1.1. introduction 1.1.1. molecular modeling and drug...

Studies based on Supramolecular Chemistry in Drug Design and Improvement of Pharmaceutical Solids

35

1.1. INTRODUCTION

1.1.1. Molecular Modeling and Drug Design

Drug design is a creative act of the same magnitude as composing, sculpting,

or writing. The results can touch the lives of millions and bring dollars of millions. It

is an iterative process which begins when a chemist identifies a compound that

displays an interesting biological profile and ends when both the activity profile and

the chemical synthesis of the new chemical entity are optimized. Traditional

approaches to drug discovery rely on a step-wise synthesis and screening program for

large numbers of compounds to optimize activity profiles. Over the past 30 years,

scientists have used computer models of new chemical entities to help define activity

profiles, geometries and reactivities [Edgar et al. 2000]. The development of

molecular modeling programs helping the discovery to be happened fast and their

application in pharmaceutical research has been formalized as a field of study known

as computer assisted drug design (CADD) or computer assisted molecular design

(CAMD).

Computational chemistry/molecular modeling is the science (or art) of

representing molecular structures numerically and simulating their behavior with the

equations of quantum and classical physics [Diane et al. 1999]. Computational

chemistry programs allow scientists to generate and present molecular data including

geometries (bond lengths, bond angles, and torsion angles), energies (heat of

formation, activation energy, etc.), electronic properties (moments, charges, ionization

potential, and electron affinity), spectroscopic properties (vibrational modes, chemical

shifts) and bulk properties (volumes, surface areas, diffusion, viscosity, etc.). As with

all models however, the chemist's intuition and training is necessary to interpret the

results appropriately. Comparison to experimental data, where available, is also

important to guide both laboratory and computational work.

The approach used in CADD is dependent upon the amount of information

that is available about the ligand and receptor. Based on the information that is

available, one can apply either structure-based or ligand-based molecular design

methods [Christoph et al. 2002]. Structure-based drug design, or rational drug design,

as it is sometimes called, refers to the intricate process of using the supramolecular

information contained in the three-dimensional structure of a macromolecular target

and of related ligand-target complexes to design novel drugs for important human


36

diseases. Computational methods are needed to exploit the structural information to

understand specific molecular recognition events and to elucidate the function of the

target macromolecule. This information should ultimately lead to the design of small

molecule ligands for the target, which will block its normal function and thereby act

as improved drugs.

The ligand-based approach is applicable when the structure of the receptor site

is unknown, but when a series of compounds have been identified that exert the

activity of interest. To be used most effectively, one should have structurally similar

compounds with high activity, with no activity, and with a range of intermediate

activities. In recognition site mapping, an attempt is made to identify a

pharmacophore, which is a template derived from the structures of these compounds.

It is represented as a collection of functional groups in three-dimensional space that is

complementary to the geometry of the receptor site.

In applying these approaches, conformational analysis will be required, the

extent of which will be dependent on the flexibility of the compounds under

investigation. One strategy is to find the lowest energy conformers of the most rigid

compounds and superimpose them. Conformational searching on the more flexible

compounds is then done while applying distance constraints derived from the

structures of the more rigid compounds. Ultimately, all of the structures are

superimposed to generate the pharmacophore. This template may then be used to

develop new compounds with functional groups in the desired positions. In applying

this strategy, one must recognize that one is assuming that it is the minimum energy

conformers that will bind most favorably in the receptor site. In fact, there is no a

priori reason to exclude higher energy conformers as the source of activity.

Once potential drugs have been identified by the methods described above,

other molecular modeling techniques may then be applied. For example, geometry

optimization may be used to "relax" the structures and to identify low energy

orientations of drugs in receptor sites. Molecular dynamics may assist in exploring the

energy landscape, and free energy simulations can be used to compute the relative

binding free energies of a series of putative drugs. Even after many cycles of the

structure-based design process, when a compound that binds to the target with a very

high level of activity (typically at nanomolar concentrations) has been developed, it is

still a long way from being a drug on the market. The compound still has to pass


37

through animal and clinical trials, where factors that have not been considered, such

as toxicity, bioavailability, and resistance, often determine its fate. There is now a

greater emphasis on incorporating some of these factors in the initial screening and

optimization process that leads to a drug.

1.1.2. Anticoagulants

Anticoagulants are used for the prevention and treatment of venous and arterial

thromboembolic disorders. Many approaches have been explored in the development

of antithrombotic drugs that inhibit enzymes in the coagulation pathways. However,

most currently approved drugs for the prevention and treatment of thromboembolic

disorders have been on the market for a long time. Heparin (UFH), which was

discovered in 1916 (McLean et al. 1916) targets multiple factors in the coagulation

cascade2, but has a number of limitations, including a parenteral route of

administration, frequent laboratory monitoring of coagulation activity and the risk for

patients of developing potentially life-threatening heparin-induced thrombocytopaenia

[Hirsh et al. 2008]. Low-molecular-weight heparins (LMWHs), which were

developed in the 1980s, promote the inactivation of both thrombin (factor IIa) and, to

a greater extent, factor Xa.

1.1.3. Factor Xa

Factor X has long been known to have a key role in haemostasis and factor Xa plays a

central part in the blood coagulation pathway by catalysing the production of

thrombin, which leads to clot formation and wound closure. Conversely, deficiency of

factor Xa may disturb haemostasis. In the very rare factor X deficiency disorder (for

which 1 in 500,000 is homozygous and 1 in 500 heterozygous), very low plasma and

activity levels of factor Xa manifest as severe bleeding tendencies [Brown et al.

2008, Hougie et al. 1957 and Telfer et al. 1956]. studies of variants of factor X

deficiency indicate that factor X plasma activity levels must be as low as 6–10% of

the normal range (approximately 50–150% of the population average) to be

considered a mild deficiency; cases with factor X activity levels below 1% are

considered to be severe [Brown et al. 2008 and Butenas et al. 1999] thus it seems that

factor X activity can be markedly suppressed without affecting haemostasis. An ideal

anticoagulant would prevent thrombosis without inducing systemic hypocoagulation,

and would thereby avoid unintended bleeding complications. therefore, a factor Xa

inhibitor could potentially have the properties of a desirable anticoagulant.


38

Figure 1.1. Blood Coagulation Cascade

Xa

XIIa

XIa

IXa

VII

VIIa

Thrombin

Va

Tissue

factor

Intrinsic pathway

Extrinsic pathway

Warfarin

UFHs

LMWHs

Fibrin clot

Dabigatran

Argatroban

Hirudins

Rivaroxaban

Apixaban

Edoxaban

Betrixaban

YM-150

LY-517717

TAK-442 etc.


