chemical function based pharmacophore models as suitable filters for virtual 3d-database screening

Chemical function based pharmacophore models as suitable filtersfor virtual 3D-database screening

T. Langera,* , R.D. Hoffmannb, F. Bachmaira, S. Beglea

aInstitute of Pharmaceutical Chemistry, University of Innsbruck, Innrain 52a, A-6020 Innsbruck, AustriabMolecular Simulations SARL, Parc Club Orsay Universite´, 20, rue Jean Rostand, F-91838 Orsay, France

Abstract

In this paper a description of the concept of chemical function based pharmacophore models is given. Their generation basedon the knowledge of a set of active ligands for a biological target is discussed as well as the utility of the models obtained toretrieve new potentially bio-active compounds from large 3D databases. This concept is exemplified describing the applicationto two different biological targets: the retinoic receptor family and the enzyme aldose reductase.q 2000 Elsevier Science B.V.All rights reserved.

Keywords: Chemical feature based pharmacophores; Virtual screening; 3D database

1. Introduction

A pharmacophore (pharmacophore model, pharma-cophoric pattern) can be considered as the ensembleof steric and electrostatic features of differentcompounds which are necessary to ensure optimalsupramolecular interactions with a specific biologicaltarget structure and to trigger or to block its biologicalresponse [1]. From this definition it follows that apharmacophore does by no means represent a realmolecule or a real association of functional groups,but a purely abstract concept which, however,accounts for the common molecular interaction capa-cities of a group of molecules towards their targetstructure. Another important point to mention is thatknowledge of a given pharmacophoric pattern doesnot yield information about the intensity of inter-action, i.e. the potency, nor, in most of the cases,about the nature of the induced biological response

(e.g. agonist–antagonist). Therefore, a systematic3D database search based on the knowledge of agiven pharmacophoric pattern may select possiblecandidate molecules, but may not necessarily givereliable information on their exact affinity. Anotherlimitation of pharmacophoric pattern based computer-assisted ligand design is that this methodology canonly be applied to already discovered enzymes orreceptors for which at least a set of several ligandsis available. However, since the finding of new leadstructures is one of the most difficult tasks in the drugdevelopment process, considerable efforts have beenmade to develop computer-aided procedures forautomated pharmacophore generation, and‘virtual screening’ has become a hot spot indrug research.

In this paper a description of the concept of chemi-cal function based pharmacophore models is given aswell as their use as suitable filters for 3D databasemining. The underlying concept will be demonstratedusing two different examples in two different drug

Journal of Molecular Structure (Theochem) 503 (2000) 59–72

0166-1280/00/$ - see front matterq 2000 Elsevier Science B.V. All rights reserved.PII: S0166-1280(99)00363-2

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* Corresponding author.

discovery areas: (i) retinoic acid (RA) receptorligands as potential antitumour drug candidates; and(ii) ARIs as drugs for the treatment of long termdiabetic complications.

2. Chemical function based pharmacophoremodelling

A considerable number of different pharmacophoregeneration methods have been published [2]. In mostof these methods, pharmacophores can be generatedautomatically (DISCO [3], GASP [4], APEX-3D [5]),and the pharmacophoric anchor points are representedby atoms. However, the main molecular binding inter-actions between proteins and their ligands can besummarized as follows: hydrogen bond donating,hydrogen bond accepting, salt bridges, aromaticPring stacking and hydrophobic effect. Inclusion ofchemical features in pharmacophores has appearedonly recently [6], although it constitutes a logicalconcept used by chemists for years in the drug discov-ery process. The originality of this type of pharma-cophores resides mostly in the fact that their definitionis general and represents the different types of inter-actions between organic molecules and proteins.The same types of interactions are used within thechemical function based pharmacophore modellingapproach as implemented within the CATALYSTsoftware package [7]. The pharmacophore modelsused in this context are termed hypotheses and consistof a number of so-called features that are located rela-tive to each other in co-ordinate space as pointssurrounded by a tolerance sphere. Each sphere repre-sents the region in space that should be occupied by acertain atom or group of atoms capable of the kind ofchemical interaction specified by the feature type. Inorganic molecules, different structural motifs canexpress a similar chemical behaviour and thereforethe same biological effect. The utility of CATALYSThypotheses as queries for 3D database search hasrecently been reviewed [8].

