Design And Discovery Of Selective Mao B Inhibitors Biology Essay

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Monoamine Oxidase (MAO) is a flavinadenosine dinucleotide containing enzyme located at the outer membranes of mitochondria in the brain, liver, intestinal mucosa, and other organs. It catalyzes the oxidative deamination of biogenic amines (neuroamines, vasoactive and exogenous amines), including dopamine, serotonin, norephinephrine, tyramine, tryptamine, and MPTP neurotoxin. The end products are aldehydes and H2O2 that are involved in oxidative cellular processes25,69. MAO exists in two isoforms, i.e., MAO-A and MAO-B in humans and both are 60 kDa outer-mitochondrial membrane-bound flavoenzymes that share 70% sequence identities70,23. Due to distinct and overlapping specificities of MAO-A and MAO-B in the oxidative deamination of neurotransmitters and dietary amines, the development of specific reversible inhibitors has been a long sought goal. Expression levels of MAO-B in neuronal tissue increase 4-fold with age,45,71 resulting in an increased level of dopamine metabolism and the production of higher levels of hydrogen peroxide, which are thought to play a role in the etiology of neurodegenerative diseases such as Parkinson's and Alzheimer's diseases.45,72 MAO inhibitors demonstrated remarkable antidepressant action but their clinical value was seriously compromised with side effects like ''cheese reaction''18,73. These serious side effects stimulated a search for antidepressants that are not MAO inhibitors and to their eventual replacement using the uptake inhibitors, the tricyclic antidepressants and more recently the serotonin selective re-uptake inhibitors (Prozac). Despite the general lack of interest, Knoll & Magyar persisted in their study with an irreversible MAO inhibitor, l-Deprenyl, derived from propargylamine37-39,45,74. It is a selective MAO-B inhibitor at low doses and inhibited the oxidative deamination of dopamine, phenylethylamine and benzylamine but at higher doses the selectivity of the compound was lost. The compound also was evaluated as an antidepressant, devoid of the ''cheese reaction''18,45. Thus, the development of specific, reversible MAO-B inhibitors could lead to clinically useful neuroprotective agents. Compounds with similar structural motifs can lead to the same biological effect. It is also well accepted that bioactive ligands that bind to a common receptor must fulfill certain chemical and geometric criteria. For this purpose a knowledge-based approach is based on another popular technique applied in the drug discovery, known as scaffold-hopping75,76 where the goal is to 'jump' in chemistry space, i.e., to discover a new structure starting from a known active compound via the modification of the central core of this molecule.77 In the previous studies pharmacophore model was generated with the consideration of Thiazole derivatives and Thiosemicarbazide derivatives with features like three hydrogen bond acceptors, one hydrophobic feature and one aromatic ring.78 In the present study, the ligand and structure based design studies were done using Catalyst and Glide to design selective MAO-B inhibitors.79-82 We have considered diverse set of structural motifs like Thio- and semicarbazides, beta-Carbolines, cis- and trans-resveratrol derivatives, Eugenol derivatives, Pyrazoles, Oxazolidinones, Phenylcyclopropylamines, and Indan derivatives to describe the knowledge-based design, identification and optimization of a new lead.83-93 Most of the rationally designed and clinically useful inhibitors (revesible or irreversible) are competitive inhibitors. We have included both reversible and irreversible inhibitors in the training set to get the optimized features necessary for the enzyme inhibition.

Plan of work

The work done shows how chemical features for Selective MAO-B inhibitors along with their activities ranging over several orders of magnitudes can be used to generate pharmacophore hypotheses and can successfully predict the activity. The validated pharmacophore model is used to retrieve molecules from a virtual library and the retrieved hits/leads were further refined using the docking studies to reduce the number of false positives and false negatives. This virtual screening approach can be used to identify and design inhibitors with greater selectivity.

3.2 Computational Studies

Catalyst 4.11 software was used to generate pharmacophore models. GLIDE (Schrodinger, L.L.C., New York) docking programs were used for the structure based studies.

