Drug-Nanoparticle Composites

2017 ◽  
Vol 2 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Natalia Sizochenko ◽  
Jerzy Leszczynski
Keyword(s):  
Polymeric Nanoparticles ◽  
Drug Carriers ◽  
Binding Energies ◽  
Quantitative Structure ◽  
Surface Binding ◽  
Structure Activity Relationships ◽  
Therapeutic Drugs ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships ◽  
Approved Drugs

Polymeric nanoparticles represent attractive targets for the controlled delivery of therapeutic drugs. Drug-nanoparticle conjugates are convenient targets to enhance solubility and membrane permeability of drugs, prolong circulation time and minimize non-specific uptake. The behavior of drugs-loaded nanoparticles is governed by various factors. Understanding of these effects is very important for design of drug-nanoparticle systems, that could be suitable for treating the particular diseases. The aim of the current study is a complementary molecular docking followed by quantitative structure-activity relationships modeling for drugs payload on polymeric nanoparticles. Twenty-one approved drugs were considered. Docking of drugs was performed towards a simplified polymeric surface. Binding energies agreed well with the observed mass loading. Quantitative structure-activity relationships model supported this data. Effects of electronegativity and hydrophobicity were discussed. Developed model may contribute to the development of other useful nano-sized polymeric drug carriers to deliver a spectrum of therapeutic and imaging agents for medical purposes.

scholarly journals Targeting against HIV/HCV Co-infection using Machine Learning-based multitarget-quantitative structure-activity relationships (mt-QSAR) Methods

2019 ◽  
Author(s):  
Yu Wei ◽  
Wei Li ◽  
Tengfei Du ◽  
Zhangyong Hong ◽  
Jianping Lin
Keyword(s):  
Protein Interaction ◽  
Mean Value ◽  
Support Vector ◽  
Classification Models ◽  
Quantitative Structure ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships ◽  
Approved Drugs ◽  
Hiv 1

ABSTRACTCo-infection between HIV-1 and HCV is common today in certain populations. However, treatment of co-infection is full of challenges with special consideration for potential hepatic safety and drug-drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV co-infection. However, identification of one molecule acting on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining naive Bayesian (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints (MACCS and ECFP6), 60 classification models were constructed to predict the active compounds toward 11 HIV-1 targets and 4 HCV targets based on the multitarget-quantitative structure-activity relationships (mt-QSAR). 5-fold cross-validation and test set validation was performed to confirm the performance of 60 classification models. Our results show that 60 mt-QSAR models appeared to have high classification accuracy in terms of ROC-AUC values ranging from 0.83 to 1 with a mean value of 0.97 for HIV-1 models, and ROC-AUC values ranging from 0.84 to 1 with a mean value of 0.96 for HCV. Furthermore, the 60 models were applied to comprehensively predict the potential targets for additional 46 compounds including 27 approved HIV-1 drugs, 10 approved HCV drugs and 9 selected compounds known to be active on one or more targets of HIV-1 or those of HCV. Finally, 18 hits including 7 HIV-1 approved drugs, 4 HCV approved drugs and 7 compounds were predicted to be HIV/HCV co-infection multitarget inhibitors. The reported bioactivity data confirmed that 7 compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. Of those remaining predicted hits and chemical-protein interaction pairs involving the potential ability to suppress HIV/HCV co-infection deserve further investigation by experiments. This investigation shows that the mt-QSAR method is available to predict chemical-protein interaction for discovering multitarget inhibitors and provide a unique perspective on HIV/HCV co-infection treatment.

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