Pharmacophore models for GABA A modulators: implications in CNS drug discovery

1. Introduction

2. Pharmacophore models for

GABA analogues binding at

the orthosteric GABA site

3. Pharmacophore models for BZ

binding site

4. Orthosteric vs allosteric drug

discovery for GABAA: pros and

cons

5. Success stories of

pharmacophore models in lead

optimization and scaffold

hopping

6. Conclusion

7. Expert opinion

Review

Pharmacophore models forGABAA modulators: implicationsin CNS drug discoveryNanda Ghoshal† & R Suyambu Kesava Vijayan†Indian Institute of Chemical Biology (A unit of CSIR), Structural Biology and Bioinformatics

Division, 4, Raja S.C. Mullick Road, Kolkata-700032, India

Importance of the field: GABAA ion channel is a validated drug target,

implicated in the pathophysiology of various neurological and psychiatric

disorders. Structural investigations on GABAA are currently precluded in the

absence of experimentally resolved structure. Pharmacophore modeling

circumvents such issues and proves to be a powerful and successful method

in drug discovery.

Areas covered in this review: The present reviews encompass pharmacophoric

models available in the literature for the orthosteric GABA and the allosteric

benzodiazepine binding site. Success stories from these simplistic pharmaco-

phore models in scaffold hopping and strategic lead optimization have

been highlighted. Recent advances in pharmacophore modeling that can

leverage CNS drug discovery programs and deliver astounding results have

been reviewed.

What the reader will gain: Readers are bound to gain a comprehensive insight

on different computational techniques used by different groups to arrive at

simple, yet sophisticated pharmacophore models. In the absence of experi-

mentally unresolved active site geometry of GABAA, these models will provide

the reader an opportunity to translate these pharmacophoric features to the

microscopic phenomenon of supramolecular ligand interaction.

Take home message: Pharmacophore modeling has now evolved as a

mainstay approach for lead generation and optimization in drug discovery

programs. Of late, many advances in pharmacophore perception have

emerged. Such advancements should be used to confront activity profiling

and early stage risk assessment in a high-throughput fashion. Extending

such technologies has the potential not only to reduce time and cost, but

also to prevent late stage attrition in drug discovery.

Keywords: benzodiazepines, GABA, GABAA, pharmacophore, QSAR, SAR

Expert Opin. Drug Discov. (2010) 5(5):441-460

1. Introduction

Human brain is a highly complex organ, which functions through various types ofneurons and neurotransmitters. Neurotransmitters which impede electrical activityare termed as ‘inhibitory’ and those which stimulate electrical activity are called‘excitatory’. In healthy brain, a delicate counter balance between excitation and inhi-bition is orchestrated by these endogenous chemicals for optimal function. GABA isthe predominant inhibitory neurotransmitter that counteracts the excitatory gluta-matergic neurotransmission in the mammalian CNS. GABA exerts its physiologicaleffects by binding to the ionotropic (GABAA, GABAc) and the metabotropic(GABAB) receptors. Of these, GABAA receptor has received the greatest attentionin terms of research because of its importance as a biological target for clinicallyimportant CNS drugs such as barbiturates, neurosteroids, loreclezole, anesthetics,

10.1517/17460441003789363 © 2010 Informa UK Ltd ISSN 1746-0441 441All rights reserved: reproduction in whole or in part not permitted

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ethanol and benzodiazepines (BZs) [1]. Hypoactive GABAmediated synaptic transmission is implicated as an etiologicalfactor in many pathophysiological conditions namely anxiety,neuropathic pain, insomnia, depression, bipolar disorder,schizophrenia and epilepsy [2]. Drugs, modulating GABA-mediated transmission, have been successful in improvingthe disease outcome, which vindicates its significance as avalidated CNS drug target.GABAA receptor is a pentameric membrane-spanning

protein that belongs to the Cys loop super family of ligand-gated ion channels [3]. Initial studies on GABA receptorclassification began with the use of competitive antagonists.Evidences emerging from such studies revealed the heteroge-neous nature of GABA receptor and it was establishedthat bicuculline, baclofen and 1,2,5,6-tetrahydropyridin-4-ylphosphinicacid are selective competitive antagonists forGABAA, GABAB and GABAC receptors, respectively. Later,a water soluble pyridazinyl GABA termed SR 95531 wasfound to be the most potent competitive antagonist forGABAA. With the advent of molecular biology, a more com-prehensive view of the human GABAA receptor, consisting of21 subunits arranged within eight families (a1 -- 6, b1 -- 4,g1 -- 4, 1d, 1e, 1p, 1q and r1 -- 3), has been identified [4].Structural investigations on GABAA receptor is precluded

due to a host of issues such as large size (~ 50 kD), heteroge-neity, low abundance (pmol/mg of protein), together withother inherent difficulties associated in isolation and purifica-tion of integral membrane proteins [5]. The lack of an experi-mentally resolved structure of GABAA receptor hampers theapplication of structure-based virtual screening for CNSdrug discovery programs. Homology modeling proves to bea welcome reprieve in such circumstances, and a few groupsincluding ours have developed homology models of GABAA

receptor [6-9]. However, the quality of these models and their

applicability in structure-based drug design are open tocriticisms as these models are derived from templates havingmoderate homology and resolution.

Ligand-based 3D pharmacophore modeling confronts thisissue by ascribing a set of common chemical features sharedby a group of compounds that are known to bind to the targetstructure. The official IUPAC definition is: ‘A pharmaco-phore is the ensemble of steric and electronic features that isnecessary to ensure the optimal supramolecular interactionswith a specific biological target structure and to trigger (orto block) its biological response.’ The deduced pharmaco-phore model finds applicability in scaffold hopping, as align-ment inputs for 3D-QSAR (quantitative structure--activityrelationship) studies and as descriptors (3 point and 4 point)for QSAR studies. Given the number of clinically importantsites and its complicated pharmacology compounded bynumber of receptor subtypes involved, it would be beyondthe scope of this article to encompass the entire spectrum ofpharmacophore models available for GABAA receptor. Hence,the present review confines to pharmacophore models derivedfor ligands modulating GABA-mediated transmission bybinding to the orthosteric GABA site and the allosteric BZsite of GABAA receptor. The pharmacophore models outlinedin this review provide a framework that could inspire thedesign of new therapeutics and highlight some applicationsof these models in the identification of lead molecules. Foran exclusive review pertaining to the GABA site, readers areencouraged to refer Krogsgaard-Larsen et al. review [10] andfor an update exclusively on the BZ site to Clayton et al.review [11].

