3D-QSAR Modelling and Synthesis of New Fusidic Acid Derivatives as

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3D-QSAR Modelling and Synthesis of New Fusidic Acid Derivatives as Antiplasmodial Agents Gurminder Kaur, Elumalai Pavadai, Sergio Wittlin, and Kelly Chibale J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00105 • Publication Date (Web): 24 Jul 2018 Downloaded from http://pubs.acs.org on July 25, 2018

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Journal of Chemical Information and Modeling

3D-QSAR Modelling and Synthesis of New Fusidic Acid Derivatives as Antiplasmodial Agents Gurminder Kaur a, Elumalai Pavadaia∇, Sergio Wittlinb,c , and Kelly Chibalea, d * a

Department of Chemistry, University of Cape Town, Rondebosch 7701, South Africa.

b c

Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland

University of Basel, 4002 Basel, Switzerland

d

South African Medical Research Council Drug Discovery and Development Research Unit,

Department of Chemistry and Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Rondebosch 7701, South Africa. ∇

Current address: Department of Physics, Florida International University, Miami 33199, FL,

USA.

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ACS Paragon Plus Environment

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GRAPHICAL ABSTRACT

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ABSTRACT

Wide spread Plasmodium falciparum (P. falciparum) resistance has compromised existing antimalarial therapies to varying degrees. Novel agents, able to circumvent antimalarial drug resistance, are therefore needed. Fusidic acid is a unique antibiotic with a unique mode of action, which has shown weak in vitro antiplasmodial activity. Towards identifying new fusidic acid derivatives with superior antiplasmodial activity, a 3D-QSAR model was developed based on the antiplasmodial activity of previously synthesized fusidic acid derivatives. The validated Hypo 2 model was used as the 3D-structural search query to screen a fusidic acid-based combinatorial library. Based on the predicted activity and pharmacophore fit value, eight virtual hit compounds were selected and synthesized, including C-21 amide and C-3 ether derivatives. All synthesized hit compounds showed superior antiplasmodial activity compared to fusidic acid. Two C-21 amide derivatives displayed significant activity against the CQ-sensitive NF54 strain with IC50 values of 0.3 µM and 0.7 µM, respectively. These two derivatives also displayed activity against the CQresistant K1 strain, with an IC50 value of 0.2 µM and were found to be relatively noncytotoxic. 1. INTRODUCTION

Since 2000, malaria incidence and mortality has lowered by 41% and 62% respectively1, due to various preventive and treatment measures such as insecticide-treated bed nets, insecticide spraying, diagnostic testing and appropriate chemotherapy. Despite this remarkable progress, malaria continues to affect people’s health and livelihoods. An estimated 216 million malaria cases occurred globally in 2016, which led to 445,000 deaths.1 The disease is predominant in Africa, which accounts for 90% of malaria infections and malaria-related deaths worldwide.1 Most of the malaria infections and deaths occur due to Plasmodium falciparum (P. falciparum), the most virulent of all plasmodium species that causes human malaria. Although a number of drugs and drug combination regimens are available to treat malaria, P. falciparum has developed resistance to almost all of them. Delayed parasite clearance by the artemisinin combination therapy (ACT) regimen, the backbone of modern antimalarial combination therapies and last line of defence against resistant parasites, has recently been observed,2–7. Novel antimalarials are therefore urgently needed to confront the threat of resistance and to provide alternative compounds for inclusion in future combination therapies.

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Fusidic acid (Figure 1) is a unique antibiotic with a unique mode of action. The chemical structure of fusidic acid includes a steroid-type tetracyclic ring system in which the three cyclohexyl rings are arranged in trans-syn-trans manner. The tetracyclic ring system also bears two hydroxyl groups, one acetate group, and a lipophilic side chain bearing a carboxylic acid group. All these features are important for its antibiotic activity.8,9 Fusidic acid is clinically used for the treatment of Gram-positive bacterial infections, most notably infections caused by Staphylococcus aureus.10–14

Its mechanism involves inhibition of

bacterial protein synthesis through interference with the elongation factor G (EF-G)/ribosome complex.15,16 Apart from antibacterial activity, fusidic acid has also shown in vitro antiplasmodial activity against the P. falciparum strains with IC50 values of 52.8 µM (D10)17 and 59 µM (NF54).18 Although its IC50 value is high, it has the potential for repositioning in malaria through semisynthesis. EF-Gs of P. falciparum (PfEF-Gs) located in the apicoplast and mitochondria have been suggested as targets of fusidic acid.17,19

