International Journal of Applied Science - Research and Review ISSN 2394-9988

2018Vol.5 No.2:10

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Research Article

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DOI: 10.21767/2394-9988.100075

Naveeda Riaz1, Anum Shahbaz1 and Saima kalsoom2*

1 DepartmentofBioinformaticsandBiotechnology,InternationalIslamicUniversity,Islamabad,Pakistan

2 (SA-CIRBS),FacultyofBasicandAppliedSciences,InternationalIslamicuniversity,Islamabad,Pakistan

*Corresponding author: Saimakalsoom

genious_chemist@yahoo.com

(SA-CIRBS),FacultyofBasicandAppliedSciences,InternationalIslamicuniversity,Islamabad,Pakistan.

Citation: RiazN,ShahbazA,KalsoomS

(2018)LigandBasedPharmacophoreModelDevelopmentfortheIdentificationofNovelAnti-PsychoticDrugs.ApplSciResRevVol.5No.2:10

Ligand Based Pharmacophore Model Development for the Identification of

Novel Anti-Psychotic Drugs

AbstractBipolardisorder(BD)isaseverebiologicaldisorderthataffectingapproximately1.2%oftheadultpopulation.Itcanbeconfusedwithotherdisorders,includingavarietyofanxietydisordersandpsychoticdisorders.Personswithbipolardisorderalso suffer repeatedly frompsychiatric disorders that are “comorbid”with thebipolar illness. Antipsychotics are recommended for usewith the schedules ofmanic or depression episodes with mental illness, and as likely aides in non-psychoticepisodes.Antipsychoticdrugsolanzapineandrisperidoneweregenerallychosen over conventional antipsychotics. A ligand-based pharmacophoreapproach has been used for twenty compounds for designing new drugs byusing ligand scout software and distance estimation by using VisualMolecularDynamics (VMD). Four pharmacophoric features i.e. two aromatic ring, onehydrophobic center and one hydrogenbond acceptor featurewere identifyingforantipsychoticdrugs.Pharmacophoricmodelsarethenappliedondatasetoftwentycompoundswithknownantipsychoticactivity.AllcompoundswerefilteroutbyusingPharmacophorebasedvirtualscreening.Thispharmacophoricmodelcanbefurtherusedforidentificationofnovelantipsychoticdrugs.

Keywords:Ligandscout;Bipolardisorder(BP);Antipsychotic;Pharmacophore

Received: March06,2018; Accepted: June01,2018; Published: June09,2018

IntroductionBipolardisorder isachronicpsychiatric illnesscharacterizedbydepressionandatleast1manicorhypomanicepisodeduringthelifetimeof the illness [1]. In this disease illness vary frommilddepressiontosevereformofmania,andeffectbothsexesequally.,environmentalandneurochemicalfactorsareresponsiblefortheprognosis of this disease. 3 major neurochemicals involve areserotonin,dopamineandnoradrenaline.Itoccursinspecificpartofbrainandinducedepressionandmaniaasspecificsymptoms.Misspecificationofwhohasadisease(lowspecificity)isgenerallymore damaging to scientific inference than under detection(low sensitivity) [2]. 29 standard drugs were selected in thisresearch having anti-depressant and anti-psychotic activities.Many drugs have beenmarketed for the treatment of bipolardisorder. Using pharmacophoric patterns comparative analysisofthesedrugswaschecked.Asapharmacophoremodelshouldbe able to discriminate between molecules with and withoutbioactivity,thesetofmoleculesshould includebothactiveandinactive compounds. Different classes of compounds called

antipsychotic, anticonvulsant, selective serotonin reuptakeinhibitors (SSRIs), serotoninnorepinephrine reuptake inhibitors(SNRIs), norepinephrine and dopamine reuptake inhibitors(NDRIs),noradrenergicandspecificserotonergicantidepressants(NASSAs), Cyclics, and monoamine oxidase inhibitors (MAOIs)were studied and their structureswere also obtainedby usingChemDrawalongwiththeLogP,molarrefractivity(MR),criticalvolume (CV) and molecular weights (MW) values. Out of 29compoundsonly20compoundswereextractedfordrug-likenessevaluation.