3.2.1 Database Mining and Training Set

The compound selection process implemented is shown as a flowchart in Figure 3.1. The pharmacophore model (Hypo2) was used as a query to screen an in house database consisting of 80,000 compounds which were passed drug like filters. The search retrieved 5500 compounds were selected for cluster analysis and a set of 530 cluster representative hits with predicted IC50 value of less than 10 µM were chosen for docking onto the active site of MAO-B. Highly scored compounds were selected to carry out the docking analysis onto the active site of MAO-A.

Figure 3.1: Schematic representation of in silico screening protocol implemented in the identification of MAO-B inhibitors.

Figure 3.2: Structurally diverse set of compounds retrieved from the database and proposed for synthesis.

3.2.2 Pharmacophore Model Generation and Validation

Pharmacophore modeling correlates activities with the spatial arrangement of various chemical features in a set of active analogues. A set of 70 human MAO-B inhibitors83-92 with an activity range (IC50) spanning over 5 orders of magnitude, i.e., 0.014-980 µM were selected. Molecules were chosen based on the MAO-B inhibitory assay tested under similar experimental conditions. This initial group was then divided into the training and test sets. The training set of 22 molecules was designed to be structurally diverse with a wide activity range. The training set molecules play a critical role in the pharmacophore generation process and the quality of the resultant pharmacophore models relies solely on the training set molecules. The test set of remaining 48 molecules is designed to evaluate predictive ability of the resultant pharmacophore. Highly active (+++ or <20 µM), moderately active (++ or 20-50 µM) and inactive (+ or >50 µM) compounds were added to the training set to obtain critical information on pharmacophore requirements for MAO-B inhibition. The molecules selected as the training set are given in Figure 3.3. This training set was then used to generate quantitative pharmacophore models.

Qualitative pharmacophore models were generated using a set of highly active molecules. To confirm essential features prevailing among the MAO-B inhibitors, 10 common feature hypotheses were generated using the most active molecules 1-6 (Figure 3.3). The common features for all 10 hypotheses are hydrogen bond donor, hydrogen bond acceptor and ring aromatic features. However, these models cannot be directly used to predict biological activity of the compounds retrieved from a database. We have generated quantitative pharmacophore models to predict the biological activities of novel compounds. While generating the quantitative model, a minimum of 0 to a maximum of 5 features involving HBA, HBD, and RA features were selected and used to build a series of hypotheses using a default uncertainty value of 3. The quality of HypoGen models are best described by Debnath and Vadivelan93-96 in terms of Fixed Cost, Null Cost and total Cost and other statistical parameters. According to which, a large difference between the fixed cost and null cost, and a value of 40-60 bits for the unit of cost would imply 75-90% probability for experimental and predicted activity correlation. In general, pharmacophore models should be statistically significant, predict the activity of molecules accurately, and retrieve active compounds from a database. The derived pharmacophore models were validated using a set of parameters including cost analysis, test set prediction, enrichment factor, and goodness of hit. HipHop and HypoGen modules within Catalyst were then used to generate qualitative pharmacophore and quantitative pharmacophore models, respectively.

Figure 3.3: Training set molecules for MAO-B inhibitors. IC50 values of each molecule are given in parentheses.

