Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning.

Manufacturers of nanomaterial-enabled products need models of endpoints that are relevant to human safety to support the "safe by design" paradigm and avoid late-stage attrition. Increasingly, embryonic zebrafish () are recognised as a key human safety relevant in vivo test system. Hence, machine learning models were developed for identifying metal oxide nanomaterials causing lethality to embryonic zebrafish up to 24 hours post-fertilisation, or excess lethality in the period of 24-120 hours post-fertilisation, at concentrations of 250 ppm or less.

Machine learning methods for multi-walled carbon nanotubes (MWCNT) genotoxicity prediction.

Multi-walled carbon nanotubes (MWCNTs) are made of multiple single-walled carbon nanotubes (SWCNTs) which are nested inside one another forming concentric cylinders. These nanomaterials are widely used in industrial and biomedical applications, due to their unique physicochemical characteristics. However, previous studies have shown that exposure to MWCNTs may lead to toxicity and some of the physicochemical properties of MWCNTs can influence their toxicological profiles.

QSAR modeling of the toxicity classification of superparamagnetic iron oxide nanoparticles (SPIONs) in stem-cell monitoring applications: an integrated study from data curation to model development.

The use of approaches for the prediction of biomedical properties of nano-biomaterials (NBMs) can play a significant role in guiding and reducing wetlab experiments. Computational methods, such as data mining and machine learning techniques, can increase the efficiency and reduce the time and cost required for hazard and risk assesment and for designing new safer NBMs. A major obstacle in developing accurate and well-validated models such as Nano Quantitative Structure-Activity Relationships (Nano-QSARs) is that although the volume of data published in the literature is increasing, the data are fragmented in many different publications and are not sufficiently curated for modelling purposes.

A novel amyloid designable scaffold and potential inhibitor inspired by GAIIG of amyloid beta and the HIV-1 V3 loop.

The GAIIG sequence, common to the amyloid beta peptide (residues 29-33) and to the HIV-1 gp120 (residues 24-28 in a typical V3 loop), self-assembles into amyloid fibrils, as suggested by theory and the experiments presented here. The longer YATGAIIGNII sequence from the V3 loop also self-assembles into amyloid fibrils, of which the first three and the last two residues are outside the amyloid GAIIG core. We postulate that this sequence, with suitably selected modifications at the flexible positions, can serve as a designable scaffold for novel amyloid-based materials.

Multiscale simulations reveal IRMOF-74-III as a potent drug carrier for gemcitabine delivery.

A multiscale computational study is reported that investigates the microscopic behavior of the anti-cancer drug gemcitabine (GEM) stored in metal organic frameworks IRMOF-74-III and the functionalized OH-IRMOF-74-III. Accurate Quantum Mechanics calculations indicate that the GEM-MOF interaction energy in both host structures is suitable for drug adsorption and delivery with a slow release. Based on Grand-Canonical Monte Carlo simulations, the predicted maximum loading of GEM is three-fold greater than in lipid-coated mesoporous silica nanoparticles and similar to liposome nanocarriers.