keyword types, such as “disease,” which describe the keyword classes typically associated with that profile. Thus, on the right of the interface are four fields where users can provide keywords describing the specific attributes of their search. Selection of a different profile may thus alter the keyword types requested from the user for a given search. QC then translates and submits search queries to the sources specified in the chosen profile, collects and processes the results, and presents them to the user as a list of documents (Figure 2). The title of a document is followed by the link and a short abstract of the content. A user can drill-down into a specific group of results by source type (eg, journal articles or guidelines).
Figure 2
Screenshot of a QC results page
Quick Clinical System Architecture
Overview
Most information sources such as websites, online texts, and databases have their own proprietary search interface, including query language and format for the display of results. Therefore, a federated meta-search engine that wishes to query a number of different information sources needs to first represent a user query using some internal query language [13] and then translate that internal query into the specific query languages of the relevant data sources. A well-documented [12] approach to this problem is to use a “wrapper” (Figure 3), which acts as an adapter between the proprietary language of individual information sources and the internal language used within a meta-search system. In QC, the internal query language is called the unified query language (UQL). Each information source known to QC has its own wrapper that translates queries from UQL into the native language of the source. As a result, internal components of QC only need to know UQL and not the individual query languages of the data sources. System maintenance is also simplified since the introduction of a new data source to the system only requires one new wrapper component to be generated. Once the results of a search are returned by an information source, the information must again be translated into a standard output format for presentation to the user, which, in QC, is called the unified response language (UReL). UReL also allows other components in the system to modify the presentation of search results without needing to understand the presentation format of individual sources (eg, to remove duplicate documents). In Figure 3, a search is initiated from the user interface, which forwards a query (in XML) to the mediator. The mediator splits the query into several subqueries and sends these to the appropriate wrapper (via a capability manager if required). Finally, the wrapper translates the query into the native query language of the data source (eg, in HTML for Web data sources). Similarly, the result from the data source gets translated back into the system’s XML representation and sent back to the user interface.
Figure 3
Architecture overview of Quick Clinical
Unified Query Language
UQL is used to represent queries obtained from users in a consistent internal way, and UQL statements identify query elements such as the external information sources to be searched and a set of search attributes used to delimit the search. For example, UQL expressions can store date range delimiters for a search. UQL also contains statements that indicate whether or not QC needs to process the query further. For example, we may wish to remove duplicate items obtained from different sources. In our current implementation, UQL is implemented using XML. To define the structure of the data within the XML document we use a data type definition (DTD), which allows various internal components of QC to validate the XML data received in the UQL query. The following example illustrates how a UQL query might look in XML.
<QUERY keyword = "iron AND deficiency"
profile = "treatment"
duplicateRemoval = "yes"
sortBy = "rank"
useLexicalVariants = "yes"
timeout = "20"
dateRangeBeginDay = "1"
dateRangeBeginMonth = "1"
dateRangeBeginYear = "1999" >
<SOURCE name = "PubMed" />
<SOURCE name = "Harrison’s online" />
<SOURCE name = "Merck" />
<SOURCE name = "MIMS" />
</QUERY>
Unified Response Language
Similarly to the UQL, the unified response language (UReL) is used internally to guide display of information to users, also represented using XML. Each separate result, or “article,” from a source can be broken up into smaller chunks and given meta-data labels to represent the different sections of the data (eg, abstracts from journal articles). Since the majority of sources accessed by QC are journals, the data that are retrieved typically contain document elements such as Title, Author(s), Journal Name, Date of Publication, and the URL where the electronic version of the paper is accessed. Other sources, such as drug descriptions from pharmaceutical compendia, have sections such as Drug Name and Manufacturer. These different document elements, based upon the typical sources QC expects to find, are defined as specific fields in the UReL definition. The following example illustrates how a set of documents retrieved by QC might be represented in UReL.
<RESULT>

<LINK>
<HREF>
http://www.ncbi.nlm.nih.gov:80
/entrez/query.fcgi?cmd=Retrieve&db=PubMed
&list_uids=12198020&dopt=Abstract
</HREF>
<LINKNAME>Abstract</LINKNAME>
</LINK>
<AUTHORLIST>Heath AL,
Skeaff CM,
Gibson RS.
</AUTHORLIST>
<TITLE>
Dietary treatment of iron deficiency
</TITLE>
<DATE>
<YEAR>2002</YEAR>
<MONTH>9</MONTH>
</DATE>
<SOURCE>PubMed</SOURCE>

<LINK>
<HREF>
http://mims.hcn.net.au
/ifmx-nsapi/mims-data/?MIval=2MIMS_abbr_pi
&product_code=288
&product_name=Ferrum+H+Injection
</HREF>
<LINKNAME>More Information</LINKNAME>
</LINK>
<AUTHORLIST>
Sigma Pharmaceuticals Pty Ltd.