3.2.3 Docking Studies

Crystal structure of MAO-A (PDB code: 2BXR) and MAO-B (PDB code: 2V5Z) 24 were used for the docking studies97. The protein 3D structures were downloaded from the protein databank (PDB). The hydrogen atoms were added to the proteins and further minimization was performed using protein preparation wizard. A set of 70 human MAO-B inhibitors were docked on to the active site of MAO-A and MAO-B using Glide docking program. During the docking process, Glide initially performs a complete systematic search of the conformation, orientation, and position of a compound in the defined binding site and eliminates unwanted poses using scoring and energy optimization. Finally the conformations were further refined via a Monte Carlo sampling. The MAO-A and MAO-B grid boxes were defined by the center of the bound inhibitors of the MAO-A and MAO-B proteins respectively. The enclosing box and binding box dimensions were fixed to 14 and 10 Å, respectively. The top 20 poses were collected for each compound. Docking poses were energy minimized using the OPLS-2001 force field. The best pose was selected based on Glide score and the favorable interactions formed between the compound and amino acid residues of the MAO-A and MAO-B active site. The selectivity index of the compoundwas calculated using the Glide score ofMAO-A and MAO-B (MAO-B Glide score/MAO-A Glide score >1).

3.3 Results and Discussion

3.3.1 Pharmacophore Studies

The best common feature pharmacophore model18 indicated the importance of H-bond acceptor (HBA), H-bond donor (HBD) and ring aromatic (RA) features, which were further confirmed in the quantitative models. Several quantitative models were generated utilizing the training set (1-22) along with MAO-B inhibitory activities (Figure 3.3 and Table 3.1). The top ten hypotheses were composed of HBA, HBD, and RA features. The values of ten hypotheses such as cost, correlation (r), and root-mean-square deviations (rmsd) are statistically significant (Table 3.2). It is evident that as error, weight and configuration components are very low and not deterministic to the model, the total pharmacophore cost is also low and close to the fixed cost. Also, as total cost is less than the null cost, this model accounts for all the pharmacophore features and has good predictive ability. In addition to an estimation of activity of the training set molecules, the pharmacophore model should also accurately predict the activity of the test set molecules. Two statistical methods were employed to rank the ten resultant hypotheses. In the first method, all ten hypotheses were evaluated using a test set of 48 known MAO-B inhibitors, which are not included in the training set. Predicted activities of the test set were calculated using all ten hypotheses and correlated with experimental activities. Of the ten hypotheses, Hypo2 showed a better correlation coefficient (0.945) compared to the other nine hypotheses. A second statistical test includes calculation of false positives, false negatives, enrichment, and goodness of hit to determine robustness of hypotheses. Under all validation conditions, Hypo2 performed superior as compared to the other nine hypotheses. Hypo2 demonstrated excellent prediction of MAO-B inhibitory activities of the training set compounds (Table 3.1). Analyzing the results, it was observed that out of the 9 highly active molecules, all were predicted correctly as highly active. Among the 6 moderately active molecules, except one, which was predicted as highly active and the rest were correctly predicted. Out of the 6 low active molecules, 3 were predicted as moderately active and the rest was predicted as low active. Activities of the compounds were not only correctly predicted but also the fit values confer a good measure of how well the pharmacophoric features of Hypo2 were mapped onto the chemical features of the compounds. Figure 3.4A shows the HypoGen pharmacophore features with their geometric parameters, all features of Hypo2 (HBA, HBD and RA) were mapped onto the highly active compounds of the training set (2) as well as the moderate compound (8) shown in Figure 3.4B and Figure 3.5C respectively. Many of the low active compounds in the training set (22) were mapped partially by the features of Hypo2 (Figure 3.4D).

The correlation values along with the predictions above make the pharmacophore suitable to predict molecular properties well. The plot showing the correlation between the actual and predicted activities for the test set and the training set molecules is given in Figure 3.5