2. Pharmacophore models for GABAanalogues binding at the orthostericGABA site

Though direct administration of GABA into the brainhas been demonstrated to inhibit seizures, its clinical use islimited due to the lack of BBB permeability [12]. Hence,GABA was considered as a prototype for the design ofGABA agonists and partial agonists, which would essentiallymimic the action of GABA and act as GABA-mediatedagents. Given its chemical makeup, GABA can exist in severalconformations, ranging from fully extended, partially folded,to a fully folded conformation. Consideration of the confor-mational flexibility of GABA was noted as an early pharmaco-phoric criterion for selective interaction with the orthostericsite [13]. Theoretical studies based on high level QM calcula-tions have also revealed that the stable structure of GABAexists in the extended form as shown in Figure 1.

In 1960, Curtins and Watkins [14] commented on theimportance of a zwitterionic structure of GABA for GABAA

receptor agonism. Further, it was concluded that the zwitter-ionic form is the one involved in neuronal activity. Thispartial neutralization seems to be a prerequisite to pass

Article highlights.

• In the absence of an experimentally resolved structureof GABAA, pharmacophore modeling proves to be amainstay approach for rapidly identifying newmolecular entities.

• In light of the paradoxical pharmacological actionaccounted due to allosteric regulation at thebenzodiazepine site of GABAA-R, it is high time todevelop pharmacophore models that relate chemicalproperty with well-defined biological end point.

• The diversity of sites on GABAA receptors opensavenues for further development of specific agentsacting on particular GABAA receptor subtypes.

• Orthosteric versus allosteric drug discovery for GABAA:which should we embark on?

• The pharmacophore models have producedsuccess stories.

• Recent advances and challenges can accelerate CNSdrug discovery programs.

This box summarizes key points contained in the article.

Pharmacophore models for GABAA modulators -- implications in CNS drug discovery

442 Expert Opin. Drug Discov. (2010) 5(5)

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through nonpolar membranes. In 1961, McGeer et al. [15]

proposed that the acidic and basic functionality groups pres-ent in GABA should be sterically unhindered, ~ 4 A apart.Further investigation of this criterion by Lorenzini et al. [16]

using ab initio calculations concluded that the distancebetween the ionized moieties should be > 5.3 A for displayingagonistic action and also commented that a distance ofaround 4.5 A is generally evident in antagonistic molecules.Attempts to lengthen, shorten or substitute the butyryl chainof GABA results in substantial loss of agonist activity [17,18].Also, alkyl or halogen substituents on carbon atoms C2-C4are not preferred [19]. Altering the acidic and basic groups

has also been explored. Replacement of the carboxylgroup by sulfonic and sulfinic analogues is also found toconfer GABAA-receptor activity [20,21]. For example,3-aminopropylsulfonicacid is more potent than GABA [22].

On the other hand, incorporation of substituents on theterminal amino group of GABAA agonists normally results ina significant or complete loss of GABAA agonism [23], for exam-ple,N-methyl-GABA is almost inactive,N,N-dimethyl- and N,N,N-trimethyl-GABA are completely inactive.

The only alterations of the amino group that retains activityare those where it is replaced by a charge delocalized guani-dino group, or by the corresponding S-thiourenyl groupwith a similar charge delocalization pattern [24]. This ledWermuth and Biziere [24] to suggest that the delocalizationof cationic charge is important for improving GABAA activity.With conformational rigidity being an important pharmaco-phoric criterion, Johnston et al. [25] have surveyed the possibleways in which conformational restriction can be achieved. Itwas found that introduction of double bond on the butyrylchain brings about rigidification.

This further confirmed the notion for the preference of anextended conformation over a folded conformation of GABA.Alteration of the basic functionality led to the identification of(Z)-3[(aminoiminomethyl)thio] prop-2-enoic acid (ZAPA),an isothiouronium analogue of GABA. ZAPA is again ofconsiderable interest because the double bond restricts theavailable configurations that it can assume, reinforcing thepreference for an extended form [26].

Muscimol, an alkaloid obtained from mushroom Amanitamuscaria, was first introduced as a conformationally restrictedGABA analogue [27].

The 3-hydroxy isoxazole moiety essentially mimics amasked carboxyl group functionality of GABA. Thoughmuscimol displayed enhanced activity than GABA, itspotential as a drug candidate was overshadowed for toxicreasons. Muscimol, in turn, served as a prototype for thedesign of cyclic analogues. The 3-isoxazolol carboxyl groupof muscimol when replaced by its bioisosters, 3-isothiazololand 3-hydroxyisoxazoline group gave thiomuscimol anddihydromuscimol, respectively, without significant loss ofGABAA receptor agonism [29].

On the other hand, structurally related muscimol analogues,isomuscimol and azamuscimol, are virtually inactive [30].

Also, N-methyl derivatives of muscimol and thiomuscimolare weak GABAA agonists, reinforcing the fact that theterminal amino group is not open to substitution [29,31].

The S-(-)-isomer of 5¢-Me-muscimol was found to havea 31-fold higher binding affinity than the R-(+)-isomer,emphasizing the very strict structural constraints imposed onthe agonist molecules by the GABAA receptors [25].

More conformationally constrained analogues wereobtained by incorporating the aminomethyl substituent ofmuscimol into a second ring, which resulted in bicycliccompound termed as THIP, also known as gaboxadol or4,5,6,7-tetrahydroisoxazolopyridin-3-ol [32].

H3N+

4 AO-

O

Anionic site

Cationic site

Figure 1. A pharmocophoric model of GABA highlighting

the importance of the zwitterionic nature and the distance

of the charged species in GABA.

3-aminopropylsulfonicacid (3-APS)

H2N SO3H

HN

O

OH

NO

OH

N+OH

O

N-Methyl GABA N,N-Di-Methyl GABA

N,N,N-trimethyl-GABA

H2N COOH

Trans-4-aminocrotonicacid (TACA)

S COOH

H2N

H2N+

(Z)-3[(Aminoiminomethyl) thio] prop-2-enoic acid (ZAPA)

O N

OH

H2N

Muscimol (IC50 6 nM) [28]

Ghoshal & Vijayan

Expert Opin. Drug Discov. (2010) 5(5) 443

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S N

OH

H2NO N

OH

H2N

Thiomuscimol Dihydromuscimol

O N

OH

H2N

O N

OH

H2N HNN

O

OH

HNN

O

OH

Muscimol (IC50 0.006 μM) [37] 4-Me Muscimol (IC50 26 μM) [37] THIP (IC50 0.10 μM) [37] 4-Me-THIP(IC50 >100 μM) [37]

ON

OH

HN

ON

OH

HN

ON

OH

HN

4-PIOL(IC50 6 μM) [37]

4-Naph-Me-4-PIOL (IC50 0.09 μM) [37]

4-Me-4-PIOL(IC50 7 μM) [37] 4-Propyl-4-PIOL(IC50 9 μM) [37]

ON

OH

HN

NO

H2N

OH

HN N

H2N

OH

Isomuscimol Azamuscimol

ON

NH

OH

N-Methyl Muscimol (IC50 6000 nM)

NO

H2N OH

NO

H2N OH

S- (-) – 5′-Me-Muscimol R- (+) – 5′-Me-Muscimol

HNN

O

OH

THIP (IC50 130 nM) [28]

HNN

S

OH

Thio-THIP

N

O

OH

HN

5-(4-piperidyl) isoxazol-3-ol (4-PIOL) (IC50 6 μM) [37]

HN

OH

O

Isoguvacine (IC50 37 nM) [28]



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On expected lines, increased conformational rigidityresulted in enhanced specificity. Though THIP was aspecific GABAA agonist, it exerted a different pharmacolog-ical profile. Unlike muscimol which displays psychoactiveproperties, THIP exerts analgesic properties, and is a provenantinociceptive agent as effective as morphine [33,34]. Its anal-gesic action, therefore, highlights the possible relevance ofGABA-mediated pathway in pain. However, replacementof oxygen in the iosoxazole moiety of THIP by sulfur togive thio-THIP resulted in decreased activity.

