Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloCaco-2 cell permeability modelling: a neural network coupled genetic algorithm approach
Anno di pubblicazione2007
Autore/iDi Fenza* A., Alagona G., Ghio C., Leonardi R., Giolitti A., Madami A.
Affiliazioni autoriD.F.A., A.G. & G.C.: Molecular Modelling Lab, Institute for Physico-Chemical Processes (IPCF) - CNR, Via G. Moruzzi 1, 56124 Pisa, Italy; L.R.: Ion Trading s.r.l., Via S. Martino 52, 56125 Pisa, Italy; G.A.: Menarini Ricerche SpA, Via Sette Santi 3, 50131 Firenze, Italy; M.A.: Menarini Ricerche SpA, Via Tito Speri 10, 00040 Pomezia, Roma, Italy
Autori CNR e affiliazioni
AbstractThe ability to cross the intestinal cell membrane is a fundamental prerequisite of a drug compound. However, the experimental measurement of such an important property is a costly and highly time consuming step of the drug development process because it is necessary to synthesize the compound first. Therefore, in silico modelling of intestinal absorption, which can be carried out at very early stages of drug design, is an appealing alternative procedure which is based mainly on multivariate statistical analysis such as partial least squares (PLS) and neural networks (NN). Our implementation of neural network models for the prediction of intestinal absorption is based on the correlation of Caco-2 cell apparent permeability (P(app)) values, as a measure of intestinal absorption, to the structures of two different data sets of drug candidates. Several molecular descriptors of the compounds were calculated and the optimal subsets were selected using a genetic algorithm; therefore, the method was indicated as Genetic Algorithm–Neural Network (GA-NN). A methodology combining a genetic algorithm search with neural network analysis applied to the modelling of Caco-2 P(app) has never been presented before, although the two procedures have been already employed separately. Moreover, we provide new Caco-2 cell permeability measurements for more than two hundred compounds. Interestingly, the selected descriptors show to possess physico-chemical connotations which are in excellent accordance with the well known relevant molecular properties involved in the cellular membrane permeation phenomenon: hydrophilicity, hydrogen bonding propensity, hydrophobicity and molecular size. The predictive ability of the models, although rather good for a preliminary study, is somewhat affected by the poor precision of the experimental Caco-2 measurements. Finally, the generalization ability of one model was checked on an external test set not derived from the data sets used to build the models. The result obtained is of interesting practical application and underlines that the successful model construction is strictly dependent on the structural space representation of the data set used for model development.
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Pagine da207
Pagine a221
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RivistaJournal of computer-aided molecular design
Attiva dal 1987
Editore: ESCOM Science Publishers - Leiden
Paese di pubblicazione: Paesi Bassi
Lingua: inglese
ISSN: 0920-654X
Titolo chiave: Journal of computer-aided molecular design
Titolo proprio: Journal of computer-aided molecular design.
Titolo abbreviato: J. comput.-aided mol. des.
Titolo alternativo: Computer aided molecular design
Numero volume della rivista21
Fascicolo della rivista-
Verificato da referee-
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)-
Parole chiaveNeural network, Genetic algorithm, Caco-2 cell permeability, Intestinal absorption, Oral bioavailability
Link (URL, URI)-
Titolo parallelo-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioniElectronic supplementary material The online version of this article (doi:10.1007/s10822-006-9098-3) contains supplementary material, which is available to authorized users.
Strutture CNR
  • IPCF — Istituto per i processi chimico-fisici
Moduli/Attività/Sottoprogetti CNR
  • MD.P01.013.001 : Modellizzazione di proprietà e reattività di molecole biologiche e biomimetiche
Progetti Europei-

Dati storici
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Area disciplinarePhysical Chemistry/Chemical Physics
Area valutazione CIVRScienze chimiche