Figure 1: Structure of fusidic acid, its derivatives a-c and fusidic acid-like compound d

We have previously reported on a series of antiplasmodial fusidic acid derivatives. Of the many carboxylic acid bioisostere derivatives that showed a 2–35 fold increase in antiplasmodial activity

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relative to fusidic acid, compound a (Figure 1) was found to be the most active with an IC50 value of 1.7 µM, against the CQ-sensitive NF54 strain of P. falciparum.18 From a series of ester and amide derivatives, compounds b and c (Figure 1) were found to be the most active with IC50 value of 1.2 µM and were relatively non-cytotoxic.20 In addition to these, we recently employed 2D and 3D similarity-based virtual screening methods to identify new growth inhibitors of P. falciparum using fusidic acid as a search query.21 From this work, steroid-like compound d (Figure 1) showed improved antiplasmodial activity with an IC50 value of 1.39 µM. The superior antiplasmodial activity of the above mentioned compounds relative to fusidic acid indicates that it is feasible to develop yet more active fusidic acid derivatives as antiplasmodial agents. This therefore prompted work described in this manuscript, In the absence of a 3D crystal structure of a known target, we chose a ligand-based 3DQSAR (Three-Dimensional Quantitative Structure-Activity Relationship) modelling approach to design fusidic acid derivatives towards the identification of derivatives with improved antiplasmodial activity. This approach has been widely used to identify new hit compounds for a particular target.22–26 The 3D-QSAR modelling superimposes a set of active compounds and extracts the common chemical features that are essential for their biological activity. After validating the model on various active and inactive compound data sets, it is then used for virtual screening against a library of compounds to identify new hit compounds. Application of such a technique prior to synthesis of any compound has the advantage of not only reducing the number of compounds for synthesis but also potentially increases the % hit rate. In this study, 3D-QSAR models were developed based on the antiplasmodial activities of previously synthesized fusidic acid derivatives. A combinatorial library was designed and screened through the validated model in order to identify new fusidic acid derivatives for synthesis and evaluation as antiplasmodial agents. Selected compounds were accordingly synthesized and evaluated for antiplasmodial activity. Of all compounds that showed superior antiplasmodial activity relative to fusidic acid, two C-21 amide derivatives, 68 and 69, displayed significant activity against the CQ-sensitive NF54 strain with IC50 values of 0.3 µM and 0.7 µM, respectively. In addition, both compounds were equipotent against the CQresistant K1 strain with an IC50 value of 0.2 µM and were found to be relatively noncytotoxic.

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2. MATERIALS AND METHODS 2.1. 3D-QSAR pharmacophore modelling 2.1.1. Data preparation: Sixty one fusidic acid derivatives were used to generate and

validate the 3D-QSAR model. These compounds were synthesized in our laboratory and in vitro biological activities were determined as the concentration of the compound that inhibited P. falciparum growth by 50% (IC50).18,20,27 Two dimensional (2D) chemical structures of the compounds were drawn using ChemBioDraw Ultra 12.0 and saved in MDL (Molecular Design Limited) mol file format. Subsequently, they were imported into Discovery Studio 4.0 (DS4) (Dassault Systèmes BIOVIA, Discovery Studio Modeling Environment, Release 4.0, San Diego: Dassault Systèmes, 2015) to provide the corresponding standard 3D-structures, which were then used to generate conformations of the compounds. According to the Catalyst program guidelines, to build and validate the pharmacophore models, 61 compounds were divided into two sets: a training set and a test set. The training set was used to generate models and consisted of 29 structurally diverse compounds with biological activities ranging from 390 to 91000 nM (Table S1). The test set, consisting of the remaining 32 compounds, was used to evaluate the predictive capability of the models (Table S2). The ‘Generate Conformation’ protocol of DS4 was used to create a conformational space for each of the training and test set compounds. A maximum number of 255 conformations were generated for each compound using generalized CHARMm force field.28 All conformations were generated with ‘Best’ conformational search option and within a relative energy threshold of 10 kcal/mol. Other parameters were used at their default values. 2.1.2. Generation of pharmacophore hypotheses: The ‘HypoGen’ algorithm of DS4 was

employed to generate 3D-QSAR pharmacophore hypotheses/models with a set of selected 29 training set compounds. According to this algorithm, a minimum of 0 to a maximum of 5 features were selected and used to build a series of pharmacophore hypotheses using an uncertainty value of 1.5. Features were selected with the help of ‘Feature Mapping’ protocol, which identified the common features present in active compounds. Accordingly, HB_acceptor, HB_donor, Hydrophobic and Ring aromatic were used as key input pharmacophore features for the generation of the pharmacophore hypotheses. All other parameters of the algorithm were used at their default values. The ‘HypoGen’ algorithm generated top 10 pharmacophore hypotheses ranked by their cost values and listed other 6


