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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: 5
Citation - Scopus: 7
Five New Cardenolides Transformed From Oleandrin and Nerigoside by Alternaria Eureka 1e1bl1 and Phaeosphaeriasp. 1e4cs-1 and Their Cytotoxic Activities
(Elsevier Ltd., 2021) Karakoyun, Çiğdem; Küçüksolak, Melis; Bilgi, Eyüp; Doğan, Gamze; Çömlekçi, Yiğit Ege; Bedir, Erdal
Biotransformation of oleandrin (1) and nerigoside (2) by endophytic fungi; Alternaria eureka 1E1BL1 and Phaeospheria sp. 1E4CS-1, has led to the isolation of five new metabolites (3, 5, 6, 7 and 8) together with a known compound (4). The structures of the biotransformation products were elucidated by 1D-, 2D NMR and HR-MS. Phaeospheria sp. mainly provided monooxygenation reactions on the A and B rings, whereas A. eureka afforded both monooxygenated and desacetylated derivatives of the substrates. Cytotoxic activity of the compounds was tested against a non-cancerous (HEK-293) and four cancer (PANC-1, MIA PaCa-2, DU 145 and A549) cell lines by MTT cell viability assay. All compounds were less cytotoxic than oleandrin, which had IC50 values ranging between 2.7 and 41.9 nM. Two of the monohydroxylated metabolites, viz. 7(?)-hydroxy oleandrin (3) and 1(?)-hydroxy oleandrin (7), were also potent with IC50 values from 18.45 to 39.0 nM, while desacetylated + monohydroxylated, or dihydroxylated products had much lower cytotoxicity. Additionally, the lesser activity of 2 and its metabolite (6) possessing diginose as sugar residue inferred that oleandrose moiety is important for the toxicity of oleandrin as well as hydrophobicity of the steroid core. © 2020 Phytochemical Society of Europe

Bioengineering / Biyomühendislik

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