Pharmacophoremodels are useful for designing lead structureand identified also for Binding explaining site (Khan et al.).Ligand-basedmethodsuseonlyligandinformationforpredictingactivity depending on its similarity/dissimilarity to previouslyknownactiveligands.Itreliesonknowledgeofothermoleculesthatbindtothebiologicaltargetofinterest,whichmaybeusedtoderiveapharmacophoremodelthatwilldefinetheminimumnecessarystructuralcharacteristicsamoleculemustpossess tobindtothetarget.Itreliesonknowledgeofothermoleculesthatbind to thebiological targetof interest,whichmaybeused to

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International Journal of Applied Science - Research and Review ISSN 2394-9988

derive a pharmacophoremodel that will define theminimumnecessary structural characteristics a molecule must possessto bind to the target. A Pharmacophore may be defined asthe essential geometric arrangement of atoms or functionalgroups necessary to produce a given biological response.IUPAC defines pharmacophore as: the ensemble of steric andelectronic features that is necessary to ensure the optimalsupramolecular interactions with a specific biological targetstructure and to trigger (or to block) its biological response.Recent pharmacophore models can be classified into twocategories: receptor-based pharmacophores and ligand-basedpharmacophores.Forareceptorwithaknownthree-dimensionalstructure, receptor-based pharmacophores have been studiedwhicharebasedonthefamousconceptofakeyforthelock[3].Pharmacophoremodels areuseful fordesigning lead structureandforexplainingpossible interactionsofanidentifiedbindingsite.ManyinvestigationsindicatedthatthepresenceofatleastoneHydrophobicUnit,OneOrtwoelectrondonoratoms,and/or anNHgroup in as special spatial arrangement seems tobenecessaryinthestructureofanticonvulsants[4].Inrecentyears,technologicaladvancesinvirtualscreeningmethodologieshaveallowedmedicinal chemists to rapidly screendrug libraries fortherapeuticactivityagainstnewbiomoleculartargets inacost-effectivemanner [5]. Adverse effects are the leading cause ofconcerninanymedicatedregimens[6].In silicoapproachescanhelpustodesigndrugswithmoreefficacyandlesssideeffects.

MethodologiesDrawing of structuresThestructuresof29compoundsweredrawnusingChemDrawUltra8.0(CambridgesoftCorp(www.cambridgesoft.com),USA)andsavedonMOLformat.TheyareshownintheTable 1.Twentycompoundsweretakenoutof29forfurtherevaluation.

Pharmacophore generationsLigand scout is a software tool that allows to rapidly andtransparentlyderive3Dpharmacophoresfromstructuraldataofligandsinafullyautomatedandconvenientway.Thealgorithmsare scientifically published and based on several years ofexperienceinpharmacophorecreation[7,8].

Thereare somegeneral steps that shouldbe followed for thisactivitytheworking.Suchas:

• Input a set of collected compound structures of drug-likemoleculesthatareknowntobindthereceptor.

• These compoundsweredrawnonChemDrawand saved inMOLfile.

• Alltheparametersweresettodefault.

• Once the pharmacophore of all compoundswas identified,theligandsarethensuperimposed.

• Superimposition allows the pharmacophore elements tooverlap and a common template i-e the pharmacophoremodelisidentified[9-13].

Thepharmacophoremodelwillthenbetestedusingavalidationset of knownpharmacophore compounds available for bipolardisorder.

Visualization and distance calculationVMD is designed for modeling, visualization, and analysis ofbiological systems such as proteins, nucleic acids, lipid bilayerassemblies, etc. VMD provides a wide variety of methods forrendering and coloring a molecule: simple points and lines,CPKspheresandcylinders, licoricebonds,backbone tubesandribbons,cartoondrawings,andothers[14-20].

Result and DiscussionPharmacophorePharmacophorewasdefinedbyPeterGund:“asetofstructuralfeaturesinamoleculethatisrecognizedatareceptorsiteandisresponsibleforthatmolecule'sbiologicalactivity”.Thismoderndefinitionisremarkablyloyaltotheearliestdefinitions.Perceivingapharmacophoreisthefirstessentialsteptowardsunderstandingtheinteractionbetweenareceptorandaligand.(Tables 2 and 3)onceapharmacophoreisestablished,abeneficialuseofitis3Ddatabasesearchingtoretrievenovelcompoundsthatwouldmatch thepharmacophore,withoutnecessarilyduplicating thetopologicalfeaturesofknownactivecompounds(henceremainindependentofexistingpatents)(Figures 1-5).

Lipinski’s rule of fiveTheLipinski’sruleoffivecontainscertaincriteriaoffeaturesofcompounds in specificamount.TheTable 4 shows thegeneralformofLipinski’sruleoffive.

