An annotated corpus with nanomedicine and pharmacokinetic parameters

A vast amount of data on nanomedicines is being generated and published, and natural language processing (NLP) approaches can automate the extraction of unstructured text-based data. Annotated corpora are a key resource for NLP and information extraction methods which employ machine learning. Although corpora are available for pharmaceuticals, resources for nanomedicines and nanotechnology are still limited. To foster nanotechnology text mining (NanoNLP) efforts, we have constructed a corpus of annotated drug product inserts taken from the US Food and Drug Administration’s Drugs@FDA online database. In this work, we present the development of the Engineered Nanomedicine Database corpus to support the evaluation of nanomedicine entity extraction. The data were manually annotated for 21 entity mentions consisting of nanomedicine physicochemical characterization, exposure, and biologic response information of 41 Food and Drug Administration-approved nanomedicines. We evaluate the reliability of the manual annotations and demonstrate the use of the corpus by evaluating two state-of-the-art named entity extraction systems, OpenNLP and Stanford NER. The annotated corpus is available open source and, based on these results, guidelines and suggestions for future development of additional nanomedicine corpora are provided

Using natural language processing techniques to inform research on nanotechnology

Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP)-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics

Physico-chemical characterization and oxidative reactivity evaluation of aged brake wear particles

Brake wear dust is a significant component of traffic emissions and has been linked to adverse health effects. Previous research found a strong oxidative stress response in cells exposed to freshly generated brake wear dust. We characterized aged dust collected from passenger vehicles, using microscopy and elemental analyses. Reactive oxygen species (ROS) generation was measured with acellular and cellular assays using 2′7-dichlorodihydrofluorescein dye. Microscopy analyses revealed samples to be heterogeneous particle mixtures with few nanoparticles detected. Several metals, primarily iron and copper, were identified. High oxygen concentrations suggested that the elements were oxidized. ROS were detected in the cell-free fluorescent test, while exposed cells were not dramatically activated by the concentrations used. The fact that aged brake wear samples have lower oxidative stress potential than fresh ones may relate to the highly oxidized or aged state of these particles, as well as their larger size and smaller reactive surface area

Human inhalation exposure to iron oxide particles

In the past decade, many studies have been conducted to determine the health effects induced by exposure to engineered nanomaterials (NMs). Specifically for exposure via inhalation, numerous in vitro and animal in vivo inhalation toxicity studies on several types of NMs have been published. However, these results are not easily extrapolated to judge the effects of inhaling NMs in humans, and few published studies on the human response to inhalation of NMs exist. Given the emergence of more industries utilizing iron oxide nanoparticles as well as more nanomedicine applications of superparamagnetic iron oxide nanoparticles (SPIONs), this review presents an overview of the inhalation studies that have been conducted in humans on iron oxides. Both occupational exposure studies on complex iron oxide dusts and fumes, as well as human clinical studies on aerosolized, micron-size iron oxide particles are discussed. Iron oxide particles have not been described to elicit acute inhalation response nor promote lung disease after chronic exposure. The few human clinical studies comparing inhalation of fine and ultrafine metal oxide particles report no acute changes in the health parameters measured. Taken together existing evidence suggests that controlled human exposure to iron oxide nanoparticles, such as SPIONs, could be conducted safel

Machine Assisted Experimentation of Extrusion-Based Bioprinting Systems

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time- and resource-intensive and not easily translatable to other laboratories. This study approaches EBB parameter optimization through machine learning (ML) models trained using data collected from the published literature. We investigated regression-based and classification-based ML models and their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite bioinks. In addition, we interrogated if regression-based models can predict suitable extrusion pressure given the desired cell viability when keeping other experimental parameters constant. We also compared models trained across data from general literature to models trained across data from one literature source that utilized alginate and gelatin bioinks. The results indicate that models trained on large amounts of data can impart physical trends on cell viability, filament diameter, and extrusion pressure seen in past literature. Regression models trained on the larger dataset also predict cell viability closer to experimental values for material concentration combinations not seen in training data of the single-paper-based regression models. While the best performing classification models for cell viability can achieve an average prediction accuracy of 70%, the cell viability predictions remained constant despite altering input parameter combinations. Our trained models on bioprinting literature data show the potential usage of applying ML models to bioprinting experimental design

Development of a targeted and controlled nanoparticle delivery system for FoxO1 inhibitors

