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`

`fd Cherm tak Compt: Set. QE 42: BOIBIR
`
`869
`
`Siructure-Based Classification of Antibacterial Activity
`
`Mark-T.D. CroninAviOF Aptia' John -C. Dearden! Judith-C. Ditty," Tatiana dl: Netzeva,!4
`Hiren Patel. Philip HO Rowe:? Ts Wavrie: Schulte)! Andrew Po Worth,"
`Konstantinos Voutzoulidis,” and: Gerrit Sehitriann®
`
`School of Pharmacy andl Chemistry, Liverpoal John Moores University, Byraar Sireet:
`Liverpool, [3 SAP England. Departtieit of Chemical Eeatoxieoligy: UFZ Cane fot Eevinaiedtal
`Research, Permosertuusse 15, D-04318 Leipzig: Germany, Department of Chemisity, Pagaly of Pharmacy,
`MU>Sofia, 2 Denav Street, 1000 Sofia, Bulgaria, Department of Comparative Medicine, College of
`Veterinary Medicine; [he University of Tennesaer, 2407. River: Brive, Knoxville, Tennessee 37996-4500, and
`ECVAM,Institute for Health & Consumer Protection, Joint Research: Centre, European Commission,
`21020: Tspra (WA), leaky
`
`Received January 3; 2002
`
`‘The aim of this stidy was to. develop-a simple. quantilative structureactivity. relationship (OSAR) for the
`classification and prediction of antibacterial activity,.a0 as'to enable dy 'sifico screuniag: Toth! s énd'a database
`of b6L compounds, classilied according to whether dhey bad: anibadtérial activity, and. tor which-4 total of
`167 physicochemical and stractural deseriptors were: ouleulated, was analyzed: To identity descriptors that
`alowed Separation oFihe two Classes (Ce, “thdse coripounds with dnd witout antibachoril dchvity); analwels
`of variance was lived and models were developed using linear digeriminant.and binary logistic tegression
`analyses, Model predictivily ag assedsed andValidated by the raiddm veaigval of 30% of the cdmpounda
`to fora a teat get, Tor which predictions were made fione theamedel, The results ot the analyses indicated
`thal six descptors; accounting for hydrophobicity andinter and inramolecilar hydrogen bonding, provided
`excellent Séparation af the datLogisis regression. anadlygia owas, Show to model the slate slightly more
`accurately: than discriminant analysis.
`
`toc ellninaté compounds: ahat
`terial braves ‘aee-sereened:
`would be considered-notto:be biowvallable, Soveral quantita:
`tive, supchirecadtivity telationehips Nave also been developed
`to predict oavailabiity per se, These attempts will’ always
`be. Tosited: by acauniber cet! factors: Including the, lintited
`availability (of
`‘date: (particularly: for poorly: bidavailable
`drugs), the considerable experimental: etrot-andinterlaboru:s
`tory wvarlabilty: presentwithin the:-data, and:
`the:
`intrinsic
`problerhs associated with the modeling of such a coimplex
`phononienon with relatively Siviplisne models; The -ara-of
`ADME prediction has: been well reviewed. recently by Ekins
`tial?
`
`INROOUCTION
`Corbinatorial libraries: are. generally stractmrally diverse
`gollections of chamicals and have become the’starting plate
`Tor many dig discovery-progranis From: such hibraries, often
`ot 700.0005 of gompounds, lead conipounds may. be. |denti-
`fied? High throughput and ultrahigh throughput sereening
`HTS and URTS) are methods.to: identify such veadscom«
`peundsHowever, whilé these sisthods. are. rabid, they are
`still relatively costly, aad for nany pharmacaldgical -activi-
`Hes, HTS endpoints may not be-available, WHi-that ia. mand,
`tere has bed considerable interhst tn the in silico identitica-
`fica, of pharniscologically active compounds,7
`in-silicg scteeniig stidies, particularly tor ise in conjune-
`fon with Combinatorial chemistry have taken a number’ of
`routes, A number of workers have altempled ty assess how
`“drup-like”: a.molecole is. Perhaps best known amorig these
`studies is: Lipinski's: Crule of five which las. been applied
`bya large pumber ‘of companies.” The “rile of five’ is
`intenied-to identity qualitatively potertial leads that are not
`capable of uptake following oral administration, The Lipinaki
`role-hes been used by many sesearchers to prediét whether
`
`Wlead daig will be bioavailable, and often novel covnblaa-
`iioannuus
`si
`
`P08; lass ch dd (0h
`* Gorresponding author phone:
`t+ 44 (0) 154
`LAL SS 210, sounails
`im,Leraniuehyjim agatk,
`Uhiverpad) JabMiotes Univerany,
`EUR2 Centre for Envronmental Reseda
`SeeSofa,
`:
`The: University of Tennesse,
`VECVAM, Instiute for Health & Consumer Protection; Joint Researth
`ence, European Cornmilssion.,
`
`Other atteripis:to screen combinatorial Hivariés if sthéo
`have involyed the idenufication of compounds. with specific
`phariaacolopical activities, Over-the past decade there have
`been many: approaches Including the use of pharmacephore
`searching of databases.’ The search tor Compounds with a
`given activity.
`is appropriate when. the phartnacaphiore is
`Known, and when. pharmacological activity js relate to
`interaction ata. staple receptor site. Many examples of the
`shocesstil ise ofphannacophorg-based searching abound in
`thé. Vteratdee O° However, Yor soine "pharmacological
`activities, receptors have: nor been déevermined of Bclivity may
`not: result, from: binding ata patticular receptor, dn such
`iistances. 101s fot. welsrally possible to. search for pharma
`oophores.
`‘fo -siroumvent the-use-of pharmacophores: for database
`and: eotbinatorial brary. screening: effort has alae. been
`POCO e10850 GOD, 299.a)
`© 2002 Awerican Chemien) Society
`Published:on. Web: Q6/07/2002
`
`This material may be protected by Capyright law (Title 17 U.S. Cade)
`
`
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`