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significant statistical parameters of the hypotheses such as root mean square deviation (RMSD) and correlation coefficient. 2.1.3. Validation of pharmacophore hypotheses: Pharmacophore hypotheses generated by

‘HypoGen’ were validated through following steps: cost analysis, Fischer’s randomization test, and the test set prediction. Cost analysis: The statistical relevance of the hypotheses was assessed on the basis of their

cost values, such as, fixed cost, null cost and total cost. The fixed cost is the lowest possible cost representing a hypothetical simplest model that fits all data perfectly. The null cost is the highest cost of a pharmacophore with no features, and estimates activity to be the average of the activity data of the training set compounds. The total cost is the actual cost of hypothesis generation.29 The greater the difference between null cost and total cost and the closer the total cost of the generated hypothesis is to the fixed cost, the more statistically significant is the generated hypothesis. Generally, a cost difference of 40–60 indicates a 75–90% chance of representing a true correlation in the data. A cost difference value greater than 60 may represent excellent true correlation. However, if the cost difference is below 40, the likelihood of the hypothesis representing a true correlation in the data rapidly drops below 50%.29 Another parameter that also determines the quality of any pharmacophore hypothesis is the error cost which represents the root-mean squared deviation (RMSD) between the predicted and experimental activities of the training set. On the basis of the above mentioned statistical data, the best pharmacophore model should have the highest cost difference, the least RMSD, and the best correlation coefficient. Fisher’s randomization test: Fisher’s randomization test validates the correlation between

the chemical structures and the biological activity of the compounds. This validation technique uses the CatScramble program to randomise the activity data among the training set compounds and generates new pharmacophore hypotheses under the same features and parameters as used in the original hypothesis generation. If these random pharmacophores show similar or better statistics such as cost values and correlation coefficients, than the original hypothesis, then the original hypothesis is considered as generated by chance. Test set validation: The capability of each pharmacophore hypothesis to predict the activity

of external compounds was assessed using the test set compounds. A conformational space for 32 test set compounds was prepared in the same manner as in the training set compounds.

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Further, the ‘Ligand pharmacophore mapping’ protocol of DS4 was used to map ligands (test set compounds) and to estimate their activity. 2.2. Combinatorial library design and pharmacophore-based virtual screening: In order

to identify hit compounds, the validated pharmacophore model was used as the 3D structural search query to screen a combinatorial library of fusidic acid derivatives. The combinatorial library was generated using the ‘Enumerate Library from Ligands’ protocol of DS4. A conformational ensemble of each library compound was then generated in the ‘Generate Conformation’ protocol, setting the confirmation method to ‘Best’, the maximum number of conformations to 255, and the relative energy threshold to 10 kcal/mol. All these conformations were used in the pharmacophore-based virtual screening which was performed using the ‘Ligand Pharmacophore Mapping’ protocol with ‘Best mapping’ and ‘Flexible fit’ options. The mapped compounds were ranked by their fit value computed by the pharmacophore model. The selection of compounds for synthesis was based on their good fit value and feasibility towards synthesis. 2.3 In vitro biological assay: Synthesized hit compounds were screened for in vitro

antiplasmodial activity against the CQ-sensitive NF54 strain of P. falciparum using the modified [3H]-hypoxanthine incorporation assay, and chloroquine and artesunate were used as reference drugs in all the experiments. The most active compounds were also screened for cytotoxicity against the Chinese Hamster Ovarian (CHO) mammalian cell line, using the 3(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazoliumbromide (MTT)-assay. Emetine was used as a reference drug. Details of the assay procedures are provided in the supporting information. 3. RESULTS AND DISCUSSION 3.1. Generation of 3D-QSAR model and validation

Sixty one fusidic acid derivatives with IC50 values determined against the NF54 strain of P. falciparum,18,20,27 were divided into two sets: a training set of 29 compounds and a test set of 32 compounds. To ensure the quality of the model generated and that the critical information on the pharmacophoric requirements has been embedded into the model, the training set compounds were selected in such a way that they show diversity in terms of both their structural features as well as biological activity. Structures and biological activities of the training (1-29) and test set (30-61) compounds are shown in Table S1 and Table S2,

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respectively (See supporting information). Using the training set compounds, the ‘HypoGen’ algorithm of DS4 generated a set of top 10 pharmacophore hypotheses (Hypo1 - Hypo10) with corresponding statistical parameters such as cost values, root mean square deviation (RMSD), and correlation coefficient as shown in Table 1.