</AUTHORLIST>
<TITLE>Ferrum H Injection</TITLE>
<SOURCE>MIMS</SOURCE>

</RESULT>
Wrappers
For every information source known to QC, there is a specific wrapper that translates a UQL query into the native query language and format of the source. The wrapper also extracts the relevant information from the HTML result pages returned by the search engine and re-expresses it in UReL. Figure 4 shows the basic architecture of wrappers in our current system. Each wrapper has three main components: a feeder, extraction rules, and a sieve. The feeder converts the user query into the native query language of the data source. The data source responds to the query and returns HTML raw data. The feeder passes the raw data to the sieve, which converts it to UReL in XML format by using the extraction rules for the data source. The UReL is then sent back via other components to the user interface, which can interpret the XML and display the results.
Figure 4
Wrapper components
Mediator
A key requirement of a multisource information retrieval system is the ability to perform concurrent searches on multiple sources with a single query [6,7]. The mediator addresses this requirement. The mediator first analyzes a query and determines how many sources are to be searched. It then creates a separate search job for each of these sources and forwards the search job to other system components. Additionally, the mediator collects individual results as they arrive and amalgamates them for the user into a single result. By introducing parallelism, the time to perform a search across a number of resources should be reduced to the duration of the slowest source. However, the potential drawback of parallel processing is the increased administration overhead of running multiple parallel processes within a system. As a rule of thumb, we would expect the benefits of parallel execution should increase with the number of sources queried, as response times for Web resources can be many seconds long, and computational execution of processes to manage parallel search are typically much less than one second.
Connection speed and latency of response time from sources are, for practical purposes, nondeterministic in an Internet environment, and a meta-search engine can therefore experience large fluctuations in responses from the same source under different circumstances. Latency is subject to network traffic conditions, making it impossible to guarantee that all resources that are queried at a particular time will respond predictably and equally. To counter this, the mediator has a time-out feature. If a response is not received within the time-out specified by a profile, the mediator will cancel a subsearch and forward all the results currently available from other sources to the user interface. This effectively guarantees a defined response time irrespective of the state of the individual data sources and provides some control over the speed/accuracy trade-off.
Capability Manager
Search capabilities vary considerably between the search engines that QC might wish to interrogate, and some sources will have limitations in their ability to process search queries. One approach to this problem is to try to raise all sources to as high a level of common performance as possible by emulating missing capabilities locally, usually by modifying the query and/or search result [13]. A trivial example is mimicking the ability to perform a Boolean search when a data source does not have this capability. To emulate a Boolean AND, a meta-search engine would perform two parallel individual searches on the source and then itself perform the Boolean operation on the two results.
In QC, a capability manager (CM) is responsible for mimicking a range of search capabilities and is located between the mediator and wrapper. The CM may modify a query and/or the result depending on the capabilities of the sources about to be queried. Capabilities of the CM within the QC system included the following:
Date-CM: search within a date range
Duplicate-CM: remove document duplicates
Sort-CM: sort results by title, author, document rank, or date
Lexical-CM: expand a search term with lexical variants of the term. A lexical variant is a synonym, pluralization, hyphenation, or other modification that changes the text but not its meaning. Lexical variants are particularly important in the medical domain [14] because many concepts can be expressed in Latin or English (eg, cardio vs heart). Moreover, there is a common confusion between terms in American English versus British English (eg, hemoglobin vs haemoglobin, epinephrine vs adrenaline).
QC uses a stacking mechanism to insert individual CMs into the processing of queries for wrappers and the processing of results from a source. A component called the search planner, containing simple rules, is responsible for stacking the CMs. This means that the sequence of CMs can be ordered to ensure the correct outcome of query or result translations. Theoretically, this corresponds to a composition of operations. A lexical variant CM, for example, has to replace the search terms in the query before the wrapper executes the search. The Date-CM, on the other hand, can only perform its job after the successful execution of the wrapper.
Search Filters
Expert searchers typically will use search strategies that are more likely to accurately locate information, based upon an understanding of the specific capabilities of an evidence source. There is an increasing interest in the writing of search filters which capture such strategies, usually focusing on the major evidence repositories like Medline [10,11]. Search filters are designed for typical clinical queries such as “diagnosis” or “prescribing,” and they are crafted to find evidence most likely to satisfy the query by first selectively searching resources identified to be of high quality and, second, by automatically adding specialist keywords to the general question posed by a user. Within QC, search filters are stored in the profiles function. For example, if a clinician selects the “diagnosis” filter and enters the search term “asthma,” QC can add in the additional terms when it queries Medline [10]:
sensitivity and specificity [MESH] OR sensitivity [WORD] OR diagnosis [SH] OR diagnostic use [SH] OR specificity [WORD]
These terms have been shown to significantly enhance the quality of Medline results, but they are unlikely to be known to a typical clinical user.