39

1.2. LITERATURE SURVEY

Although factor Xa was identified as a promising target for the development

of new anticoagulants in the early 1980s, the viability of factor Xa inhibition was not

tested before the end of that decade. Antistatin was the first first factor Xa inhibitor,

the naturally occurring compound isolated from the salivary glands of the Mexican

leech Haementeria officinalis in 1987. It is a slow, tight-binding, potent factor Xa

inhibitor. Another naturally occurring factor Xa inhibitor, the tick anticoagulant

peptide (tAP), was isolated in 1990 from extracts of the soft tick Ornithodoros

moubata [Waxman et al. 1990]. similarly to antistasin, tAP is a slow, tight-binding

inhibitor of factor Xa. Comparative animal studies suggested that direct factor Xa

inhibitors might be a more effective approach to anticoagulation [Nicolini et al. 1996

and Lynch et al. 1994], and might also offer a wider therapeutic window, particularly

with regard to primary haemostasis [Sitko et al. 1992 and Lefkovits et al. 1996].

Although antistasin and tAP provided support for the concept of factor Xa inhibition,

development of these compounds was discontinued. the reasons were never disclosed.

Nonetheless, the encouraging results from studies using recombinant versions of the

natural factor Xa inhibitors prompted several pharmaceutical companies to initiate

chemistry programmes to develop selective, small-molecule, direct inhibitors of fXa..

The design of selective small molecule fXa inhibitors has profited from X-ray

crystallography of several enzyme–inhibitor complexes, molecular modeling and

three-dimensional QSAR studies [Maignan et al. 2001] . Factor Xa contains a serine

protease domain in a trypsin-like closed β -barrel fold encompassing the catalytic triad

Ser195-His57-Asp102 and two essential subsites S1 and S4. The search for ligands

providing optimal interactions within S1 and S4 pockets, combined with suitable

scaffolds, has been a major focus in structure-based design of selective fXa inhibitors.

Early fXa inhibitors contained benzamidine, naphtylamidine or other basic groups

[Al-Obeidi et al. 1999] , thought to be necessary for binding in the S1 pocket, but the

poor bioavailability often associated with the amidine group directed efforts to replace

this functionality with less basic or nonpolar neutral groups [Lam et al. 2003 and

Agustin et al. 2006] . Examples of benzamidine-containing fXa inhibitors are DX-

9065a ( 1 ), developed by Daiichi Pharmaceutical Co. (Tokyo, JP) [Nagahara et al.

1994] , and otamixaban ( 2 ), developed at Sanofi-Aventis (Frankfurth aM, DE)

[Guertin et al. 2002] . DPC-423 ( 3 ), disclosed by DuPont Pharmaceuticals (Newark,

Delaware, US), was the first orally active fXa inhibitor that went into the clinic [Pinto


40

et al. 2001 and Wong et al. 2002] . A range of potent and orally bioavailable fXa

inhibitors has since emerged, which include compounds containing either less basic

amidine isosters, such as razaxaban (DPC-906, 4 ) [Quan et al. 2005] , or neutral P1

substituents, such as rivaroxaban (BAY 59-7939, 5 ) [Roherig et al. 2005] and

apixaban (BMS-562247-1, 6 ) [Pinto et al. 2007]. Several other heterocycles were

also evaluated for FXa inhibition. Isoxazolines under bisamidino class were

extensively studied for their FXa activity, the SAR of these compounds therefore

analyzed in the present study with the aid of molecular modeling tools.


41

1.3. OBJECTIVE

The study was aimed to design and predict the activity of factor Xa binding

compounds as anticoagulants via the below approach.

Developing QSAR among reported analogues of factor Xa inhibitors

Identification of Pharmacophore in the selected series

Docking of a set of diverse structures to correlate the activity

De novo design of ligands based on active site interation points

Prediction of activity for the designed ligands


42

1.4. PLAN OF WORK

Regression

method- G/PLS,

GFA & Stepwise

Compare/Fit

MFA / MSA –

QSAR Model

Activity Prediction Validation of QSAR

model

Pharmacophore

model

Interaction Energy

Calculation

Alignment and

Descriptors

Factor Xa-Inhibitor

Complex structure (1LPG)

Active Molecules

from Literature

Ludi- De novo

drug design

LigandFit-

Docking studies

CATALYST 3D-QSAR

Lead Molecules

STRUCTURE BASED

APPROACH

ANALOGUE BASED

APPROACH


43

1.5. ANALOGUE BASED APPROACH

1.5.1. 3D-QSAR

A QSAR (quantitative structure-activity relationship) is a multivariate,

mathematical relationship between a set of 2D and 3D physicochemical properties

(descriptors) and a biological activity. The QSAR relationship is expressed as a

mathematical equation. Analysis of the statistical relationships between molecular

structure and various properties provided by QSAR facilitates an understanding of

how chemical structure and biological activity are related [Charifson 2007]. In a

QSAR study the biological activity is correlated with changes in measured or

computed molecular features of the molecules. These features could be hydrophobic,

steric, electronic, thermodynamic, structural or molecular shape related and these may

influence biological activity. Regression analysis can be applied to the data to create a

model of activity based upon all or some of the features. The number of compounds

for which biological activity is known is usually small as compared to the number of

features, which can be measured or calculated.

A QSAR generally takes the form of a linear equation

Biological Activity = Const + (C1 P1) + (C2 P2) + (C3 P3) + ...

Where the parameters P1 through Pn are computed for each molecule in the series and

the coefficients C1 through Cn are calculated by fitting variations in the parameters

and the biological activity.

An underlying assumption in QSAR analyses is that all molecules in the data

set showing high activity bind to their receptor in a similar way. If the molecules

present similar molecular skeletons or similar binding groups, molecular alignment

can be performed by skeleton or binding group superimposition _i.e. the

pharmacophore using the most active compound in the series as a template.

Alternatively, the alignment can be performed on the basis of similarities in 3D

interaction fields. Molecular alignment is probably the most crucial problem of local

methods in 3D-QSAR analyses as all these methods require an alignment criterion

before developing the quantitative model. Poor alignment can result in an inadequate

statistical model.

