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Pharmacophore model for pentamidine analogs active against Plasmodium falciparum
European Journal of Medicinal Chemistry 45 (2010) 6147e6151
Contents lists available at ScienceDirect
European Journal of Medicinal Chemistry journal homepage: http://www.elsevier.com/locate/ejmech
Preliminary communication
Pharmacophore model for pentamidine analogs active against Plasmodium falciparum Prashanth Athri a, *, Tanja Wenzler b, c, Richard Tidwell d, Svetlana M. Bakunova d, W. David Wilson a a
Department of Chemistry, Georgia State University, 50 Decatur Street, Atlanta, GA 30303, USA Swiss Tropical and Public Health Institute, Department of Medical Parasitology and Infection Biology, 4002 Basel, Switzerland c University of Basel, Basel, Switzerland d Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 May 2010 Received in revised form 4 September 2010 Accepted 8 September 2010 Available online 18 September 2010
Pentamidine and its analogs constitute a class of compounds that are known to be active against Plasmodium falciparum, which causes the most dangerous malarial infection. Malaria is a widespread disease known to affect hundreds of millions of people and presents a perceivable threat of spreading. Hence, there is a need for well-defined scaffolds that lead to new, effective treatment. Here we present a pentamidine-based pharmacophore constructed using GALAHAD that would aid targeted synthesis of leads with enhanced properties, as well as the development of lead scaffolds. The study was supported by high-quality biological in vitro data of 22 compounds against the P. falciparum strains NF54 and K1. The model established reveals the importance of hydrophobic phenyl rings with polar oxygen and amidine substituents and the hydrophobic linking chain for the activity against malaria. Ó 2010 Elsevier Masson SAS. All rights reserved.
Keywords: Pharmacophore GALAHAD Pentamidine P. falciparum Malaria Molecular modeling
1. Introduction 1.1. Pentamidine and malaria Malaria, a eukaryotic parasitic disease that is transmitted through infected mosquitoes, causes approximately 250 million infections and kills almost 1 million people annually (WHO Fact sheet, World Malaria Report 2009 [1]). Visitors from malaria-free areas to regions where the disease is common are especially vulnerable. In spite of the huge human cost of this disease, it has not been a top priority for therapeutic development and drugs currently available for treatment of malaria as well as other protozoan diseases have significant deficiencies [2]. The spread of drug resistant Plasmodium falciparum (P. falciparum), the parasite that causes the most severe form of malaria, as well as cross-resistance among available drugs, has created an urgent need for new drugs with improved properties [3]. New drugs that can target unique sites in the parasite are essential to deal with cross-resistance and to lower toxicity. The cost of a new drug should be affordable for populations in disease endemic countries.
* Corresponding author. Tel.: þ1 404 413 5503; fax: þ404 413 5505. E-mail address: [email protected] (P. Athri). 0223-5234/$ e see front matter Ó 2010 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.ejmech.2010.09.012
Pentamidine is an aromatic diamidine that has been used as a general antiprotozoal agent for over 50 years [4]. Diamidines, in general, have exhibited broad spectrum antiparasitic activity [5]. However, pentamidine is the only compound of the group to see significant human use. Although it has activity against P. falciparum, pentamidine has numerous, serious side effects, including renal toxicity and cardiotoxicity and is far from what is desired in a frontline drug [6e8]. In spite of its promise against malaria, the target and the mechanism of action of pentamidine against that disease are not established. Possible mechanisms that have been proposed include binding to toxic heme formed from metabolism of host hemoglobin and inhibition of formation of non-toxic haemozoin in the food vacuole of P. falciparum [9]. Pentamidine and related diamidines are also known to bind to AT sequences of DNA that contain four or more AT base pairs, such as the trypanosome kinetoplast DNA. Furamidine (or DB75), a heterocyclic aromatic analog of pentamidine that has intrinsic fluorescence, is localized in DNA-containing organelles in trypanosomes [10,11]. Given the very high AT content of the malaria genome, DNA is also clearly a potential target in this organism. In support of this mode of action it has recently been demonstrated with fluorescence microscopy that DB75 localizes in the malaria parasite nucleus but is not detected in other organelles [12]. Irrespective of the targets, enrichment of databases can be
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achieved by identification of a feature-based pharmacophore. In this work we have used 3D alignment methodologies along with high-quality biological results for a series of pentamidine analogs to develop a pharmacophore that can assist in developing new scaffolds without any prerequisite knowledge of the mechanism of action or structural information about the target. 1.2. Computational approach A number of new diamidines have been synthesized in an effort to maintain or enhance the excellent therapeutic properties of pentamidine while decreasing side effects associated with pentamidine treatment [13,5]. In this study, we develop a pharmacophore based on an automated computational alignment technique. We have used a new set of pentamidine analogs (Fig. 1) and collected particularly high-quality biological testing results. GALAHAD (Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Datasets) [14e16] is a module in the Sybyl [17] modeling environment. GALAHAD seeks to develop pharmacophores based on of a set of ligands supersposed to maximize steric overlap and minimizing strain energy, and uses a genetic algorithm to search for candidate solutions. The biological activity results were used in conjunction with GALAHAD to develop a pharmacophore based on hypotheses generated by compound molecular structures and properties. The pharmacophore model delineates ten features that can be used for database searching as well as a guide for chemists to rationally design and synthesize potentially high-activity compounds. GALAHAD generates a multitude of competitive pharmacophore hypotheses and alignments from a set of test molecules [16]. It uses a generalized multi-dimensional cost function that takes into account the hydrophobic, ionic, hydrogen bonding and steric attributes of the test compounds when generating various possible alignments, and consequently, pharmacophores [18]. The pharmacophore ensemble generated by GALAHAD is the result of a series of sophisticated model building processes. The GALAHAD alignments are the result of a two-step process: the first step comprises a flexible alignment in which the ligands are represented by their constituent torsions. A genetic algorithm (GA) is used to manipulate a set of torsions that are in turn applied on the compounds to develop multiple conformations [18]. The GA uses a multi-objective function that receives information about conformation similarity using Tuplets [19] and also considers individual conformational energy to evaluate a particular alignment. Alignments produced are ranked using Pareto ranking [20,21]. This ensures that the final set of pharmacophore models is ranked independently with respect to all criteria, which are: energy of the compounds, hydrogen bonding partners present, steric overlap among the compounds and the agreement between the compound tuplet and the query tuplet. The second step in the GALAHAD pharmacophore development process generates a hypermolecule that contains pharmocophoric information from multiple molecules in the dataset. This step uses an extension of the LAMDA methodology [22] to aggregate features into a single hypermolecule by sequentially processing structurally similar compounds [18]. 2. Materials and methods 2.1. Dataset selection We have used a set of 22 pentamidine related analogs (Fig. 1), which have terminal amidines and two phenyl rings connected by a 5-membered aliphatic chain (for details on synthesis see Bakunova et al. [23]). In vitro antimalarial activity was tested in quadruplicates to obtain high-quality IC50 data for the modeling.