`S700 1 Chem: (ag Comput Sei, Vol 42, Noo d, 2008
`
`placed inte the developinenr of structure-based Classtfioation
`methods dorQSARs), uilliging pabern mecognition techniques
`1: predier
`-Blolbplaally. active. miolecules,|* SVBile lbesd
`methods are-often used to predict the poteney of digs (Le.
`a contirdous response), they may also be utilized to predict
`whether wscompound is active Ge. a Calegonic response)"
`Such approaches are commonplace in predictive toxicology
`for the cathnation of endgame sich iva careihogenicnty, eye
`irritadon; ete." Recent elfarisand new trends indhe use of
`OSARS to. predict, pharmucologi¢al activites have been
`reviewedrecently)?
`Compounds wath antibacterial activity: are ao class of
`phirridceutiodly (hal clearly do not shake: a’ common mode
`of action: Murthermore) there is nd: “typical” antibacterial
`pharniicentionl compound, ane they. are strenitally diverse)?
`Therefore development-of a single phartnacopnore and is
`
`tide in Screening are unlikely tobe
`successful. Despite: this,
`‘fonds:Vert etaldeveloped i dlasvi fication model for the
`prediction of aritibactertal Gamipounds oa the basis ofa neural
`network ‘trained upon 62 descriptors inchiding Aumbers cf
`oarticnlar atoms and! tepolodieal infuamation: There area
`dumberof advantages and disadvantagesto the ase of neural
`networks: (AOSARES: Maucal cuetworks dine
`“eapabhe: Of
`ispdeling worlihedt rlationehips much better than are sinietly
`linger techinidies: such as Rearession analyais or diseriminant
`anilysis. However, desnite then utility neural networks rin
`a‘significait tisk of overtraining: Pudthermote; they ane sot
`transparent: he. the developer is iol ablilé to view, compre:
`hend:: Ge
`interpret:
`the ‘relationship “benyeen -aouvitys and
`structural properties, as can be done when aanote empitical
`
`technique’ suchas regression. “or Miscominant analysis 18
`applied”?
`The aii ofthis verte, therefore, Was tb develop4
`the prediction: of
`for
`(or QSAR)
`classification model’
`antibacterial icnvityantizing trangporent and easily portable
`techniques. Te accentiate further the simplicity of the
`approach, 2-Dstructural descripiors were utilized to develop
`rmodels:"Tovocomplementiity statistical techniques appropri:
`ate for the classification of categorie data, discrininant and
`biliary lopigths: celpeession: afialvsie, were applied:develop
`trahapareht and! Interpremble models”
`
`METHODS.
`
`total of “661 compounds were
`Biological Activity. A”
`classified ns-etther-having: or lacking antibacterial activity,
`‘These classifications wert recorded origmally by: Tomda-
`Vert et al? Biological data weretuken as this fundamental
`categorie response) analyses With quantitalive data were ot
`attempted The compounds are ulliged, aol their classified:
`tions are listed ja Table 1. TW should be aoted thal the data
`sets asymmetric with fewer active dein inactive compounds,
`Calculation of Physicochemical Bescriptors: The names
`of the drugs were taken from. Tomds-“Vert etal”) strucnires
`weeConvertedinto SMILES: sttings®: for the calealatlotof
`21) physicochemical and structural destriptois, The loga-
`thm ofthe. achinolwiter partion coethoient war pbiained
`ublig:
`the “ClagP’
`for’ Windows: Oversion DLGQ) -soflware
`(Biobyte Corp. Claremont CA}, measured values being used
`in phaference! bo daleuliad values,
`SMILES “strings were:
`then centered “inte The, OSARIs
`Wwerl 1) sottware (SeiVision: Academic Press, San Dingo;
`
`
`
`CRONIN BE AL:
`
`the: calénlation of 240° 25D. descriptors of the
`CAS for
`structural, steric, clécioaic, and bydroeen bonding Hilutes
`of the moleciiles: Exanination’ of the descnplory calculated
`pdieated that.64 hada value-ot0 for inore than 95% ofthe
`compounds. Ge.
`they contained litle if any,
`imeaningty
`ieformalion), “To eisure btatihtieal Vahdlty, these denenpints
`were omitted frony the unalyess, so that 1662-0 deserintors
`in addition36 Ing P wer used. A sormmary ofthe descriptors
`equlied is given in Table 2.
`Statistical Analyses, Preselection of Varlables. Priorta
`statistical analysis for the classification ofactivity, deseniptors
`were. selected on the basis of their being able (o discriminate
`between antibacterial and nonantibactertal activity, Selection,
`maybe perfonmed by qniember-of approaches. La this study,
`oy ANAIVSS ct valnice ANCONA) ite performed ion)
`each desoniptor (prior to Clagaifiention mexioling) to. determine
`those that were best able to saparete ihe two. classes of
`compounds. This texbniqne was found to: compare Javorably
`io the stepwise: detenmination of deseriptors trom):
`for
`inslanes, siepwise discriminant analysis, ANOVA was
`perforiied “Oa cach variable using’ SPSS “for: Windows
`statistical software: (ver LO.Oc SPSS Ane, Gea. Lae did
`the Foratios were recordail, The deserptors found tobe the
`mostManificant from ANOVA were chosen for the develop
`iment of the clasdification models despribed below.
`Classification, Once significant descriptors for the. clas
`SHRGAHOs: OF activity. Gad been eitablished, wo stodsiical
`techniques wert applied to developdunetions io classify the
`
`compounds:
`linear discriminant analysis (LDA) and: binary
`
`jogistic serression TBUR). Bothtechniques are highly suited
`tothe tandeling of carezorio: date? Por the purposes: of
`qddeling. a value of 1 was assinnedto compounds with
`dntibactérial activity, and a value ot O assigned to those
`lacking scuvity: Linear diseriraiiantanaiys|s was performed
`usingthe SPSS slatistion| software, Using this package, prior
`probabitities were computed: trou proup $ize-(0.020" and
`O77 for the onantibactenials andantibacterials respectively)
`and Withit Srdup cowatlined inalnik was used Results are
`roportedbas' aperceituge-at Coneéctly ¢lanaited cases. Binary
`Jogistic. repression. was alse. performed, using “ie SPSS
`statigneal sotiware, on the virlables deemed to be iniporiant
`forthe classification of bulogsieal aetivity Resalig: are
`reported a8 a percentage of correctly classified: cases: ata
`
`probability culofl value oF 03.
`Validation, The models were initially usedtoelassily the
`compounds in the complete training set, While this provides
`some assessment af the goodness oft of a model. i does
`not privide athorough and. independant assessment of now
`a model. May predich new. compounds. To. assess, such
`prediodwity the ase of a test sets essentialWith suea
`techitiques:
`a: proportions oF he data tis “removed before
`inodeling {the test set), and the remaining data (ihe training
`sen are utilized: fo aake “predichons. Vo “alleviate: die
`problems -ot-a-prior-identification: of the test and. training
`Sotaaihis stinly 30% oF the ‘compounils were randamly
`vonved: (a fonw the test. set) Mand he remaining 70% of
`compounds: were -utilived to: formulate: nomodel. This: pro
`
`ceak Wale TepeatedtolCol TO tinige 10 Gene Mar chance
`effects in the random selection of variables: were elit:
`Hated,
`
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`