Statistical methods are an essential component of QSAR work. They help to

build models, estimate a model's predictive abilities, and find relationships and


44

correlations among variables and activities. Regression methods are used to build a

model in the form of an equation that gives one or more dependent variables (usually

activity) in terms of independent variables ("descriptors"). The model can then be

used to predict activities for new molecules, perhaps prioritizing or screening a large

group of molecules whose activities are not known.

Methods of Statistical analysis available for the QSAR studies can be classified as:

Data analysis methods

Principal components analysis (PCA)

Cluster analysis

Regression methods

Simple linear regression (simple)

Multiple linear regression (linear)

Stepwise multiple linear regression (stepwise)

Principal components regression (PCR)

Partial least squares (PLS)

Genetic function approximation (GFA)

Genetic partial least squares (G/PLS)

Validation methods

Cross validation

Randomization test

Following are the different modules available in Cerius2 for QSAR study.

Molecular Field Analysis (MFA) [Hirashima et al. 1999], which quantifies the

interaction energy between a probe molecule and a set of aligned target molecules in a

QSAR. Interaction energies measured and analyzed for a set of 3D structures can be

useful in establishing QSARs.

Molecular Shape Analysis (MSA) [John et al. 1994], which extends QSAR

operations for performing 3D QSAR studies. This technique generates quantitative

measurements of molecular shape properties as part of QSAR analysis.

http://www.sinica.edu.tw/~scimath/msi/cerius45/qsar/theory_methods.html#517850












45

1.5.1.1. MATERIALS AND METHODS

Factor Xa (fXa) binding affinity data reported by Quan et al. has been used for

the present QSAR study. A set of aryl amidino isoxazoline derivatives which were

previously synthesized and evaluated for their activity taken from various references

[Quan et al. 1997, 1999,1999, 2003 and Pancras et al. 2000] were used in this study

(Table 1.1). The affinity data [ Ki(nM)] of aryl amidino isoxazoline derivatives for

factor Xa have been converted to the logarithmic scale [pKi)] and then used for

subsequent QSAR analyses as the response variable. All computational experiments

were conducted with Cerius2 4.11 version QSAR environment from Accelrys (San

Diego, USA) on a Silicon Graphics O2 workstation running under the IRIX 6.5

operating system. Molecular shape analysis (MSA) and Molecular field analysis

(MFA) were used as the 3D-QSAR techniques.

The MSA [John et al. 1994] is a formalism that deals with the quantitative

characterization, representation and manipulation of molecular shape in the

construction of a QSAR. The overall aim of MSA was to identify the biologically

relevant conformation without knowledge of the receptor geometry and to explain in a

quantitative fashion the activity of the series of congeners. The major steps of MSA

were (1) generation of conformers and energy minimization; (2) hypothesizing an

active conformer (global minimum of the most active compound); (3) selecting a

candidate shape reference compound (based on the active conformation); (4)

performing pairwise molecular superimposition using the maximum common

subgroup (MCSG) method; (5) measuring molecular shape commonality using MSA

descriptors; (6) determining other molecular features by calculating quantum

mechanical, spatial, electronic and conformational parameters; (7) selection of

conformers; (8) generation of QSAR equations by genetic function algorithm (GFA)

or stepwise regression. A complete list of descriptors used for the QSAR study were

given in Table 1.2. Multiple conformations of each molecule were generated using the

Boltzmann jump as a conformational search method. The upper limit of the number of

conformations per molecule was 150. Each conformer was subjected to an energy

minimization procedure using the smart minimizer with the Drieding force field to

generate the lowest energy conformation for each structure. The lowest energy

conformer of the most active inhibitor 51 and the best binding pose of most active

inhibitor that was obtained in flexible docking study were selected as shape references


46

in two separate studies for alignment. A rigid fit of atom pairings was performed to

superimpose each structure so that it overlays the shape-reference compound.

The major steps of MFA were (1) generating conformers and energy

minimization; (2) matching atoms using maximum common substructure (MCS)

search and aligning molecules using the default options; (3) setting MFA preferences

(rectangular grid with 2.00 Ao step size, charges by Gasteiger algorithm, H

+, CH3 and

HO- as probes); (4) creating the field; (5) analysis by the Genetic partial least squares

(G/PLS) method. The MFA models were predictive and sufficiently reliable to guide

the design of novel compounds. The MFA was attempted to postulate and represent

the essential features of a receptor site from the aligned common features of the

molecules that bind to it. The method generated multiple models that were checked

for validity. The MFA calculated probe interaction energies on a rectangular grid

around a bundle of active molecules. The surface was generated from a ‗‗Shape

Field‘‘. The atomic coordinates of the contributing models were used to compute field

values on each point of a 3D grid. Grid size was adjusted to default 2Å. The MFA

evaluated the energy between a probe (H+, CH3 and HO

-) and a molecular model at a

series of points defined by a rectangular grid. Fields of molecules were represented

using grids in MFA and each energy associated with an MFA grid point could serve

as input for the calculation of a QSAR. These energies were added to the study table

to form new columns headed according to the probe type. Statistical analysis of data

was done using techniques like G/PLS, genetic function approximation (GFA) and

stepwise regression for MSA and G/PLS, GFA for MFA using QSAR+ environment

of Cerius2 software.

The GFA technique was used to generate a population of equations rather than

one single equation for correlation between biological activity and physicochemical

properties. The GFA provided an error measure, called the lack of fit (LOF) score that

automatically penalized models with too many features. The GFA was done as

follows: (1) an initial population of equations is generated by random choice of

descriptors; (2) pairs from the population of equations are chosen at random and

‗‗crossovers‘‘ are performed and progeny equations are generated; (3) it is better at

discovering combinations of features that take advantage of correlations between

multiple features; (4) the fitness of each progeny equation is assessed by the LOF

measure; (5) it can use a larger variety of equation-term types in construction of its

models; (6) if the fitness of a new progeny equation is better, then it is preserved. The


47

model with a proper balance of all statistical terms was used to explain the variance of

the biological activity. The G/PLS algorithm was used as an alternative to a GFA

calculation. The G/PLS algorithm used GFA to select appropriate basis functions to

be used in a model of the data and PLS regression as the fitting technique to weigh the

basis functions‘ relative contributions in the final model. The method gave a reduced

solution that was statistically more robust than multiple linear regression (MLR). To

avoid overfitting, a strict test for the significance was done by cross-validation. The

use of G/PLS thus allowed the construction of larger QSAR equations while still

avoiding overfitting and eliminating most variables. For PLS equations r2, r and least

square error (LSE) were taken as statistical measures while LOF was noted for the

GFA-derived equations.