2.2. Preparation of compounds Compounds were dissolved in 100% dimethyl sulfoxide (DMSO) and finally diluted in culture medium prior to the assay. The DMSO concentration was kept below 1%. In vitro activity on P. falciparum was assessed using the strains NF54 (sensitive to all known drugs) and K1 (resistant to chloroquine and pyrimethamine). The determination of IC50 values against erythrocytic stages of P. falciparum was carried out four times independently and each in duplicate using the [3H]hypoxanthine incorporation assay [24,25]. Briefly, the compounds were tested in 10.44 g/L RPMI 1640 medium, supplemented with 5.94 g/L Hepes, 5 g/L AlbuMAX II, 2.1 g/L sodium bicarbonate, and 100 mg/L neomycin in 96-well microtiter plates. Infected human red blood cells in medium (1.25% hematocrit, 0.3% parasitemia) were incubated with serial drug dilutions in an atmosphere of 93% N2, 4% CO2, 3% O2 at 37 C. After 48 h [3H]hypoxanthine (0.5 mCi/well) was added and the plates were incubated for an additional 24 h under the same conditions. The parasites were harvested with a Betaplate cell harvester and transferred on a glass fiber filter. Viability was assessed by measuring the incorporation of [3H]hypoxanthine by a Betaplate liquid scintillation counter (Wallac, Zurich, Switzerland). The IC50 values were calculated from the sigmoidal inhibition curves using Microsoft Excel. 2.3. Alignment and pharmacophore generation The first step in pharmacophore generation is optimized molecular alignment of all compound structures, which is complex for flexible molecules such as those in Fig. 1. We used the GALAHAD program for alignment assistance and used the Tripos force field for optimization. All compounds were built and minimized in the Sybyl [17] software platform on a Red Hat Linux workstation. The alignment was performed in two stages. In the first stage, 3 of the most active molecules (for the K1 strain e 5MAA089, 3KEG083 and 1EVK061; for the NF54 strain e 3KEG083, 3SMB019 and 5MAA089) were aligned flexibly by GALAHAD, completely independent of a template. Using all molecules in this stage to generate a flexible alignment may lead to some features correlated to an interaction being neglected [18]. Using more than 3 molecules led to a reduction in the number of features identified and the quality of the pharmacophore, while using a single molecule could induce non-specific feature set. GALAHAD produces a set of probable hypotheses following the flexible alignment. Table 1 provides the following characteristics about each of these models generated using the 3 molecules most active against the K1 strain: Specificity, Number of Hits, Features, Pareto Ranking, Energy, Sterics, Hydrogen Bonds and Molecular Query. In addition, information about how the individual compound (used to generate the pharmacophore) compares to the query tuplet is also given in the last four columns. Specificity is an index to the expected discrimination for that specific model. The number of hits gives the actual number that hit the query, among the ligands used to generate the model. The number of common features that were identified is indicated in the column titled Features. The Pareto rank of the model, the total energy of the model, the steric energy and the pharmacophoric concordance (see Tripos Bookshelf 7.3 [18]) are given in the next four columns. Molecular query quantifies the agreement between the query tuplet and pharmacophore. Finally, information of how the individual ligands compare to the query tuplet with respect to energy, sterics, hydrogen bond characteristics and query tuplet is shown in the last four columns. In the second stage, one of the models generated above was used as a rigid template to align the remaining molecules. The selection of the model selected from the first stage was based on three criteria:
P. Athri et al. / European Journal of Medicinal Chemistry 45 (2010) 6147e6151
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Fig. 1. All compounds used in this study.
1. The model needed to “hit” all 3 active molecules. 2. The model needed to have reasonable energies (within the same order of magnitude as compared to other competing models). 3. The model needed to have a minimum of 8 pharmacophoric features.
Once a model was selected, the associated pharmacophore was used as a template to align all the molecules using GALAHAD’s Align to template procedure. These results (for the K1 strain) are presented in Table 2. Finally, the same procedure was repeated to generate a pharmacophore using activities against the NF54 strain.
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Table 1 Various pharmacophores proposed by GALAHAD (for K1 strain). The gray rows are the models that were discarded based on the criteria for model selection (Section 2.3). Among the remaining models, Model_05 (in bold above) was selected as the final template because a larger number of molecules had corresponding pharmacophoric features.
