Development of predictive in silico model for cyclosporine- and aureobasidin-based P-glycoprotein inhibitors employing receptor surface analysis

Zalloum, Hiba; Taha, Mutasem O.

doi:10.1016/j.jmgm.2008.07.005

Cited by 8 publications

(8 citation statements)

References 61 publications

(118 reference statements)

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“…The performance of this model is quite similar to that of previously developed models [12][13][14][15][16][17][18][19][20][21][22][23]25] and approaches the upper bound of 85% of good classifications in Pgp models estimated by Zhang et al on the basis of the variability of experimental data on Pgp-related assays [16] (only the work from Huang et al reports a performance of 90%, above the calculated upper bound [24]). The percentage of well-classified training set substrates is 73.1%, while for the non-substrates the percentage rises to 82.2%.…”

Section: Models Built From the Random Poolssupporting

confidence: 83%

“…A 31st pool of descriptors that we will call 'rational pool' was also considered by reviewing past reports of models related to Pgp affinity and designing a subset with those Dragon descriptors possibly related to key features identified in previous modeling efforts [12][13][14][15][16][17][18][19][20][21][22][23][24][33][34][35]. For example, several of the previous studies on Pgp affinity models reported that the numbers of H-bond donors and acceptors are important features for the recognition event; therefore, the number of H-bond donors, the number of H-bond acceptors, the number of primary, secondary and tertiary N atoms and the number of OHs were included in the rational pool.…”

Section: Molecular Descriptor Calculation and Modeling Techniquementioning

confidence: 99%

“…Most of the recently reported computational models to recognize Pgp substrates or inhibitors are either phamacophore hypothesis or QSAR models that rely on 3D molecular descriptors [12][13][14][15][16][17][18][19][20][21][22][23], with some exceptions such as the work by Huang et al [24] and Cabrera et al [25], who reported models that include either constitutional or topological (0D-2D) descriptors. Since currently public chemical repositories, such as Pubchem or ZINC databases, hold several millions of drug-like compounds and the small organic compound chemical space grows exponentially toward the hypothetical estimate of 10 60 to 10 100 feasible chemical entities [26,27], 3D models might not be the most efficient choice for exploration of this vast universe, since they require previous conformational analysis to obtain a probable conformer or an ensemble of probable conformers of the repository compounds.…”

Section: Introductionmentioning

confidence: 99%

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Development of a highly specific ensemble of topological models for early identification of P‐glycoprotein substrates

Ianni

Talevi

Castro

et al. 2011

Journal of Chemometrics

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a P-glycoprotein (Pgp) is an ATP-dependent efflux transporter protein associated with multidrug resistance in several diseases such as cancer, epilepsy and AIDS. It is preferentially expressed in organs and tissues that function as a barrier (e.g. the gut walls or the blood-brain barrier) or promote the elimination of xenobiotics from the organism (e.g. liver and kidney). Pgp limits drug bioavailability; thus, the recognition of Pgp substrates at the early stages of the drug development cycle is essential for the development of new chemotherapeutic agents to deal with multidrug resistance issues. Here we present the development of several classifier models based on topological descriptors to identify potential Pgp substrates, aimed to be applied as secondary filter in virtual screening campaigns. Receiver Operating characteristic (ROC) curves show that combination of individual models, through data fusion, in a three-model ensemble, allows attaining higher areas under the curve and an overall better behavior in terms of sensitivity or specificity. The individual discriminant functions (dfs) presented have a performance similar to that of the previously reported models and, remarkably, our models only include low-dimensional (up to 2D) molecular descriptors, which makes them adequate for the virtual screening of increasingly large virtual chemical repositories.

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Section: Models Built From the Random Poolssupporting

confidence: 83%

Section: Molecular Descriptor Calculation and Modeling Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of a highly specific ensemble of topological models for early identification of P‐glycoprotein substrates

Ianni

Talevi

Castro

et al. 2011

Journal of Chemometrics

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“…There is firm evidence showing the importance of using combinatorial therapeutic approaches that can target the hallmarks of cancer when treating tumors [6]. Tumor cells can adapt to single therapeutic modality by switching different signaling pathways, which provide higher chances of tumor survival and relapse [7, 8].…”

Section: Introductionmentioning

confidence: 99%

Co-encapsulation of thymoquinone with docetaxel enhances the encapsulation efficiency into PEGylated liposomes and the chemosensitivity of MCF7 breast cancer cells to docetaxel

Odeh

Naffa

Azzam

et al. 2019

Heliyon

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Combinatorial therapeutic strategies to eradicate tumors can be superior to a single therapeutic modality. Docetaxel (DT) has been approved for the treatment of local or metastasized breast cancer alone or in combination with other chemotherapeutic agents. Thymoquinone (TQ) originated from the seeds of Nigella Sativa plant has been reported to possess in vitro and in vivo antitumor activity against variety of tumors. In the current study, we have investigated the synergistic anticancer efficacy of a novel combination of DT and TQ on MCF7 breast cancer cell line using MTT cell viability assay. Moreover, this study describes for the first time the co-encapsulation of DT and TQ into PEGylated liposomes. The results showed that the combination of DT and TQ resulted in significant synergistic cytotoxicity compared to DT and TQ alone. Moreover, DT and TQ have been successfully co-encapsulated into PEGylated liposomes with higher encapsulation efficiency compared to DT and TQ alone. In conclusion, DT and TQ combination poses a synergistic effect and may aid in decreasing the required doses of DT. Also, the co-encapsulation of DT and TQ into PEGylated liposomes can provide a promising DT and TQ delivery system into cancer cells.

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“…For instance, Ekins et al [30] and Pajeva et al [29], [32] recruited the chemical feature HBD to develop their pharmacophore hypotheses. Wang et al [72], Zalloum and Taha [73] and Chen et al [74] employed HBD related descriptor to construct their QSAR models; and even the CoMSIA model proposed by Labrie et el. [75] also used the field HBD.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Promiscuous P-Glycoprotein Inhibition Using a Novel Machine Learning Scheme

2012

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Background P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the over-expression of P-gp by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop a P-gp inhibition predictive model in the process of drug discovery and development. Methodology/Principal Findings An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those structurally diverse molecules in the training set ( n = 31, r 2 = 0.89, q 2 = 0.86, RMSE = 0.40, s = 0.28), the test set ( n = 88, r 2 = 0.87, RMSE = 0.39, s = 0.25) and the outlier set ( n = 11, r 2 = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model. Conclusions/Significance This accurate, fast and robust PhE/SVM model that can take into account the promiscuous nature of P-gp can be applied to predict the P-gp inhibition of structurally diverse compounds that otherwise cannot be done by any other methods in a high-throughput fashion to facilitate drug discovery and development by designing drug candidates with better metabolism profile.

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Development of predictive in silico model for cyclosporine- and aureobasidin-based P-glycoprotein inhibitors employing receptor surface analysis

Cited by 8 publications

References 61 publications

Development of a highly specific ensemble of topological models for early identification of P‐glycoprotein substrates

Development of a highly specific ensemble of topological models for early identification of P‐glycoprotein substrates

Co-encapsulation of thymoquinone with docetaxel enhances the encapsulation efficiency into PEGylated liposomes and the chemosensitivity of MCF7 breast cancer cells to docetaxel

Prediction of Promiscuous P-Glycoprotein Inhibition Using a Novel Machine Learning Scheme

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