Table 1: Statistical results of the top 10 pharmacophore hypotheses generated by ‘HypoGen’

algorithm

a

Cost Diff.

b

RMSD

c

Features

Correlation Training Set Test Set 0.919 0.620

Hypothesis

Total Cost

Hypo 1

115.806

111.460

1.303

Hy, Hy, Hy, RA

Hypo 2

118.772

108.490

1.349

Hy, Hy, Hy, RA

0.913

0.832

Hypo 3

127.014

100.250

1.631

Hy, Hy, Hy, RA

0.870

0.434

Hypo 4

127.593

99.668

1.646

Hy, Hy, Hy, RA

0.867

0.574

Hypo 5

127.798

99.463

1.520

Hy, Hy, Hy, RA

0.888

0.629

Hypo 6

131.349

95.912

1.730

Hy, Hy, Hy, RA

0.852

0.366

Hypo 7

132.382

94.879

1.748

Hy, Hy, Hy, RA

0.849

0.274

Hypo 8

136.537

90.724

1.816

HBA, Hy, Hy, RA

0.836

0.584

Hypo 9

137.504

89.757

1.804

HBA, Hy, Hy, RA

0.838

0.594

Hypo 10

138.409

88.852

1.815

HBA, Hy, Hy, RA

0.836

0.466

a

Cost difference between the null and the total cost. The null cost, the fixed cost and the

configuration cost are 227.261, 87.4891 and 17.7258, respectively. bRMSD, root mean square deviation. cAbbreviation used for features: HBA, hydrogen bond acceptor; Hy, hydrophobic and RA, ring aromatic. The best model was selected on the basis of the highest cost difference, a good correlation coefficient, the least RMSD, and the lowest total cost values.22 As shown in Table 1, Hypo 1 and Hypo 2 have lower total cost values (115.806 - Hypo 1, 118.772 - Hypo 2) and higher cost differences (111.460-Hypo 1 and 108.490-Hypo 2) compared to the other hypotheses. The RMSD value represents the deviation of the predicted activity value from the experimental value; therefore, a lower RMSD value indicates a better predictive ability of the model. The RMSD values of Hypo 1 (1.303) and Hypo 2 (1.349) were found to be lower than other models. The correlation coefficient is the linear regression derived from the geometric fit index and a higher correlation coefficient signifies better predictive ability of the model. 9



The correlation coefficients for Hypo 1 and Hypo 2 are 0.919 and 0.913, respectively, which are prominently higher than other hypotheses (0.836-0.870). Both hypotheses have the same pharmacophore features: 3 hydrophobic regions and one aromatic ring. The selection of the best model for virtual screening was performed after confirming the reliability of all hypotheses on the test set as explained below. The highest correlation coefficient was obtained for Hypo 2 (R=0.832) while Hypo 1 showed a correlation coefficient of 0.619 (Table 1). However, none of the other 8 hypotheses (Hypo 3- Hypo 10) showed a correlation coefficient close to Hypo 2. Consequently, Hypo 2 was selected as the best model for virtual screening of a combinatorial library. The fit values of all the training set compounds, as computed by Hypo 2, are shown in Table 2. The most active compound (1, IC50 = 390 nM) overlapped with all four pharmacophore features of Hypo 2 (Figure 2B) and showed the highest fit value of 3.69. On the other hand, the least active compound (29, IC50 = 91000 nM) missed one ring aromatic and one hydrophobic features of Hypo 2 (Figure 2C) and thus showed the lowest fit value of 1.53. Further, the ratio between experimental and predicted activities (error) for most of the compounds was found to be < 3, representing a good consistency of Hypo 2 in terms of predicting biological activity (IC50).

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Figure 2: (A) The best model, Hypo 2. (B) Hypo 2 mapping with one of the most active compounds (IC50 = 390 nM). (C) Hypo 2 mapping with one of the least active compounds (IC50 = 91000 nM) from the training set. Pharmacophore features are color-coded: orange for ring aromatic (RA) and cyan for hydrophobic (Hy).