It appears from the medicinal chemist’s point of viewthat the chemical features hypotheses concept allows astraightforward drug design strategy. The construc-tion of these hypotheses can be done either manually:(i) based on the assumed bio-active conformation of acertain ligand; or (ii) using positioning data from

X-ray crystallography or NMR experiments, or (iii)automatically using algorithms implemented withinthe CATALYST package. Both aspects—the manualand the automated hypothesis generation—will bepresented in the following sections as well as theutility of such models to perform rapid virtual screen-ing in structural databases.

3. Retinoic acid receptor ligands as potentialantitumour drug candidates

Retinol (Vitamin A) and its biologically active deri-vatives, retinal and RA, are essential for health andsurvival of the individual. These natural compounds,together with a large repertoire of synthetic analogues,are collectively referred to as retinoids. In addition totheir well known role in vision, retinoids are alsorequired for reproduction, act as morphogenic agentsduring embryonic development, and regulate tightlythe growth and differentiation of a wide variety of celltypes throughout life of the organism [9]. A consider-able advance in understanding the molecular mechan-ism of retinoid utilization in development andphysiology arose with the identification of specificreceptors that mediate the effects of RA. These recep-tors belong to the nuclear receptor family and repre-sent transcription factors. Retinoid receptors comprisetwo distinct groups of the nuclear receptors, namelythe RARs and RXRs, consisting each of threemembers, namelya, b, andg [10]. The large spec-trum of biological processes affected by RA suggeststhat each RAR and RXR isoform play a unique role asa regulator of embryonic development and homeo-stasis. The antitumour activity of retinoids appearsto be mediated by inhibition of cell proliferation orby induction of cell differentiation and induction ofprogrammed cell dead (apoptosis). In particular, theantiproliferative activity has been extensivelyexploited for the treatment of a variety of skin dis-orders [11] and more recently, for the treatment ofcertain types of cancer [12,13]. The relatively simplechemical structure of retinoids has made them interest-ing candidates for drug development. Unfortunately,some retinoids show a high degree of undesirableside effects including skin irritation, lipid and bonetoxicity, visual effects as night blindness or dryeyes, and teratogenicity. This provides impetus for

T. Langer et al. / Journal of Molecular Structure (Theochem) 503 (2000) 59–7260

identifying new retinoids which have unique biologi-cal profiles and potentially larger therapeutic indices.In this section we describe the results of a recent study[14]: the generation of a predictive 3D QSAR modelbased on the CoMFA method [15] as well as the use ofa manually derived chemical function based pharma-cophore model based on the structure of a rigid andhighly potent ligand of RAR for the retrieval ofretinoids and possible other ligands for the retinoicreceptors within the Derwent World Drug Index(WDI) [16].

For model generation, a training set consisting of32 compounds was selected (Ki for RAR a: 5 nM–50mM; Ki for RAR b: 3 nM–50mM; Ki for RAR g:1 nM–50mM). The structures were submitted to acombined simulated annealing and potential energy

minimization procedure in order to determine ensem-bles of energetically favourable structures for eachcompound. The molecular alignment for theCoMFA study was done using compoundCD367[17]—the most potent rigid analogue of the trainingset series—as a template (Fig. 1).

The interaction energy fields were calculated usingthe default parameters and satisfactory models for allthree RAR subtypes were obtained: ther2-valuescalculated using the cross-validation procedure werefound 0.622 for RARa, 0.596 for RARb, and 0.576for RAR g (final models, three components: RARa:r2 � 0:83; s� 0:48; RAR b: r2 � 0:91; s� 0:39;RAR g: r2 � 0:92; s� 0:40�: The validation studywas performed using a test set containing 12compounds and we obtained a satisfactory predictionof activity data for all three models.

Finally, a chemical feature based pharmacophorewas manually built using the compound CD367 as atemplate together with the information originatingfrom the structural alignment for the CoMFA study.This model consists of five features: one negativeionizable feature (or acidic function) mapping thecarboxylic acid function, and four hydrophobicfeatures mapping the aromatic moieties of the mole-cule and presenting extended tolerance radii (Fig. 2)in order to mimic the observed steric bulk.

T. Langer et al. / Journal of Molecular Structure (Theochem) 503 (2000) 59–72 61

OH

O

Fig. 1. Compound CD367 (Ki for RAR a: 5.3 nM; Ki for RAR b:3 nM; Ki for RAR g: 1.3 nM).

Fig. 2. CATALYST hypothesis for RAR receptor ligands.


