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Pregled bibliografske jedinice broj: 639167

Accurate Models for P-gp Drug Recognition Induced from a Cancer Cell Line Cytotoxicity Screen


Levatić, Jurica; Ćurak, Jasna; Kralj, Marijeta; Šmuc, Tomislav; Osmak, Maja; Supek, Fran
Accurate Models for P-gp Drug Recognition Induced from a Cancer Cell Line Cytotoxicity Screen // Journal of medicinal chemistry, 56 (2013), 14; 5691-5708 doi:10.1021/jm400328s (međunarodna recenzija, članak, znanstveni)


Naslov
Accurate Models for P-gp Drug Recognition Induced from a Cancer Cell Line Cytotoxicity Screen

Autori
Levatić, Jurica ; Ćurak, Jasna ; Kralj, Marijeta ; Šmuc, Tomislav ; Osmak, Maja ; Supek, Fran

Izvornik
Journal of medicinal chemistry (0022-2623) 56 (2013), 14; 5691-5708

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
P-gp ; ABCB1 ; drug efflux ; cell line screen ; chemoterapy resistance

Sažetak
P-glycoprotein (P-gp, MDR1) is a promiscuous drug efflux pump of substantial pharmacological importance. Taking advantage of large-scale cytotoxicity screening data involving 60 cancer cell lines, we correlated the differential biological activities of 13 000 compounds against cellular P-gp levels. We created a large set of 934 high-confidence P-gp substrates or nonsubstrates by enforcing agreement with an orthogonal criterion involving P-gp overexpressing ADR-RES cells. A support vector machine (SVM) was 86.7% accurate in discriminating P-gp substrates on independent test data, exceeding previous models. Two molecular features had an overarching influence: nearly all P-gp substrates were large (>35 atoms including H) and dense (specific volume of <7.3 Å3/atom) molecules. Seven other descriptors and 24 molecular fragments (“effluxophores”) were found enriched in the (non)substrates and incorporated into interpretable rule-based models. Biological experiments on an independent P-gp overexpressing cell line, the vincristine-resistant VK2, allowed us to reclassify six compounds previously annotated as substrates, validating our method’s predictive ability. Models are freely available at http://pgp.biozyne.com.

Izvorni jezik
Engleski

Znanstvena područja
Biologija, Računarstvo, Temeljne medicinske znanosti



POVEZANOST RADA


Projekt / tema
098-0000000-3168 - Strojno učenje prediktivnih modela u računalnoj biologiji (Tomislav Šmuc, )
098-0982464-2514 - Uloga različitih mehanizama odgovora stanica na terapiju oštećenjem DNA (Marijeta Kralj, )

Ustanove
Institut "Ruđer Bošković", Zagreb

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


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