The 3D-QSAR equations generated were validated by PRESS (leave-one-out)

and bootstrap statistics which were calculated using the QSAR+ module of the

Cerius2 software and the reported parameters were cross-validation r2 (q

2), predicted

residual sum of squares (PRESS), standard deviation based on PRESS (SPRESS),

standard deviation of error of prediction (SDEP) and bootstrap r2 (bsr

2). Both the

model development process and finally developed models were subjected for

validation purposes. Additionally, the final models were subjected to leave-20%-out

crossvalidation with 15 trials in each case.

Table 1.1 Structure of QSAR study molecules

Basic Structure Molecule

Number R R1 R2

R

R2

N

O

O

NHR1

1 m-C=NH(NH2) CH2COOH p-C=NH(NH2)

2 m-C=NH(NH2) CH2COOCH3 p-C=NH(NH2)

3 m-C=NH(NH2) CH2CONHCH2COOCH3 p-C=NH(NH2)

4 p-C=NH(NH2) CH2COOCH3 m-C=NH(NH2)

5 m-C=NH(NH2) CH2COOCH3 m-C=NH(NH2)

6 m-C=NH(NH2) H p-C=NH(NH2)


48

Table 1.1 Structure of QSAR study molecules contd...


Number R1

N

OO

NH

N

R1

Cl

NH2

S

O

O

NH2

7 NHCOCH3

8 NHCOOH

9 NHSO2NH2

10 NHSO2CH3

11 NHSO2(CH2)2CH3

12 NHSO2CH2CF3

13 NHSO2C6H5

14 NHSO2-thiophen-3-yl

15 NHSO2-3-pyridyl

16 NHSO2CH2C6H5

17 N

N

NN

18 N

N

19 N

N

N

20 NHCONH2

21 NHCONHCH2CH3


Number P1

N

OO

NH

N

S

O

O

NH2

NH

SO

O

CH3

P1

22

NO

NH2

23

CH3

O


49



Number R1 R3 X Y

N

OO

NH

X

Y

R1

S

O

O

NH

NH2

NH

R3

24 CH2CH2OCH3 H CH CH

25 COONH2 H CCH3 CH

26 CH2COOH H CH CH

27 CH2SO2CH2CH3 H CH CH

28 CH2OCH3 H CH CH

29 CH2OCH2CH3 H CH CH

30 CH2O-n-Pr H CH CH

31 CH2O-i-Pr H CH CH

32 CH2O-n-Bu H CH CH

33 CH2O-i-Amyl H CH CH

34 H H CH CH

35 CH3 H CH CH

36 CF3 H CH CH

37 CH2C6H5 H CH CH

38 CH2-1-(1,2,4-triazole) H CH CH

39 CH2-1-tetrazole H CH CH

40 CH2-2-tetrazole H CH CH

41 CH2OCH3 H N N

42 CH2OCH2CH3 H N N

43 CH2SCH2CH3 H N N

44 CH2SO2CH2CH3 H N N

45 CH2-1-tetrazole H N N

46 CH3 H N CH

47 CH2OCH3 H N CH

48 CH2OCH2CH3 H N CH

49 CH2SCH2CH3 H N CH

50 CH2SO2CH2CH3 H N CH

51 CH2-1-tetrazole H N CH

52 CH2-1-tetrazole CH3 CH CH

53 CH2-1-tetrazole (CH2)2CH3 CH CH

54 CH2-1-tetrazole H CCH3 CH

55 CH2-1-tetrazole H CF CH


50



Number R2 X Y Z

N

OO

NH

X

Y

Z

NH2

NH

O

OCH3

R2

56 SO2NH2 CH CH CH

57 H CH CH CH

58 3‘-CH3 CH CH CH

59 2‘-CH3 CH CH CH

60 3‘-CF3 CH CH CH

61 2‘-CF3 CH CH CH

62 3‘-OCH3 CH CH CH

63 2‘-OCH3 CH CH CH

64 3‘-SO2NH2 CH CH CH

65 2‘-SH CH CH CH

66 2‘-COOCH3 CH CH CH

67 2‘-SO2CH3 CH CH CH

68 2‘-SO2NH2 CH CCH3 CH

69 2‘-SO2NH2 CH CF CH

70 2‘-SO2NH2 CH CH N

71 2‘-SO2NH2 N CH CH

72 2‘-SO2NH2 N N CH


51

Table 1.2 Descriptors used for QSAR Study

Sl. No. Descriptor Title Type/Family

1 DIFF V A3 MSA

2 COS V A3 MSA

3 F0 MSA

4 NCOS V A3 MSA

5 SHAPE RMS MSA

6 SR VOL A3 MSA

7 LUMO_M0Ev QUANTUM

MECHANICAL

8 DIPOLE_Mopac debye QUANTUM

MECHANICAL

9 HF_MOPAC kcal QUANTUM

MECHANICAL

10 HOMO_MOev QUANTUM

MECHANICAL

Sl. No. Descriptor Title Type/Family

11 H-BOND ACCEPTOR STRUCTURAL

12 H-BOND DONOR STRUCTURAL

13 ALogP98 THERMODYNAMIC

14 Fh2o THERMODYNAMIC

15 Foct THERMODYNAMIC

16 LogP THERMODYNAMIC

17 MOL REF THERMODYNAMIC

18 ZAGREB TOPOLOGICAL

19 RADIUS OF GYRATION SPATIAL

20 PMI_Mag SPATIAL

21 SHADOW-XY SPATIAL

22 SHADOW-XZ SPATIAL

23 SHADOW-YZ SPATIAL

24 SHADOW-XY SPATIAL

25 SHADOW-XZ SPATIAL

26 SHADOW-YZ SPATIAL

27 SHADOW-nu SPATIAL

28 SHADOW-XI SPATIAL

29 SHADOW-YI SPATIAL

30 SHADOW-ZI SPATIAL


52

Table 1.3. Importanat descriptor values for QSAR Study

MOLECULE

NUMBER

ACTIVITY

(pKi)

NCOSV

A3

DIFF V

A3

DIPOLE

MOMENT

HF_MOPAC

HOMO

PMI

MOL ref

ZAGREB

AlogP

Fh2O

Foct

HIGH

51 9.770 245.525 45.974 8.6680 451.1754 -9.3766 3630.164 143.7 212 0.51 -63.145 -49.440

55 9.357 229.408 49.350 8.7390 440.5836 -9.5678 3569.156 145.8 218 1.33 -60.796 -48.850