Table 1Selected compound retrieved from the WDI database

Archidonate Am93 Aseanostatin P5COOH

N

O

O

OHHO

O

AT-346 Boswellate α Boswellate βO

OHNCl

Cl

HO

OHO

HO

OHO

Boswellate γ BRL-35390 Bromopalmitate - 2

HO

OHO

HO

O O O

OH

OH

HO

O Br

HO O

Calendulate Caprate Catalpate

HO

O

HO

O O

HO

CD-1530 CD-235 CD-1905

HO

OH

O

O

OH

O

O

N

O

OH

O

H


Yohimbinesulfate Chaulmoograte Clomoxir

N

N

OO

OS

O

OOH

OH

O

Cl

O

HOO

CV-5356 Dillenate E Dodecyl-benzene-sulfate

N

OHO

OH

HO

O

OHS

O

O

OH

Etylthioundecanoate

-

11Undecanoate

Haloxyfop Hexylsalicylate

SHO

O

HO

O

N

O

O

HO O

Cl

F

FF

HO O

OH

Laurate Mycomycin Myristoleate

HO

O

O

HOHO

O

Table 1 (continued)


Namirotene ND-186 ND-186-H-5

S OH

O

N

O

OH

O

N

O

OH

O

F FF

Pelretin RO-21-6667 RP-64966

OH

O

OH

O

O

S

O

HOO

SKF-105656 SKF-7249 SMR-6

HO

O

N

O

HO

J

J

J

OH

O

OH

O

SR-3986 SRI-4657-47 Targretin

OH

O

S

OH

O

O

OH

Table 1 (continued)

The Derwent-WDI was searched using our RARpharmacophore hypothesis. This flexible 3D databasecontains approximately 48000 different compoundswith a total of 2.8 Mio conformers. 323 compoundswere found to match the defined features of the query.Tables 1 and 2 show a selection of compoundsretrieved from this virtual screening together withaffinity values calculated using the CoMFA models.As expected, a considerable number of known reti-noid receptor ligands were present among the struc-tures extracted, like CD-1530, CD-1905, CD-235,Namirotene, Pelretin, RO-21-6667, and Targretin.Interestingly, all of the selected compounds werepredicted to exhibit high binding affinity to each ofthe retinoid acid receptor subtypes.

The arising consequence therefrom would be tosearch for references which establish a connectionbetween these compounds and RA receptor mediatedaction. Indeed, for example thev-6-fatty acid, arachi-donic acid and related metabolites seem to beinvolved in the development and progression of pros-tate cancer [18]. There are also hints in the literaturethat arachidonic acid substantially suppresses theinduction of apoptosis, which can be induced by reti-noids, thus mediating the carcinoma cell survival [19].These findings together with the predicted bindingdata possibly suggest a connection between fattyacids and RA receptor binding. The biological activepentacyclic triterpene, boswellic acid, an anticancerdrug originating from Boswellia serrata was foundto be an effective inducer of cell differentiation inHL-60 cells [20]. The model predicted binding dataof boswellatea, b, andg together with the ability ofboswellic acid to induce differentiation in HL-60cells. These biological observations should justifyexperiments to measure the binding affinity of theboswellates to RA receptors. Another interesting find-ing resulting from this database search experimentwas the indication of haloxyfop, an aryloxyphenoxyherbicide [21], as a RA receptor ligand. This herbicideexhibits teratogenic effects and it would be interestingto find out if RA receptors are involved in haloxyfopinduced teratogenic effects. For compound RP-64966we also got predicted high affinity binding data for RAreceptors. The compound is involved in the leuko-triene synthesis as it acts as leukotriene A4 hydrolaseinhibitor [22].

In conclusion, this example demonstrates that a

manually derived pharmacophore hypothesiscombined to 3D-database search together with aclassical CoMFA model can be used to retrievefrom a large number of compounds those with highaffinity for RA receptors. This approach can lead tothe identification of structures which exert their activ-ity via the specific receptor pathway but which up tonow, were not recognized to act as ligands of thesereceptors. In our case this procedure might allow theidentification of novel RA receptor ligands.

