Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
Nearly all waterborne products, such as food, beverages, pharmaceuticals, paints, biological (medical) samples, cosmetics and wood require preservation to prevent decomposition of the product due to microbial growth. Most non-food preservatives such as isothiazolinones, bronopol, and pyrithiones, are derived from oil and are increasingly more strictly regulated due to hazards such as ecotoxicity, sensibilization and development of allergies. The low legally permitted concentrations will not only become too low to realize preservation, they will also induce antimicrobial resistance. A chemical transition towards new, innovative, biobased, and eco-friendly preservatives is therefore required. Wydo NBD is dedicated to research towards sustainable ingredients for waterborne paints. For this, together with the Hanze University, non-hazardous, eco-friendly and biobased natural preservatives will be identified and further developed towards marketable products. The knowledge obtained in this project will contribute to the development of biological (paint) conservatives knowledge and improvement of current production methods of Wydo, with the potential for wider application in food and medical products. This project aims to identify natural antimicrobial additives and consists of three consecutive stages. First, an extensive, unbiased bioinformatics guided literature mining will be performed to find relationships between biological antimicrobial compounds and microbes found in paint. The most promising antimicrobials from this mining will be made available by chemical synthesis. Subsequently, the compounds will be assessed for their potential as novel natural preservatives for waterborne paints, by testing for their antimicrobial activity and stability.
Nearly all waterborne products, such as food, beverages, pharmaceuticals, paints, biological (medical) samples, cosmetics and wood require preservation to prevent decomposition of the product due to microbial growth. Most non-food preservatives such as isothiazolinones, bronopol, and pyrithiones, are derived from oil and are increasingly more strictly regulated due to hazards such as ecotoxicity, sensibilization and development of allergies. The low legally permitted concentrations will not only become too low to realize preservation, they will also induce antimicrobial resistance. A chemical transition towards new, innovative, biobased, and eco-friendly preservatives is therefore required. Wydo NBD is dedicated to research towards sustainable ingredients for waterborne paints. For this, together with the Hanze University, non-hazardous, eco-friendly and biobased natural preservatives will be identified and further developed towards marketable products. The knowledge obtained in this project will contribute to the development of biological (paint) conservatives knowledge and improvement of current production methods of Wydo, with the potential for wider application in food and medical products.This project aims to identify natural antimicrobial additives and consists of three consecutive stages. First, an extensive, unbiased bioinformatics guided literature mining will be performed to find relationships between biological antimicrobial compounds and microbes found in paint. The most promising antimicrobials from this mining will be made available by chemical synthesis. Subsequently, the compounds will be assessed for their potential as novel natural preservatives for waterborne paints, by testing for their antimicrobial activity and stability
Plastic waste is one of the largest environmental problems in the 21st century. By 2050, up to 12,000 Mt of plastic waste is estimated to be in landfills or in the natural environment. Biochemical recycling by using modified microbial enzymes have shown potentials in the back-to-monomer (BTM) recycling of polyethylene terephthalate by breaking down the polymers into re-usable monomers. These enzymes can be produced via fungal species. In order to make this biochemical BTM process viable a process integrated enzyme production is key in increasing the efficiency and reducing the cost of enzymes. For this a molecular monitoring method, such as RNA-seq (RNA-sequencing), is needed. RNA-seq can achieve a snapshot on enzyme producing process inside of the cell by semi-quantitatively measuring the volume of enzyme encoding RNAs. This information can bring hints on fungal strain improvement by promoting the desired enzymes. It also helps to instantly monitor the BTM production outcomes. However, conventional RNA-seq platforms can only be performed via service providers or startup investments reaching 2 million euros. Each round of analysis could take as long as 6 weeks turnaround time. Furthermore, the method creates huge amount of complicated datasets, only by expert skills and specialized high performance computing the data can be sorted in a comprehensive manner. To solve these problems, in this project, by combining the expertise on plastic end-of-life control, fungal enzyme production, molecular monitoring and Bioinformatics from both the UAS and SME sides, we aim to implement a novel RNA-seq based system to monitor the in-process enzyme production for plastic degradation. We will optimize the existing portable RNA-seq prototype machinery for semi-real time monitoring of the BTM recycling process. The downstream data will be handled by a tailored analysis pipeline designed with expert knowledge via an user-friendly interface.