52 9.036 243.158 62.773 7.7740 489.7000 -9.5697 3674.995 150.5 216 1.33 -57.056 -45.410

71 9.018 224.787 47.423 7.6390 268.8943 -9.3640 3547.148 137.0 202 1.16 -55.773 -45.600

MEDIUM

29 8.456 227.920 47.1910 7.1910 289.8063 -9.3510 3459.226 139.0 196 2.03 -52.654 -47.090

11 8.432 277.038 41.4990 11.4990 343.6474 -8.8568 4052.572 150.6 214 1.92 -55.707 -47.070

46 8.387 196.119 34.7570 4.7570 302.7423 -9.3492 3342.220 126.2 184 1.55 -51.363 -44.940

67 8.377 236.775 27.0320 7.0320 335.6898 -9.4134 3501.064 140.2 202 2.59 -43.732 -38.100

LOW

57 6.657 224.589 21.263 0.6400 323.107 -8.7641 2727.018 126.6 178 3.06 -40.685 -36.880

58 6.620 200.389 17.250 1.5750 341.5050 8.7521 2975.891 131.7 184 3.55 -40.592 -37.510

6 6.569 128.069 -90.808 1.5750 281.5358 -9.0681 1843.436 96.7 134 0.68 -50.515 -44.760

5 6.097 188.241 -34.421 3.0750 307.9451 -9.1838 1901.327 112.3 160 0.60 -54.613 -45.940


53

1.5.1.2. RESULTS & DISCUSSION

Molecular field analysis

The generated field was of the rectangular type. The probes used in this study

(MFA1) were H

+ and CH3. In another study (MFA

2) HO

- was also used as extra probe.

The charge method used was Gasteiger and the energy cutoff was kept at -30 to +30

kcal. QSAR equations were generated using both G/PLS and GFA method. The

number of iterations was set to 1000,000 to obtain the final equation. The mutation

probabilities were set to the system defaults. The final best result was obtained with

G/PLS in MFA2

and was discussed here. A view of aligned molecules studied in the

field is shown in Figure 1.2 The following equation was obtained from the MFA:

G/PLS equation

Activity = 6.10935 + 0.101911 H+/220 + 0.010303 CH3/674 - 0.01462 H

+/570 +

0.02806 HO-/569 + 0.015087 HO

-/653 - 0.039155 H

+/213 + 0.027586 HO

-/659 +

0.017601 H+/752 + 0.032525 CH3/870 + 0.013759 H

+/552 - 0.037206 H

+/840 -

0.035787 H+/527 + 0.002849 H

+/751 - 0.054315 H

+/949.----------- Equation 1.1.

In Eq. 1.1, H+ /220, CH3 /674..., and so on were the probes and their

numbering (corresponding to spatial positions as shown in Figure 1.3); i.e., these

represent interactions at points 220 by H+, 674 by CH3, etc. The equation was of very

good statistical quality. It shows 90.2% explained variance while leave-one-out cross-

validation r2

is found to be 80.1%. The final models were also subjected to leave-

20%-out cross-validation tests with 15 trials and the r2 value between the observed

and predicted values were found to be 0.871 and 0.870 respectively (Table 1.4).

Predicted activity values for each molecule by the QSAR model were given in Table

1.5. Figure 1.3 to 1.6 show the plots between actual and predicted activity values.


54

Figure 1.2. Alignment of molecules for MFA study

Table 1.4 Statistics of MFA QSAR Equation

Statistical

Parameters

MFA1 MFA2

G/PLS GFA G/PLS GFA

r2

q2

LOO r2

20% out r2

BS r2

F-Test

PRESS

LOF

LSE

Outliers

0.881

0.666

0.777

0.870

0.740

13.780

0.068

4

0.902

0.816

0.801

0.871

0.903

37.562

7.568

0.150

0.056

8

0.893

0.715

0.807

0.871

0.797

11.742

0.061

4

0.877

0.840

0.829

0.868

0.877

49.226

6.608

0.125

0.070

6


55

Table 1.5 QSAR Activity Table

Molecule

Number

Actual

Acivity

(pKi)

MFA2 MSA

1

G/PLS

Redicted

GFA

Predicted

G/PLS

Predicted

GFA

Predicted

Stepwise

Predicted

1 6.845 6.625 6.813 60862 6.945 6.941

2 7.027 6.568 6.788 6.990 7.032 7.032

3 7.745 7.785 7.658 7.457 7.711 7.780

4 6.932 6.686 7.008 6.962 7.027 7.032

5 6.097 6.294 6.159 6.997 7.053 7.043

6 6.569 6.710 6.622 6.674 6.529 6.438

7 7.959 7.981 8.068 7.766 7.794 7.802

8 7.824 8.108 8.433 7.722 7.675 7.701

9 8.260 8.333 8.314 8.407 8.447 8.466

10 8.721 8.347 8.304 8.331 8.293 8.310

11 8.432 8.400 8.646 8.386 8.326 8.354

12 8.481 8.533 8.425 8.200 8.206 8.308

13 8.770 8.829 8.367 8.629 8.565 8.548

14 8.921 8.807 8.495 8.613 8.587 8.580

15 8.523 8.899 8.427 8.553 8.702 8.714

16 8.658 8.615 8.658 8.617 8.593 8.614

17 8.337 8.223 8.480 8.659 8.782 8.793

18 8.000 8.124 8.032 8.463 8.468 8.552

19 8.000 8.255 8.264 8.642 8.638 8.494

20 7.854 7.664 7.754 8.133 7.970 7.897

21 7.468 7.789 7.842 8.409 8.285 8.187

22 8.602 7.966 8.090 7.661 7.750 7.747

23 7.161 7.335 7.368 7.716 7.859 7.869

24 8.300 8.481 8.224 8.007 8.115 8.130

25 8.131 8.326 8.499 8.246 8.462 8.478

26 7.699 8.492 8.295 8.121 8.121 8.144

27 8.456 8.127 7.863 8.471 8.515 8.593

28 8.469 8.422 8.416 8.161 8.129 8.124

29 8.456 8.535 8.385 8.225 8.156 8.167


56

Molecule

Number

Actual

Acivity

(pKi)