A close scrutiny revealed that the pKa values of THIP (4.4;8.5) and Thio-THIP (6.1; 8.5) to be different. On compari-son of the acid ionization constant values with GABA (4 forthe carboxylate group and 10.7 for the amine) and muscimol(4.8 and 8.4), it was evident that the pKa of the ionized groupsplays a crucial role [35]. This led to rationalize that the differ-ence in the degree of charge delocalization of the zwitterionicforms of THIP and Thio-THIP explains the difference in thepotencies. Deannulation of THIP yielded 5-(4-piperidyl)isoxazol-3-ol (4-PIOL) and its related analogues [36].

4-PIOL is a low-affinity partial GABAA agonist, and anumber of 4-PIOL analogues have been synthesized with sub-stituents in the 4-position of the 3-isoxazolol ring in order toinvestigate the steric tolerance of this position. Frølund et al.came out with the first detailed pharmacophore for GABAA

agonists, binding at the GABA site [38]. They proposedthat the introduction of alkyl groups in the 4-position of the3-isoxazolol ring of muscimol and THIP severely inhibitsinteraction with the GABAA receptor recognition site asshown in Figure 2.

In contrast, introduction of alkyl groups in the 4-positionof the 3-isoxazolol ring of 4-PIOL does not bring aboutmarked change in activity, with the exception being4-Naph-Me-4-PIOL wherein introduction of a naph-methylmoiety at 4th position enhances activity.

Based on SAR evidences they concluded that muscimol,THIP and 4-PIOL bind in a different fashion in theorthosteric GABA site. Further, using THIP as lead, a mono-heterocyclic GABAA agonist, isoguvacine, was developed.Isoguvacine is a specific ligand for the GABAA receptor andwas approximately equipotent as GABA in competing forthe binding of [3H] GABA, but displayed less potency thanGABA in mediating chloride ion flux [39].

Given the importance of an arginine residue involved in therecognization of the anionic part of the ligand, Frølund et al.proposed a hypothetical binding mode [38,40] which signifiesthat the 4-position of the 3-isoxazolol ring of muscimoldoes not correspond with the 4-position of the 3-isoxazololring of 4-PIOL during interaction with the GABA site.Substitution in the 4-position of the 3-isoxazolol ringbeing tolerable, it prompted them to further probe thesteric tolerance at this position. Alkyl or benzyl groups sub-stituted at this position resulted in affinites comparablewith 4-PIOL [37]. Interestingly, introduction of more bulkygroups, such as diphenylalkyl and naphthylalkyl groups, as

exemplified by 2-Naph-methyl analogue, 4-Naph-Me-4-PIOL, proved not only to be tolerable but also demonstratedenhanced affinity [37].

This led to the speculation that in the case of 4-PIOLanalogues a large cavity accommodating the 4-substituent islocated at the subunit interface between the a and b sub-units, which houses the orthosteric binding site. With allof the afore mentioned models largely being obtained byligand-based approaches, it is also worth mentioning somecomplementary and parallel experimental research undertakenby several groups to narrow down on the GABA bindingpocket using techniques such as photoaffinity labeling, site-directed mutagenesis and substituted cysteine accessibilitymethod [41-44].

Westh-Hansen et al. [45] in 1999 envisaged that an argi-nine residue at the GABAA receptor recognition site isdirectly involved in the binding of the receptor to theanionic part of the ligand. Westh-Hansen et al. [45] revealedthe importance of the role of a1Arg 120 in binding and gat-ing. Czajkowski and co-workers 2004 [46] have proposedthat three arginine residues at the a1 and b2 domains(b2-Arg207, a1-Arg66 and a1-Arg131) directly stabilizeGABA during binding by electrostatic interaction betweenthe positively charged guanido group at the end of the argi-nine side chain and the negatively charged carboxylategroup on the GABA molecule.

3. Pharmacophore models for BZ binding site

One of the most thoroughly investigated modulatory sites onthe GABAA receptor is the BZ binding site. The BZ site gainsimmense importance, given its pharmacological significanceas a biological target for a class of drugs called BZs [47].This allosteric site is located at the a1(+)/g2(-) subunitinterface [48].

Chlordiazepoxide and diazepam, the prototypical BZs,were introduced as anxiolytics long before GABA was thoughtto be an inhibitory transmitter [49].

Only some 15 years later the BZ receptor (BZR) site wasdefined [50]. A number of pharmacologically distinct BZ bind-ing sites have been identified on the basis of differential drugeffects. Classical BZs, such as diazepam are insensitive atGABAA receptor containing a4 or a6 subunit, whereas atypi-cal BZs belonging to the imidazobenzodiazepine class haveaffinity for all subunits [51]. Models, which attempt to explainligand efficacy as a function of ligand receptor interaction atthe molecular level, has been put forth by many groups.Crippen came out with the earliest pharmacophoric model,deduced using a set of 29 drugs representing five chemicalclasses [52].

Crippen proposed 15 site points using a distance geometryapproach method to arrive at nine attractive site points(Sl -- S9) and six sterically repulsive points SlO -- S15. In1985, Codding and Muir [53] determined the X-ray structureof an imidazobenzodiazepine antagonist, Ro15-1788, and

Ghoshal & Vijayan


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Figure 2. A pharmacophore model for GABAA receptor agonists showing the proposed binding modes of muscimol (green

carbon atoms), 4-PIOL (light gray carbon atoms) and THIP (orange carbon atoms). The cyan-colored tetrahedrons represent

sterically ‘forbidden’ volumes (receptor essential volumes). Dashed lines indicate hydrogen bonding interactions with two

different conformations of an arginine residue.Reproduced with permission from [40]. Copyrights 2006, American Chemical Society.

A color version of this figure is available online at http://informahealthcare.com/doi/abs/10.1517/17460441003789363.

4-PIOL: 5-(4-Piperidyl) isoxazol-3-ol; THIP: 4,5,6,7-Tetrahydroisoxazolopyridin-3-ol.

