scholarly journals Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme

Molecules ◽  
2018 ◽  
Vol 23 (7) ◽  
pp. 1820 ◽  
Author(s):  
Chun Chen ◽  
Ming-Han Lee ◽  
Ching-Feng Weng ◽  
Max Leong
Keyword(s):  
Support Vector Regression ◽  
Atp Hydrolysis ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
The Novel ◽  
Glycoprotein P ◽  
P Glycoprotein ◽  
Wide Range ◽  
Substrate Binding Sites

P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood–brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites. A nonlinear quantitative structure–activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 50, r2 = 0.96, qCV2 = 0.94, RMSE = 0.10, s = 0.10) and test set (n = 13, q2 = 0.80–0.87, RMSE = 0.21, s = 0.22). When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

scholarly journals Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme

2018 ◽  
Author(s):  
Chun Chen ◽  
Ming-Han Lee ◽  
Ching-Feng Weng ◽  
Max K. Leong
Keyword(s):  
Support Vector Regression ◽  
Atp Hydrolysis ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
The Novel ◽  
Glycoprotein P ◽  
P Glycoprotein ◽  
Wide Range ◽  
Substrate Binding Sites

P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood-brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites. A nonlinear quantitative structure-activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 50, r2 = 0.96, q2CV = 0.94, RMSE = 0.10, s = 0.10) and test set (n = 13, q2 = 0.80–0.87, RMSE = 0.21, s = 0.22). When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

scholarly journals In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression

2020 ◽  
Vol 21 (10) ◽  
pp. 3582
Author(s):  
Ming-Han Lee ◽  
Giang Huong Ta ◽  
Ching-Feng Weng ◽  
Max K. Leong
Keyword(s):  
Drug Discovery ◽  
Support Vector Regression ◽  
Intestinal Permeability ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
The Novel ◽  
Drug Discovery And Development ◽  
Important Drug ◽  
Good Agreement

The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure–activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r2 = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q2 = 0.75–0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q2 = 0.78–0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

scholarly journals Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability

Pharmaceutics ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 174 ◽  
Author(s):  
Giang Huong Ta ◽  
Cin-Syong Jhang ◽  
Ching-Feng Weng ◽  
Max K. Leong
Keyword(s):  
Drug Discovery ◽  
Support Vector Regression ◽  
Human Colon ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Human Colon Carcinoma Cell ◽  
Drug Discovery And Development ◽  
Training Samples ◽  
Experimental Values

Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.

scholarly journals Quantitative Structure–Activity Relationships for the Flavonoid-Mediated Inhibition of P-Glycoprotein in KB/MDR1 Cells

Molecules ◽  
2019 ◽  
Vol 24 (9) ◽  
pp. 1661 ◽  
Author(s):  
Mengmeng Xia ◽  
Yajing Fang ◽  
Weiwei Cao ◽  
Fuqiang Liang ◽  
Siyi Pan ◽  
...  
Keyword(s):  
Inhibitory Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Glycoprotein P ◽  
Chemotherapy Drugs ◽  
Structure Activity ◽  
P Glycoprotein ◽  
Daily Diet ◽  
Low Toxicity

P-glycoprotein (P-gp) serves as a therapeutic target for the development of inhibitors to overcome multidrug resistance (MDR) in cancer cells. In order to enhance the uptake of chemotherapy drugs, larger amounts of P-gp inhibitors are required. Besides several chemically synthesized P-gp inhibitors, flavonoids as P-gp inhibitors are being investigated, with their advantages including abundance in our daily diet and a low toxicity. The cytotoxicity of daunorubicin (as a substrate of P-gp) to KB/MDR1 cells and the parental KB cells was measured in the presence or absence of flavonoids. A two-dimensional quantitative structure–activity relationship (2D-QSAR) model was built with a high cross-validation coefficient (Q2) value of 0.829. Descriptors including vsurf_DW23, E_sol, Dipole and vsurf_G were determined to be related to the inhibitory activity of flavonoids. The lack of 2,3-double bond, 3′-OH, 4′-OH and the increased number of methoxylated substitutions were shown to be beneficial for the inhibition of P-gp. These results are important for the screening of flavonoids for inhibitory activity on P-gp.

