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

doi:10.1021/jm400328s

Cited by 50 publications

(38 citation statements)

References 69 publications

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“…Recently, Levatić et al developed new SVM models based on radial basis functions and polynomial kernels by which 958 molecules were represented by 183 2D molecular descriptors. The best model correctly predicted P‐gp substrates with an accuracy of 88.2% in the training set and 86.7% in the test set.…”

Section: Sar Computational Modelsmentioning

confidence: 99%

Reversing cancer multidrug resistance: insights into the efflux by ABC transports from in silico studies

Ferreira

Santos

2014

WIREs Comput Mol Sci

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One of the greatest threats to cancer treatment is the development, by some tumors, of resistance to the pharmacological action of several structurally unrelated cytotoxic agents-multidrug resistance (MDR). As P-glycoprotein (P-gp) is one of the most studied ATP-dependent efflux pumps that are frequently involved in drug efflux from cancer cells, the development of MDR modulators with the ability to inhibit P-gp efflux is considered a promising approach for overcoming MDR. However, the development of P-gp modulators have been hampered due to the absence of knowledge on the intrinsic molecular aspects by which efflux occurs, namely the specific steps that correlates drug recognition, ATP binding and efflux-related conformational changes. Experimental evidences for these processes are also difficult to obtain and only provide small pieces of information that need to be assembled for better comprehension of a wider and complex process that is drug efflux. A promising alternative relies on cutting-edge computational techniques to provide new insights on key aspects that are determinant to understand how P-gp efflux can be effectively reversed. With the contribution of ligand-based or structure-based computational methods, P-gp drug efflux is slowly becoming a dynamic and reactive process rather than a simple response to drug binding, with the complex architecture of ABC transporters playing a determinant role not only in drug recognition but in the coordination of ATP-driven conformational changes that ultimately drives drug efflux. The major enlightenments that computational studies provided toward a better comprehension of MDR and P-gp efflux phenomena are hereby described.The demographic effect of this pathology on the general population, especially in the industrialized countries, is becoming severely affected by a rapid increase in the number of new cancers (Figure 1). It is estimated that by 2030 the number of new cases (excluding nonmelanoma skin cancer) will grow 32.8% (more than 21 million), with an estimated increase of 34.6% (1.3 million) in the total number of deaths. 2,3 Thus, new and improved chemotherapy regimens are needed to counterweight such predictions. However, cancer therapy evolution is directly related to a greater knowledge on the basic molecular mechanisms Volume 5, January/February 2015 Although the above structures contributed to a better comprehension of the ABC exporters' topology, a Volume 5, January/February 2015

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Section: Sar Computational Modelsmentioning

confidence: 99%

Reversing cancer multidrug resistance: insights into the efflux by ABC transports from in silico studies

Ferreira

Santos

2014

WIREs Comput Mol Sci

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“…We used curated datasets from the scientific literature for all the properties (Pgp inhibition 13 , Pgp recognition 27 , OCT2 inhibition 28 , OATP1B1 inhibition 29 , Clearance 10 and MRDD 26 ) except for inhibition of and recognition by BCRP, for which we curated the collection by using data from several articles. Foundation of QSPR classification studies were the categorical classification of compounds, based on experimental measures as suggested by the authors of the curated collections (mostly IC 50 or percentage of inhibition).…”

Section: Resultsmentioning

confidence: 99%

“…27 , who classified compounds as P-glycoprotein substrates or nonsubstrates based on high-throughput data on different cancer cell lines. We used their dataset for modelling, composed of 934 molecules.…”

Section: Methodsmentioning

confidence: 99%

ADME-Space: a new tool for medicinal chemists to explore ADME properties

Bocci

Carosati

Vayer

et al. 2017

Sci Rep

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We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.

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“…Demel i saradnici [55] iskoristili su podatke o citotoksičnosti eksperimentalnih jedinjenja jedne farmaceutske kompanije na ćelijskoj liniji koja prekomerno eksprimira Pgp, za kreiranje seta od 1877 jedinjenja. Levatić i saradnici [58] opisali su sličan pristup, korišćenjem podataka o citotoksičnosti na 60 različitih ćelijskih linija (NCI60) koje uniformno prikuplja američki Nacionalni institut za kancer, u okviru svog Programa razvoja lekova [60]. Obe grupe autora su pretpostavile da u slučaju Pgp supstrata postoji inverzan odnos između osetljivosti ćelija na citotoksične efekte jedinjenja i nivoa ekspresije Pgp-a u datoj ćelijskoj liniji.…”

Section: Modeli Zasnovani Na Molekulskim Deskriptorimaunclassified

Computational models for predicting drug transport mediated by P-glycoprotein

Erić¹,

Kalinić²

2015

Arh farmaciju

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Kratak sadržajP-glikoprotein (Pgp) je transmembranski transporter koji, transportujući strukturno raznovrsne lekove iz unutrašnjosti ćelije u ekstracelularnu sredinu, može uticati na resorpciju, distribuciju i efikasnost većeg broja lekova. Prekomerna ekspresija Pgp-a u ćelijama predstavlja jedan od mehanizama razvoja rezistencije na lekove. Iz ovih razloga, potrebno je u ranoj fazi otkrića leka predvideti da li je potencijalni lek supstrat za Pgp, idealno i pre same sinteze. U tu svrhu, tokom poslednje decenije razvijen je veliki broj računarskih modela koji omogućavaju predviđanje transporta posredstvom Pgp-a samo na osnovu hemijske strukture. U ovom radu prikazan je pregled različitih pristupa koji su korišćeni u razvoju modela, razmotrene su njihove prednosti i nedostaci, kao i faktori koji u najvećoj meri utiču na pouzdanost predviđanja. Polispecifičnost ovog transportera predstavlja značajan izazov za većinu metoda računarske hemije. Ipak, dostignut nivo tačnosti modela koji su prikazani u litearaturi ukazuje na činjenicu da oni mogu doprineti racionalizaciji procesa dizajniranja novih lekova. Šira dostupnost eksperimentalnih podataka, kao i kombinovanje različitih pristupa modelovanju transporta, mogu dodatno unaprediti postojeće modele.Ključne reči: P-glikoprotein, računarski modeli, rezistencija, dizajn lekova.

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

Cited by 50 publications

References 69 publications

Reversing cancer multidrug resistance: insights into the efflux by ABC transports from in silico studies

Reversing cancer multidrug resistance: insights into the efflux by ABC transports from in silico studies

ADME-Space: a new tool for medicinal chemists to explore ADME properties

Computational models for predicting drug transport mediated by P-glycoprotein

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