Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches

Papa, Ester; Doucet, Jean‐Pierre; Sangion, Alessandro; Doucet-Panaye, A.

doi:10.1080/1062936x.2016.1197310

Cited by 44 publications

(28 citation statements)

References 54 publications

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“…This is in agreement with previous findings from other groups (Walkey et al, 2014; Liu et al, 2015; Papa et al, 2016). It is however interesting to note that prediction intervals are significantly more inaccurate than those from other descriptors and the percentage of measurements within the prediction interval is usually lower than 90% instead of expected 95%.…”

Section: Resultssupporting

confidence: 94%

“…For the Protein corona dataset a couple of QSAR studies with global models have been published (Walkey et al, 2014; Liu et al, 2015; Papa et al, 2016), but unfortunately their results are also not directly comparable, because we report results for the complete dataset with 121 Gold and Silver particles, while other authors report results only for a subset of Gold particles.…”

Section: Discussionmentioning

confidence: 86%

“…Papa et al (2016) developed global models for 84 Gold compounds with eleven algorithms and reported r 2 and RMSE values for training set retrofitting, leave-one-out crossvalidation ( LOO ) and stratified external test set predictions (64 particles training set, 20 particles test set). There was little difference between good performing models (PPR, EARTH, SVM-linear, SVM-radial, MLR, and PLS) and the authors conclude that Projection Pursuit Regression (PPR) gives the most robust models (LOO r 2 0.81, RMSE 1.01, external r 2 0.79, RMSE 1.01).…”

Section: Discussionmentioning

confidence: 99%

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Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties

2017

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The lazar framework for read across predictions was expanded for the prediction of nanoparticle toxicities, and a new methodology for calculating nanoparticle descriptors from core and coating structures was implemented. Nano-lazar provides a flexible and reproducible framework for downloading data and ontologies from the open eNanoMapper infrastructure, developing and validating nanoparticle read across models, open-source code and a free graphical interface for nanoparticle read-across predictions. In this study we compare different nanoparticle descriptor sets and local regression algorithms. Sixty independent crossvalidation experiments were performed for the Net Cell Association endpoint of the Protein Corona dataset. The best RMSE and r2 results originated from models with protein corona descriptors and the weighted random forest algorithm, but their 95% prediction interval is significantly less accurate than for models with simpler descriptor sets (measured and calculated nanoparticle properties). The most accurate prediction intervals were obtained with measured nanoparticle properties (no statistical significant difference (p < 0.05) of RMSE and r2 values compared to protein corona descriptors). Calculated descriptors are interesting for cheap and fast high-throughput screening purposes. RMSE and prediction intervals of random forest models are comparable to protein corona models, but r2 values are significantly lower.

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Section: Resultssupporting

confidence: 94%

Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

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Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties

2017

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“…Few of the studies applied more than one feature selection technique. Papa et al [40] used GA optimized for Multiple Linear Regression (MLR) models based on ordinary least squares (MLR-OLS) and for support vector machines (SVMs). Using both methods revealed differences in the results related to optimizations for either linear or non-linear approaches.…”

Section: Feature Selectionmentioning

confidence: 99%

Practices and Trends of Machine Learning Application in Nanotoxicology

et al. 2020

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Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

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“…Recent advances and still unresolved issues in the application of computational modelling of nano-bio interactions have been reviewed recently (Winkler, 2015). Computational models of corona composition and its effect on biological activity, using molecular dynamics methods, nonlinear dynamics equations, or machine learning have also been reported recently (Darabi Sahneh et al, 2013; Papa et al, 2016). …”

Section: Nano-bio Interaction Data Nanoinformatics and Modellingmentioning

confidence: 99%

Toward a systematic exploration of nano-bio interactions

Bai

Liu

et al. 2017

Toxicology and Applied Pharmacology

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Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability. They can provide a viable method of reducing or eliminating animal testing. However, careful experimental design with the modelling of the results in mind is a proven and efficient way of exploring large materials spaces. This approach, coupled with high speed automated experimental synthesis and characterization technologies now appearing, is the fastest route to developing models that regulatory bodies may find useful. We advocate greatly increased focus on systematic modification of physicochemical properties of nanoparticles combined with comprehensive biological evaluation and computational analysis. This is essential to obtain better mechanistic understanding of nano-bio interactions, and to derive quantitatively predictive and robust models for the properties of nanomaterials that have useful domains of applicability.

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Investigation of the influence of protein corona composition on gold nanoparticle bioactivity using machine learning approaches

Cited by 44 publications

References 54 publications

Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties

Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties

Practices and Trends of Machine Learning Application in Nanotoxicology

Toward a systematic exploration of nano-bio interactions

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