Unlike standard search filters, QC profiles are meta-search filters because they encode search filters for multiple different sources. Profiles thus encode expert search strategies that are most likely to answer a certain class of query, and they encode, among other things, the most appropriate content sources to search (Table 1). For a primary care physician, these search profiles might be for diagnosis, prescription, review, and treatment [4], but any set of profiles can be created within QC to meet the specific query types and search contexts of different users. In Table 1, the Treatment profile describes a set of nine separate source-specific search filters, which collectively describe the search strategy believed most likely to retrieve an accurate search result from each resource. The # symbol delimits keyword variables that are to be instantiated with user keywords. For example, #1# represents the keyword type “disease,” and QC’s mediator component will substitute the user-provided keywords for “disease” throughout the profile, prior to sending the query to the individual wrappers for the different sources. More than one search string can be created for an individual source (eg, TGL 1 and TGL 2) as a single strategy may not always retrieve all the relevant documents.
Table 1
Quick Clinical meta-search filters
Source
Search String
TGL1
(#1# AND #2# AND #3# AND #4#) AND+ ("treatment" OR "therapy" OR "therapeutic use")
TGL2
(#1# AND #3#) AND+ ("treatment" OR "therapy" OR "therapeutic use")
HealthInsite3
#1# AND #2# AND #3# AND #4#
HealthInsite4
#1# AND #3#
PubMed5
(#1# ATTR+ [Title] AND #3# ATTR+ [Title] AND #4# ATTR+ [Title] ATTR+ /ther)English 10 years Human
PubMed6
#1# ATTR+ [Title] AND (#3# ATTR+ [Title] OR #4# ATTR+ [Title]) ATTR+ /drug ATTR+ therEnglish 10 years Human
Merck7
((#1# AND #3# AND #4#) OR (#1# AND #3#)) AND+ ("treatment" OR "therapy")
Harrison’s8
Disconnected
Harrison’s9
Disconnected
System Platform
The system was constructed using Java, the Struts Web application framework, and a MySQL database and is deployed on a RedHat Linux platform. The user interface (JSP, servlet, and HTML pages) is deployed through an Apache Web server connected to a Tomcat servlet engine. The Apache-Tomcat platform incorporates load balancing and fail-over and is suitable for scalability and large-scale deployment.
Technical Evaluation
QC has undergone a series of clinical evaluations, which have been reported separately [4,15,16].
In total, 227 family physicians from across Australia participated in a trial of QC. Clinicians who had a computer with Internet access in their consulting rooms were recruited and asked to use QC for 4 weeks in routine care. Each participant was given a personal username and password to access the system. All clinicians completed an online pre-trial survey. QC was configured to search a set of eight sources, including remote sites such as PubMed, online journals such as BMJ and the Medical Journal of Australia (MJA), and locally cached sources such as The Merck Manual and Therapeutic Guidelines Australia.
For every search, the time from the request arriving at the system to the time when the results were sent back to the user was recorded (Figure 5; search time = system time + slowest source time). Note that there is a cap on search time when the time-out cuts in. Time-outs are search-profile dependent and were set at either 15 or 30 s. The time it took to conduct the search on the individual sources was also recorded. The time taken to send data between QC and the user’s computer (user time) is not incorporated in these measurements.
In the following section we report on the technical performance of the architecture and then reflect on its suitability for supporting evidence retrieval in clinical practice.
Figure 5
Search time metrics
Results
In the pre-trial questionnaire, 40% of the clinicians reported having a broadband (ADSL, cable, satellite) connection, while 43% used a 56k or 64k modem connection. The remaining 17% either did not know the type of connection used or had a slower connection. A total of 1662 searches were performed over the trial.
Search Speed
Under local network conditions (LAN, 100MBit), the user time (from starting the search on a client computer to displaying the results) was approximately 1.5 s. However, since most users accessed the system through the Internet, latency was significantly longer and slowed down the overall search speed.
The average search time was 4.9 s, with a standard deviation of 3.2 s (N = 1662 searches). Figure 6 shows the distribution of all search times over the trial. There are four distinctive features in this chart. The first is a small peak at 1 s (ie, searches that took up to 1 s to complete). The second feature is a peak around the mean value. Third, there is a small peak at 15 s, and, fourth, there is a small peak at 30 s.
Figure 6
Distribution of search time for all 1662 searches
System Time
System time for a search was computed by subtracting the duration of the slowest source in every search from the search time (see Figure 5). From the system time histogram in Figure 7, it can be seen that for the majority of the searches the system takes between 100 ms and 130 ms (mean = 117.9 ms; SD = 68.4 ms; N = 1614 [48 searches had missing data, hence 1614 searches]).
Figure 7
Distribution of the system time for 1614 searches
System Time Versus Number of Individual Sources Involved
Depending on the search profile selected, the system will query a certain number of information sources and combine the results. To illustrate the dependency between system time and the number of sources queried, Table 2 shows average system time versus the number of sources queried in a search. The number of sources queried is predefined by the search profile, and none of the search profiles tested queried five, six, or eight sources.
Table 2
System time vs number of sources queried
Number of Sources Queried
N
Average System Time (ms)
1
48
18.1
2
9
31.8
3
15
73.3
4
7
59.7
5
0
-
6
0
-
7
1373
122.2
8
0
-
9
162
122.6
Speed and Reliability of Individual Data Sources
In addition to the performance measurements of the whole searches, the speed and reliability of the individual data sources was measured. Reliability was measured as the number of error cases (ie, queries that were not answered due to an error condition, such as a network error, an HTTP error, or queries that timed out). Reliability and speed figures are summarized in Table 3.