MFA2 MSA

1

G/PLS

Redicted

GFA

Predicted

G/PLS

Predicted

GFA

Predicted

Stepwise

Predicted

30 8.538 8.657 8.476 8.337 8.164 8.190

31 8.367 8.789 8.554 8.319 8.222 8.252

32 7.886 7.895 7.644 8.483 8.181 8.220

33 7.432 7.623 7.558 8.376 8.225 8.308

34 8.143 8.101 8.031 7.907 7.772 7.693

35 7.959 8.087 8.062 8.003 7.841 7.805

36 7.638 7.595 7.894 7.855 7.720 7.766

37 8.070 8.150 8.294 8.480 8.253 8.198

38 8.770 8.532 8.652 8.594 8.617 8.556

39 8.796 8.862 8.850 8.860 8.879 8.768

40 8.796 8.714 8.709 8.733 8.679 8.579

41 8.004 8.053 8.055 8.050 8.101 8.121

42 8.168 8.055 8.106 8.167 8.158 8.186

43 8.620 8.448 8.229 8.279 8.166 8.126

44 8.276 8.381 8.448 8.498 8.613 8.679

45 8.886 9.209 9.550 8.723 8.887 8.836

46 8.387 8.633 8.473 7.885 7.694 7.682

47 8.602 8.248 8.526 7.931 7.951 7.955

48 7.553 8.057 8.104 8.178 8.015 8.041

49 8.658 8.514 8.280 8.220 7.985 7.947

50 8.770 8.432 8.461 8.538 8.481 8.572

51 9.770 9.237 9.584 9.819 9.844 9.863

52 9.036 8.819 8.769 8.798 8.920 8.832

53 8.678 8.783 8.734 9.003 8.981 8.944

54 9.000 8.937 8.888 8.790 8.908 8.801

55 9.357 8.877 8.886 8.711 8.853 8.761

56 8.201 7.943 8.046 7.916 8.039 8.073

57 6.657 6.629 6.577 6.693 6.567 6.646

58 6.620 6.653 6.611 6.991 6.926 6.874

59 6.678 6.882 6.829 6.859 6.893 6.840


57

Molecule

Number

Actual

Acivity

(pKi)

MFA2 MSA

1

G/PLS

Redicted

GFA

Predicted

G/PLS

Predicted

GFA

Predicted

Stepwise

Predicted

61 7.553 7.430 7.621 7.070 7.024 7.069

62 6.699 6.742 6.706 6.982 7.036 7.037

63 7.208 7.479 7.493 6.806 6.966 6.953

64 7.168 7.308 7.219 7.800 7.968 8.013

65 7.180 7.595 7.482 7.000 7.076 6.996

66 7.276 7.416 7.344 7.109 7.243 7.258

67 8.377 7.990 7.998 8.167 8.085 8.121

68 8.056 7.948 8.009 8.184 8.261 8.285

69 8.377 7.950 7.952 7.895 8.089 8.138

70 7.721 8.317 8.154 8.171 8.204 8.233

71 9.018 8.348 8.959 8.212 8.147 8.213

72 8.367 8.739 8.585 8.034 8.211 8.267

Molecular shape analysis

A view of aligned molecules in MSA1 and MSA

2 study were shown in Figure

1.7. and 1.8 respectively. MSA2 was found to be superior to MSA

1. The best equation

obtained from GFA regression (at 100,000 crossovers and F value for inclusion of

variables was set to 4) in MSA2 was,

Activity = -2.1378 – 0.022792*<Foct> - 0.008514*<NCOSV> - 0.324377*

<AlogP98> + 0.045833*<Mol Ref> + 0.69775*<Rad of Gyration> -----Equation 1.2.

Equation 1.2. could explain 73.6% of the variance and predict 64.1% of the

variance. The final model was also subjected leave-one-out and leave-20%-out cross-

validation tests with 15 trials and the r2 value between the observed and predicted

values was found to be 0.727 and 0.743 respectively (Table 1.6). Predicted activity

values for each molecule by the QSAR model were given in Table 1.5. Figure 1.9 to

1.11 show the plots between actual and predicted activity values.


58

Figure 1.3. Actual Vs Predicted

Acitvity values obtained in MFA1 by

G/PLS regression


Acitvity values obtained in MFA1 by

GFA regression

Figure 1.5.: Actual Vs Predicted

Acitvity values obtained in MFA2

by

G/PLS regression


Acitvity values obtained in MFA2

by

GFA regression


59

The following two equations were among those obtained from the G/PLS and

Stepwise regressions respectively,

Activity = -3.55282 + 0.06584*<DIPOLE_MOPAC> -0.00127*<Vm> -

0.177066*<AlogP98> -0.720516*<HOMO_MOPAC> + 0.048567*<Mol Ref> -

0.005897*<NCOSV> ----------------- Equation 1.3.

Activity = -4.14186 + 0.0064018*<COSV> + 0.062217*<DIPOLE_MOPAC> -

0.751026* <HOMO_MOPAC> + 0.028011*<Mol Ref> -0.18584*<AlogP98> ----

Equation 1.4.

Table 1.6 Statistics of MSA QSAR Equation

Statistical

Parameters

MSA1 MSA2

G/PLS GFA STEPWISE G/PLS GFA STEPWISE

r2

q2

LOO r2

20% out r2

BS r2

F-Test

PRESS

LOF

LSE

Outliers

0.730

0.639

0.727

0.780

0.695

14.878

0.155

3

0.736

0.641

0.727

0.743

0.736

30.211

14.733

0.217

0.151

7

0.731

0.623

0.721

0.691

0.731

35.841

15.516

7

0.695

0.648

0.682

0.694

0.679

14.516

0.175

1

0.696

0.628

0.686

0.753

0.696

30.241

15.347

0.234

0.174

4

0.677

0.605

0.648

0.724

0.677

35.136

5


60

Figure 1.7. Alignment of molecules for MSA1 study

Figure 1.8. Alignment of molecules for MSA2 study


61

Figure 1.9. Actual Vs Predicted Acitvity values obtained in MSA2 by GFA regression

Figure 1.10. Actual Vs Predicted Acitvity values obtained in MSA2 by G/PLS

regression

Figure 1.11. Actual Vs Predicted Acitvity values obtained in MSA2 by Stepwise

regression


62

1.5.2. PHARMACOPHORE MODELING (CATALYST)

One of the basic tenets of medicinal chemistry is that biological activity is

dependent on the three-dimensional placement of specific functional groups (the

pharmacophore). A pharmacophore is defined as an ensemble of universal chemical

features that characterize a specific mode of action of a ligand in the active site of the

macromolecule in 3D space [Barnum et al. 1996]. Chemical features are e.g.

hydrogen bonds, charge interactions, hydrophobic areas. CATALYST is one of such a

pharmacophore generating modeling tool is being successfully used, in conjunction

with traditional research techniques, to examine the structural properties of existing

compounds, develop and quantify a hypothesis which relates these properties to

observed activity and utilize these "rules" to predict properties and activities for new

chemical entities [Patel et al. 2002]. Catalyst tools help to rationally design small

molecules as drug candidates using 3D pharmacophore and shape-based models, and

to suggest potentially active compounds suitable for synthesis and biological testing.