MODEL_01 MODEL_02 MODEL_03 MODEL_04 MODEL_05 MODEL_06 MODEL_07 MODEL_08 MODEL_09 MODEL_10 MODEL_11 MODEL_12 MODEL_13 MODEL_14 MODEL_15 MODEL_16 MODEL_17 MODEL_18 MODEL_19 MODEL_20
SPECIFICITY
N_HITS
FEATS
PARETO
ENERGY
STERICS
H-BOND
MOL_QRY
5.74 3.75 3.52 3.53 3.14 3.31 3.31 3.14 3.53 6.01 4.72 5.77 3.52 5.96 3.52 4.49 4.43 3.18 4.21 4.31
3 2 2 2 3 3 3 3 2 3 3 3 2 3 2 2 3 2 3 3
9 7 8 8 10 9 9 10 8 8 8 9 8 9 8 4 9 10 5 5
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18.47 18.2 16.85 18.16 40.51 34.6 33.05 36.15 24.55 38.09 26 17.71 18.9 84917.79 4608.67 16.15 87152.77 17325.34 1675.99 28.58
2362.2 3550.3 3539.3 3520.5 2737.2 2391.5 2201.6 2044.1 3417.1 3634.4 3607.9 3290.1 3604.7 2694 3876.2 2254.1 3697.2 3465.6 3832.3 2506.2
588.1 496.3 479.7 479.7 583.7 583.1 575.7 583.1 496.3 485.7 484.5 441.6 479.7 565.5 479.7 510.8 479.7 479.7 485.7 516.4
416.77 167.67 209.67 322 414.21 386.33 446.32 453.26 209.67 207.88 168.13 338.66 111.62 435.26 288 14 365.26 415 20.66 27.23
IND_ENERGY MODEL_01 MODEL_02 MODEL_03 MODEL_04 MODEL_05 MODEL_06 MODEL_07 MODEL_08 MODEL_09 MODEL_010 MODEL_011 MODEL_012 MODEL_013 MODEL_014 MODEL_015 MODEL_016 MODEL_017 MODEL_018 MODEL_019 MODEL_020
17.78 26.03 20.70 18.93 36.89 33.63 21.76 17.56 21.94 37.97 38.94 19.49 20.09 254686.56 13799.00 17.28 261430.58 51902.54 4948.59 17.16
IND_STERICS 16.79 15.52 19.27 19.47 66.88 51.52 58.74 72.72 38.30 59.41 22.83 17.44 20.41 51.53 13.63 21.38 14.32 58.10 61.77 49.26
20.85 13.03 10.58 16.07 17.75 18.65 18.65 18.18 13.41 16.90 16.21 16.21 16.20 15.28 13.38 9.80 13.41 15.39 17.60 19.34
18362.83 27291.17 26865.00 26498.50 19763.17 17137.83 15597.00 14566.17 26527.50 28189.67 26490.83 24681.67 28124.33 18268.33 31635.00 16767.83 28143.33 28714.67 31510.17 17724.50
IND_HBOND 19860.83 30588.50 29607.00 29200.50 23693.83 20155.83 17545.67 16898.83 28651.50 30227.00 30718.83 27506.33 31077.00 23020.33 31775.67 15821.17 31657.33 29359.33 32941.50 21416.50
3. Results and discussion As described above, the three most active molecules with IC50s from 0.003 to 0.007 mM (pIC50: 2.47e2.07) against K1 were used to generate the initial pharmacophore and the GALAHAD results are presented in the format shown in Table 1. Each row is a probable alignment based on the overlap of pharmacophoric features of the three molecules that can be used as a template to align the remaining molecules in the second stage. Each of the columns is a model property that can help isolate the ideal hypothesis that can be used to align the remaining molecules. Among the 20 models that were produced by GALAHAD, 8 of them were used as potential alignment schemes. Seven models (MODEL_02, MODEL_03, MODEL_04, MODEL_09, MODEL_13, MODEL_15, MODEL_16, MODEL_18) were discarded since they failed to include all three most active molecules in the proposed pharmacophore. The energies of a few alignments were up to 2 orders of magnitudes higher as compared to other members. They are MODEL_14, MODEL_15, MODEL_17, MODEL_18 and MODEL_19 and these were discarded based on criterion 2 (Section 2.3). Finally, any remaining models that had fewer than 8 pharmacophoric features were discarded (MODEL_20). Each of the 8 viable models was considered individually as a rigid template to align the remaining molecules using GALAHAD’s Align to template option. Among the optimum 8 models, MODEL_05
7676.17 15725.83 17048.33 16872.50 12038.50 10923.17 11019.00 9764.17 17417.50 16989.67 18791.50 17495.00 17368.33 13309.00 17895.67 12549.83 18070.67 16768.00 17101.50 11195.17
5258.67 4467.00 4317.67 4317.67 5253.00 5213.67 5181.33 5213.67 4467.00 4371.00 4360.33 3657.00 4275.00 4982.67 4317.67 4338.67 4317.67 4275.00 4371.00 4647.67
IND_MOL_QRY 5254.67 4467.00 4317.67 4275.00 5217.00 5173.67 5088.00 5173.67 4467.00 4371.00 4360.33 3974.33 4317.67 5050.67 4317.67 4529.33 4317.67 4317.67 4371.00 4575.67
5293.33 4467.00 4275.00 4317.67 5253.00 5248.33 5181.33 5248.33 4467.00 4371.00 4275.00 3974.33 4317.67 4933.33 4275.00 4382.67 4275.00 4317.67 4371.00 4497.00
1.40 0.00 0.00 0.00 L1.20 0.10 0.10 0.80 0.00 1.10 0.50 0.10 3.40 0.00 0.10 0.00 0.70 0.00 0.10 0.30
0.70 0.00 0.00 0.00 L1.10 0.00 0.20 0.70 0.00 1.10 0.50 0.30 3.40 0.00 0.10 0.00 0.70 0.20 0.10 0.30
1.30 0.00 0.00 0.00 L1.20 0.10 0.10 0.80 0.00 1.10 0.60 0.30 3.40 0.70 0.00 0.00 0.70 0.20 0.10 0.50
(Fig. 2) performed better than the others based on the number of molecules that had corresponding features as compared to the pharmacophore. Hence, MODEL_05 was considered for the final rigid alignment of the remaining compounds (results shown in Table 2). GALAHAD was not able to align 8 molecules in the set and the model was used to manually align these 8 molecules. Nevertheless, upon further investigation, the manually aligned molecules had very high steric energy clashes. It was observed that high MOL_QRY (Table 2, Column 7) scores closely correlated with the non-alignment of molecules. All molecules that did not align well with the model had high MOL_QRY scores. The optimized pharmacophore from this procedure is shown in Fig. 2 and includes two hydrophobes centered on the benzene rings as well as one on the aliphatic linking chain, two acceptor atom centers on the two oxygen atoms and four donor atoms centered on the amidines. A single nitrogen atom is identified on one of the ends. The program would identify all nitrogens common to the three compounds only if it was able to align them without a high cost to the steric and energy terms. Hence, only one of the nitrogens is seen in the model even though there are other candidates among all three compounds used to build the initial pharmacophore. The pharmacophore model clearly shows the importance of the hydrophobic phenyl rings with polar oxygen and amidine substituents to antimalarial activity. The hydrophobic linking chain is also important for activity.