O

O

O

O

Br

6-methylflavone (Ki = 125 nM) [64] 6-bromoflavone (Ki = 70 nM) [64]

N+

N

Cl

NH

O-

N

N

O

Cl

Chlordiazepoxide Diazepam

O

O

6-ethylflavone(Ki = 180 nM) [64]

O

O

3

56

7

8

Propylflavone(Ki = 480 nM) [64]



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compared it with the structures of 1,4-benzodiazepineagonists and with two other types of antagonists, b-carbolinesand a pyrazoloquinolinone named CGS-8216. They arrivedat a model that provided an explanation for the spectrum ofresponses elicited by receptor binding.

In 1987, Wermuth and co-workers [54] came out witha unique pharmacophore model that had six critical zonesnamely a p-electron rich aromatic (PAR) zone, twoelectron-rich zones d 1 and d 2 placed at 5 and 4.5 A dis-tance, respectively, from the reference centroid in the PARzone, a freely rotating aromatic ring termed as FRA region,an out-of-plane region, strongly associated with agonistproperties and an additional hydrophobic region. The modelnot only accounted for all the BZs known at the time, butwas also able to explain the difference in affinities of R andS enatiomers in distinguishing agonist versus non-agonistactivity profiles. Later many models were also proposedby Fryer [55], Borea et al. [56] and Allen et al. [57]. All ofthe models were derived from a limited number ofcompounds available.

3.1 Agonist/inverse agonist and antagonist

pharmacophore modelCook and co-workers reported the first detailed pharma-cophore model [58,59] that explained both inverse agonistand antagonistic properties. In order to define an inverse ago-nist/antagonist pharmacophore model, a series of ligandsbelonging to different structural classes namely diindole,BCCE, pyridodiindole, phenylpyrazoloquinolinone and thie-nylpyrazoloquinolinone were used. Key features of the inverseagonist/antagonistic model (Figure 3A) include two hydrogenbonding sites (A2 and H1) and two hydrophobic pockets. A2

is a hydrogen bond accepting site and H1 is hydrogen donat-ing site and the two hydrophobic sites differs in their size.They also found that all diindoles, with an inverse agonist/antagonist profile, had their D and E ring unsubstituted,which reinforced an earlier belief of planar or psuedoplanartopography. Further, they concluded that electron releasinggroups substituted in E ring elicit inverse antagonistic actionwhereas halogen substitution results in agonistic profile(Figure 3A). For the pyridodiindole series it was found thatthe N5 pyridine nitrogen atom and the hydrogen atomattached at the N7 position are valuable features for antago-nistic activity. It has been proposed that the hydrogenbond accepting site A2 interacts with the N9 hydrogen atomand the N7 hydrogen nuclei of b-carbolines and diindoles,respectively. Likewise, the hydrogen bond donating site H1 isbelieved to interact with the N2 and N5 nitrogen atom of theb-carbolines and diindoles, respectively. The hydrophobic sitesassociated with the BZ binding pocket appear to place restric-tive dimensional parameters on the nature of the substituents.

In contrast to the inverse agonist/antagonistic model,the agonist pharmacophore model (Figure 3B) contains anadditional hydrogen bonding group H2. On receptor essentialvolume analysis, they identified a volume element that is notcommon to agonist but common to inverse agonist. This lipo-philic region was subsequently named as L2. In addition, theother features are a lipophilic region reached out by the5-phenylring of classical BZs (L3), a sterically inaccessibleregion (S1) and a hydrophobic site L1.

3.2 Diazepam insensitive pharmacophore modelClassical 1,4-benzodiazepines, such as diazepam or flunitraze-pam, exhibit high affinity for receptors composed of a1b2g2,a2b2g2, a3b2g2 and a5b2g2 subunits and hence characterizedas diazepam sensitive (DS) receptors. On the contrary,a4/6b2g2 receptors that lack sensitivity to classical diazepamare named as diazepam insensitive (DI), receptors. AtypicalBZs such as imidazobenzodiazepines, pyrazoloquinolinonesand b-carbolines exhibit high to moderate potency towardsthese subtypes. It has been revealed by Seeburg andco-workers [60] that histidine 101 present in DS receptorsappears to be a key residue for the action of clinically usedclassical BZ ligands, as it is replaced by its variant argininein DI receptor. Studies carried out by Skolnick and

O

O

6-(1-methylethyl) flavones(Ki = 720nM) [64]

O

O

HO

7-Hydroxy flavones (Ki = 4200nM) [64]

O

O

R8

R7

R6

R5

R2′

R3′

R4′

A B

C

Flavonoid scaffold

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Wong [61,62] on a series of imidazobenzodiazepines suggestthat substitutions at position 7 and 8 are essential for bindingaffinity. The binding affinity also linearly correlates with thesize of the ester substitution at position R3. The oxygenatom on the ester group is important because one of theseoxygen atoms is presumed to be involved in the formationof a hydrogen bond with the H2 site (Figure 4).

3.3 Unified pharmacophore modelCook proposed a more refined pharmacophore model termedas the unified pharmacophore model [63] by considering> 150 agonists, antagonists and inverse agonists acting at theDS--BZ site spanning across 15 structural families. Briefly,the proposed unified pharmacophore model (Figure 5) consistsof two hydrogen bond donating features (H1 and H2), onehydrogen bond accepting feature (A2) and one lipophilicfeature (L1). H2/A3 represents a bifunctional site with theability to act as a hydrogen bond donor as well as an acceptor.In addition to these features, there are regions for lipophilic

interaction (L2, L3 and LDi) as well as regions of negativesteric repulsion that could also be termed as excluded volumeregions (S1, S2 and S3).For positive allosteric modulation, occupancy of sites L2

and/or L3 together with H1, H2 and L1 are deemed to beimportant. Inverse agonist requires interactions only with sitesH1, L1 and A2 for potent activity in vivo.The LDi feature is a region of lipophilic interaction,

for which the difference between the DS and the DIsub-pharmacophore models is most pronounced.

3.4 Pharmacophore model for flavones acting at the

BZ site of GABAA

Although the unified pharmacophore model proposed byCook was comprehensive, neuroactive flavonoids were not

considered during the development of the model. Flavonoidswhich represent a class of non-Bz ligands obtained from avariety of herbal plants were used as tranquilizers in folk med-icine. Dekermendjian et al. carried out a detailed pharmaco-phoric analysis using a set of 21 flavonoids [64]. They foundthat the flavonoids fitted well within the unified pharmaco-phore/receptor model, with the only exception, the A2 sitebeing unoccupied, as shown in Figure 6.