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Table 2: Experimental (NF54) and predicted IC50 values of the training set compounds based on Hypo 2 Compound No.

a

b

Fit Value

Exp. IC50 (nM)

Pred. IC50 (nM)

1

3.69

390

500

1.3

2

3.14

500

1800

3.6

3

3.52

1100

750

-1.4

4

3.05

1200

2200

1.8

5

3.14

1400

1800

1.3

6

3.13

1500

1800

1.2

7

3.13

1700

1800

1.1

8

3.01

2000

2400

1.2

9

3.03

2000

2300

1.2

10

3.13

2600

1800

-1.4

11

2.75

2600

4400

1.7

12

2.96

2900

2700

-1.1

13

3.03

3200

2300

-1.4

14

3.06

3400

2100

-1.6

15

3.06

3600

2100

-1.7

16

3.04

3800

2200

-1.7

17

2.98

4700

2600

-1.8

18

2.53

6600

7300

1.1

19

2.18

7000

16000

2.3

20

2.11

8200

19000

2.3

21

2.85

8400

3500

-2.4

22

2.11

9200

19000

2.1

23

2.38

14000

10000

-1.3

24

2.11

15000

19000

1.3

25

2.11

21000

19000

-1.1

26

2.11

36000

19000

-1.9

27

1.63

43000

57000

1.3

28

2.11

59000

19000

-3.1

29

1.53

91000

73000

-1.2

12


Error

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a

Fit value indicates how well the features in the pharmacophore overlap the chemical

features in the molecule. bDivision of higher value of experimental/predicted IC50 by lower predicted/experimental IC50 value, ‘+’ indicates that the predicted IC50 is higher than the experimental IC50, ‘–’ indicates that the predicted IC50 is lower than the experimental IC50, a value of 1 indicates that the predicted IC50 is equal to the experimental IC50. The statistical significance of Hypo 2 model was further evaluated using the Fischer’s randomization test. This cross-validation method produced 19 random pharmacophore spreadsheets for each hypothesis, at 95% confidence level, using the same features and parameters as used in original hypothesis generation. The higher correlation and the lower cost value of Hypo 2 than random pharmacophore models shows that the Hypo 2 is superior and was not generated by chance (Figure S1). This result provided confidence that the Hypo 2 could be the best hypothesis that contains all the necessary chemical features required for activity. In addition to having good statistical data, a good model should also be able to accurately predict the activity of compounds and retrieve active compounds from the library or database. Therefore, the selected model ‘Hypo 2’ was subjected to a reliability check using test set and experimental decoys. As explained earlier, the test set validation, which uses a test set of 32 fusidic acid derivatives (not included in building of the models), was performed on all the 10 hypotheses. Of these, Hypo 2 showed the highest correlation between the experimental and predicted activity (Table 1). The IC50 values predicted by Hypo 2 for the test set compounds together with their experimental IC50 values and the error values for the predictions are shown in Table S3. With the exception of compound 30, all compounds showed error values below 8, most of them showing error values below 3. A high correlation (0.832, Figure S3) together with closely predicted activity values for the test set compounds indicate that Hypo 2 is capable of predicting, not only training set compounds, but also the external compounds. In addition, Hypo 2 was tested with the experimental decoys consisting of four fusidic acid derivatives with IC50 > 100 µM (Figure S4). The model predicted the four compounds to be inactive by rejecting these compounds during virtual screening, demonstrating that the model is also capable of classifying inactive compounds accurately. Therefore, Hypo 2 was selected for screening a fusidic acid-based combinatorial library.

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3.2. Pharmacophore-based virtual screening with the combinatorial library: Based on the modifications in the molecules used to build the model, two sites in fusidic acid were chosen to construct a combinatorial library. These sites included modifications either at C-3 or C-21 positions. A general structure representing the library design is shown in Figure 3.

Figure 3: A combinatorial library design for new fusidic acid derivatives. A total of 120 compounds were screened through Hypo 2 using the ‘Ligand Pharmacophore Mapping’ protocol, which computed a fit value for each compound. The selection of hit compounds was based on these fit values. The threshold for the fit value was decided from the fit values of the two most active compounds (compounds 1and 2, Table 2) used in 3DQSAR model generation. Accordingly, compounds with fit values > 3 were prioritized. After considering factors like higher fit values, availability of the starting materials and diversity in chemical structure, eight compounds were selected for synthesis. These 8 hits include derivatization at either the C-21 carboxylic acid to generate C-21 amide derivatives 65-69 or at the C-3 position to generate C-3 ethers 62-64. The chemical structures of the compounds with their fit value and predicted value of activity are shown in Figure 4. These selected hit compounds were synthesized and evaluated for biological activity.

