A nanoinformatics decision support tool for the virtual screening of gold nanoparticle cellular association using protein corona fingerprints

The increasing use of nanoparticles (NPs) in a wide range of consumer and industrial applications has necessitated significant effort to address the challenge of characterizing and quantifying the underlying nanostructure – biological response relationships to ensure that these novel materials can be exploited responsibly and safely. Such efforts demand reliable experimental data not only in terms of the biological dose-response, but also regarding the physicochemical properties of the NPs and their interaction with the biological environment. The latter has not been extensively studied, as a large surface to bind biological macromolecules is a unique feature of NPs that is not relevant for chemicals or pharmaceuticals, and thus only limited data have been reported in the literature quantifying the protein corona formed when NPs interact with a biological medium and linking this with NP cellular association/uptake. In this work we report the development of a predictive model for the assessment of the biological response (cellular association, which can include both internalized NPs and those attached to the cell surface) of surface-modified gold NPs, based on their physicochemical properties and protein corona fingerprints, utilizing a dataset of 105 unique NPs. Cellular association was chosen as the end-point for the original experimental study due to its relevance to inflammatory responses, biodistribution, and toxicity in vivo. The validated predictive model is freely available online through the Enalos Cloud Platform (http://enalos.insilicotox.com/NanoProteinCorona/) to be used as part of a regulatory or NP safe-by-design decision support system. This online tool will allow the virtual screening of NPs, based on a list of the significant NP descriptors, identifying those NPs that would warrant further toxicity testing on the basis of predicted NP cellular association.</p

Prospects and challenges for FAIR toxicogenomics data

Peer reviewe

D10.6 - Final Version of NanoCommons Sustainability Plan

NanoCommons was funded as an infrastructure project for a starting community. This means that it was supposed to build the concepts and foundation on which the community can continue to build solutions and services; in the case of NanoCommons, the infrastructure goal was to address the starting community’s data and nanoinformatics needs. NanoCommons did not start entirely from scratch, as it was building on efforts of the Nanosafety Cluster’s Working Group F on data management, and benefited from a general appreciation of the value of data reuse and computational predictions in the community. The push towards increasing use of chemoinformatics and nanoinformatics approaches was also endorsed by the public, regulatory and funding agencies, including being accelerated by the European ban on animal testing in the cosmetics industry and the European Green Deal. Similarly, industry is increasingly acting as a driver: fostering implementation and adoption of data harmonisation, FAIRness (Findability, Accessibility, Interoperability and Reusability of data) and openness and recognising that these activities require targeted and centralised efforts, which were provided by NanoCommons. However, a starting community is just that: a start upon which the community can build, a coalescence point around which collective efforts can nucleate. Our journey is still at the earliest stages, and much is needed in terms of automation, tooling, and continued training and education to drive the mindset changes within the community to fully embed data management at the start of the data lifecycle. Sustained and continuous support will be needed to achieve sufficient levels of digitalisation, global adoption of reporting standards both in scientific and regulatory settings, and machine-readability and machine-actionable data, all of which will lead to better quality and reproducible research, and more trust in the data and understanding of its applicability and suitability for reuse thus enhancing the value of the data and knowledge generated. This starts with sustaining what we already have, which in our case is the NanoCommons Knowledge Infrastructure, the implemented services from NanoCommons, as well as other associated partners and projects, and the collaboration with other projects established beyond the borders of nanosafety research. The term sustainability can be described as “the ability to be maintained at a certain rate or level”. Applied to NanoCommons, this means that the services/tools/materials that were designed and developed during the project and are already being offered to support the nanosafety community will continue to be maintained and ideally further developed, beyond the end of the funded period of the project, ensuring future accessibility for users and potential customers. Since there will be no direct public funding for these services anymore (pending further applications via Horizon Europe for example), planning for sustainability and creation of a (not necessarily commercial) business model were started very early in the project as a central task of WP10 and possible options were continuously evaluated and adapted based on stakeholder feedback coming from surveys and, more importantly, from users of the starting infrastructure services and expertise who received support in the form of Transnational Access (TA) projects or as part of the Demonstration Cases (see deliverable reports D9.3 and D9.4 for details of the first and second round Demonstration Cases, respectively). Deliverable D10.6 presented here builds on the previous deliverables D10.4 “First Testing and Evaluation Results of NanoCommons Sustainability Plan” and D10.5 “Second Testing and Evaluation Results on the NanoCommons Sustainability Plan”, proposing the first version of the business model and analysing all project activities related to sustainability during the last period, respectively. Together, these three reports outline the considerations and activities undertaken with the aim of ensuring the sustained existence and utilisation of the NanoCommons project outcomes beyond the project lifetime. A major NanoCommons objective has been to achieve a sustainable and open knowledge infrastructure for the whole nanosafety community, and thus a considerable effort was invested in exploring the options and approaches, focussing on those business models consistent with the ethos of openness and accessibility, given the public funding used to develop the services, and the critical importance of access to Environmental Health and Safety (EHS) data globally. In this final deliverable, evaluation of the TAs and Demonstration Cases with respect to their (potential) contributions to the UN Sustainable Development Goals (SDGs) is completed by looking at the results from the third funding period. Additionally, the targeted activities with the strategic partners most of whom were previously identified as significant routes via which to sustain and further develop the NanoCommons tools and services, are summarised. The NanoCommons focus areas for short/long term sustainability are presented, along with the justifications of these choices. All of this information is then condensed into the final NanoCommons sustainability plan