This finding was quite interesting as earlier studies revealed itto be an important interaction site for compounds that displaypotent inverse agonism. Flavonoids when fitted to the unifiedpharmacophore model revealed that the lone pair of electronsof the carbonyl oxygen (O) atom interacts with the hydrogenbond-donating site H1. The oxygen lone pair present in theflavones mapped to the H2 site of the receptor. The fused phe-nyl ring in the flavones occupied the lipophilic region L1. Inaddition, the larger methoxy and bromo substituents as seenin 6-methoxyflavone and 6-bromoflavone efficiently fill outthe lipophilic pocket L2, which is in agreement with theproposed pharmacological effects of such an interaction.

To probe the dimension of the L2 site, they increased thesize of the 6-substituent from ethyl to propyl and isopropyland found that the affinity decreases as the steric nature ofthe substituent increases.

This indicates that substituent of the size of a propyl groupor larger is not tolerated at this position due to stericrepulsions with the receptor. Based on these SAR findings,it can be inferred that a subtle balance between bulk andpolarity of the substituent is tolerated. Also, substituent atthe 7-position, as evident in 7-hydroxy flavone, does notenhance affinity, presumably, due to repulsive interactionswith the receptor essential volume site S2.

In their initial study using a limited number of flavonoids,they concluded that the flavonoids currently in question

N

N

X

H

N

H

Small lipophilic site

H1

Large lipophilic site

H

A

B C

D

E

A2

L1

L3

L2

S1

NN

HNO

ClH2

H1

A. B.

Figure 3. Schematic representation of the inverse agonistic/antagonistic mapped to a diindole scaffold (3A) and the agonistic

pharmacophoric model (3B) for the BZ site.BZ: Benzodiazepine.



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were not able to interact with the hydrogen bond acceptingsite A2.

Based on these findings, they proposed a refined pharmaco-phore model [65], an extension to the Cook’s unified pharma-cophore model, with two additional sterically excluded sitesS4 and S5 and concluded that a 3¢-substituent should be

directed ‘downwards’ and the region around 4¢-and 5¢-positions are sterically repulsive regions (Figure 7). The term‘interface’ is used to denote a partly lipophilic region in thevicinity of the 5¢-position in the flavone series and this regionhas been proposed to represent the interface between a and gsubunit in the GABAA receptor. To investigate if interactionswith the A2 site could be achieved on substitution at positions2¢ and 6¢, they ended up by synthesizing 5¢-bromo-2¢-hydroxy-6-methylflavone [66] that occupies the A2 site(Figure 7) and exhibited the highest potency (0.9 nM).

3.5 A predictive pharmacophore model for

flavonoidsThe above models, proposed by Liljefors and co-workers, werequalitative in nature [64-66]. A quantitative pharmacophoremodel for flavones was rationalized by Huang et al. [67] consid-ering a set of 38 flavonoid molecules. They carried out 3DQSAR (CoMFA, CoMSIA, HQSAR) and ab initio (HF/6-31 G)studies to calculate (electrostatic potential) ESP maps. Aplausible interaction model for flavonoids within the BZ siteproposed by them is illustrated in Figure 8.

Accordingly, the oxygen atom and the carbonyl group inthe flavonoids are involved in hydrogen bonding. The twohydrogen bond acceptor sites A1 and A2 correspond withthe sites H1 and H2 in the Cook’s model, respectively. Thelipophilic site (L1) involving in a hydrophobic interactionwith the flavone nucleus has a striking resemblance withthe LDi site of Cook’s model. Further, they speculated thepresence of two sub sites (S1 and S2) near the lipophilicsite (L1). As far as S1 and S2 sites are concerned, substituentsat position 6 and 7 of the flavone scaffold engage either in anelectrostatic or steric interaction according to the nature ofthe substituents present at these positions. The aromaticside chains in the binding pocket may have strict geometricalorientation, thus, making these hydrophobic sites very sensi-tive to the structural properties of the substituents on aro-matic rings A and B. Further, Hong and Hopfinger [68]

developed a 3D pharmacophoric model using a receptor-independent 4D QSAR approach. Using a 4D QSAR para-digm that incorporates ligand conformational flexibilityand multiple alignment exploration they concluded that Ki

values are highly dependent on the size and electrostaticcharacter of the substituents at position R3¢ and R6 positionsof the flavonoid scaffold (shown below). Additionally, polarnegative groups at R3¢ and/or R6 substituents are deemednecessary for activity.

Duchowicz et al. [69] has carried out QSAR studiesusing linear modeling techniques and estimated the bindingaffinities for some newly synthesized flavonoids having2,7-substitutions in the benzopyrane backbone. RecentlyGoodarzi, et al. [70] has carried out QSAR modelingstudies on a data set of 92 flavonoids using a novel nonlinearmodeling algorithm termed Hybrid Genetic AlgorithmBased Support Vector Regression.

LP

LP

LP

LPLP

NN

NN

N

ClCl

L2S2

H2

LDi

A2H1

S1

S3

L3

L1

NNN

O O

Figure 5. Cook’s unified pharmacophoric model for the BZ

site fitted to CGS-9896 (dotted line), a diazadiindole (thin

line) and diazepam (thick line) along with the pharmaco-

phoric elements.Reproduced with permission from [84].

Copyrights 2000, American Chemical Society.

BZ: Benzodiazepine.

N

N

NO

O

OR7

R10

R8

R9R3

L1

LDi

H1

H2

Figure 4. DI-pharmacophore model mapped to imidazo-

benzodiazepine scaffold.DI: Diazepam insensitive.

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3.6 Pharmacophore models for defined

pharmacological end pointsThe binding of BZ ligands to the BZ receptor system elicitsmultiple pharmacological end points. Harris et al. [71,72] devel-oped distinct 3D pharmacophore models for ligands elicitingpharmacological end points such as anxiolytic, hyperphagia,sedation and contextual memory. In the absence of adequatesubtype selective modulators, linking structural variationwith changes in pharmacology could help in arriving atpharmacophore models for a defined pharmacological end

point. Such an approach proves to be paradigm shift fromthe conventional approach of relating chemical propertieswith binding affinity/activity. Relating chemical structurewith pharmacological end point makes more sense, especiallyfor the allosteric segment when the biological end point isgoverned by the subtle changes that take place on binding ofthe allosteric ligand.

Using AM1 level of energy minimization and Quanta/CHARMM as a conformation generation tool, a pharmaco-phore model was developed using 17 diverse BZR ligandsthat display anxiolytic response [71]. A five component anxio-lytic pharmacophore consisting of two proton acceptors, ahydrophobic group, an aromatic electron accepting ring anda ring containing polar moieties was hypothesized as shownin Figure 9.