Quantitative Structure-activity Relationship of Acetylcholinesterase Inhibitors based on mRMR Combined with Support Vector Regression

2019 ◽  
Vol 16 (4) ◽  
pp. 311-316
Author(s):  
Jiaxiang Wu ◽  
Guozhao Mai ◽  
Bowen Deng ◽  
Jeong Younseo ◽  
Dongsu Du ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Feature Selection Method ◽  
Acetylcholinesterase Inhibitors ◽  
Qsar Model ◽  
Accessible Surface Area ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Accessible Surface ◽  
Relative Errors ◽  
Leave One Out

In this work, support vector regression (SVR), an effective machine learning method, proposed by Vapnik was applied to establish QSAR model for a series of AchEI. Fourteen descriptors were selected for constructing the SVR mode by using mRMR-Forward feature selection method. The parameters (ε, C) were adjusted by leave-one-out cross validation (LOOCV) method which was used to judge the predictive power of different models. After optimization, one optimal SVR-QSAR model was attained, and the mean relative errors (MRE) of LOOCV by using SVR is 1.72%. As a result, LogP negatively affected the activity, Refractivity and Water Accessible Surface Area positively affected the activity.

scholarly journals ATP-dependent thermostabilization of human P-glycoprotein (ABCB1) is blocked by modulators

2019 ◽  
Vol 476 (24) ◽  
pp. 3737-3750 ◽  
Author(s):  
Sabrina Lusvarghi ◽  
Suresh V. Ambudkar
Keyword(s):  
Conformational Changes ◽  
Drug Transport ◽  
Atp Hydrolysis ◽  
Atp Binding ◽  
Dependent Manner ◽  
Deficient Mutant ◽  
Concentration Dependent Manner ◽  
Glycoprotein P ◽  
Binding Domains ◽  
P Glycoprotein

P-glycoprotein (P-gp), an ATP-binding cassette transporter associated with multidrug resistance in cancer cells, is capable of effluxing a number of xenobiotics as well as anticancer drugs. The transport of molecules through the transmembrane (TM) region of P-gp involves orchestrated conformational changes between inward-open and inward-closed forms, the details of which are still being worked out. Here, we assessed how the binding of transport substrates or modulators in the TM region and the binding of ATP to the nucleotide-binding domains (NBDs) affect the thermostability of P-gp in a membrane environment. P-gp stability after exposure at high temperatures (37–80°C) was assessed by measuring ATPase activity and loss of monomeric P-gp. Our results show that P-gp is significantly thermostabilized (>22°C higher IT50) by the binding of ATP under non-hydrolyzing conditions (in the absence of Mg2+). By using an ATP-binding-deficient mutant (Y401A) and a hydrolysis-deficient mutant (E556Q/E1201Q), we show that thermostabilization of P-gp requires binding of ATP to both NBDs and their dimerization. Additionally, we found that transport substrates do not affect the thermal stability of P-gp either in the absence or presence of ATP; in contrast, inhibitors of P-gp including tariquidar and zosuquidar prevent ATP-dependent thermostabilization in a concentration-dependent manner, by stabilizing the inward-open conformation. Altogether, our data suggest that modulators, which bind in the TM regions, inhibit ATP hydrolysis and drug transport by preventing the ATP-dependent dimerization of the NBDs of P-gp.

scholarly journals Transport of Alzheimer’s associated amyloid-β catalyzed by P-glycoprotein

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250371
Author(s):  
James W. McCormick ◽  
Lauren Ammerman ◽  
Gang Chen ◽  
Pia D. Vogel ◽  
John G. Wise
Keyword(s):  
Cell Culture ◽  
Atp Hydrolysis ◽  
Amyloid Β ◽  
Md Simulations ◽  
Membrane Transporter ◽  
Biochemical Activity ◽  
Glycoprotein P ◽  
P Glycoprotein ◽  
Hydrolysis Activity

P-glycoprotein (P-gp) is a critical membrane transporter in the blood brain barrier (BBB) and is implicated in Alzheimer’s disease (AD). However, previous studies on the ability of P-gp to directly transport the Alzheimer’s associated amyloid-β (Aβ) protein have produced contradictory results. Here we use molecular dynamics (MD) simulations, transport substrate accumulation studies in cell culture, and biochemical activity assays to show that P-gp actively transports Aβ. We observed transport of Aβ40 and Aβ42 monomers by P-gp in explicit MD simulations of a putative catalytic cycle. In in vitro assays with P-gp overexpressing cells, we observed enhanced accumulation of fluorescently labeled Aβ42 in the presence of Tariquidar, a potent P-gp inhibitor. We also showed that Aβ42 stimulated the ATP hydrolysis activity of isolated P-gp in nanodiscs. Our findings expand the substrate profile of P-gp, and suggest that P-gp may contribute to the onset and progression of AD.