ConclusionPharmacophoremodelcanbeamultipurposetoolwhichaidsinthediscoveryanddevelopmentofnewleadcompounds.Inthisstudy,pharmacophoremodel for20newchemical compoundswerevalidated.Aligand-basedpharmacophoreapproachisusedforthepredictionofantipsychoticactivitytogetthecompoundsthat are biologically active, and this eventually leads to anantipsychoticdrugwhichisdoneusingLigandScout.Thegeneralpharmacophore consisted of three main features namely,hydrophobic unit, hydrogen bonding acceptor and aromaticrings.ThederivedpharmacophoremodelsarethenfilteredusingLipinski’s rule of five andorally bio-available compoundswereobtained. These compounds will be use for the treatment ofbipolardisorders.

2D and 3D pharmacophore model of compoundLamotrigine.Pharmacophorefeaturesarecolor-coded.Hydrophobic features (Yellow spheres), Hydrogen-Bond acceptors (Red spheres) and Hydrogen-Bonddonorgroups(Greenspheres).

Figure 1

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2D and 3D pharmacophoremodel of compound Aripiprazole. Pharmacophore features are color-coded. Hydrophobic features(Yellowspheres),Hydrogen-Bondacceptors(Redspheres)andHydrogen-Bonddonorgroups(Greenspheres).

Figure 3

2D and 3D pharmacophore model of compound Quetiapine. Pharmacophore features are color-coded. Hydrophobic features(Yellowspheres),Hydrogen-Bondacceptors(Redspheres)andHydrogen-Bonddonorgroups(Greenspheres).

Figure 2

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Suggested distance estimation of compounds showing that the model comprises aAromatic1toAromatic2indistancerangeof6.20-6.95,Aromatic1toHBAindistanceranges of 5.11-5.98, Aromatic 2 to HBA in distance range of 3.20-3.90, Aromatic toHydrophobic in ranges 3.23-3.92, Aromatic 2 toHydrophobic in Ranges 4.27-4.97 andHBAtoHydrophobicindistancerangeof6.05-6.75.

Figure 4

3-DimensionalorientationofsuperimpositionofcompoundLamotrigine(teapink),compoundPerphenazine(orange),compoundClonidine(purple),andcompoundEscitalopram(turquoise).

Figure 5

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Table 1ListofcompoundsshowingIUPACnamesandstructures.

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7© Under License of Creative Commons Attribution 3.0 License

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Compound No. No. of HBA No. of HBD No. of Ar No. of Hyd1 3 2 2 32 4 1 4 23 2 1 3 44 2 1 3 35 5 0 3 36 1 1 2 37 2 2 2 28 5 0 4 39 2 1 4 2

10 1 1 3 312 4 1 2 314 1 1 2 416 1 1 2 317 2 1 2 219 2 1 2 221 3 0 3 322 3 0 3 423 1 1 4 325 2 1 2 226 6 1 4 3

Table 2 3DPharmacophoricfeaturesofselectedcompoundsshowingnumberofhydrogenbondacceptor,hydrogenbonddonor,aromaticringsandhydrophobicunitsagainstantipsychoticdrugs.

Interactionsofdifferentcompoundshavebeenobserved.

Compound No Ar1-Ar2 (nm) Ar1-HBA Ar2-HBA Ar1-Hyd (nm) Ar2-Hyd (nm) HBA-Hyd(nm) (nm) (nm)

1 6.95 5.92 3.9 3.9 4.76 6.352 6.73 5.77 3.28 3.87 4.97 6.093 6.59 5.48 3.56 3.42 4.51 6.264 6.67 5.11 3.58 3.92 4.39 6.055 6.7 5.13 3.3 3.25 4.29 6.076 6.52 5.58 3.8 3.91 4.4 6.597 6.32 5.98 3.64 3.87 4.83 6.448 6.66 5.88 3.7 3.4 4.8 6.39 6.84 5.9 3.46 3.86 4.27 6.52

10 6.74 5.47 3.76 3.23 4.54 6.7512 6.41 5.9 3.58 3.41 4.64 6.3614 6.23 5.5 3.6 3.84 4.74 6.516 6.92 5.75 3.81 3.89 4.95 6.4217 6.3 5.9 3.6 3.27 4.38 6.2919 6.2 5.94 3.78 3.39 4.47 6.5921 6.45 5.86 3.3 3.25 4.28 6.0822 6.55 5.1 3.86 3.84 4.57 6.7423 6.82 5.72 3.71 3.33 4.96 6.3525 6.24 5.95 3.42 3.91 4.36 6.5826 6.48 5.25 3.56 3.44 4.87 6.66

Table 3Pharmacophoricdistancesofeachtwentycompounds.

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Properties RangeHBA ≤6

Aromatic ≤4Hydrophobic ≤4

LogP <5Molwt. <500

Table 4 Lipinski’sRuleoffiveshowinggeneralform.

Inthisstudy26compoundswereobservedunderthefulfillmentofLipinski’sruleoffive.

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