A 3D pharmacophore model for BZR ligands initiating thehyperphagic response was derived using a set of 17 structurallydiverse compounds [71]. This resulted in the development of afour-component 3D pharmacophore. The resulting pharma-cophore model for hyperphagia had two proton acceptors, aring aromatic functionality and a hydrophobic functionalityin a common geometric arrangement for all compoundswith this defined end point. Further, to identify the pertinentfeatures for selectively influencing contextual memory bymodulating a5 subunit, they developed a 3D pharmacophoremodel using a diverse set of non-selective ligands. Using theobtained pharmacophore model as an alignment input strat-egy for 3D QSAR (CoMSIA), they identified an electrostaticcontour near the ligands’ terminal substituent region as a dis-criminating factor for selective binding to a1b2g2 (sedation)versus a5b2g2 (memory) (Figure 10) [72].

Further, they carried out fragment-based QSAR using QMand physico-chemical descriptors and concluded that the frag-ment length (Sterimol L) and frontier molecular orbital ener-gies (HOMO-LUMO) of the substituent correlated with thecompounds influence on electrophysiological activity.

3.7 Subtype selective pharmacophore modelsIn recent years, it has been discovered that although theGABAA receptor is widely distributed in the brain, the sub-unit composition of the receptor exhibits regional variations.Heterogeneity displayed by GABAA receptor led to a specula-tion that different subtypes may mediate specific effects ofBZs such as anxiety or sedation and opened avenues for thediscovery of novel subtype specific BZ ligands. Current phar-macological studies, combined with the use of geneticallyengineered mice, have led to a general consensus that GABAA

a1 receptor mediate sedation and has a prominent role inseizure susceptibility and sedation [73-76], GABAA a2/a3

receptors are involved in anxiety [73], whereas GABAA a5

receptors are relevant to memory function [73]. Therefore, cur-rent research is revived towards the identification of isoform-selective modulators with improved potency and decreasedside effects. Merck research laboratory has initiated drugdiscovery projects aimed at the identification of selective

S1

S2

S3

S4

S5

L3 H2/A3

0

0

L1

3′4′

5′6′

H1

A2

Channel

L2

Figure 6. An extended view of the pharmacophoric model as

proposed by Liljeforis.Reproduced with permission from [65].


S1

S2

S3

S4

S5

L3 H2/A3

L1

Br

5′

6

2′

H1A2

Interface

L2

Figure 7. Binding mode of a high affinity flavone fitted to

the refined pharmacophoric model.Reproduced with permission from [66].




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GABAA a2/a3 receptor agonists as anxioselective agents,devoid of sedative effects [77-83], and a5 selective inverse ago-nists as cognitive enhancers [79]. A simple classification ofGABAA receptor and their subtypes of pharmacologicalimportance is shown in Table 1.

With efforts centered on the identification of subtype selec-tive modulators, it is worth reviewing pharmacophoric modelsfor GABAA subtypes available in the literature.

The commonality and uniqueness of the pharmaco-phore models for various subtypes were rationalized byHuang et al. via a comprehensive ligand mapping tech-nique [84], in a very similar fashion, and used for the creationof unified pharmacophore model. They carried out includedvolume analysis, based on their unified pharmacophoremodel, by aligning subtype selective ligands as indicated bytheir selective affinities. The included volumes thus con-structed were used to compare the topologies of the BZ bind-ing pockets for the a1b3g2, a5b3g2 and a6b3g2 receptorsubtypes. GABAA a1 subtype selective ligands.

CL-218,872, a partial agonist, and zolpidem, an agonist,were used for included volume analysis of the a1b3g2 pocket.

On analysis of the included volume, they concluded thatspace need for accommodating a1 subtype selective ligandsis slightly different from the volume need to accommodatea2 subtype as defined by ligands. This result implies thatoccupation of LDi may be critical for the selectivity of aligand at receptors which contain a1 subunits, as the LDiregion is much larger in a1 receptors. Results from other lab-oratories also suggest that occupation of region LDi is highlyrecommended for selective binding at receptors which containa a1 subunit. Results from both SAR studies and includedvolume analysis implied that the a2 and a3 subtypes arevery similar in shape, polarity and lipophilicity. L3 region isvery small or non-extinct for the a4 and a6 subtypes (Figure 11and Figure 6 of [11]). The lack of this region (L3) could beattributed for the diazepam-insensitive nature displayed bythese receptor subtypes. L2 is believed to be deeper in thepharmacophore of the a5 receptor subtype than in the a1

or a6 isoform. It has been observed that ligands with a5

selectivity are generally imidazobenzodiazepines.The C8 substituent of this series of ligands is known to

occupy the L2 region of the pharmacophore. For example,the acetyleno function of RY80, one of the most potenta5-selective ligands, occupies this region L2 and displayeda5 selectivity.

Occupation of the region LDi by the ligands belonging tothe imidazobenzodiazepines was poor, as shown in Figure 12.

Cook also carried out CoMFA-based 3D QSAR studies ona series of substituted imidazobenzodiazepines using affinityvalues reported for the recombinant a1b3g2, a2b3g2,a3b3g2, a5b3g2, and a6b3g2 GABAA/BZ receptor subtypesto deduce predictive pharmacophore models [85]. Inspectionof these models suggested that L2 site within the pharmaco-phore/receptor model of the a1b3g2 receptor subtypecorresponds to the region occupied by a C-8 substituent in

O

O

S1

S2

A2

A1

L2 site

Figure 8. CoMFA, CoMSIA and HQSAR-based pharmacophore

model for flavones.

N

N

N

CF3

NN

N

N

Zolpidem (Ki = 26.7 nM at α1) [11]

CL-218,872 (Ki = 57 nM at α1) [11]

N

N

N

O

O

O

H

RY80 (Ki = 0.49 nM for α5)

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the unified pharmacophore/receptor model. This pocket wasneither deep nor as large as the analogous lipophilic pocketwithin the a5 subtype. It also appears that region L2 in thea2 and a3 subtypes is neither as deep nor as large as that ofthe a5 subtype. It is evident that the included volume of thea6 subtype is significantly smaller than that of the a1 subtype.Especially the LDi region is much larger in a1 receptor.

Lu and Zhou developed a predictive pseudo receptormodel using a 3D QSAR approach termed Flexible AtomReceptor Model for five recombinant receptor subtypes(a1--3,5,6b3g2) [86]. The pseudo receptor model developed bythem was congruent with that of the Cooks unified pharma-cophore model. Vijayan and Ghoshal [87] carried out 3DQSAR and pharmacophore modeling studies on a series of

D(1-2) = 4.6 ± 1.8 Å

D(1-3) = 5.8 ± 1.6 Å

D(1-4) = 3.4 ± 0.7 Å

D(1-5) = 5.5 ± 1.3 Å

D(2-3) = 4.5 ± 1.3 Å

D(2-4) = 4.3 ± 0.8 Å

D(2-5) = 6.4 ± 1.5 Å

D(3-4) = 4.3 ± 0.95 Å

D(3-5) = 5.6 ± 1.1 Å

D(4-5) = 3.0 ± 0.8 Å

3

5 = Aromatic (LUMO) Ring

5 = Ringpolar moieties

1,2 = Proton donatingreceptor points

3 = Variablehydrophobicregion

21

5

D(1-3)

D(3-4)

D(3-5)

D(1-4)

D(1-5)

D(4-5)

D(1-2)

D(2-3

)

Figure 9. A five component anxiolytic pharmacophore along with inter pharmacophoric distances.Image reproduced from [71].