scholarly journals Prediction of Commuter’s Daily Time Allocation

2013 ◽  
Vol 25 (5) ◽  
pp. 445-455 ◽  
Author(s):  
Fang Zong ◽  
Jia Hongfei ◽  
Pan Xiang ◽  
Wu Yang
Keyword(s):  
Support Vector Regression ◽  
Travel Time ◽  
Time Allocation ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Model System ◽  
Support Vector ◽  
Activity Duration ◽  
Wide Range ◽  
Duration Prediction

This paper presents a model system to predict the time allocation in commuters’ daily activity-travel pattern. The departure time and the arrival time are estimated with Ordered Probit model and Support Vector Regression is introduced for travel time and activity duration prediction. Applied in a real-world time allocation prediction experiment, the model system shows a satisfactory level of prediction accuracy. This study provides useful insights into commuters’ activity-travel time allocation decision by identifying the important influences, and the results are readily applied to a wide range of transportation practice, such as travel information system, by providing reliable forecast for variations in travel demand over time. By introducing the Support Vector Regression, it also makes a methodological contribution in enhancing prediction accuracy of travel time and activity duration prediction.

scholarly journals Support vector regression-based QSAR models for prediction of antioxidant activity of phenolic compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ying Shi
Keyword(s):  
Phenolic Compounds ◽  
Support Vector Regression ◽  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Qsar Model ◽  
Heat Of Formation ◽  
Trolox Equivalent Antioxidant Capacity ◽  
Support Vector ◽  
Prediction Ability ◽  
Qsar Models

AbstractThe Support vector regression (SVR) was used to investigate quantitative structure–activity relationships (QSAR) of 75 phenolic compounds with Trolox-equivalent antioxidant capacity (TEAC). Geometric structures were optimized at the EF level of the MOPAC software program. Using Pearson correlation coefficient analysis, four molecular descriptors [n(OH), Cosmo Area (CA), Core-Core Repulsion (CCR) and Final Heat of Formation (FHF)] were selected as independent variables. The QSAR model was developed from the training set consisting of 57 compounds and then used the leave-one-out cross-validation (LOOCV) correlation coefficient to evaluate the prediction ability of the QSAR model. Used Artificial neural network (ANN) and multiple linear regression (MLR) for comparing. The RMSE (root mean square error) values of LOOCV in SVR, ANN and MLR models were 0.44, 0.46 and 0.54. The RMSE values of prediction of external 18 compounds were 0.41, 0.39 and 0.54 for SVR, ANN and MLR models, respectively. The obtained result indicated that the SVR models exhibited excellent predicting performance and competent for predicting the TEAC of phenolic compounds.

scholarly journals Transmembrane segment 1 of human P-glycoprotein contributes to the drug-binding pocket

2006 ◽  
Vol 396 (3) ◽  
pp. 537-545 ◽  
Author(s):  
Tip W. Loo ◽  
M. Claire Bartlett ◽  
David M. Clarke
Keyword(s):  
Atpase Activity ◽  
Atp Hydrolysis ◽  
Binding Pocket ◽  
Drug Binding ◽  
Covalent Attachment ◽  
Acetoxymethyl Ester ◽  
Glycoprotein P ◽  
P Glycoprotein ◽  
Cysteine Scanning ◽  
Cysteine Scanning Mutagenesis

P-glycoprotein (P-gp; ABCB1) actively transports a broad range of structurally unrelated compounds out of the cell. An important step in the transport cycle is coupling of drug binding with ATP hydrolysis. Drug substrates such as verapamil bind in a common drug-binding pocket at the interface between the TM (transmembrane) domains of P-gp and stimulate ATPase activity. In the present study, we used cysteine-scanning mutagenesis and reaction with an MTS (methanethiosulphonate) thiol-reactive analogue of verapamil (MTS-verapamil) to test whether the first TM segment [TM1 (TM segment 1)] forms part of the drug-binding pocket. One mutant, L65C, showed elevated ATPase activity (10.7-fold higher than an untreated control) after removal of unchanged MTS-verapamil. The elevated ATPase activity was due to covalent attachment of MTS-verapamil to Cys65 because treatment with dithiothreitol returned the ATPase activity to basal levels. Verapamil covalently attached to Cys65 appears to occupy the drug-binding pocket because verapamil protected mutant L65C from modification by MTS-verapamil. The ATPase activity of the MTS-verapamil-modified mutant L65C could not be further stimulated with verapamil, calcein acetoxymethyl ester or demecolcine. The ATPase activity could be inhibited by cyclosporin A but not by trans-(E)-flupentixol. These results suggest that TM1 contributes to the drug-binding pocket.

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