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Fit value: 4.00 Predicted activity: 242 nM








Figure 4: Chemical structures of hit compounds selected for synthesis with fit values and predicted activity 3.3. Synthesis of hit compounds Two different synthetic routes (Scheme 1) were adopted for synthesis. Compounds 65-69 were synthesized by the reaction of fusidic acid (28) with the corresponding amine using T3P (50% w/v in DMF) as a coupling reagent. Synthesis of compounds 62-64 was achieved in 3 steps. Fusidic acid was first protected as pivaloyloxymethyl ester to yield 28a. This intermediate was then heated with the corresponding benzyl halides at 130oC using diisopropylethylamine (DIPEA) as a base to provide 62a-64a. Finally, the pivaloyloxymethyl group was removed using K2CO3 in methanol, yielding target compounds 62-64. Detailed synthetic procedures are described in supporting information. All target compounds were purified using column chromatography and fully characterized by analytical and spectroscopic techniques. Purities of the compounds were found to be greater than 95%. 15



Following synthesis, compounds were screened for in vitro antiplasmodial activity against the CQ-sensitive NF54 strain of P. falciparum.

Scheme 1: (a) ClCH2OCOC(CH3)3, Et3N, DMF, 25oC, 36h; (b) R1-Cl or R1-Br, DIPEA, 16h, 130oC; (c) K2CO3, Methanol, 25oC, 2.5h; (d) R-NH2, Et3N, DCM, T3P (50% w/v solution in DMF), 0oC-25oC, 5h 3.4. Antiplasmodial activity The antiplasmodial activity of fusidic acid was found to be 59 µM (IC50 against NF54). All synthesized compounds were found to be more active than fusidic acid with IC50 values ranging from 0.3 to 9.2 µM (Table 3). C-21 amide derivatives 65-69 displayed superior activities compared to C-3 ether derivatives 62-64. Among the C-21 derivatives, the antiplasmodial activity of the compounds improved significantly when either a methyl group was introduced at the benzylic position (compound 68) or when the benzylic group is omitted (compound 69). The IC50 values for these two compounds were also found to be close to those predicted by the pharmacophore model (Figure 4). However, IC50 values for other compounds were found to be higher than those predicted by the pharmacophore model. Data based on compounds 65-67 suggests that a substituent at the 4-position of the benzyl group had no statistically significant effect on antiplasmodial activity. Compounds 68 (IC50 0.3 µM) and 69 (IC50 0.7 µM) were found to be the most active. These two compounds also showed

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significant in vitro activity against the CQ-resistant K1 strain with equipotent activity, IC50 value of 0.2 µM, and were found to be relatively non-cytotoxic as determined in the Chinese Hamster Ovarian (CHO) cell line (Table 3).

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Table 3: In vitro antiplasmodial activity (72 h incubation, [3H]-hypoxanthine incorporation readout, mean (Individual data, n = 2) ) and cytotoxicity of fusidic acid derivatives

a

Compound number

R

R1

IC50 NF54 (µM)

Fusidic acid

OH

H

62

a

IC50 K1 (µM)

IC50 CHO (µM)

59

19

>194

OH

6.4 (6.7, 6.1)

ND

ND

63

OH

7.6 (8.4, 6.8)

ND

ND

64

OH

9.2 (9.7, 8.8)

ND

ND

65

H

2.7 (2.9, 2.5)

ND

ND

66

H

5.9 (6.9, 5.0)

ND

ND

67

H

3.6 (3.9, 3.3)

ND

ND

68

H

0.3 (0.3, 0.2)

0.2 (0.19, 0.17)

>153

69

H

0.7 (0.8, 0.6)

CQ

16 nM

0.2 (0.19, 0.15) 215 nM

Artesunate

4 nM

2.6 nM

Emetine *ND = not determined;

a

134

0.05

IC50 values are the mean of two independent experiments. The values

for each individual experiment are shown in brackets 18


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4. CONCLUSION 3D-QSAR models based on the antiplasmodial activities of 61 previously synthesized fusidic acid derivatives were developed using the ‘Hypogen’ algorithm of Discovery Studio 4.0. The selected model Hypo2, after validation, was used as a 3D-query for virtual screening to search for potential hits from a fusidic acid-based combinatorial library. Eight hit compounds were synthesized and evaluated for their antiplasmodial activity against the NF54 strain of P.