Table 3
Reliability and speed of data sources
Source
Type
Number of Searches
Number of Errors
Error (%)
Mean Speed (s)
SD (s)
Min (s)
Max (s)
Merck
Local
2144
0
0.0
0.06
0.11
0.01
2.89
TGL
Local
2193
0
0.0
0.05
0.12
0.01
2.85
BMJ
Remote
73
1
1.4
4.55
3.92
0.99
17.5
HealthInsite
Remote
2993
55
1.8
3.09
1.08
1.08
22.3
MedlinePlus
Remote
653
0
0.0
1.87
1.36
1.09
12.5
MIMS
Remote
650
3
0.5
0.98
1.14
0.28
8.30
MJA
Remote
58
1
1.7
0.25
0.31
0.10
1.73
PubMed
Remote
3288
39
1.2
3.76
1.69
1.87
15.0
Total
12052
99
Mean
0.8
1.83
0.63*
0.68
10.4
* standard error of the mean
The most reliable sources were the locally indexed sources Merck (The Merck Manual) and TGL (Therapeutic Guidelines Australia), both which did not have any error cases. On the other end of the scale are HealthInsite (a national consumer site for health information) and MJA. The slowest source in the trial was BMJ, with an average of 4.55 s to process a query (SD = 3.92 s; N = 73). This was followed by PubMed, which returned results at an average of 3.76 s (SD = 1.69 s; N = 3288). The two locally indexed sources (Merck and TGL) returned search results within an average of 0.061 s and 0.047 s, respectively. However, the two local sources do have a relatively large standard deviation. Figure 8 shows the distribution of query times to the eight individual data sources.
Figure 8
Histogram of search times for each of the eight data sources (x-axis is time taken for a search, in ms; y-axis is number of searches)
Discussion
System Time
From the results of the system time versus source time, we can observe that system-processing time is only a fraction of the total search time. However, there are exceptions, namely when local data sources are used exclusively. From a user’s perspective this still would not be an issue as the overall user time is greater by at least a factor of ten. It could, however, become a problem in a situation where many searches are dependant on the result of a previous search and have to be executed in series. System time has thus been kept relatively short, removing the initial reservation that too much parallelism could slow down the system excessively. From Table 3 it can be seen that the system time generally increases in line with the number of sources queried (with the exception of four sources queried). However, the order of this increase does not appear to be squared or even exponential, but rather linear.
Search Times
The four distinct features in the histogram of search times described in Figure 6 are due to the nature of the data sources and the value of the time-outs. The first small peak at 1 s is from search profiles that use exclusively local data sources. The second feature is a peak around the mean value and is caused by the six Internet resources. The small peak at 15 s is due to the large number of search profiles that have this value as a time-out. And finally, the tiny peak at 30 s is where the remaining searches time out.
It was to be expected that local sources would be more reliable and have a shorter latency in response time. This is due to the controlled environment, compared to the uncontrolled Internet environment of the external sources. It is interesting to note the difference between the six external data sources. While some sources are very popular (eg, PubMed) and therefore are expected to be busy, others might lack the resources to keep up with demand. The time-out value of individual data sources is a trade-off between speed and quality of results and is determined by the intended usage of the system. However, under certain circumstances there are optimizations that can be carried out without affecting quality of results. For example, the search duration histogram for HealthInsite (Figure 8; top right) reveals that if a search has not completed within 10 s it is highly unlikely it will complete within 15 s. Therefore, a time-out value of 15 s can safely be reduced to 10 s without significantly compromising search quality.
Future Work
The current QC architecture has demonstrated in trials that it meets the technical design goals set for it, and it provides good evidence that our general approach to federated searching is sustainable and maintainable. We intend to pursue research and development in areas of current interest to meta-search engines, information retrieval systems, and artificial intelligence. These include automatic wrapper generation [17,18] so that new data sources can be easily integrated into QC. Using this approach, a component could automatically generate a wrapper from knowledge of the data source query inputs and results. Another area of continued research will be automated data source consistency checking. Data sources often change in their formats, and this needs to be monitored with either automated or human intervention in order to modify wrappers accordingly. A third area will be intelligent search agents [19]. We envisage incorporating an intelligent agent that will guide users through the search process, using domain knowledge to help frame clinical questions and choose search parameters. This agent could learn to work with its user. An area of continued development will be semantic understanding of result sets. We would like QC to combine search results into a meaningful coherent story that presents a concise, relevant, and digestible response to the user [20]. These approaches, coupled with user support, will allow us to develop and improve the system with a view to it becoming an integral part of a clinician’s daily practice. Even without these enhancements, we have demonstrated that the QC framework is a functional and useful approach for the delivery of online, just-in-time clinical evidence.