Advances in De Novo Drug Design : From Conventional to Machine Learning Methods

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development.Peer reviewe

Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials

Toxicogenomics (TGx) approaches are increasingly applied to gain insight into the possible toxicity mechanisms of engineered nanomaterials (ENMs). Omics data can be valuable to elucidate the mechanism of action of chemicals and to develop predictive models in toxicology. While vast amounts of transcriptomics data from ENM exposures have already been accumulated, a unified, easily accessible and reusable collection of transcriptomics data for ENMs is currently lacking. In an attempt to improve the FAIRness of already existing transcriptomics data for ENMs, we curated a collection of homogenized transcriptomics data from human, mouse and rat ENM exposures in vitro and in vivo including the physicochemical characteristics of the ENMs used in each study.Peer reviewe

Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

Identification of Endocrine Disrupting Chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause Estrogen Receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous Quantitative Structure-Activity Relationships (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R2=0.71, STL R2=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R2=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation

Comparative study of the AT1 receptor prodrug antagonist candesartan cilexetil with other sartans on the interactions with membrane bilayers

AbstractDrug–membrane interactions of the candesartan cilexetil (TCV-116) have been studied on molecular basis by applying various complementary biophysical techniques namely differential scanning calorimetry (DSC), Raman spectroscopy, small and wide angle X-ray scattering (SAXS and WAXS), solution 1H and 13C nuclear magnetic resonance (NMR) and solid state 13C and 31P (NMR) spectroscopies. In addition, 31P cross polarization (CP) NMR broadline fitting methodology in combination with ab initio computations has been applied. Finally molecular dynamics (MD) was applied to find the low energy conformation and position of candesartan cilexetil in the bilayers. Thus, the experimental results complemented with in silico MD results provided information on the localization, orientation, and dynamic properties of TCV-116 in the lipidic environment. The effects of this prodrug have been compared with other AT1 receptor antagonists hitherto studied. The prodrug TCV-116 as other sartans has been found to be accommodated in the polar/apolar interface of the bilayer. In particular, it anchors in the mesophase region of the lipid bilayers with the tetrazole group oriented toward the polar headgroup spanning from water interface toward the mesophase and upper segment of the hydrophobic region. In spite of their localization identity, their thermal and dynamic effects are distinct pointing out that each sartan has its own fingerprint of action in the membrane bilayer, which is determined by the parameters derived from the above mentioned biophysical techniques