falciparum. All compounds were found to be more active than fusidic acid. Compounds 68 and 69 displayed significant activity with IC50 values of 0.3 µM and 0.7 µM, respectively. Compounds 68 and 69 were relatively non-cytotoxic and also displayed significant in vitro activity against the resistant strain (K1) with an IC50 value of 0.2 µM. AUTHOR INFORMATION Corresponding author * Phone: +27 21 650 2553; Fax: +27 21 650 5195; E-mail: [email protected] ORCID Kelly Chibale: 0000-0002-1327-4727 ACKNOWLEDGEMENTS The University of Cape Town, South African Medical Research Council, and South African Research Chairs initiative of the Department of Science and Technology administered through the South African National Research Foundation are gratefully acknowledged for support (KC). We thank Peter J. Smith and Carmen de Kock for generating cytotoxicity data. REFERENCES (1)

WHO | World Malaria Report; 2017.

(2)

Wellems, T. E.; Plowe, C. V. Chloroquine Resistant Malaria. J. Infect. Dis. 2001, 184, 770–776.

(3)

Petersen, I.; Eastman, R.; Lanzer, M. Drug-Resistant Malaria: Molecular Mechanisms and Implications for Public Health. FEBS Lett. 2011, 585, 1551–1562.

(4)

Bosman, P.; Stassijns, J.; Nackers, F.; Canier, L.; Kim, N.; Khim, S.; Alipon, S. C.; Chuor Char, M.; Chea, N.; Dysoley, L.; Van den Bergh, R.; Etienne, W.; De Smet, M.; Ménard, D.; Kindermans, J.-M. Plasmodium Prevalence and Artemisinin-Resistant Falciparum Malaria in Preah Vihear Province, Cambodia: A Cross-Sectional Population-Based Study. Malar. J. 2014, 13, 394.

19



(5)

Wongsrichanalai, C.; Meshnick, S. R. Declining Artesunate-Mefloquine Efficacy against Falciparum Malaria on the Cambodia-Thailand Border. Emerg. Infect. Dis. 2008, 14, 716–719.

(6)

Cottrell, G.; Musset, L.; Hubert, V.; Le Bras, J.; Clain, J. Emergence of Resistance to Atovaquone-Proguanil in Malaria Parasites: Insights from Computational Modeling and Clinical Case Reports. Antimicrob. Agents Chemother. 2014, 58, 4504–4514.

(7)

Blasco, B.; Leroy, D.; Fidock, D. A. Antimalarial Drug Resistance: Linking Plasmodium Falciparum Parasite Biology to the Clinic. Nat. Med. 2017, 23, 917–928.

(8)

Godtfredsen, W. O.; Daehne, V. W.; Tybring, L.; Vangedal, S. Fusidic Acid Derivatives. I. Relationship between Structure and Antibacterial Activity. J. Med. Chem. 1966, 9, 15–22.

(9)

Duvold, T.; Sørensen, M. D.; Bjorkling, F.; Henriksen, A. S.; Rastrup-Andersen, N. Synthesis and Conformational Analysis of Fusidic Acid Side Chain Derivatives in Relation to Antibacterial Activity. J. Med. Chem. 2001, 44, 3125–3131.

(10)

Spelman, D. Fusidic Acid in Skin and Soft Tissue Infections. Int. J. Antimicrob. Agents 1999, 12, S59–S66.

(11)

Verbist, L. The Antimicrobial Activity of Fusidic Acid. J. Antimicrob. Chemother. 1990, 25, 1–5.

(12)

Godtfredsen, W.; Roholt, K.; Tybring, L. Fusidic Acid: A New Orally Active Antibiotic. Lancet 1962, 1, 928–931.

(13)

O’Neill, A. J.; McLaws, F.; Kahlmeter, G.; Henriksen, A. S.; Chopra, I. Genetic Basis of Resistance to Fusidic Acid in Staphylococci. Antimicrob. Agents Chemother. 2007, 51, 1737–1740.

(14)

Collignon, P.; Turnidge, J. Fusidic Acid in Vitro Activity. Int. J. Antimicrobial . Agents 1999, 12 Suppl 2, S45–S58.

(15)

Besier, S.; Ludwig, A.; Brade, V.; Wichelhaus, T. A. Molecular Analysis of Fusidic Acid Resistance in Staphylococcus Aureus. Mol. Microbiol. 2003, 47, 463–469.