meta-search filters
information retrieval
evidence-based medicine
clinical decision support systems


Recruiting Injection Drug Users: A Three-Site Comparison of Results and Experiences with Respondent-Driven and Targeted Sampling Procedures

Several recent studies have utilized respondent-driven sampling (RDS) methods to survey hidden populations such as commercial sex-workers, men who have sex with men (MSM) and injection drug users (IDU). Few studies, however, have provided a direct comparison between RDS and other more traditional sampling methods such as venue-based, targeted or time/space sampling. The current study sampled injection drug users in three U.S. cities using RDS and targeted sampling (TS) methods and compared their effectiveness in terms of recruitment efficiency, logistics, and sample demographics. Both methods performed satisfactorily. The targeted method required more staff time per-recruited respondent and had a lower proportion of screened respondents who were eligible than RDS, while RDS respondents were offered higher incentives for participation.
Introduction
Injection drug users (IDU) remain an important population in the study of HIV/AIDS. The Centers for Disease Control and Prevention (CDC) estimates that 31.8% of cumulative male U.S. AIDS cases through 2004 and 40.7% of female cases were exposed through injection drug-use or male-to-male sexual contact and injection drug use.1 Unfortunately traditional sampling methods are inadequate when the population of interest consists of individuals who perform illegal or stigmatized behaviors. Injection drug users are one such population. Traditional probability-based sampling methods require the development of a sampling frame enumerating the entire population. This is prohibitive for these ‘hidden’ populations because their size is unknown, and their members are often reluctant to participate in studies due to legal issues or social stigmatization.2,3
Because of these difficulties, a number of alternative sampling methods have historically been employed to study hidden populations, including injection drug users.4 These methods have included convenience sampling, time/space or venue-based sampling and chain-referral methods. Chain-referral or “snow-ball” sampling5,6 methods involve selection of an initial set of members of the target population, or ‘seeds’, who provide information on, or referrals for, subsequent waves of respondents. The number of waves and potential referrals, as well as the seed selection methods, may vary. Versions of these methods have proven successful at quickly and easily identifying members of hidden populations.7–9 However, results obtained are thought to be biased by the initial seed selection method as well as characteristics of the respondents' social network and have generally been classified as convenience samples.6,10,11
Another method proposed by Watters and Biernacki3 known as targeted sampling (TS) has had some success in recruiting injection drug users.12 Targeted sampling requires extensive ethnographic and formative research to describe the area and population of interest and to identify appropriate locations within that area for inclusion in a sampling plan.13 Existing secondary data sources are also reviewed to further describe the target population and geographic areas of interest. Qualitative data in the form of focus groups and in-depth interviews with key informants may be conducted to gain a more complete understanding of the population of interest as well as to identify possible specific locations for sampling. These locations are then systematically mapped and ethnographic observations are conducted. Based on all of this information a sampling frame of locations is developed and weighted by the density of observed indicators. Locations are randomly selected and recruitment is conducted within the selected locations. Targeted sampling, as well as other time–space or venue-based sampling methods, may be biased to the extent that members of the population of interest do not attend the venues or areas that have been identified.
Heckathorn10,14,15 has recently proposed a modified chain-referral method called respondent-driven sampling (RDS) as a promising strategy in surveying hidden populations. This approach retains the advantages of chain-referral sampling in terms of ease of respondent identification and recruitment while allowing population estimates and inferences to be made.10,15 Initial seeds are identified from known members of the population or through key informants. Information about the respondent's social network is collected, and seeds are provided with a set number of coupons for distribution to recruits within their own network. Eligible recruits are interviewed and provided coupons for distribution to the next ‘wave’. Recruitment continues in this way until the desired sample size is reached. Network information is then used to derive asymptotically unbiased population estimates for variables of survey interest.
RDS has been shown to be a useful strategy for the recruitment of drug users.16–18 To date, however, no direct comparison of RDS with other sampling methods has been made in terms of the ability to recruit hidden populations such as IDUs, the “representativeness” of sample characteristics, and the practicality of administration.2,4 The objective of the current study is to compare and contrast RDS and TS methods on demographics, recruitment efficiency, and logistic concerns.
Materials and Methods
Three sites in large Metropolitan Statistical Areas (MSA) (Detroit, Houston and New Orleans) that participated in the CDC-funded National HIV Behavioral Surveillance among injecting drug users (NHBS-IDU) conducted a pilot investigation comparing RDS and TS. Each site assessed the feasibility of recruiting at least 100 eligible IDUs utilizing RDS and TS methods over an 8-week period in late 2004. What follows is a brief presentation of the strategies used by each site to locate and recruit potential respondents using RDS and TS methods.
Targeted Sampling
Formative Research
Extensive ethnographic and formative research was conducted at all sites prior to sampling. Each site completed a thorough review of secondary data sources dealing with the IDU population. Data were compiled and synthesized from several sources including number of persons living with HIV/AIDS who have IDU as a reported mode of exposure, archival census data and findings from previous published and unpublished research.