4. Aldose reductase inhibitors

Aldose reductase (EC 1.1.1.21) is the first enzymeof the so-called ‘polyol pathway’. In the presence ofNADPH it converts glucose to sorbitol, which isfurther transformed into fructose by the enzyme sorbi-tol dehydrogenase [23]. The affinity of aldose reduc-tase for glucose is low and therefore flux through thispathway is low in most tissues under physiologicalconditions. Under diabetic conditions however, intissues such as nerve, lens, and retina, in which insulinis not necessary for glucose transport across themembrane, glucose concentration is sufficiently highto provide a substrate for aldose reductase. Theincreased glucose flux through the polyol pathwayand the high intracellular accumulation of sorbitolare believed to be involved in the etiology of theknown diabetic complications such as retinopathy,cataract, neuropathy, and nephropathy [24]. Accord-ingly, suitable aldose reductase inhibitors (ARIs) havebeen proposed as therapeutic agents capable of delay-ing the onset of long-term diabetic complications or ofminimizing their severity [25]. From the initial find-ings that long chain fatty acids inhibit aldose reduc-tase, a variety of structurally diverse compounds havebeen observed to inhibit this enzyme. These includecompounds containing the chromone, flavone, quino-line, coumarin, xanthone, 11-oxo-11H-pyrido[2,1-b]quinazoline, naphthalene, 4-oxo-3H-phthalazine, 2,4-dioxo,1,2,3,4-tetrahydroquinazoline, 3-thioxo-2H-1, 4-benzoxazine, or rhodanine ring system. Some ofthese compounds have progressed up to the clinicallevel. Several X-ray structures of complexes betweenporcine or human aldose reductase and inhibitors(sorbinil, tolrestat, zopolrestat) have been publishedso far. These studies provide useful information on the


interactions between these inhibitors and the activesite of the enzyme. In this section we describe theresults of a recent study aimed at the construction ofchemical feature basedpharmacophore models for ARIsand their use as template for 3D database search [26].

As stated above, chemical feature based pharma-cophore hypotheses can be generated automatically

using the HypoGen algorithm within the softwarepackage CATALYST, provided that structure–activ-ity data of a well-balanced set of compounds is avail-able. Two assumptions must be made about the data:(1) all compounds used in the training set have to bindto the same receptor in roughly the same fashion; and(2) compounds having more binding interactions with


Table 2Predicted pKd andKd values from CoMFA models of RARa, b, andg ligands

Compounds RARa model RARb model RARg model

pKdpred (nM) Kdpred. (nM) pKdpred (nM) Kdpred. (nM) pKdpred. (nM) Kdpred. (nM)

AM 93 7.6 23.6 8.9 1.1 9.3 0.4Archidonate 7.7 17.3 7.9 10.2 8.5 2.8Aseanostatin P5 7.6 24.8 8.4 3.7 9.0 0.9AT 346 7.2 62.4 7.8 12.6 8.2 5.8Boswellatea 7.3 42.6 7.6 20.6 8.0 8.6Boswellateb 7.4 35.6 7.7 16.0 8.1 7.1Boswellateg 7.5 30.1 7.7 17.5 8.1 6.6BRL-35390 7.5 27.0 8.0 9.1 8.5 2.6Bromopalmitate-2 7.8 15.2 8.3 4.7 8.9 1.1Calendulate 7.4 34.5 8.0 8.3 8.7 1.7Caprate 7.4 38.8 8.0 8.65 8.7 2.2Catalpate 7.4 35.1 7.8 12.6 8.7 1.9CD1530 7.1 71.6 7.9 12.4 8.2 5.4CD 1905 7.8 14.5 8.0 9.0 8.3 4.1CD 235 7.3 47.0 8.1 6.5 8.2 5.6Chaulmoograte 7.7 16.6 8.5 2.6 9.1 0.6Clomoxir 7.7 18.0 7.5 27.4 8.0 9.7CV 5356 7.6 21.6 8.2 5.7 8.8 1.3Dillenate E 7.2 63.1 7.2 55.8 7.5 26.1Dodecyl-benzene-sulphate 7.4 34.8 7.9 10.4 8.8 1.3Ethylthioundecanoate 7.5 30.5 7.5 27.2 8.0 9.9Haloxyfop 7.1 70.1 7.7 18.1 8.5 2.7Hexylsalicylate 7.3 45.8 7.4 39.0 8.0 9.8Laurate 7.5 26.8 7.8 15.4 8.4 3.2Mycomycin 7.4 33.6 7.6 25.0 8.1 7.1Myristoleate 7.5 30.4 7.8 14.7 8.7 5.3Namirotene 7.8 14.7 7.8 15.6 8.4 3.9ND-186 7.2 61.9 7.8 12.6 8.3 4.6ND-186-H5 7.5 27.6 8.1 6.7 8.4 3.4Pelretin 7.3 43.3 7.6 20.2 8.5 2.9RO-21-6667 7.2 56.7 7.6 22.8 8.1 6.6RP-64966 7.7 18.6 7.6 21.5 8.1 7.3SKF-105656 7.2 53.8 8.0 9.0 8.5 3.1SKF-7249 7.2 53.4 7.9 10.7 8.6 2.4SMR 6 7.4 39.7 7.9 10.7 8.5 2.6SR-3986 7.1 70.0 7.7 18.6 8.2 5.9SRI-4657-47 7.4 35.4 8.3 4.7 8.8 1.4Targretin 7.3 40.4 8.2 5.8 8.9 1.1Undecanoate 7.4 36.1 8.1 7.0 8.4 3.7Yohimbinesulphate 7.8 15.4 8.2 5.7 8.2 5.1