(16)

Bodley, J. W.; Zieve, F. J.; Lin, L. Studies on Translocation. IV. The Hydrolysis of a Single Round of Guanosine Triphosphate in the Presence of Fusidic Acid. J. Biol .Chem. 1970, 45, 5662–5667.

(17)

Johnson, R. A.; McFadden, G. I.; Goodman, C. D. Characterization of Two Malaria Parasite Organelle Translation Elongation Factor G Proteins: The Likely Targets of the Anti-Malarial Fusidic Acid. PLoS ONE 2011, 6, e20633.

(18)

Kaur, G.; Singh, K.; Pavadai, E.; Njoroge, M.; Espinoza-moraga, M.; De Kock, C.; Smith, P. J.; Wittlin, S.; Chibale, K. Synthesis of Fusidic Acid Bioisosteres as Antiplasmodial Agents and Molecular Docking Studies in the Binding Site of Elongation Factor-G. Med. Chem. Commun. 2015, 6, 2023–2028. 20


Page 20 of 22

Page 21 of 22 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(19)

Gupta, A.; Mir, S. S.; Saqib, U.; Biswas, S.; Vaishya, S.; Srivastava, K.; Siddiqi, M. I.; Habib, S. The Effect of Fusidic Acid on Plasmodium Falciparum Elongation Factor G (EF-G). Mol. Biochem. Parasitol. 2013, 192, 39–48.

(20)

Espinoza-Moraga, M.; Singh, K.; Njoroge, M.; Kaur, G.; Okombo, J.; De Kock, C.; Smith, P. J.; Wittlin, S.; Chibale, K. Synthesis and Biological Characterisation of Ester and Amide Derivatives of Fusidic Acid as Antiplasmodial Agents. Bioorg. Med. Chem. Lett. 2017, 27, 658–661.

(21)

Pavadai, E.; Kaur, G.; Wittlin, S.; Chibale, K. Identification of Steroid-like Natural Products as Antiplasmodial Agents by 2D and 3D Similarity-Based Virtual Screening. Med. Chem. Commun. 2017, 8, 1152–1157.

(22)

Debnath, A. K. Generation of Predictive Pharmacophore Models for CCR5 Antagonists: Study with Piperidine- and Piperazine-Based Compounds as a New Class of HIV-1 Entry Inhibitors. J. Med. Chem. 2003, 46, 4501–4515.

(23)

Ghoneim, O. M.; Ibrahim, D. A.; El-deeb, I. M.; Ha, S.; Booth, R. G. A Novel Potential Therapeutic Avenue for Autism : Design , Synthesis and Pharmacophore Generation of SSRIs with Dual Action. Bioorg. Med. Chem. Lett. 2011, 21, 6714– 6723.

(24)

Braga, R. C.; Andrade, C. H. Assessing the Performance of 3D Pharmacophore Models in Virtual Screening: How Good Are They? Curr. Top. Med. Chem. 2013, 13, 1127–1138.

(25)

Sabatini, S.; Gosetto, F.; Iraci, N.; Barreca, M. L.; Massari, S.; Sancineto, L.; Manfroni, G.; Tabarrini, O.; Dimovska, M.; Kaatz, G. W.; Cecchetti, V. Re-Evolution of the 2-Phenylquinolines: Ligand-Based Design, Synthesis, and Biological Evaluation of a Potent New Class of Staphylococcus Aureus NorA Efflux Pump Inhibitors to Combat Antimicrobial Resistance. J. Med. Chem. 2013, 56, 4975–4989.

(26)

Chang, C.; Ekins, S.; Bahadduri, P.; Swaan, P. W. Pharmacophore-Based Discovery of Ligands for Drug Transporters. Adv. Drug Deliv. Rev. 2006, 58, 1431–1450.

(27)

Kaur, G. A Medicinal Chemistry Approach to Drug Repositioning in the Treatment of Tuberculosis and Malaria, University of Cape Town, 2016.

(28)

Brooks, B. R.; Bruccoleri, R. E.; Olafson, B. D.; States, D. J.; Swaminathan, S.; Karplus, M. CHARMM: A Program for Macromolecular Energy, Minimization, and Dynamics Calculations. J. Comput. Chem. 1983, 4, 187–217.

(29)

Li, H.; Sutter, J.; Hoffmann, R. Pharmacophore Perception, Development, and Use in Drug Design; Guner, O. F., Ed.; International University Line: California, 2000.

21



22


Page 22 of 22

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