P. Athri et al. / European Journal of Medicinal Chemistry 45 (2010) 6147e6151 Table 2 Comparative energy, sterics, H-bond and the Molecular Query (MOL_QRY) score provided by GALAHAD (Columns 3, 4, 5 & 6), the pIC50 for the malarial K1 strain (Column 2) and the NF54 strain (Column 8) of all the molecules used in this study (Column 1). Column 7 notes whether the particular molecule was aligned with the proposed pharmacophore, i.e. Model_05 (Fig. 2) for the K1 strain. pIC50 (K1) 5MAA089 2.47 3KEG083 2.3 1EVK061 2.07 3STL057 1.91 3SMB019 1.84 3SMB101 1.77 1EVK057 1.74 1KAO009 1.74 21DAP023 1.63 5MAA137 1.62 3SMB043 1.58 5MAA123 1.49 3SMB045 1.48 3SMB079 1.32 1EVK097 1.24 5EVK038 0.91 2EVK008 0.61 1EVK060 0.19 4EVK055 0.13 4EVK051 0.2 5BGR006 0.25 5MAA135 0.3
ENERGY STERICS HMOL_QRY Align to BOND M05? 15.84 65.07 28.54 61.66 95774.26 63.58 16.48 54.55 63.86 11.49 74.26 43.73 19.32 66.21 26.64 1259.15 18.44 2.84 23.52 10.7 106.54 29.42
8300 7983.6 5469.2 2538.3 3770.9 711.6 3024.3 1944.2 716.1 2757.2 1452.5 1983.7 4306.9 1775 2222.3 4479.4 3511.1 7290.1 3414.9 1782 3368 5837.9
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0.8 0.8 2.6 0.8 1.6 1.1 0.8 0.8 5.8 0.8 0.8 0.9 0.8 0.8 2.6 0.8 2.8 0.8 2.3 0.8 0.6 2.2
Yes Yes No Yes Yes No Yes Yes No Yes Yes Yes Yes Yes No Yes No Yes No Yes No No
pIC50 (NF54) 1.46 1.57 1.3 1.25 1.55 0.92 1.09 1.28 1.46 0.95 1.01 0.98 0.63 0.52 0.33 1.23 1.46 0.72 0.02 0.39 0.55 0.69
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among a pool of structurally related compounds. The pharmacophore delineates important features that are common to pentamidine-based ligands active against P. falciparum. The corresponding features on either sides of the hydrophobic chain provide the necessary interactions with binding pockets. The flexibility of the hydrophobic linking chain is important to aligning these pharmacophoric features in the binding pocket with very little or no energy costs. Also, the model does not preclude either DNA or heme targets and, in effect, might suggest that molecules that fit this pharmacophore bind to multiple targets to achieve high activity. This qualitative model can be used to drive future synthesis of ligands that optimize the perceived features, and also assist 3D database querying. Funding Funding for this project was provided by the Bill and Melinda Gates Foundation. Contributors PA and WDW conceived the project and wrote the paper. TW conducted all the biological assays that provided the activity data. RT and SMB synthesized all the compounds that were used in this study. PA designed and implemented the computational modeling pipeline described in the paper. References
Fig. 2. The 3 active analogs that were aligned using GALAHAD are shown above. The hypothesized pharmacophore for these pentamidine analogs active against P. falciparum, shown above, includes three hydrophobic centers in cyan, four donors in magenta, one nitrogen in red and two acceptor atoms in green. For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.
As mentioned earlier, the same procedure was used to generate a pharmacophore using the NF54 activities. The optimal pharmacophore closely replicated the features present in the MODEL_05 model for the K1 strain. To avoid repetition, we have not presented this model. Most pentamidine analogs evaluated in this study are relatively active and the variance in activities in this dataset is quite low. The dataset does not allow for 3D QSAR type studies since only 22 compounds existed in our database, which were structurally related and in vitro active with IC50s ranging from 0.003 mM to 2 mM against the P. falciparum strain K1 and 0.02 mMe3 mM against NF54. Several compounds did show slightly higher IC50s in the case of NF54, as compared to K1. This is attributed to its slightly impaired resistance. Potential cross-resistance with chloroquine and pyrimethamine was observed for 5MAA135 since only this compound has a higher IC50 against the resistant strain K1. 4. Conclusion The pharmacophore presented in this study provides rational avenues to enrich the present database with high activity leads
[1] WHO, World Malaria Report 2009 This report is available at. World Health Organisation, Geneva, 2009. (accessed on 6.6.10). [2] S. Nwaka, Trans. R. Soc. Trop. Med. Hyg. 99 (Suppl. 1) (2005) S20eS29. [3] T. Mita, K. Tanabe, K. Kita, Parasitol. Int. 58 (2009) 201e209. [4] K. Werbovetz, Curr. Opin. Investig. Drugs 7 (2006) 147e157. [5] M. Demeunynck, C. Bailly, W.D. Wilson (Eds.), DNA and RNA Binders, vol. 2, Wiley-VCH, Weinheim, Germany, 2003 (Chapter 16). [6] A. Feddersen, K. Sack, J. Antimicrob. Chemother. 28 (1991) 437e446. [7] T. Antoniou, K.A. Gough, Pharmacotherapy 25 (2005) 899e903. [8] R. Assan, C. Perronne, D. Assan, L. Chotard, C. Mayaud, S. Matheron, D. Zucman, Diabetes Care 18 (1995) 47e55. [9] A. Stead, P.G. Bray, I.G. Edwards, H.P. DeKoning, B.C. Elford, P.A. Stocks, S.A. Ward, Mol. Pharmacol. 