Ludi can also be used in analog-based design strategies without the knowledge of the

receptor structure. Given a series of superimposed analogs, identifies the potential

interaction sites and searches library for molecules that match these interaction sites.

The aim of this study was the generation of selective pharmacophore models

that describe the type, the size, and the position of chemical functions essential for a

compound‘s anticoagulent activity via factor Xa inhibition, for a set of aryl amidino

isoxazoline derivatives.


63


All molecular modeling studies were performed using Catalyst 4.11 installed

on a Silicon Graphic Octane desktop Workstation. The flexibility of each molecule

was represented by a set of energetically reasonable conformers which were generated

with the Catalyst catConf module choosing a maximum number of 250 conformers,

the best quality generation type, and an energy threshold of 20 kcal/mol beyond the

calculated global energy minimum. The number of conformers generated for each

molecule was limited to a maximum of 255. Ten hypotheses were generated using

these conformers for each of the molecules and estimated activity values are

generated after selection of the following features for the drugs; hydrogen bond

donor, hydrophobic, negative charge, positive ionizable and ring aromatic. After

assessing all 10 hypotheses generated for each data set, the lowest energy cost

hypothesis was considered the best. The goodness of the structure activity correlation

was estimated by means of the correlation coefficient (r). Also calculated the total

energy cost of the generated pharmacophores from the deviation between the

estimated activity and the observed activity, combined with the complexity of the

hypothesis (i.e., the number of pharmacophore features). A null hypothesis was

additionally calculated, which presumes that there is no relationship in the data and

that experimental activities are normally distributed about their mean. Hence, the

greater the difference between the energy cost of the generated hypothesis and the

energy cost of the null hypothesis, the less likely it is that the hypothesis reflects a

chance correlation. This criterion was then used as an assessment of the

pharmacophore model selected.


64


Catalyst was used on the set of molecules with activity spanning orders of

magnitude to construct a useful model of the chemical features and their position in

3D space (Figure 1.12 and Table 1.7) necessary for a biological response. After

several iterations the model of our 30-molecule data set produced a good correlation

when compared with the estimated (r of 0.92). The model contained four features

necessary for activity, namely two hydrogenbond donor, a hydrophobic aromatic, and

a ring aromatic feature (Figure 1.12 and Table 1.8). The features have been compared

with highest and low activity molecules in the dataset using comparefit and found that

ring aromatic feature has been missed for the later one (Figure 1.13 & 1.14). The

generated catalyst hypotheses can serve as query features in 3D database (DB) search

for virtual screening to detect novel lead compounds as factor Xa inhibitors.

Table 1.7. Pharmacophore Activity Table

Molec

ule

Numb

er Fit

Actual

Activity

Ki(Nm)

Estimat

ed

Activity

Ki'

(Nm) Error

Mole

cule

Num

ber Fit

Actual

Activity

Ki(Nm)

Estimat

ed

Activity

Ki'

(Nm) Error

51 8.66 0.17 0.16 -1.1 3 5.98 18 77 -1.6

71 7.57 0.96 2 2 70 6.78 19 12 -1.3

50 7.61 1.7 1.8 1.1 26 6.67 20 16 -1.4

14 7.19 2.8 4.7 1.7 36 6.65 23 16 1.1

11 6.7 3.7 15 4 61 6.38 28 30 -2.4

17 6.61 4.6 18 3.9 21 6.71 34 14 -1

9 7.14 5.5 5.2 -1.1 66 6.15 53 52 -1.7

56 7.26 6.3 4 -1.6 64 6.26 68 40 -1.3

25 6.92 7.4 8.9 1.2 2 6.01 94 72 -1.3

68 7.29 8.8 3.8 -2.3 4 5.9 120 92 -1.3

41 6.74 9.9 13 1.3 1 5.96 140 81 -1.8

7 6.67 11 16 1.4 62 5.68 200 150 -1.3

32 6.87 13 10 -1.3 59 5.87 210 98 -2.1

20 6.73 14 14 -1 58 5.62 240 180 -1.4

8 6.68 15 15 1 6 5.33 270 340 1.3


65

Table 1.8. Pharmacophore Geometry Table

DISTANCE Ra-Hd2 Ra-Hd1 Ra-La La-Hd2 La-Hd1 Hd1-Hd2

Min.

Max.

5.800

7.800

4.666

6.666

4.153

6.153

9.810

11.810

4.338

6.338

10.891

12.891

ANGLE Hd1RaHd2 Hd1LaHd2 Hd1RaLa LaRaHd2

Min.

Max.

139.0o

149.0o

83.1o

93.0o

53.9o

63.9o

124.0o

133.9o

Figure 1.12: Pharmacophore model showing various features required for activity

Hd1

Hd2

Ra

La

Ra- Ring aromatic

La- Hydrophobic aromatic

Hd- Hydrogen bond donor


66

Figure 1.13. Complete alignment of Pharmacophoric features observed for the high

active compound of the series

Figure 1.14. Partial alignment of Pharmacophoric features observed for the low active

compound of the series.


67

1.6. STRUCTURE BASED APPROACH

1.6.1. DOCKING

To discover novel ligands for receptors of known structure, investigators often

use docking computer programs to screen multi-compound databases for molecules

that fit a binding site on the receptor. For each molecule, many orientations and

conformations are sampled; based on these configurations, each molecule is scored

for complementarity to the receptor and ranked relative to the other members of the

database.

LigandFit [Venkatachalam et al. 2003] is one of such a drug discovery

software program. The program apprises each of hundreds of millions of molecules to

see if they are likely to interact with a target protein. It calculates and studies the

many positions, or conformers, the molecule might adopt interacting with the protein.

This process is called virtual screening of the molecules. It explores the 3-dimensional

position each molecule might adopt. Each new position of the molecule may help the

right parts of the molecule interact with the protein target. The total number of

conformations differs molecule to molecule. Some molecules may have more bonds

or flexibility, and thus will have more conformers.

LigandFit provides a cavity search algorithm for finding binding sites.

Possible binding sites are proposed to use as a binding site, or a binding site already

defined by the ligand. This is especially useful when a model or experimental

structure of the protein has been obtained but the binding site has not been identified.