Copyrights 2000, with permission from Elsevier.

Distance No’s1-21-31-41-52-32-42-53-43-54-5

Sedation4.9±1.15.6±1.84.4±1.12.3±0.94.5±1.24.7±1.23.1±1.35.1±1.13.0±0.87.8±1.3

Sedation

Memory3.2±1.73.5±1.06.9±0.7

5.2±1.28.9±1.4

4.9±0.0

Memory

2

3

5

4

1

Figure 10. A pharmacophore model for selective binding to a1b2g2 (sedation) versus a5b2g2 (memory).Reproduced with permission from [72].




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a2/a3 subtype selective modulators to arrive at a pharmaco-phoric model. They considered 13 unique scaffolds exhibitingfunctional selectivity towards a2/a3subtype to derive a a3

subtype selective pharmacophoric model (Figure 13) usingthe HipHop module of catalyst. The pharmacophoric modelconstructed was inclusive of included volume feature and itcorroborated with the Cook’s unified pharmacophore model.The quality of the model was validated using selectivitymetrics obtained by screening a decoy database seeded withboth selective and non-selective ligands.

4. Orthosteric vs allosteric drug discovery forGABAA: pros and cons

Designing drugs targeting the orthosteric site that mimic orinhibit the effects of the endogenous/cognate ligand throughcompetitive inhibition demands a pharmacophoric modelthat shares a spatial overlap with the endogenous ligand.

Hence, agonists targeted towards the orthosteric GABA siteoffer limited scope for pharmacophoric diversity. Mostimportantly, accomplishing subtype/functional selectivity bytargeting the conserved GABA binding site would be daunt-ing as the orthosteric site on all the GABAA isoforms areconserved, as they are evolved to interact with GABA. For amedicinal chemist, optimizing a GABA agonist for enhancedbinding affinity would be a straightforward approach, as thebiological end point is the result of a mutually exclusive bind-ing phenomenon between the ligand and the orthosteric site.The advent of functional assays in mid-1990s paved way forallosteric drug discovery. The pros of pursuing allostericdrug discovery for the GABAA receptor offer scope for thedevelopment of novel molecular entities. Targeting the allo-steric site, relatively less conserved within isoforms comparedto the orthosteric site, holds promise for subtype/isoformselectivity. On the contrary, lead optimization of allostericligands for a medicinal chemist is akin to betting, as the

Table 1. A simple classification scheme of GABAA and their subtypes of pharmacological importance.

Subunit Preferred subunit

combinations

Suggested subcellular

localization

Pharmacological effect

a1 a1b2g2 Synaptic Sedation, anterograde amnesiaSome anticonvulsant action, ataxia

a2 a2b2/3g2, a2bxg1 Synaptic Anxiolytic, hypnotic some muscle relaxationa3 a3bxg2/3 Synaptic/extra synaptic Some anxiolytic action, some anticonvulsant action

Maybe some muscle relaxationa4 a4b2/3g2 Extra synaptic Diazepam-insensitive site anxiety, amnesia, alcoholisma5 a5b3g2/3 Extra synaptic Memory and learning anxiety, amnesia, myorelaxationa6 a6b2/3g2 Extra synaptic Diazepam-insensitive site

A.

B.

Figure 11. A. Orthogonal views of the overlap of included volumes of the pharmacophore/receptor models for a1b3g2 (blue)

and a5b3g2 (yellow) receptor subtypes. B. Orthogonal views of the overlap of included volumes of the pharmacophore/

receptor models for a5b3g2 (purple) and a6b3g2 (green) receptor subtypes.Reproduced with permission from [84]. Copyrights 2000, American Chemical Society.

A color version of this figure is available online at http://informahealthcare.com/doi/abs/10.1517/17460441003789363.

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conformational changes that occur on the binding of theallosteric ligand dictate pharmacological end point.

5. Success stories of pharmacophore modelsin lead optimization and scaffold hopping

Pharmacophore-based screening has now become a mainstayapproach in the field of computer-assisted drug design.Preclinical literature is replete with examples of many successstories from pharmacophore modeling. Nevertheless, thesuccess of pharmacophore models in the CNS segment isrelatively less. The limited success could be attributed to theallostery phenomenon involved in the GABAA receptor,

which adds up a new dimension that is not reflected in allthese models. The extended pharmacophore model (Figure 7)proposed by Lijeforis and co-workers was retrospectivelyused by them to design the most potent flavonoid5¢-bromo-2¢-hydroxy-6-methylflavone [66].

Later, they converted the pharmacophore model conceivedby them as shown in Figure 6 into a catalyst pharmacophoremodel (Figure 14) and queried the Maybridge and ACDcompound database in an attempt to find novel scaffolds.

On pharmacological evaluation of the 22 hits that wereretrieved from the Maybridge database and the 76 hits obtainedfrom the ACD database, a 4-quinolone derivative was found tobe the most potent with a Ki value of 121 nM [88].

Figure 12. RY80 (gray) and a substituted pyrazoloquinolinone (black) fitted to the unified pharmacophore/receptor model.Reproduced with permission from [84].


O

O

OH

Br

5′-bromo-2′-hydroxy-6-methylflavone (Ki 0.9 nM) [66]

NH

O

O

O

CF3

6-Trifluromethyl-3-ethoxycarbonyl-4-quinolone (Ki = 121 nM)

NH

O O

NH

6-Benzyl-3-propylaminocarbonyl-4-quinolone (Ki = 0.048 nM)

NH

N

O O

O

O

6-PBC (Ki = 0.49 nM) [89]



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Further, optimization of 3-ethoxycarbonyl-4-quinoloneled to the identification of an amide derivative 6-benzyl-3-propylaminocarbonyl-4-quinolone as a better candidate witha Ki value of 0.048 nM on biological screening [66].

A predictive inverse agonist/antagonist binding pharmaco-phore model developed for b-carbolines via CoMFA/GOLPEapproaches and further refined, using ab initio calculations,was used to design and synthesize an anxiolytic/anticonvulsant.

6-(n-Propyloxy)-4-methoxymethyl-b-carboline-3-carboxylicacid ethyl ester (6-PBC): on validation in biological assaysit was found that 6-PBC, a partial agonist, had a Ki valueof 0.49 nM [89].

