Modeling uptake of nanoparticles in multiple human cells using structure–activity relationships and intercellular uptake correlations

Basant, Nikita

doi:10.1080/17435390.2016.1257075

Cited by 20 publications

(9 citation statements)

References 62 publications

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“…The final assessments of the developed models were done by defining the applicability domain (AD) of obtained models using the distance to model in the X -space (DModX) approach using SIMCA-P 57 software. The compounds in the training set outside this domain were considered as outliers, and the compounds in the test set having DModX values greater than the threshold value are called outside AD.…”

Section: Resultsmentioning

confidence: 99%

QSPR Modeling of the Refractive Index for Diverse Polymers Using 2D Descriptors

2018

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In the present work, predictive quantitative structure–property relationship models have been developed to predict refractive indices (RIs) of a set of 221 diverse organic polymers using theoretical two-dimensional descriptors generated on the basis of the structures of polymers’ monomer units. Four models have been developed by applying partial least squares (PLS) regression with a different combination of six descriptors obtained via double cross-validation approaches. The predictive ability and robustness of the proposed models were checked using multiple validation strategies. Subsequently, the validated models were used for the generation of “intelligent” consensus models ( ) to improve the quality of predictions for the external data set. The selected consensus models were used for the prediction of refractive index values of various classes of polymers. The final selected model was used to predict the refractive index of four small virtual libraries of monomers recently reported. We also used a true external data set of 98 diverse monomer units with the experimental RI values of the corresponding polymers. The obtained models showed a good predictive ability as evidenced from a very good external predicted variance.

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Section: Resultsmentioning

confidence: 99%

QSPR Modeling of the Refractive Index for Diverse Polymers Using 2D Descriptors

2018

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“…To date, modeling efforts for prediction of ENM-bio interactions focus primarily upon cellular response and toxicity. 15–18 To improve accuracy, there is a movement toward inclusion of PC information in modeling cellular response 5,7 , but the current models for ENM biological response rely upon expansive PC databases 7,19–22 . Despite this recognition that the ENM PC plays a key role in biological response to ENMs 5 , no modeling efforts to date have focused upon prediction of the PC fingerprint.…”

mentioning

confidence: 99%

Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties

Findlay

Freitas

Mobed-Miremadi

et al. 2018

Environ. Sci.: Nano

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Proteins encountered in biological and environmental systems bind to engineered nanomaterials (ENMs) to form a protein corona (PC) that alters the surface chemistry, reactivity, and fate of the ENMs. Complexities such as the diversity of the PC and variation with ENM properties and reaction conditions make the PC population difficult to predict. Here, we support the development of predictive models for PC populations by relating biophysicochemical characteristics of proteins, ENMs, and solution conditions to PC formation using random forest classification. The resulting model offers a predictive analysis into the population of PC proteins in Ag ENM systems of various ENM size and surface coatings. With an area under the receiver operating characteristic curve of 0.83 and F1-score of 0.81, a model with strong performance has been constructed based upon experimental data. The weighted contribution of each variable provides recommendations for mechanistic models based upon protein enrichment classification results. Protein biophysical properties such as pI and weight are weighted heavily. Yet, ENM size, surface charge, and solution ionic strength also proved essential to an accurate model. The model can be readily modified and applied to other ENM PC populations. The model presented here represents the first step toward robust predictions of PC fingerprints.

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“…The molecular descriptors used to predict uptake of functionalised NPs in different cell lines included their hydrophobicity, lipophilicity, hydrogen bonding ability, polarizability, molecular topological complexity and presence of heteroatoms, these characteristics are some of the most important characteristics determining the uptake of a nanoparticle in a cell and are important in facilitating surface interactions, protein binding affinity and cell permeability [102][103][104]. The study found that functionalised NPs that exhibited hydrophobicity and lipophilicity showed a positive correlation with cellular uptake in U937 and HUVEC cell lines [101].…”

Section: Quantitative Structure-activity Relationship (Qsar) Modelmentioning

confidence: 96%

“…DTB and DTF are essentially two separate systems that can be used to create a predictive decision tree, which is essentially a graph that uses a branching method to illustrate every possible outcome of a decision [100]. One particular study by Basant and co-workers used QSAR modelling with DTB to predict the uptake of functionalised NPs in six different cell lines-PaCa2, HUVEC, RestMph, GMCSF_Mph and U937 [101]. The molecular descriptors used to predict uptake of functionalised NPs in different cell lines included their hydrophobicity, lipophilicity, hydrogen bonding ability, polarizability, molecular topological complexity and presence of heteroatoms, these characteristics are some of the most important characteristics determining the uptake of a nanoparticle in a cell and are important in facilitating surface interactions, protein binding affinity and cell permeability [102][103][104].…”

Section: Quantitative Structure-activity Relationship (Qsar) Modelmentioning

confidence: 99%

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Exploiting Endocytosis for Non-Spherical Nanoparticle Cellular Uptake

2022

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Several challenges exist for successful nanoparticle cellular uptake—they must be able to cross many physical barriers to reach their target and overcome the cell membrane. A strategy to overcome this challenge is to exploit natural uptake mechanisms namely passive and endocytic (i.e., clathrin- and caveolin-dependent/-independent endocytosis, macropinocytosis and phagocytosis). The influence of nanoparticle material and size is well documented and understood compared to the influence of nanomaterial shape. Generally, nanoparticle shape is referred to as being either spherical or non-spherical and is known to be an important factor in many processes. Nanoparticle shape-dependent effects in areas such as immune response, cancer drug delivery, theranostics and overall implications for nanomedicines are of great interest. Studies have looked at the cellular uptake of spherical NPs, however, fewer in comparison have investigated the cellular uptake of non-spherical NPs. This review explores the exploitation of endocytic pathways for mainly inorganic non-spherical (shapes of focus include rod, triangular, star-shaped and nanospiked) nanoparticles cellular uptake. The role of mathematical modelling as predictive tools for non-spherical nanoparticle cellular uptake is also reviewed. Both quantitative structure-activity relationship (QSAR) and continuum membrane modelling have been used to gain greater insight into the cellular uptake of complex non-spherical NPs at a greater depth difficult to achieve using experimental methods.

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Modeling uptake of nanoparticles in multiple human cells using structure–activity relationships and intercellular uptake correlations

Cited by 20 publications

References 62 publications

QSPR Modeling of the Refractive Index for Diverse Polymers Using 2D Descriptors

QSPR Modeling of the Refractive Index for Diverse Polymers Using 2D Descriptors

Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties

Exploiting Endocytosis for Non-Spherical Nanoparticle Cellular Uptake

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