LigandFit uses the energy of the ligand-receptor complex to automatically find the

best binding modes of the ligand to the receptor. A grid method is used for the

evaluation of non-bonded interactions between the rigid protein and the movable

atoms from the flexible ligand [Venkatachalam et al. 2003].


68


All molecular modeling studies were performed using Accelrys Discovery

studio installed on a Silicon Graphic Octane desktop Workstation. Shape and volume

of the active site has been identified by selecting the aminoacid residues around the

non stranded residue of the protein at 5 Å distance. A set of aryl amidino isoxazoline

derivatives which were previously synthesized and evaluated for their activity taken

from various references [Quan et al. 1997, 1999,1999, 2003 and Pancras et al 2000]

were used in this study (Table 1.1). Ten Molecules with varied range of activities

have been selected from the study set molecules. Conformational search for each

selected molecules was done by simplex search method. Generated conformers were

docked in the active site and calculated for their interactions. Ten best conformations

for each molecule are selected by the system based on dock score and ligand

interaction energies.


Results of docking studies were displayed in Table 1.9. Difference in

interaction energy values and the binding mode of conformations (Figure 1.15 &

1.16) can be used to explain the activity variance between the molecules. Conformer

with better interaction energy values for 51 was used as shape reference to align all

other study molecules for 3D-QSAR studies.

Table 1.9. Dock score and interaction energy values

Molecule

Number

Dock

Score

Ligand

Interaction Energy

Actual

Acivity (pKi)

51 73.660 -9.682 9.770

3 71.861 -5.479 7.745

14 78.664 -9.339 8.921

17 79.067 -10.071 8.337

56 77.955 -7.561 8.201

60 71.702 -8.440 6.620

25 75.043 -10.075 8.131

71 72.448 -9.925 9.018


69

Figure 1.15: Comparative view on binding modes between high (stick model) and low

(cylinder model) active molecules

Figure 1.16: Binding mode of another conformer of the high active molecule


70

1.6.2. DE NOVO LIGAND DESIGN

The receptor-based approach to CADD applies when a reliable model of the

receptor site is available, as from X-ray diffraction, NMR, or homology modeling.

With the availability of the receptor site, the problem is to design ligands that will

interact favorably at the site, which is a docking problem.

Ludi is a de novo ligand design program [Bohm 1992] which provides a

starting point for the design. It offers significant time saving in the search for new and

potentially improved ligands. It quickly generates a series of potential ligands for

molecular receptors. From the structure of the target receptor or from a set of ligands

for the target receptor, Ludi derives the potential binding interaction sites and then

searches a library for complementary small molecules. Ludi automatically fits and

scores members of a library against a receptor site, and ranks those candidate

structures as a prioritized list. The potential ligands suggested by this method are

selected based on their ability to participate in nonbonded interactions with the

receptor. Both hydrogen bonding and hydrophobic interactions are considered in the

selection process. Alternatively, this programme can also be used to optimize a given

ligand by performing targeted modifications to those sites that develop key

interactions with the receptor.


71


All molecular modeling studies were performed using Accelrys Discovery

studio installed on a Silicon Graphic Octane desktop Workstation. Defining the active

site is the prerequisite for De novo design. PDB structure of factor Xa with bound

ligand (1LPG) revealed by X-ray crystallography was downloaded and used in this

study. Interactions of bound ligand with active site residues of protein can be

visualized from Figure 1.17. The active site of factor Xa was defined with 568 atoms

surrounding the ligand at a distance of 5 Å. Five different centers were defined in the

active site (Figure 1.18) and subjected to find hits from the library of fragments. A hit

fragment in each run was selected based upon the match of the atoms with the

interaction points generated around respective center. After positioning the hits on

their site points linker fragments were generated to connect the hit fragments and

finally built the lead molecule after several runs and selections (Figure 1.19, 1.20 and

1.21). Later the generated molecules were subjected to docking studies to study the

interaction energies.


Six molecules were designed as lead compounds. Structure were shown in

Figure 1.22. Three molecules were found to have good predicted activity values by

the MSA QSAR model (Table 1.10). The dock scores and interaction energy values

for these molecules were comparable with current active set of molecules. Dock score

and interaction energy value for lead molecules 1,3 & 5 were found to be good and

advocated the predicted activity. We presume that lead1 molecule can be developed

into drug candidate by further optimization.

Table 1.10: Predicted activity for designed structures

Lead

Molecules

Predicted Activity (pKi) By QSAR Model Dock Score

G/PLS

Equation

GFA

Equation

STEPWISE

Equation

1 8.093 7.578 7.931 -6.8306

2 6.003 5.993 5.622 -4.4158

3 9.267 5.241 5.113 -6.10635

4 7.089 5.241 6.296 -5.3038

5 6.898 6.014 5.901 -5.70561


72

Figure 1.17. Active site residues of factor Xa showing non bonded interactions with

non stranded residue(bound ligand).

Figure 1.18. Active site of factor Xa with interaction points generated by Ludi

program


73

Figure 1.19. Hits/fragments generated at center ‗A‘ of the active site

Figure 1.20. Hits/fragments generated at center ‗C1 & C2‘ of the active site


74

Figure 1.21. Hits/fragments generated at center ‗B & D‘ of the active site

SOH

Br

N

O

OHCH3

NN

S

Br

Br

S

Br

Br

O

O

O

CH3

S

Br

Br O

O

S

Br

Br

O

O

Figure 1.22. Structures of designed lead molecules

Lead 1

Lead 2

Lead 3

Lead 4

Lead 5


75

1.7. CONCLUSION

Designing of fXa inhibitors as anticoagulants started with structure based

approach and ended by predicting the activity for the lead molecules with the aid of

Pharmacophore and QSAR models. Three of the designed molecules found to be

promising candidates to be developed as drug candidates by further optimization. The

present 3D-QSAR analysis explores the spatial, shape and thermodynamic

requirements for the binding affinity of aryl amidino isoxazoline derivatives to factor

Xa. The MSA-derived equations shows the importance of thermodynamic and

quantum mechanical descriptors, molecular refractivity and radius of gyration

contribution to activity. The MFA-derived equation shows interaction energies at

different grid points with positive, negative and neutral bulk probes. Statistically

reliable 3D-QSAR and pharmcophore models obtained from this study suggest that

these techniques could be useful to design potent factor Xa inhibitors and predict the

activity of same.


76

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1.1. introduction 1.1.1. molecular modeling and drug...

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