59 (2001) 1298e1306. [10] A.M. Mathis, A.S. Bridges, M.A. Ismail, A. Kumar, I. Francesconi, M. Anbazhagan, Q. Hu, F.A. Tanious, T. Wenzler, J. Saulter, W.D. Wilson, R. Brun, D.W. Boykin, R.R. Tidwell, J.E. Hall, Antimicrob. Agents Chemother. 51 (2007) 2801e2810. [11] W.D. Wilson, B. Nguyen, F.A. Tanious, A. Mathis, J.E. Hall, C.E. Stephens, D.W. Boykin, Curr. Med. Chem. Anticancer Agents 5 (2005) 389e408. [12] A.E. Purfield, R.R. Tidwell, S.R. Meshnik, Malar. J. 8 (2009) 104. [13] M.P. Barrett, D.W. Boykin, R. Brun, R.R. Tidwell, Br. J. Pharmacol. 152 (2007) 1155e1171. [14] GALAHADÔ is distributed by. Tripos Inc., 1699 S. Hanley Rd., St. Louis, MO, 2009. . [15] R.D. Clark, E. Abrahamian, A. Strizhev, U. S. Patent Application 20070143030 (2007). [16] N.J. Richmond, C.A. Abrams, P.R.N. Wolohan, E. Abrahamian, P. Willett, R.D. Clark, J. Comput.-Aided Mol. Des. 20 (2006) 567e587. [17] SYBYL 7.3, Tripos International, 1699 South Hanley Rd., St. Louis, Missouri, 63144, USA. [18] Tripos Bookshelf 7.3, Tripos International, St. Louis, MO (accessed 2009). [19] E. Abrahamian, P.C. Fox, L. Nærum, I.T. Christensen, H. Thøgersen, R.D. Clark, J. Chem. Inf. Comput. Sci. 43 (2003) 458e468. [20] V.J. Gillet, P. Willett, P.J. Fleming, D.V.S. Green, J. Mol. Graph. Model. 20 (2002) 491e498. [21] S.J. Cottrell, V.J. Gillet, R. Taylor, D.J. Wilton, J. Comput.-Aided Mol. Des. 18 (2004) 665e682. [22] N.J. Richmond, P. Willett, R.D. Clark, J. Mol. Graph. Model. 23 (2004) 199e209. [23] S.M. Bakunova, S.A. Bakunov, D.A. Patrick, S.E.V.K. Kumar, K.A. Ohemeng, A.S. Bridges, T. Wenzler, T. Barszcz, S.K. Jones, K.A. Werbovetz, R. Brun, R.R. Tidwell, J. Med. Chem. 52 (2009) 2016e2035. [24] R.E. Desjardins, C.J. Canfield, J.D. Haynes, J.D. Chulay, Antimicrob. Agents Chemother. 16 (1979) 710e718. [25] J.L. Vennerstrom, S. Arbe-Barnes, R. Brun, S.A. Charman, F.C.K. Chiu, J. Chollet, Y. Dong, A. Dorn, D. Hunziker, H. Matile, K. McIntosh, M. Padmanilayam, J. Santo Tomas, C. Scheurer, B. Scorneaux, Y. Tang, H. Urwyler, S. Wittlin, W.N. Charman, Nature 430 (2004) 900e904.
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European Journal of Medicinal Chemistry journal homepage: http://www.elsevier.com/locate/ejmech
Preliminary communication
Pharmacophore model for pentamidine analogs active against Plasmodium falciparum Prashanth Athri a, *, Tanja Wenzler b, c, Richard Tidwell d, Svetlana M. Bakunova d, W. David Wilson a a
Department of Chemistry, Georgia State University, 50 Decatur Street, Atlanta, GA 30303, USA Swiss Tropical and Public Health Institute, Department of Medical Parasitology and Infection Biology, 4002 Basel, Switzerland c University of Basel, Basel, Switzerland d Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 May 2010 Received in revised form 4 September 2010 Accepted 8 September 2010 Available online 18 September 2010
Pentamidine and its analogs constitute a class of compounds that are known to be active against Plasmodium falciparum, which causes the most dangerous malarial infection. Malaria is a widespread disease known to affect hundreds of millions of people and presents a perceivable threat of spreading. Hence, there is a need for well-defined scaffolds that lead to new, effective treatment. Here we present a pentamidine-based pharmacophore constructed using GALAHAD that would aid targeted synthesis of leads with enhanced properties, as well as the development of lead scaffolds. The study was supported by high-quality biological in vitro data of 22 compounds against the P. falciparum strains NF54 and K1. The model established reveals the importance of hydrophobic phenyl rings with polar oxygen and amidine substituents and the hydrophobic linking chain for the activity against malaria. Ó 2010 Elsevier Masson SAS. All rights reserved.
Keywords: Pharmacophore GALAHAD Pentamidine P. falciparum Malaria Molecular modeling
1. Introduction 1.1. Pentamidine and malaria Malaria, a eukaryotic parasitic disease that is transmitted through infected mosquitoes, causes approximately 250 million infections and kills almost 1 million people annually (WHO Fact sheet, World Malaria Report 2009 [1]). Visitors from malaria-free areas to regions where the disease is common are especially vulnerable. In spite of the huge human cost of this disease, it has not been a top priority for therapeutic development and drugs currently available for treatment of malaria as well as other protozoan diseases have significant deficiencies [2]. The spread of drug resistant Plasmodium falciparum (P. falciparum), the parasite that causes the most severe form of malaria, as well as cross-resistance among available drugs, has created an urgent need for new drugs with improved properties [3]. New drugs that can target unique sites in the parasite are essential to deal with cross-resistance and to lower toxicity. The cost of a new drug should be affordable for populations in disease endemic countries.