Table 3Training set for inhibitors of rat lens aldose reductase (compound, IC50 (nM))

N

N

F

BrCl

O

O

COOH

FK 366 (Ref. [27])

4.4

NS

NCl

N

O

O

COOH

ARI-1 (Ref. [28])

5.7

CF3

N

CH3

S

MeO

COOH

Tolrestat (Ref. [29])

11

NH

NH

O

FF

O

Imirestat (Ref. [29])

24

N

N

O

Br

F

COOH

Ponalrestat (Ref. [29])

16

S

NCH3

O

S

COOH

Epalrestat (Ref. [27])

23

O

NH

NH

F

O

O

O

NH

OMe

ARI-2 (Ref. [30])

33

O

N

COOH

AD 5467 (Ref. [27])

130

O

Br

SN

NH

O

OO2

M 16209 (Ref. [31])

120

OHO

OH

NO2

O

O

OCH2CH3

NH

ARI-3 (Ref. [29])

100

N

OHO

O

OCH2CH3

EBPC (Ref. [32])

840

NO O

COOH

Alrestatin (Ref. [29])

1500

O

O

OH COOH

ARI-4 (Ref. [29])

2200

HOOC COOH

TMG (Ref. [29])

2200

O

OH

OH

O

OH

OH

OH

Quercetin (Ref. [29])

1100

the receptor are more active than those with fewer. Aproblem that must be addressed is that since adversesteric interactions are not recognized during the auto-matic pharmacophore model generation, moleculesthat exhibit low activity due to ‘forbidden’ volume

should not be present in the data set. After model gener-ation, however, this information may be included inthe model by adding exclusion spheres manually.

Training set selection is the crucial step to asuccessful application of a HypoGen analysis. Theset must be populated by structurally diverse repre-sentatives covering an activity range of at least fourorders of magnitude. Taking into account theserequirements, we defined a training set of 15compounds with IC50 values for rat lens aldose reduc-tase ranging from 4.4 nM to 2.2mM (Table 3).

Conformational models of the training setcompounds were generated using a Monte Carlo likealgorithm together with poling [33]. The number ofconformations generated for each compound and usedfor hypothesis generation is given in Table 4.

From the set of generated hypotheses, the first twoperformed the best regarding their predictive capacity.We selected them as queries for 3D-database search.Both models exhibit two hydrogen bond acceptorfunctions and differ by the presence of one hydro-phobic and one negative ionizable region for modelA, and two hydrophobic features for model B (Fig. 3).


Fig. 3. Two best CATALYST hypotheses generated for ARIs.

Table 4Conformers of the molecules of rat lens

Compound Conformers

FK 366 108ARI-1 149Tolrestat 162Imirestat 4Ponalrestat 89Epalrestat 37ARI-2 69AD 5476 50M 16209 33ARI-3 83EBPC 20Alrestatin 21ARI-4 8TMG 118Quercetin 6

As shown in Table 5, both models are able topredict the affinity of the molecules in the trainingset well (model A:r � 0:954; model B:r � 0:948�:

To probe further the prediction capability of bothmodels, we predicted activity data of a test setcontaining five compounds (Table 6).

