Machine-Learning-Based Approach to Decode the Influence of Nanomaterial Properties on Their Interaction with Cells

Abstract

In an in vitro nanotoxicity system, cell−nanoparticle (NP) interaction leads to the surface adsorption, uptake, and changes into nuclei/cell phenotype and chemistry, as an indicator of oxidative stress, genotoxicity, and carcinogenicity. Different types of nanomaterials and their chemical composition or "corona" have been widely studied in context with nanotoxicology. However, rare reports are available, which delineate the details of the cell shape index (CSI) and nuclear area factors (NAFs) as a descriptor of the type of nanomaterials. In this paper, we propose a machine-learning-based graph modeling and correlation-establishing approach using tight junction protein ZO-1-mediated alteration in the cell/nuclei phenotype to quantify and propose it as indices of cell−NP interactions. We believe that the phenotypic variation (CSI and NAF) in the epithelial cell is governed by the physicochemical descriptors (e.g., shape, size, zeta potential, concentration, diffusion coefficients, polydispersity, and so on) of the different classes of nanomaterials, which critically determines the intracellular uptake or cell membrane interactions when exposed to the epithelial cells at sub-lethal concentrations. The intrinsic and extrinsic physicochemical properties of the representative nanomaterials (NMs) were measured using optical (dynamic light scattering, NP tracking analysis) methods to create a set of nanodescriptors contributing to cell−NM interactions via phenotype adjustments. We used correlation function as a machine-learning algorithm to successfully predict cell and nuclei shapes and polarity functions as phenotypic markers for five different classes of nanomaterials studied herein this report. The CSI and NAF as nanodescriptors can be used as intuitive cell phenotypic parameters to define the safety of nanomaterials extensively used in consumer products and nanomedicine.

Cytotoxic Potential, Metabolic Profiling, and Liposomes of Coscinoderma sp. Crude Extract Supported by in silico Analysis

“…[38][39][40] Nanomaterials with smaller particle sizes are easier to be up taken via endocytosis. 41 Since the low water solubility of extract components can be an obstacle against availability for absorption and cellular uptake, 42,43 enhancement of solubilization of the extract components, achieved by encapsulation, may have an important role in the improved cytotoxic effect against the cell lines under investigation. 44 Favored uptake by interstitial leaky vasculature of tumor tissues can be another scenario for the accelerated cellular internalization.…”

Nanomedicine-Based Therapeutics to Combat Acute Lung Injury

“…The toxicity and safety concerns are great challenges for nanomedicine clinical translation. Recent advances in machine learning and artificial intelligence immensely decoded and empowered the cell-nanomaterial interaction, which gifted the computational tool for the prediction process 125,126 and in-silico methods 127,128 to potentially decipher the quantitative nanostructure activity-relationship (Nano-QSAR) for nanotoxicology and nanotherapeutics ( Figure 6).…”

Rapid Quantum Dot Nanobead-mAb Probe-Based Immunochromatographic Assay for Antibody Monitoring of Trichinella spiralis Infection

“…CLP antibody (CLP Ab) was purified by saturated ammonium sulfate solution 29 from the recombinant CLP (rCLP)-immunized pig, whose preimmunized serum served as the source of background in calibration curves. Sera from pigs infected with 10,000 larvae were collected at 7,11,17,19,21,25,30,35,45,60, 90 and 120 dpi, and were used in antibody kinetics.…”

“…15,16 Traditional ICAs employ gold nanoparticles (AuNPs) and provide a binary yes/no answer that corresponds to the presence if the concentration of target antibody is over or under a specific cut-off level. [17][18][19][20] Disturbed red optical intensity of AuNPs in complex biological body fluid condition and reduced colloidal stability in harsh conditions, such as in salt solutions, restrict their further applications in traditional ICAs for serum antibody test. Currently, quantum dot nanobeads (QBs) with resistance to matrix interference are used to improve the sensitivity and stability of ICAs, because of their controllable fluorescent emission wavelengths, narrow peaks and more intense fluorescence signals than the original quantum dots.…”