* Corresponding author. Tel.: þ1 404 413 5503; fax: þ404 413 5505. E-mail address: [email protected] (P. Athri). 0223-5234/$ e see front matter Ó 2010 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.ejmech.2010.09.012
Pentamidine is an aromatic diamidine that has been used as a general antiprotozoal agent for over 50 years [4]. Diamidines, in general, have exhibited broad spectrum antiparasitic activity [5]. However, pentamidine is the only compound of the group to see significant human use. Although it has activity against P. falciparum, pentamidine has numerous, serious side effects, including renal toxicity and cardiotoxicity and is far from what is desired in a frontline drug [6e8]. In spite of its promise against malaria, the target and the mechanism of action of pentamidine against that disease are not established. Possible mechanisms that have been proposed include binding to toxic heme formed from metabolism of host hemoglobin and inhibition of formation of non-toxic haemozoin in the food vacuole of P. falciparum [9]. Pentamidine and related diamidines are also known to bind to AT sequences of DNA that contain four or more AT base pairs, such as the trypanosome kinetoplast DNA. Furamidine (or DB75), a heterocyclic aromatic analog of pentamidine that has intrinsic fluorescence, is localized in DNA-containing organelles in trypanosomes [10,11]. Given the very high AT content of the malaria genome, DNA is also clearly a potential target in this organism. In support of this mode of action it has recently been demonstrated with fluorescence microscopy that DB75 localizes in the malaria parasite nucleus but is not detected in other organelles [12]. Irrespective of the targets, enrichment of databases can be
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achieved by identification of a feature-based pharmacophore. In this work we have used 3D alignment methodologies along with high-quality biological results for a series of pentamidine analogs to develop a pharmacophore that can assist in developing new scaffolds without any prerequisite knowledge of the mechanism of action or structural information about the target. 1.2. Computational approach A number of new diamidines have been synthesized in an effort to maintain or enhance the excellent therapeutic properties of pentamidine while decreasing side effects associated with pentamidine treatment [13,5]. In this study, we develop a pharmacophore based on an automated computational alignment technique. We have used a new set of pentamidine analogs (Fig. 1) and collected particularly high-quality biological testing results. GALAHAD (Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Datasets) [14e16] is a module in the Sybyl [17] modeling environment. GALAHAD seeks to develop pharmacophores based on of a set of ligands supersposed to maximize steric overlap and minimizing strain energy, and uses a genetic algorithm to search for candidate solutions. The biological activity results were used in conjunction with GALAHAD to develop a pharmacophore based on hypotheses generated by compound molecular structures and properties. The pharmacophore model delineates ten features that can be used for database searching as well as a guide for chemists to rationally design and synthesize potentially high-activity compounds. GALAHAD generates a multitude of competitive pharmacophore hypotheses and alignments from a set of test molecules [16]. It uses a generalized multi-dimensional cost function that takes into account the hydrophobic, ionic, hydrogen bonding and steric attributes of the test compounds when generating various possible alignments, and consequently, pharmacophores [18]. The pharmacophore ensemble generated by GALAHAD is the result of a series of sophisticated model building processes. The GALAHAD alignments are the result of a two-step process: the first step comprises a flexible alignment in which the ligands are represented by their constituent torsions. A genetic algorithm (GA) is used to manipulate a set of torsions that are in turn applied on the compounds to develop multiple conformations [18]. The GA uses a multi-objective function that receives information about conformation similarity using Tuplets [19] and also considers individual conformational energy to evaluate a particular alignment. Alignments produced are ranked using Pareto ranking [20,21]. This ensures that the final set of pharmacophore models is ranked independently with respect to all criteria, which are: energy of the compounds, hydrogen bonding partners present, steric overlap among the compounds and the agreement between the compound tuplet and the query tuplet. The second step in the GALAHAD pharmacophore development process generates a hypermolecule that contains pharmocophoric information from multiple molecules in the dataset. This step uses an extension of the LAMDA methodology [22] to aggregate features into a single hypermolecule by sequentially processing structurally similar compounds [18]. 2. Materials and methods 2.1. Dataset selection We have used a set of 22 pentamidine related analogs (Fig. 1), which have terminal amidines and two phenyl rings connected by a 5-membered aliphatic chain (for details on synthesis see Bakunova et al. [23]). In vitro antimalarial activity was tested in quadruplicates to obtain high-quality IC50 data for the modeling.
2.2. Preparation of compounds Compounds were dissolved in 100% dimethyl sulfoxide (DMSO) and finally diluted in culture medium prior to the assay. The DMSO concentration was kept below 1%. In vitro activity on P. falciparum was assessed using the strains NF54 (sensitive to all known drugs) and K1 (resistant to chloroquine and pyrimethamine). The determination of IC50 values against erythrocytic stages of P. falciparum was carried out four times independently and each in duplicate using the [3H]hypoxanthine incorporation assay [24,25]. Briefly, the compounds were tested in 10.44 g/L RPMI 1640 medium, supplemented with 5.94 g/L Hepes, 5 g/L AlbuMAX II, 2.1 g/L sodium bicarbonate, and 100 mg/L neomycin in 96-well microtiter plates. Infected human red blood cells in medium (1.25% hematocrit, 0.3% parasitemia) were incubated with serial drug dilutions in an atmosphere of 93% N2, 4% CO2, 3% O2 at 37 C. After 48 h [3H]hypoxanthine (0.5 mCi/well) was added and the plates were incubated for an additional 24 h under the same conditions. The parasites were harvested with a Betaplate cell harvester and transferred on a glass fiber filter. Viability was assessed by measuring the incorporation of [3H]hypoxanthine by a Betaplate liquid scintillation counter (Wallac, Zurich, Switzerland). The IC50 values were calculated from the sigmoidal inhibition curves using Microsoft Excel. 2.3. Alignment and pharmacophore generation The first step in pharmacophore generation is optimized molecular alignment of all compound structures, which is complex for flexible molecules such as those in Fig. 1. We used the GALAHAD program for alignment assistance and used the Tripos force field for optimization. All compounds were built and minimized in the Sybyl [17] software platform on a Red Hat Linux workstation. The alignment was performed in two stages. In the first stage, 3 of the most active molecules (for the K1 strain e 5MAA089, 3KEG083 and 1EVK061; for the NF54 strain e 3KEG083, 3SMB019 and 5MAA089) were aligned flexibly by GALAHAD, completely independent of a template. Using all molecules in this stage to generate a flexible alignment may lead to some features correlated to an interaction being neglected [18]. Using more than 3 molecules led to a reduction in the number of features identified and the quality of the pharmacophore, while using a single molecule could induce non-specific feature set. GALAHAD produces a set of probable hypotheses following the flexible alignment. Table 1 provides the following characteristics about each of these models generated using the 3 molecules most active against the K1 strain: Specificity, Number of Hits, Features, Pareto Ranking, Energy, Sterics, Hydrogen Bonds and Molecular Query. In addition, information about how the individual compound (used to generate the pharmacophore) compares to the query tuplet is also given in the last four columns. Specificity is an index to the expected discrimination for that specific model. The number of hits gives the actual number that hit the query, among the ligands used to generate the model. The number of common features that were identified is indicated in the column titled Features. The Pareto rank of the model, the total energy of the model, the steric energy and the pharmacophoric concordance (see Tripos Bookshelf 7.3 [18]) are given in the next four columns. Molecular query quantifies the agreement between the query tuplet and pharmacophore. Finally, information of how the individual ligands compare to the query tuplet with respect to energy, sterics, hydrogen bond characteristics and query tuplet is shown in the last four columns. In the second stage, one of the models generated above was used as a rigid template to align the remaining molecules. The selection of the model selected from the first stage was based on three criteria:
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Fig. 1. All compounds used in this study.