6. Conclusion

The pharmacophore models hereby reviewed were largelydeduced by analyzing SAR and mapping common structuralfeatures of active analogues and few models from statisticallysignificant 2D QSAR and 3D QSAR approaches. Pharmaco-phore modeling has indeed proven to be a useful frameworkfor better understanding of the existing data and has beensuccessfully used for the design and optimization of leadcompounds. As evident from the pharmacophore modelsdescribed, it is apparent that a similar pharmacophore motifis shared by diverse classes of modulators as they fit in to a

unified pharmacophoric model. This implicates a commonmechanism underlying chloride ion modulation. It shouldalso be noted that pharmacophore models only provide acrude working model that are deemed necessary, but not asufficient criterion for optimal supramolecular interaction.The allosteric transition paradigm envisaged for GABAA sys-tem adds a new dimension. The quartenary twist, evidentfrom a related system nACh, reveals that the agonists andantagonists, as well as positive and negative modulators,select and stabilize structures in different conformations.This necessitates the need for a dynamic pharmacophorethat accounts for the key features involved in allosteric transi-tion to pave way for ‘mechanism-based drug discovery’ andensure quantum leaps toward novel and effective treatments.

7. Expert opinion

Over the years, the discovery of drugs for treating neurologicaland psychiatric diseases has proven to be modest. Despite thehuge investments and great strides made in CNS drug discov-ery, a ‘magic bullet’ that could revolutionize disease treatmentstill seems to be a long run in conceiving realistic therapeuticstrategies. The CNS segments particularly witness much attri-tion in the translational phase, with only 3 -- 5% of the pro-spective drug candidates becoming marketed therapeutics.Though in the initial phase of drug discovery pharmacophoremodeling proves to be a mainstay approach, it is not immuneto short comings. Finding successful drug candidates via data-base searching is currently limited by the fact that the definedpharmacophoric features are greatly influenced by the pre-sumed bioactive conformation. The hits retrieved may havethe defined pharmacophoric elements but inconsistent solu-bility characters would subsequently show up as a false posi-tive on biological screening. Multiple-binding mode mayoften exist in a binding pocket where non-directional forces,such as van der Waals or hydrophobic, are dominant. Con-formers in the database may have high internal energies thatare unrealistic. Consideration of tautomeric form is very vitalas they are bound to the change pharmacophoric definitionfor features such as HBA and HBD. Considering these short-comings, use of other computational methods such asCoMFA, CoMSIA and GRID/GLOPE, which implicitlyallow deducing pharmacophoric features and also offer theadvantage of being quantitative, can be used. Predictivepharmacophore-based 3D QSAR modeling by Catalyst�Hypogen and Schr€odinger Phase� programs could also beused in a high-throughput fashion for profiling chemicallibraries and for prioritizing leads. Further, newer pharmaco-phore modeling techniques such as Statistical Classification ofActive Molecules for Pharmacophore Identification, Dynamicpharamcophore model that accounts for protein flexibilityand ligand entropy and the program CLEW which usesmachine learning approach, offer advantages. The use ofatom pair descriptors pioneered by Sheriadal and co-workers,Pharmaprint, MACCS fingerprint keys and other variants can

Figure 13. GABAA a3 subtype selective pharmacophore

model developed using catalyst.Reproduced from [87].


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be used for similarity searching. The use of 3-/4-point phar-macophoric features for all the accessible conformations isperceived to have advantage over atom pair descriptorswhen used for similarity searching. The allosteric andorthosteric sites are conformationally linked, and mostallosteric modulators lack an intrinsic signaling efficacy;hence, the hitherto unexplored utility of linking theorthosteric and allosteric pharmacophores could be used tosculpture novel hybrid or bitopic ligands. A recent disclosureby Valant, Christopoulos and colleagues [90] in this directionhas revealed that directed design of ligands combiningorthosteric and allosteric moieties could increase the chancesof discovering subtype and pathway-selective GPCR ago-nists. Embarking on such directed design of bitopic ligandsfor the GABAA receptor is worth taking a plunge. Drug dis-covery is a multi-objective process often involving propertiesof conflicting nature; hence, multi-objective optimizationmethods such as Pareto method, evolutionary multi-objective technique (MOGA) and Derringer’s desirabilityfunction-based optimization allow running global QSARstudies, jointly considering multiple properties of interestin drug design process.There are many issues intrinsic to biology that merit men-

tioning. Heterogeneous pathophysiology and our limitedknowledge of etiology often disappoint when embarkingon mechanism driven drug discovery. The maxim ‘animalscan serve as models for the disease mechanism, but not forthe disease itself’ mirrors more disparity particularly for theCNS segment. Attempts should be made to translate the

advances, made in the understanding of neuronal plasticity,to drug discovery. Current pharmaceutical research forCNS segment still follows the stereotypical ‘receptor--ligand’interaction paradigm; hence, neurotransmitter and trans-porters are the mainstay drug targets. Intracellular targets,which render target and spatial function specificity, shouldbe considered, given the regional variation and functionalityin brain. Although GABAA receptor is an important clinicaltarget and current studies have highlighted the importanceof subtype selective modulation, very few subtype selectivemodulators exist. Hence, future research should be focusedtowards the development of subtype selective modulatorsas they are expected to overcome the side effects evidentfrom conventional therapy. From a computational point ofview, it is high time to construct more realistic homologymodels from the recently resolved high resolution crystalsof pentameric ligand gated ion channels. The presence oftrans membrane regions in these crystal structures couldhelp to expand our knowledge, currently confined to theligand binding domain. Homology models generated fromhomologous templates could find utility in the constructionof a structure-based pharmacophore. Further, moleculardynamics simulations could be used to create dynamic phar-macophore models that reflect the pertinent features, whichallosterically govern channel gating. The current pharmaco-phore models available in literature are based on chemicaland electronic features and are qualitative. To paraphraseon an optimistic note, it sounds that, with advancement inmembrane solubilization technique, synchrotron technology

H1

A2

S4H2S3

S2

Figure 14. A lead compound obtained via database screening, shown mapped to the extended pharmacophore model.Reproduced from [88].




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and micro-diffraction technique, the crystal structure ofGABAA can be realized sooner, ultimately opening the doorsfor structure-based drug design and accelerating CNSdrug discovery.

Declaration of interest

The authors state no conflict of interest and have received nopayment in preparation of this manuscript.

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AffiliationNanda Ghoshal MSc PhD†1 &

R Suyambu Kesava Vijayan2 BPharm MTech†Author for correspondence1Scientist-EII,

Indian Institute of Chemical Biology

(A unit of CSIR),

Structural Biology and Bioinformatics Division,

4, Raja S.C. Mullick Road,

Kolkata-700032, India

Tel: +91 33 2473 3491 ext. 854;

Fax: +91 33 2473 5197;

E-mail: [email protected] Research Fellow,

Indian Institute of Chemical Biology

(A unit of CSIR),

Structural Biology and Bioinformatics Division,

4, Raja S.C. Mullick Road,

Kolkata-700032, India



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Documents

Pharmacophore models for GABA A modulators: implications in CNS drug discovery