`
`
`
`
`
`J Chem: Inf Comput, Stig Vol. 22 No04, 2002 RTL
`CVBRIAL CLASSIFICATION
`
`
`
`
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`
`UCB Biopharma SPRL(IPR2019-00400)
`Exhibit 2029 Page 5
`
`UCB Biopharma SPRL (IPR2019-00400)
`Exhibit 2029 Page 5
`
`

`

`CRONIN ET AL,
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`
`UCB Biopharma SPRL(IPR2019-00400)
`Exhibit 2029 Page 6
`
`UCB Biopharma SPRL (IPR2019-00400)
`Exhibit 2029 Page 6
`
`

`

`
`
`
`
`
`petad CLASSIEICATION
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`
`
`
`UCB Biopharma SPRL(IPR2019-00400)
`Exhibit 2029 Page 7
`
`UCB Biopharma SPRL (IPR2019-00400)
`Exhibit 2029 Page 7
`
`

`

`B74. L Chon daft Compal. Sd, Vol. 4h, No, 4 2008
`CRONINGET AL,
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`
`UCB Biopharma SPRL(IPR2019-00400)
`Exhibit 2029 Page 8
`
`UCB Biopharma SPRL (IPR2019-00400)
`Exhibit 2029 Page 8
`
`

`

`
`
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