1. The model needed to “hit” all 3 active molecules. 2. The model needed to have reasonable energies (within the same order of magnitude as compared to other competing models). 3. The model needed to have a minimum of 8 pharmacophoric features.
Once a model was selected, the associated pharmacophore was used as a template to align all the molecules using GALAHAD’s Align to template procedure. These results (for the K1 strain) are presented in Table 2. Finally, the same procedure was repeated to generate a pharmacophore using activities against the NF54 strain.
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Table 1 Various pharmacophores proposed by GALAHAD (for K1 strain). The gray rows are the models that were discarded based on the criteria for model selection (Section 2.3). Among the remaining models, Model_05 (in bold above) was selected as the final template because a larger number of molecules had corresponding pharmacophoric features.
MODEL_01 MODEL_02 MODEL_03 MODEL_04 MODEL_05 MODEL_06 MODEL_07 MODEL_08 MODEL_09 MODEL_10 MODEL_11 MODEL_12 MODEL_13 MODEL_14 MODEL_15 MODEL_16 MODEL_17 MODEL_18 MODEL_19 MODEL_20
SPECIFICITY
N_HITS
FEATS
PARETO
ENERGY
STERICS
H-BOND
MOL_QRY
5.74 3.75 3.52 3.53 3.14 3.31 3.31 3.14 3.53 6.01 4.72 5.77 3.52 5.96 3.52 4.49 4.43 3.18 4.21 4.31
3 2 2 2 3 3 3 3 2 3 3 3 2 3 2 2 3 2 3 3
9 7 8 8 10 9 9 10 8 8 8 9 8 9 8 4 9 10 5 5
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18.47 18.2 16.85 18.16 40.51 34.6 33.05 36.15 24.55 38.09 26 17.71 18.9 84917.79 4608.67 16.15 87152.77 17325.34 1675.99 28.58
2362.2 3550.3 3539.3 3520.5 2737.2 2391.5 2201.6 2044.1 3417.1 3634.4 3607.9 3290.1 3604.7 2694 3876.2 2254.1 3697.2 3465.6 3832.3 2506.2
588.1 496.3 479.7 479.7 583.7 583.1 575.7 583.1 496.3 485.7 484.5 441.6 479.7 565.5 479.7 510.8 479.7 479.7 485.7 516.4
416.77 167.67 209.67 322 414.21 386.33 446.32 453.26 209.67 207.88 168.13 338.66 111.62 435.26 288 14 365.26 415 20.66 27.23
IND_ENERGY MODEL_01 MODEL_02 MODEL_03 MODEL_04 MODEL_05 MODEL_06 MODEL_07 MODEL_08 MODEL_09 MODEL_010 MODEL_011 MODEL_012 MODEL_013 MODEL_014 MODEL_015 MODEL_016 MODEL_017 MODEL_018 MODEL_019 MODEL_020
17.78 26.03 20.70 18.93 36.89 33.63 21.76 17.56 21.94 37.97 38.94 19.49 20.09 254686.56 13799.00 17.28 261430.58 51902.54 4948.59 17.16
IND_STERICS 16.79 15.52 19.27 19.47 66.88 51.52 58.74 72.72 38.30 59.41 22.83 17.44 20.41 51.53 13.63 21.38 14.32 58.10 61.77 49.26
20.85 13.03 10.58 16.07 17.75 18.65 18.65 18.18 13.41 16.90 16.21 16.21 16.20 15.28 13.38 9.80 13.41 15.39 17.60 19.34
18362.83 27291.17 26865.00 26498.50 19763.17 17137.83 15597.00 14566.17 26527.50 28189.67 26490.83 24681.67 28124.33 18268.33 31635.00 16767.83 28143.33 28714.67 31510.17 17724.50
IND_HBOND 19860.83 30588.50 29607.00 29200.50 23693.83 20155.83 17545.67 16898.83 28651.50 30227.00 30718.83 27506.33 31077.00 23020.33 31775.67 15821.17 31657.33 29359.33 32941.50 21416.50
3. Results and discussion As described above, the three most active molecules with IC50s from 0.003 to 0.007 mM (pIC50: 2.47e2.07) against K1 were used to generate the initial pharmacophore and the GALAHAD results are presented in the format shown in Table 1. Each row is a probable alignment based on the overlap of pharmacophoric features of the three molecules that can be used as a template to align the remaining molecules in the second stage. Each of the columns is a model property that can help isolate the ideal hypothesis that can be used to align the remaining molecules. Among the 20 models that were produced by GALAHAD, 8 of them were used as potential alignment schemes. Seven models (MODEL_02, MODEL_03, MODEL_04, MODEL_09, MODEL_13, MODEL_15, MODEL_16, MODEL_18) were discarded since they failed to include all three most active molecules in the proposed pharmacophore. The energies of a few alignments were up to 2 orders of magnitudes higher as compared to other members. They are MODEL_14, MODEL_15, MODEL_17, MODEL_18 and MODEL_19 and these were discarded based on criterion 2 (Section 2.3). Finally, any remaining models that had fewer than 8 pharmacophoric features were discarded (MODEL_20). Each of the 8 viable models was considered individually as a rigid template to align the remaining molecules using GALAHAD’s Align to template option. Among the optimum 8 models, MODEL_05
7676.17 15725.83 17048.33 16872.50 12038.50 10923.17 11019.00 9764.17 17417.50 16989.67 18791.50 17495.00 17368.33 13309.00 17895.67 12549.83 18070.67 16768.00 17101.50 11195.17
5258.67 4467.00 4317.67 4317.67 5253.00 5213.67 5181.33 5213.67 4467.00 4371.00 4360.33 3657.00 4275.00 4982.67 4317.67 4338.67 4317.67 4275.00 4371.00 4647.67
IND_MOL_QRY 5254.67 4467.00 4317.67 4275.00 5217.00 5173.67 5088.00 5173.67 4467.00 4371.00 4360.33 3974.33 4317.67 5050.67 4317.67 4529.33 4317.67 4317.67 4371.00 4575.67
5293.33 4467.00 4275.00 4317.67 5253.00 5248.33 5181.33 5248.33 4467.00 4371.00 4275.00 3974.33 4317.67 4933.33 4275.00 4382.67 4275.00 4317.67 4371.00 4497.00
1.40 0.00 0.00 0.00 L1.20 0.10 0.10 0.80 0.00 1.10 0.50 0.10 3.40 0.00 0.10 0.00 0.70 0.00 0.10 0.30
0.70 0.00 0.00 0.00 L1.10 0.00 0.20 0.70 0.00 1.10 0.50 0.30 3.40 0.00 0.10 0.00 0.70 0.20 0.10 0.30
1.30 0.00 0.00 0.00 L1.20 0.10 0.10 0.80 0.00 1.10 0.60 0.30 3.40 0.70 0.00 0.00 0.70 0.20 0.10 0.50
(Fig. 2) performed better than the others based on the number of molecules that had corresponding features as compared to the pharmacophore. Hence, MODEL_05 was considered for the final rigid alignment of the remaining compounds (results shown in Table 2). GALAHAD was not able to align 8 molecules in the set and the model was used to manually align these 8 molecules. Nevertheless, upon further investigation, the manually aligned molecules had very high steric energy clashes. It was observed that high MOL_QRY (Table 2, Column 7) scores closely correlated with the non-alignment of molecules. All molecules that did not align well with the model had high MOL_QRY scores. The optimized pharmacophore from this procedure is shown in Fig. 2 and includes two hydrophobes centered on the benzene rings as well as one on the aliphatic linking chain, two acceptor atom centers on the two oxygen atoms and four donor atoms centered on the amidines. A single nitrogen atom is identified on one of the ends. The program would identify all nitrogens common to the three compounds only if it was able to align them without a high cost to the steric and energy terms. Hence, only one of the nitrogens is seen in the model even though there are other candidates among all three compounds used to build the initial pharmacophore. The pharmacophore model clearly shows the importance of the hydrophobic phenyl rings with polar oxygen and amidine substituents to antimalarial activity. The hydrophobic linking chain is also important for activity.