A total of 81 in-depth interviews were conducted with individuals who had knowledge of local injection drug-use communities. Those interviewed included local HIV prevention personnel and community planning group members as well as law enforcement representatives and current or former injectors. Ten focus groups with a total of 78 injection drug users were also conducted in order to provide specific knowledge about local injection habits and behaviors. These structured interviews were designed to provide a fuller understanding of the injection community at large as well as to solicit locations for possible sampling areas. Sites also utilized information on areas that had been previously identified by ethnographers with knowledge of the local IDU community or had been used in other local IDU studies.
Area Identification and Recruitment
Field observations of the possible sampling areas were systematically conducted at varying times of day and week over the course of 5 months (March–July 2004). Field staff conducted ethnographic observations within each area and recorded any IDU indicators that were present. IDU indicators included physical objects such as used syringes, baggies, balloons or injection works/equipment as well as behavioral indicators such as copping activity, loitering or commercial sex work. Sites used a combination of ethnographic research, secondary data analysis, key informant interviews, and field observations to form expected yield ratings to identify areas where injection drug use was likely to occur and where potential survey respondents could be recruited.
Based on highest expected yield rating, Detroit and Houston identified four and ten sampling areas, respectively, within seven ZIP codes. For these sites line based enumeration was then used to approach possible respondents. New Orleans identified 35 sampling areas within 16 ZIP codes of high, medium and low expected yield. These ZIP codes were stratified by the expected yield, and order of ZIP codes was then randomly selected without replacement at the beginning of each 2-week period. Interviewers approached all individuals within each of the prescribed three block sampling areas within each ZIP code.
Respondent-Driven Sampling
Seed Selection and Recruitment
Possible seeds were identified through key informant referrals and street outreach. Seeds were purposefully selected to maximize diversity on race, sex and drug of choice. Each seed was screened, and eligible seeds were interviewed using the NHBS-IDU instrument and provided with a $20 cash value incentive. Each initial seed was provided with three recruitment coupons and instructed to give them to individuals they knew injected drugs. Seeds were also informed that they would be compensated with $10 cash value for each person that they recruited. Peer-recruited respondents returning these coupons were then screened for eligibility, interviewed and provided with three coupons they were instructed to give to someone who they knew injected drugs. They were told that they would be compensated $10 for each person they recruited. A specialized coupon tracking system database was used to track referrals and facilitate incentive provision.
Analysis
All RDS participants were asked to provide information about the size and characteristics of their social networks from which they would draw potential recruits. Specifically, each participant was asked to report the number, sex and race/ethnicity of injectors they knew and had seen in the past 6 months. Relationship of the respondent to the recruiter (e.g., friend, injection partner, sex partner etc.) was also assessed. This information along with data from the coupon tracking system was analyzed using Respondent-Driven Sampling Analysis Tool (RDSAT) software (RDSAT (computer version). Version 5.4.0. Ithaca, New York: Volz E, Heckathorn DD; 2005) to provide estimated population proportions and confidence intervals for age, sex and race/ethnicity variables.
Participants
All participants provided informed consent and were carefully screened for eligibility criteria. Eligibility criteria for study participation were that the respondent was at least 18 years of age, was resident of the study MSA and had injected illicit drugs within the past 12 months.
Targeted Sampling
In Detroit 273 individuals were approached during TS. Two hundred twenty one individuals agreed to participate and were screened for eligibility. Of TS screened participants, 54% (n=120) were found to be eligible and completed the survey.
In Houston 174 individuals were approached during TS. One hundred forty eight respondents agreed to participate and were screened for eligibility. Of TS screened participants, 65% (n=97) were found to be eligible and completed the survey.
In New Orleans 560 individuals were approached during TS. Three hundred eighty-nine respondents agreed to participate and were screened for eligibility. Of TS screened participants, 35% (n=137) were found to be eligible and completed the survey.
Respondent-Driven Sampling
Six seeds were approached in Detroit for participation, and three of those individuals completed the survey and agreed to be seeds. One hundred sixty-nine recruitment coupons were distributed by Detroit during RDS over nine waves of recruitment; 106 of those individuals returned and were screened for eligibility. Of RDS screened participants, 96% (n=102) were found to be eligible and completed the survey.
Thirteen seeds were approached in Houston for participation, and one additional seed was recruited after being interviewed in the targeted sampling component. Of the 14 seeds recruited, 1 was found to be ineligible. One hundred sixty-eight recruitment coupons were distributed during RDS over seven waves of recruitment; 62 of those individuals returned and were screened for eligibility. Of RDS screened participants, 77% (n=48) were found to be eligible and completed the survey.
Ten seeds were approached in New Orleans for participation, and two of those individuals completed the survey and agreed to be seeds. Three hundred sixteen recruitment coupons were distributed during RDS over 19 waves of recruitment; 133 of those individuals returned and were screened for eligibility. Of RDS screened participants, 88% (n=118) were found to be eligible and completed the survey.