Background: Poly (lactic-co-glycolic acid) (PLGA) and polyethylene glycol (PEG) are polymers approved by the United States’ Food and Drug Administration. Drugs for various medical treatments have been encapsulated in PLGA-PEG nanoparticles for targeted delivery and reduction of unwanted side effects. Methods: A flow synthesis method for PLGA-PEG nanoparticles containing FoxO1 inhibitors and adipose vasculature targeting agents was developed. A set of nanoparticles including PLGA and PLGA-PEG-P3 unloaded and drug loaded were generated. The particles were characterized by DLS, fluorescence spectroscopy, TEM, and dialysis. Endotoxin levels were measured using the LAL chromogenic assay. Our approach was compared to over 270 research articles using information extraction tools. Results: Nanoparticle hydrodynamic diameters ranged from 142.4 ±0.4 d.nm to 208.7 ±3.6 d.nm while the polydispersity index was less than 0.500 for all samples (0.057 ±0.021 to 0.369 ±0.038). Zeta potentials were all negative ranging from -4.33 mV to -13.4 mV. Stability testing confirmed that size remained unchanged for up to 4 weeks. For AS1842856, loading was 0.5 mg drug/mL solution and encapsulation efficiency was ~100%. Dialysis indicated burst release of drug in the first 4 hours. Conclusion: PLGA encapsulation of AS1842856 was successful but unsuccessful for the two more hydrophilic drugs. Alternative syntheses such as water/oil/water emulsion or liposomal encapsulation are being considered. Analysis of data from published papers on PLGA nanoparticles indicated that our results were consistent with identified process-structure relationships and few groups reported endotoxin levels even though in vivo testing was performed.https://scholarscompass.vcu.edu/gradposters/1071/thumbnail.jp

Human inhalation exposure to iron oxide particles

In the past decade, many studies have been conducted to determine the health effects induced by exposure to engineered nanomaterials (NMs). Specifically for exposure via inhalation, numerous in vitro and animal in vivo inhalation toxicity studies on several types of NMs have been published. However, these results are not easily extrapolated to judge the effects of inhaling NMs in humans, and few published studies on the human response to inhalation of NMs exist. Given the emergence of more industries utilizing iron oxide nanoparticles as well as more nanomedicine applications of superparamagnetic iron oxide nanoparticles (SPIONs), this review presents an overview of the inhalation studies that have been conducted in humans on iron oxides. Both occupational exposure studies on complex iron oxide dusts and fumes, as well as human clinical studies on aerosolized, micron-size iron oxide particles are discussed. Iron oxide particles have not been described to elicit acute inhalation response nor promote lung disease after chronic exposure. The few human clinical studies comparing inhalation of fine and ultrafine metal oxide particles report no acute changes in the health parameters measured. Taken together existing evidence suggests that controlled human exposure to iron oxide nanoparticles, such as SPIONs, could be conducted safely

Characterization of Tungsten Inert Gas (TIG) Welding Fume Generated by Apprentice Welders.

Tungsten inert gas welding (TIG) represents one of the most widely used metal joining processes in industry. Its propensity to generate a greater portion of welding fume particles at the nanoscale poses a potential occupational health hazard for workers. However, current literature lacks comprehensive characterization of TIG welding fume particles. Even less is known about welding fumes generated by welding apprentices with little experience in welding. We characterized TIG welding fume generated by apprentice welders (N = 20) in a ventilated exposure cabin. Exposure assessment was conducted for each apprentice welder at the breathing zone (BZ) inside of the welding helmet and at a near-field (NF) location, 60cm away from the welding task. We characterized particulate matter (PM4), particle number concentration and particle size, particle morphology, chemical composition, reactive oxygen species (ROS) production potential, and gaseous components. The mean particle number concentration at the BZ was 1.69E+06 particles cm(-3), with a mean geometric mean diameter of 45nm. On average across all subjects, 92% of the particle counts at the BZ were below 100nm. We observed elevated concentrations of tungsten, which was most likely due to electrode consumption. Mean ROS production potential of TIG welding fumes at the BZ exceeded average concentrations previously found in traffic-polluted air. Furthermore, ROS production potential was significantly higher for apprentices that burned their metal during their welding task. We recommend that future exposure assessments take into consideration welding performance as a potential exposure modifier for apprentice welders or welders with minimal training