The purpose of the pharmacophore model generation is not just to predict the activity of the training set compounds accurately but also to verify whether the pharmacophore models are capable of predicting the activities of external compounds of the test set series and classifying them correctly as active or inactive. The molecules were classified as highly active (+++ or <20 µM), moderately active (++ or 20-50 µM) and inactive (+ or >50 µM). Hypo2 was used to search the test set of known MAO-B inhibitors. Database mining was performed using the BEST flexible searching technique. The results were analyzed using a set of parameters such as hit list (Ht), number of active percent of yields (%Y), percent ratio of actives in the hit list (%A), enrichment factor (E), false negatives, false positives, and goodness of hit score (GH) (Table 3.3)98. Hypo2 succeeded in the retrieval of 84% of the active compounds. In addition, the pharmacophore also retrieved 6 inactive compounds (false positives) and predicted 2 active compounds as inactive (false negatives). An enrichment factor of 1.76 and a GH score of 0.73 indicate the quality of the model. Overall, a strong correlation was observed between the Hypo2 predicted activity and the experimental MAO-B inhibitory activity (IC50) of the training and test set compounds (Figure 3.5). However, the Hypo2 model has a greater tendency to show false positives. This could be attributed to high structural similarity in active and inactive MAO-B inhibitors, resulting in an inability to discriminate this pattern by the pharmacophore model. We further extended this study to structure based design and to limit the number of false positive and false negative hits and to further understand the binding of inhibitors to the active site of MAO-B complex.

Table 3.1: Experimental and Predicted IC50 data of 22 training set molecules against Hypo2 model.

Molecule

Exp.

IC50, μM

Predicted

IC50, μM

Error a

Fit value b

Experimental

scale c

Predicted

scale c

1

0.06

0.114

2.4

4.81

+++

+++

2

0.08

0.11

2.6

5.41

+++

+++

3

2.6

7.2

5.9

3.54

+++

+++

4

3

11.2

6.8

3.41

+++

+++

5

6.7

9.8

1.5

3.73

+++

+++

6

8.9

19

2.7

3.34

+++

+++

7

13

14

1.1

3.57

+++

+++

8

17

8.2

-6

4.27

+++

+++

9

19

14

-1.3

3.57

+++

+++

10

20

13

-1.6

3.62

++

+++

11

22

24

1.1

3.35

++

++

12

25

21

-1.2

3.4

++

++

13

30

25

-1.2

3.32

++

++

14

38

21

-1.8

3.41

++

++

15

40

35

-2

3.43

++

++

16

50

21

-2.4

3.41

++

++

17

61

38

-1.6

3.14

+

++

18

65

34

-1.9

3.2

+

++

19

88

190

2.15

2.26

+

+

20

92

39

-2.4

3.14

+

++

21

140

210

1.5

2.24

+

+

22

240

148

-5

3.05

+

+

a + 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.

b Fit value indicates how well the features in the Pharmacophore overlap the chemical features in the molecule. Fit = weight*[max(0,1-SSE)] where SSE = (D/T)2, D= displacement of the feature from the center of the location constraint and T=the radius of the location constraint sphere for the feature (tolerance).

c Activity scale - IC50 <20 µM = +++ (Highly active) - IC50 20-50 µM = ++ (Moderately active) - IC50 >50 µM = + (Low active).

Table 3.2: 10 Pharmacophore models generated by the HypoGen for MAO-B inhibitors.

Hypo No.

Total cost

Cost difference$

Error cost

RMS deviation

Training set

(r)

Features#

1

100.46

55.42

83.92

0.941

0.772

HBAHBDRA

2

99.04

56.84

82.42

0.875

0.961

HBAHBDRA

3

99.58

56.3

82.8

0.894

0.892

HBAHBDRA

4

99.64

56.24

82.87

0.898

0.851

HBAHBDRA

5

100.06

55.82

83.23

0.916

0.812

HBAHBDRA

6

100.11

55.77

83.54

0.931

0.783

HBAHBDRA

7

100.77

55.11

84.11

0.958

0.769

HBAHBDRA

8

100.99

54.89

83.45

0.927

0.79

HBAHBDRA

9

101.12

54.76

84.36

0.97

0.763

HBAHBDRA

10

101.19

54.69

83.35

0.922

0.793

HBAHBDRA

Hypo 2 showed a better correlation coefficient (0.945) compared to the other nine hypotheses.

a (Null costTotal cost), Null cost = 155.88, Fixed cost = 90.53, For the Hypo-2 Weight = 1.28, Configuration = 15.41. All cost units are in bits.

b HBA - Hydrogen Bond Acceptor, HBD - Hydrogen bond donor, RA - Ring aromatic.