Materials
The standardized NHBS-IDU questionnaire was administered to all eligible participants. This 68-item questionnaire on HIV risk behaviors, requiring approximately 40 min, includes items on demographic characteristics, drug use, sexual risk behaviors, access to and use of health care and utilization of HIV prevention services. Variables of interest for this study included respondent demographic information, time required for survey completion, and cost of each survey.
Results
Sample Characteristics
Demographic characteristics of the samples obtained for each site by method are presented in Tables 1, 2, and 3. Local HIV surveillance data on persons living with HIV/AIDS who have injection drug use as a reported mode of exposure are presented for comparison purposes only. Chi-square tests indicated no significant association of sampling method and gender for any site (Detroit: , P=0.33; Houston: , P=0.38; New Orleans: , P=0.23). No significant association between age and sampling method was found (Detroit: , P=0.52; Houston: , P=0.14; New Orleans: , P=0.23). Two sites showed significant association between race/ethnicity and sampling method (Detroit: , P=0.42; Houston: , P=0.01; New Orleans: , P<0.0001).
Table 1Demographic characteristics of Detroit IDUs obtained through RDS and TS, and persons living with HIV/AIDS who are injection drug users (PLWH/A-IDU) TSRDSRDS pop. estimates, % (95% CI)PLWH/A-IDU, (%)Race/ethnicityN%N%African American101949696–91White 0011–5Hispanic1111–3Other6622–0Sex/genderMale7469717163 (52–75)61Female3431242437 (25–48)39TransgenderAge18–3421220.1(0.2–0.7)435–442423202017 (7–27)2245–546560545451 (40–67)4755+1716242432 (16–43)27Table 2Demographic characteristics of Houston IDUs obtained through RDS and TS, and persons living with HIV/AIDS who are injection drug users (PLWH/A-IDU) TSRDSRDS pop. estimates, % (95% CI)PLWH/A-IDU, (%)Race/ethnicityN%N%African American42522745–69White22281423–20Hispanic10121932–10Other6700–1Sex/genderMale5367467776 (45–96)56Female2025122024 (4–55)44Transgendera6823Age18–342634122014 (0.1–31)1735–442228254141 (33–86)4045+3039244045 (5–60)43aFemale and transgender categories were combined for purposes of Houston's RDS analyses.Table 3Demographic characteristics of New Orleans IDUs obtained through RDS and TS, and persons living with HIV/AIDS who are injection drug users (PLWH/A-IDU) TSRDSRDS pop. estimates, % (95% CI)PLWH/A-IDU, (%)Race/ethnicityN%N%African American12589675877 (64–90)65White54342920 (9–34)33Hispanic11223 (0.1–3)2Othera9613110Sex/genderMale11985948174 (58–88)76Female1914221926 (12–42)24Transgender21Age18–343726342927 (15–43)1635–444431262227 (12–44)3545–544029443839 (21–57)3755+191412107 (2–13)12aHispanic and other race/ethnicities were combined for purposes of New Orleans' RDS analyses.
Also presented are the population estimates and confidence intervals obtained by RDSAT. No RDS population estimates were calculated for race/ethnicity for Detroit or Houston due to lack of cross-group recruitment (e.g., only African Americans were recruiters of other race/ethnicities in Detroit). For each RDS estimate presented, equilibrium was achieved.
Cost Effectiveness
Across all sites 47% of TS-screened respondents were found to be eligible IDUs, while 89% of screened respondents in RDS were eligible IDUs. TS required an average of 214 h performing ethnographic observation and mapping plus an average of approximately 2 h and 9 min of staff time locating, screening and interviewing each recruit (total = 4 h and 6 min per recruit) while RDS required approximately 1 h and 13 min per recruit. TS methods provided each participant with an average of $20 in incentives, while RDS recruits received an average of $26.68. RDS incentives did not total $50 for every participant because some respondents did not produce the maximum of three recruits, and some recruiters did not return to receive the added incentives for recruitment efforts.
Logistics
Staffing requirements were similar for both methods, and sites were able to use the same staff to conduct RDS and TS concurrently. Some differences were evident as a result of method selection. Targeted sampling was conducted in the field while RDS was usually conducted from a single storefront. Interviewers reported that this led to a more controlled environment when using RDS. Increased privacy, comfort and perceptions of safety were reported, while distraction level decreased. Weather was also more of a concern with TS while interviewing outdoors.
Discussion
The use of multiple sources of ethnographic data was beneficial in identifying high-yield TS recruitment areas because areas with the highest number of eligible recruits were often not necessarily areas with obvious drug use indicators but areas identified through HIV surveillance or key-informant interviews. However, even with good ethnographic information, many areas that were expected to be high-yield were not, and less than half of all individuals approached were found to be eligible. This led to low interviewer morale in Detroit, where the interviewers were paid per interview completed. It also led to some bias in recruitment in Houston, where interviewers made the decision to recruit only individuals who looked like injection drug users on one particularly unsuccessful recruitment day.