Finally, model A was used as a query to search theDerwent-WDI database. When a large database issearched with a relatively simple pharmacophorequery that contains three or four features, the mostlikely outcome will be very large hit lists of severalhundreds of compounds and the user is then facedwith a generally unpleasant task of evaluating thesehuge lists. Different possibilities of limiting thenumber of hits have been proposed [8]: for example,raising the fit number referring to how well a hitconformer maps the chemical features of the querytogether with the use of a molecular weight filter,has been shown to limit considerably the number ofhits up to enrichment factors of 60. Another possibi-lity consists in the manual inclusion of excludedvolume spheres into the hypothesis. In the presentstudy, we used the second approach, since informationon the 3D structure of the enzyme is available. Thehypothesis was fitted manually into the active site ofaldose reductase using the co-ordinates of thecomplex tolrestat–porcine aldose reductase (pdb-fileentry 1AH3) [34] and the co-ordinates of the alphacarbon atoms of all amino acids surrounding the

binding pocket were used to define centre points forexclusion volume spheres with a variable radius(Fig. 4).

Using this approach, the number of hits can bereduced considerably: Starting from 3967 hits usingmodel A as search query, a reduction of approxi-mately 73% of the hit list (1073 hits) was observedwhen exclusion spheres with a radius of 3.5 A˚ wereadded to the query. This number was increased further(approximately 92% of reduction 301 hits) with theuse of a radius of 4.0 A˚ and to six compounds withspheres of 4.5 A˚ . Even though the number ofcompounds has been drastically reduced, the utilityof the last set remains questionable, since no knownARIs are among the six compounds was retrieved. Inthe list containing 301 structures, several moleculesknown to exhibit aldose reductase inhibitory activitywere found.

Another possibility of limiting the number of hitsresulting of a 3D-database search is the use of a so-called molecular shape parameter. This method issomewhat more flexible than the use of excludedvolumes: the CATALYST shape is defined by a setof 3D co-ordinates, each with a corresponding radius.These points are the centres of atoms defined in aquery molecule and the radius associated with eachpoint corresponds to the atom type. The resultingvolume in space is defined by the union of a set ofspheres and can be merged with a hypothesis to createa combined query. During a search, each candidatemolecule is converted to a similar volume and fittedboth to the hypothesis and the shape spheres. Thesimilarity of the shape query to the molecule iscomputed as the volume of the intersection dividedby the volume of the union of the aligned shapes.Depending on the similarity tolerance that is set, ahit may have more or less volume that protrudes


Table 5Prediction of training set activities by model A and B

Compound Activity(nM)

Estimated activity(nM) model A

Estimated activity(nM) model B

FK 366 4.4 5.2 16ARI-1 5.7 4.1 5.3Tolrestat 11 26 13Imirestat 24 35 27Ponalrestat 16 22 44Epalrestat 23 33 27ARI-2 33 34 36AD 5476 130 250 62M 16209 120 52 29ARI-3 100 110 88EBPC 840 280 990Alrestatin 1500 1500 1100ARI-4 2200 2800 2800TMG 2200 480 990Quercetin 1100 2900 1300

Table 6Test set activity prediction by model A and B

Compound Activity(nM)

Estimated activity(nM) model A

Estimated activity(nM) model B

1 62 6.7 642 15 3.9 113 50 7.4 914 41 15 105 24 8.6 13


Fig. 4. Model A together with the excluded volume spheres (radius 3.5 A˚ )

Fig. 5. Model A combined with the shape defined by compound FK366.

from the shape. The result of this fuzziness is a veryrobust form of searching.

We defined a combined query using hypothesismodel A together with the shape generated with thestructure of compound FK366, the most activecompound of our training set (Fig. 5). Searching theDerwent-WDI with this query yielded a hit listcontaining 176 molecules: Expectedly, known potentARIs that had not been included in the training set likeAD2484, zenarestat, and zopolrestat were presentamong the hit list.

5. Summary and conclusions

In this paper, we have shown the interest and theuse of feature-based pharmacophores focused on theproblem of lead structure search within the drugdevelopment process. The main originality of suchmodels resides in their nature—the pharmacophorichypotheses are described by common chemical featuresthat complement the functionality present in biologi-cally interesting receptors—and not only by atoms.We have used two examples to illustrate how suchmodels can constitute powerful tools to establishquantitative structure–activity relationships as wellas search queries for 3D databases of structures tofind compounds with interesting drug developmentpotential. The methodology described in this paperallows to retrieve compounds from large databasesbelieved to exhibit a certain affinity for a given biolo-gical target and to predict the biological activity,provided a quantitative hypothesis model has beenestablished. This concept may also be extended tovirtual 3D databases and therefore may be used as afiltering method for the design of combinatorial chem-istry libraries.

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chemical function based pharmacophore models as suitable filters for virtual 3d-database screening

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