Representing and describing nanomaterials in predictive nanoinformatics

This Review discusses how a comprehensive system for defining nanomaterial descriptors can enable a safe-and-sustainable-by-design concept for engineered nanomaterials. Engineered nanomaterials (ENMs) enable new and enhanced products and devices in which matter can be controlled at a near-atomic scale (in the range of 1 to 100 nm). However, the unique nanoscale properties that make ENMs attractive may result in as yet poorly known risks to human health and the environment. Thus, new ENMs should be designed in line with the idea of safe-and-sustainable-by-design (SSbD). The biological activity of ENMs is closely related to their physicochemical characteristics, changes in these characteristics may therefore cause changes in the ENMs activity. In this sense, a set of physicochemical characteristics (for example, chemical composition, crystal structure, size, shape, surface structure) creates a unique 'representation' of a given ENM. The usability of these characteristics or nanomaterial descriptors (nanodescriptors) in nanoinformatics methods such as quantitative structure-activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimize ENMs at the design stage by improving their functionality and minimizing unforeseen health/environmental hazards. A computational screening of possible versions of novel ENMs would return optimal nanostructures and manage ('design out') hazardous features at the earliest possible manufacturing step. Safe adoption of ENMs on a vast scale will depend on the successful integration of the entire bulk of nanodescriptors extracted experimentally with data from theoretical and computational models. This Review discusses directions for developing appropriate nanomaterial representations and related nanodescriptors to enhance the reliability of computational modelling utilized in designing safer and more sustainable ENMs.Peer reviewe

Rational design, efficient syntheses and biological evaluation of N,N′-symmetrically bis-substituted butylimidazole analogs as a new class of potent Angiotensin II receptor blockers

A series of symmetrically bis-substituted imidazole analogs bearing at the N-1 and N-3 two biphenyl moieties ortho substituted either with tetrazole or carboxylate functional groups was designed based on docking studies and utilizing for the first time an extra hydrophobic binding cleft of AT1 receptor. The synthesized analogs were evaluated for their in vitro antagonistic activities (pA2 values) and binding affinities (–logIC50 values) to the Angiotensin II AT1 receptor. Among them, the potassium (–logIC50 = 9.04) and the sodium (–logIC50 = 8.54) salts of 4-butyl-N,N′-bis{[2′-(2H-tetrazol-5-yl)biphenyl-4-yl]methyl}imidazolium bromide (12a and 12b, respectively) as well as its free acid 11 (–logIC50 = 9.46) and the 4-butyl-2-hydroxymethyl-N,N′-bis{[2′-(2H-tetrazol-5-yl)biphenyl-4-yl]methyl}imidazolium bromide (14) (–logIC50 = 8.37, pA2 = 8.58) showed high binding affinity to the AT1 receptor and high antagonistic activity (potency). The potency was similar or even superior to that of Losartan (–logIC50 = 8.25, pA2 = 8.25). On the contrary, 2-butyl-N,N′-bis{[2′-[2H-tetrazol-5-yl)]biphenyl-4-yl]methyl}imidazolium bromide (27) (–logIC50 = 5.77) and 2-butyl-4-chloro-5-hydroxymethyl-N,N′-bis{[2′-[2H-tetrazol-5-yl)]biphenyl-4-yl]methyl}imidazolium bromide (30) (–logIC50 = 6.38) displayed very low binding affinity indicating that the orientation of the n-butyl group is of primary importance. Docking studies of the representative highly active 12b clearly showed that this molecule has an extra hydrophobic binding feature compared to prototype drug Losartan and it fits to the extra hydrophobic cavity. These results may contribute to the discovery and development of a new class of biologically active molecules through bis-alkylation of the imidazole ring by a convenient and cost effective synthetic strategy