The sample characteristics of those recruited through TS may not reflect the general IDU population but rather the population found during the recruitment times. New Orleans included low density areas and randomized area selection in the hopes of assembling a diverse sample, reflective of the general IDU population. However, this did not happen. The sample characteristics were reflective of the population found during recruitment times, and the sample may have missed less visible injectors, such as those with steady day time employment or child care responsibilities and those who remain indoors during the day or only come out at night. Houston and Detroit had similar problems. One solution might be to extend recruitment hours into the night or start very early in the morning, but this would further increase security concerns.
TS offered less control in the working environment, compared to RDS. Safety was a concern: The interviewers tended to spread out in the targeted area and the interviewing process drew the attention of others. Groups often gathered to inquire about the interview and the incentive, heightening security concerns. Weather was also an important factor in recruitment success, as fewer individuals were on the streets during hot, cold, or rainy days, and the spring and summer of 2005 was particularly noteworthy, weather-wise. Overall the RDS storefront was preferable to conducting TS outdoors. Participants often scheduled interview appointments and came to a central location for their interviews. Adequate staffing and office space were essential. It was found to be best to have a general waiting area, private interview spaces for each interviewer, and an area for obtaining participants' coupon information and providing incentives. In addition, all sites concurred that a single staff person to monitor the flow of participants entering, waiting, and exiting the facility would have provided even more control of the environment.
RDS success depends largely on the effective recruitment of seeds, an inter-connectedness of injection drug use networks, and the willingness of seeds and their recruits to travel to an interview location. Community partnerships were also important in the recruitment of successful seeds. In Detroit, seeds were recruited from two needle exchange locations in the city at the recommendation of staff familiar with the individuals' ability to recruit others. This worked well in Detroit, where three of six recruited seeds led to the recruitment of 106 eligible IDUs.
In Houston, the initial seeds were recruited from outreach workers who were conducting other research studies in drug-using populations. This was problematic, as some of these initial seeds were not actually linked to the injection drug using community but rather spent their days recruiting participants for these other research projects. These “research hustler” seeds were often very good at recruiting individuals (for money), just not at recruiting eligible individuals. This resulted in very short referral chains since few of their recruits were eligible.
In New Orleans, staff were only able to successfully recruit two seeds. Eight others identified through key informants agreed to participate but never came to the field location to be interviewed. The two seeds that were interviewed represented the two subgroups of the overall sample, as described in the formative research. One seed was an older African American male, the other a younger white female. The race/ethnicity and age differences between the New Orleans RDS sample (29% white, 19% under 25) and the TS sample (4% white, 5% under 25) may have reflected the characteristics of the two seeds' networks rather than methodological differences.
Based on lessons learned in this pilot, when NHBS-IDU shifted to full implementation, the sites made several adjustments to the RDS protocols. In New Orleans, prior to the discontinuation of the project due to Hurricane Katrina, seeds were recruited from among participants who were known to interviewers from the TS pilot and who encouraged others to be interviewed. Second, two field offices were established; one was close to downtown and easily accessible by public transportation and the other was near a large public housing project in an area that yielded high eligibility rates in TS. Finally, transportation tokens were purchased and made available to participants who came to the field office in an attempt to ease the burden of travel on respondents. In Houston, successful seeds were individuals the interviewers/outreach workers found during the formative work for the targeted pilot. These were influential individuals or gatekeepers within small communities of injection drug users, often the individuals who were first to approach staff and inquire why interviewers were entering their geographic space.
Discontinuing RDS recruitment required some special considerations. While targeted sampling may easily stop on any given date or after a pre-determined number of participants have been recruited, the coupons distributed to potential RDS subjects represent a commitment on the part of the researcher. It was therefore important to carefully decrease the number of coupons distributed and shorten the coupon validity period so that recruitment would continue even as the end of the study date approached. Ending the distribution of coupons too early may result in not meeting the recruitment goal (in this case 100 participants), while ending it too late may lead to a disgruntled public.
Overall, both RDS and TS proved effective in recruiting IDUs in the three pilot MSAs. Although no claim as to the actual representativeness of the obtained samples is made, both methods yielded samples that were similar in terms of many demographic characteristics (such as age, race and sex) as well as drug of choice (not presented here). Demographic results of the samples from both methods also compared favorably to each city's Census and HIV surveillance data from injection drug users. All three sites were able to reach the goal of 100 respondents in TS. Issues with initial seed selection and time constraints most likely resulted in the failure of one site to recruit 100 respondents with RDS. Total cost efficiency for the two methods was also comparable. Extensive formative research and low screened-to-eligible respondent ratios resulted in larger person-hour expenditures for TS, while RDS required higher incentive payouts to participants. Logistical concerns did differ considerably between the two methods. While RDS provided a more controlled interview environment, recruitment success seemed to be affected by the ability to identify quality seeds. Quality of ethnographic assessment and size and cultural qualities of the MSAs also affected targeted sampling success. While more research is needed in assessing the methodological impact of choice of sampling method in identifying hidden populations, these experiences should provide researchers some insight into the some of the practical strengths and weaknesses of these two methods.

injection drug use
respondent-driven sampling
hidden populations
sampling methodology
targeted sampling.