Figure 3.4: Pharmacophore model for MAO-B inhibitors. (A) Three-dimensional arrangement of pharmacophore features in the quantitative pharmacophore model (Hypo2). Pharmacophore features are: H-bond acceptor (HBA) as green, Ring aromatic (RA1) as orange and H-bond donor (HBD) as magenta. (B) Hypo2 is mapped onto high active compound (2). (C) Mapping of Hypo2 onto a moderately active compound 8. (D) Mapping of Hypo2 onto an inactive compound 22.

Figure 3.5: Scatter plot shows correlation between experimental and Hypo2 predicted activities of known MAO-B inhibitors.

Table 3.3: Statistical parameters from screening of training and test set molecules.

S. No

Parameter

MAO B

1

Total molecules in database (D)

70

2

Total Number of actives in database (A)

34

3

Total Hits (Ht)

38

4

Active Hits (Ha)

32

5

% Yield of actives [(Ha/Ht)*100]

84.21

6

% Ratio of actives [(Ha/A)*100]

94.12

7

Enrichment factor (E) [(Ha*D)/(Ht*A)]

1.76

8

False Negatives [A - Ha]

2

9

False Positives [Ht - Ha]

6

10

Goodness of Hit Score$

0.73

$ [(Ha/4HtA)(3A+Ht))*(1-((Ht-Ha)/(D-A))]; GH Score of 0.7 - 0.8 indicates a very good model

3.3.2 Docking Studies

A set of 70 MAO-B inhibitors were docked onto MAO-B active site using Glide in order to identify their binding sites. A general trend was observed, in which the more active compound complexes scored higher than moderate active and low active compounds. Overall, there was no significant correlation between the fitness scores and MAO-B inhibitory activities of the compounds. Figure 3.6 depicts a comparison between the bound conformation of representative highly active (2), moderately active (8) and low active (22) compounds inside the active site of MAO-B. The predicted binding areas of highly active compound Figure 3.6B and moderate Figure 3.6C in the MAO-B active site were similar to that of crystal ligand Figure 3.6A. Hydrogen bonding interactions and the interacting amino acid residues at the active site are also shown. These compounds commonly occupy a wide cavity surrounded by the following amino acids: Y60, P102, L171, I198, I199, Q206, I316, Y326, F343, Y398, and Y435. It was observed that the active compound established strong interactions with key amino acid residues and the amide nitrogen of highly active is engaged in two hydrogen bonds with Gln206 and an ordered water molecule and the amide oxygen is H-bonded to a water molecule. In the moderate active compound only one hydrogen bond formation was observed with water molecule where in low active compound neither showing any hydrogen bonding nor occupying the crystal ligand active site place. This analysis concludes the impartment interaction between the MAO-B protein and ligand and also correlated these interactions with pharmacophore features.

Figure 3.6: GLIDE docking studies on MAO-B inhibitors. (A) The bound conformation of compounds 2 (white) and Crystal ligand (yellow) on the Active Site of MAO-B. (B) The bound conformation of compounds 8 (white) and Crystal ligand (yellow) on the Active Site of MAO-B. (C) The bound conformation of compounds 22 (white) and Crystal ligand (yellow) on the Active Site of MAO-B.

3.4 Conclusion

Several structurally diverse compounds possessing MAO-B selective inhibitory activity were identified using pharmacophore and docking studies. These compounds bearing amenable chemical and structural features are potential leads for drug design strategies targeting MAO-B. Finally, 15 hits (Figure 3.2) were proposed for synthesis and testing. Based on selectivity index (MAO-B Glide Score/MAO-A Glide Score >1) of the compounds, structural novelty and synthetic feasibility and availability of chemicals, finally, we synthesized 10 substituted Aryl or Pyridine Pyrazoline derivatives and tested for the pharmacological activity.

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