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Citation - WoS: 13
Citation - Scopus: 17
Machine Learning-Assisted Prediction of the Toxicity of Silver Nanoparticles: a Meta-Analysis
(Springer, 2023) Bilgi, Eyüp; Öksel Karakuş, Ceyda
Silver nanoparticles are likely to be more dangerous than other forms of silver due to the intracellular release of silver ions upon dissolution and the formation of mixed ion-containing complexes. Such concerns have resulted in an ever-growing pile of scientific evaluations addressing the safety aspects of nanosilver with widely varying methodological approaches. The substantial differences in the conduct/design of nanotoxicity screening have led to the generation of conflicting findings that may be accurate in their narrative but fail to provide a complete picture. One strategy to maximize the use of individual risk assessments with potentially biased estimates of toxicological effects is to homogenize results across several studies and to increase the generalizability and human relevance of their findings. Here, we collected a large pool of data (n=162 independent studies) on the cytotoxicity of nanosilver and unrevealed potential triggers of toxicity. Two different machine learning approaches, decision tree (DT) and artificial neural network (ANN), were primarily employed to develop models that can predict the cytotoxic potential of nanosilver based on material- and assay-related parameters. Other machine learning algorithms (logistic regression, Gaussian Naive Bayes, k-nearest neighbor, and random forest classifiers) were also applied. Among several attributes compared, exposure concentration, duration, zeta potential, particle size, and coating were found to have the most substantial impact on nanotoxicity, with biomolecule- and microorganism-assisted surface modifications having the most beneficial and detrimental effects on cell survival, respectively. Such machine learning-assisted efforts are critical to developing commercially viable and safe nanosilver-containing products in the ever-expanding nanobiomaterial market.
Citation - WoS: 69
Citation - Scopus: 73
Nanoparticle-Protein Corona Complex: Understanding Multiple Interactions Between Environmental Factors, Corona Formation, and Biological Activity
(Taylor & Francis, 2021) Öksel Karakuş, Ceyda; Tomak, Aysel; Çeşmeli, Selin; Hanoğlu, Berçem Dilan; Winkler, David
The surfaces of pristine nanoparticles become rapidly coated by proteins in biological fluids, forming the so-called protein corona. The corona modifies key physicochemical characteristics of nanoparticle surfaces that modulate its biological and pharmacokinetic activity, biodistribution, and safety. In the two decades since the protein corona was identified, the importance of nano particles surface properties in regulating biological responses have been recognized. However, there is still a lack of clarity about the relationships between physiological conditions and cor ona composition over time, and how this controls biological activities/interactions. Here we review recent progress in characterizing the structure and composition of protein corona as a function of biological fluid and time. We summarize the influence of nanoparticle characteristics on protein corona composition and discuss the relevance of protein corona to the biological activity and fate of nanoparticles. The aim is to provide a critical summary of the key factors that affect protein corona formation (e.g. characteristics of nanoparticles and biological environ ment) and how the corona modulates biological activity, cellular uptake, biodistribution, and drug delivery. In addition to a discussion on the importance of the characterization of protein corona adsorbed on nanoparticle surfaces under conditions that mimic relevant physiological environment, we discuss the unresolved technical issues related to the characterization of nano particle-protein corona complexes during their journey in the body. Lastly, the paper offers a perspective on how the existing nanomaterial toxicity data obtained from in vitro studies should be reconsidered in the light of the presence of a protein corona, and how recent advances in fields, such as proteomics and machine learning can be integrated into the quantitative analysis of protein corona components.

Bioengineering / Biyomühendislik

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