P. Athri et al. / European Journal of Medicinal Chemistry 45 (2010) 6147e6151 Table 2 Comparative energy, sterics, H-bond and the Molecular Query (MOL_QRY) score provided by GALAHAD (Columns 3, 4, 5 & 6), the pIC50 for the malarial K1 strain (Column 2) and the NF54 strain (Column 8) of all the molecules used in this study (Column 1). Column 7 notes whether the particular molecule was aligned with the proposed pharmacophore, i.e. Model_05 (Fig. 2) for the K1 strain. pIC50 (K1) 5MAA089 2.47 3KEG083 2.3 1EVK061 2.07 3STL057 1.91 3SMB019 1.84 3SMB101 1.77 1EVK057 1.74 1KAO009 1.74 21DAP023 1.63 5MAA137 1.62 3SMB043 1.58 5MAA123 1.49 3SMB045 1.48 3SMB079 1.32 1EVK097 1.24 5EVK038 0.91 2EVK008 0.61 1EVK060 0.19 4EVK055 0.13 4EVK051 0.2 5BGR006 0.25 5MAA135 0.3
ENERGY STERICS HMOL_QRY Align to BOND M05? 15.84 65.07 28.54 61.66 95774.26 63.58 16.48 54.55 63.86 11.49 74.26 43.73 19.32 66.21 26.64 1259.15 18.44 2.84 23.52 10.7 106.54 29.42
8300 7983.6 5469.2 2538.3 3770.9 711.6 3024.3 1944.2 716.1 2757.2 1452.5 1983.7 4306.9 1775 2222.3 4479.4 3511.1 7290.1 3414.9 1782 3368 5837.9
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0.8 0.8 2.6 0.8 1.6 1.1 0.8 0.8 5.8 0.8 0.8 0.9 0.8 0.8 2.6 0.8 2.8 0.8 2.3 0.8 0.6 2.2
Yes Yes No Yes Yes No Yes Yes No Yes Yes Yes Yes Yes No Yes No Yes No Yes No No
pIC50 (NF54) 1.46 1.57 1.3 1.25 1.55 0.92 1.09 1.28 1.46 0.95 1.01 0.98 0.63 0.52 0.33 1.23 1.46 0.72 0.02 0.39 0.55 0.69
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among a pool of structurally related compounds. The pharmacophore delineates important features that are common to pentamidine-based ligands active against P. falciparum. The corresponding features on either sides of the hydrophobic chain provide the necessary interactions with binding pockets. The flexibility of the hydrophobic linking chain is important to aligning these pharmacophoric features in the binding pocket with very little or no energy costs. Also, the model does not preclude either DNA or heme targets and, in effect, might suggest that molecules that fit this pharmacophore bind to multiple targets to achieve high activity. This qualitative model can be used to drive future synthesis of ligands that optimize the perceived features, and also assist 3D database querying. Funding Funding for this project was provided by the Bill and Melinda Gates Foundation. Contributors PA and WDW conceived the project and wrote the paper. TW conducted all the biological assays that provided the activity data. RT and SMB synthesized all the compounds that were used in this study. PA designed and implemented the computational modeling pipeline described in the paper. References
Fig. 2. The 3 active analogs that were aligned using GALAHAD are shown above. The hypothesized pharmacophore for these pentamidine analogs active against P. falciparum, shown above, includes three hydrophobic centers in cyan, four donors in magenta, one nitrogen in red and two acceptor atoms in green. For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.
As mentioned earlier, the same procedure was used to generate a pharmacophore using the NF54 activities. The optimal pharmacophore closely replicated the features present in the MODEL_05 model for the K1 strain. To avoid repetition, we have not presented this model. Most pentamidine analogs evaluated in this study are relatively active and the variance in activities in this dataset is quite low. The dataset does not allow for 3D QSAR type studies since only 22 compounds existed in our database, which were structurally related and in vitro active with IC50s ranging from 0.003 mM to 2 mM against the P. falciparum strain K1 and 0.02 mMe3 mM against NF54. Several compounds did show slightly higher IC50s in the case of NF54, as compared to K1. This is attributed to its slightly impaired resistance. Potential cross-resistance with chloroquine and pyrimethamine was observed for 5MAA135 since only this compound has a higher IC50 against the resistant strain K1. 4. Conclusion The pharmacophore presented in this study provides rational avenues to enrich the present database with high activity leads
[1] WHO, World Malaria Report 2009 This report is available at. World Health Organisation, Geneva, 2009.