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Formulation Design and Optimization Using Molecular Dynamics
Published in Davide Fissore, Roberto Pisano, Antonello Barresi, Freeze Drying of Pharmaceutical Products, 2019
Roberto Pisano, Andrea Arsiccio
The type of information that can be obtained from MD simulations is complementary, not alternative, to experimental tests. In silico modeling can help explain experimental results, and reduce the number of experimental tests to be performed, but it would hardly be able to reproduce the complexity of reality. At the same time, the molecular details provided by MD simulations are not accessible by current experimental techniques.
Environmental Fate and Effects of Nanomaterials in Aquatic Freshwater Environments
Published in Sivashankar Krishnamoorthy, Krzysztof Iniewski, Nanomaterials, 2017
Arno C. Gutleb, Sébastien Cambier, Teresa Fernandes, Anastasia Georgantzopoulou, Thomas A.J. Kuhlbusch, Iseult Lynch, Ailbhe Macken, Kahina Mehennaoui, Ruth Moeller, Carmen Nickel, W. Peijnenburg, Tomasso Serchi
Computational modeling has emerged over the past decade as a reliable tool to estimate the underpinning parameters that control properties and effects of chemical substances on the basis of (quantitative) structure–activity relationship (Q)SAR, and read across from the vast amount of available data. Combined with powerful data-mining tools, these computational models offer a rapid way of filling data gaps due to the lack or limited availability of experimental data on new substances. The in silico models are now routinely used by researchers, industry, and regulators to estimate physicochemical properties, human health and ecotoxicological effects, and environmental behavior and fate of a wide range of chemical substances.
In Silico Methods for Nanotoxicity Evaluation: Opportunities and Challenges
Published in Vineet Kumar, Nandita Dasgupta, Shivendu Ranjan, Nanotoxicology, 2018
Natalia Sizochenko, Alicja Mikolajczyk, Jerzy Leszczynski, Tomasz Puzyn
Chemoinformatics aim at storage, indexing, search of information of the related compounds, visual representations, modeling, and docking. Chemicals are represented in silico (structure and properties) and usually stored in chemical databases (Sizochenko and Leszczynski 2016). Chemical databases are applicable for computational (virtual) screening. As the amount of nanotoxicological data grows each year, development of databases helps in data sharing and standardization. There are several nanomaterials research databases that were developed at different times. For example, eNanoMapper, caNanoLab, Nanomaterial Registry, and so on (Knowledgebase n.d.; Nanomanufacturing n.d.; National Toxicology Program Database n.d.; Registry n.d.; Thomas et al. 2013). caNanoLab is a data repository that contains information on nanoparticles: composition, biomedical, physical (size, molecular weight, etc.), and in vitro measurements. Usually, access to the associated publications is also provided. Researchers can also use this database to submit their own results. Submitters can restrict the visibility of their records to be private, to be distributed to particular collaboration groups or to be public. National Toxicology Program Database contains information about different toxicants, including nanomaterials (National Toxicology Program Database n.d.). Nanomaterial Biological Interactions Knowledgebase is a repository containing data on nanomaterial characterization (purity, size, shape, charge, composition, functionalization, and agglomeration state), synthesis methods, and nanomaterial-biological interactions (Knowledgebase n.d.). InterNano is a repository which brings together different resources related to nanotechnology (e.g., devices, materials, etc.) (Nanomanufacturing n.d.). Another popular source is The ISA-TAB-Nano database. It is a standard specifies format for representing information about nanomaterials (Thomas et al. 2013). The biggest problem of existing databases lies in the fact that standardized protocols for nanoparticles measurements may vary within different laboratories. Properties of nanoparticles can easily change if the method of preparation was changed. This may lead to inaccuracy in risk assessment, which means that this procedure could be adequately addressed only for each individual case. This raises a need for wise selection of the synthesis methods and target cells or organisms. In order to obtain qualitative results, the initial data of nanoparticles should be homogenous and obtained through a standardized measurement protocol. The quality of data is the main limiting factor for in silico modeling. The best choice is the data obtained from one laboratory. In other cases, critical revision of data origin and conditions of experiment are required. In the case of nanoparticles, standardized protocols for measurements may vary in different laboratories.
Metabolic modeling of synthetic microbial communities for bioremediation
Published in Critical Reviews in Environmental Science and Technology, 2023
Lvjing Wang, Xiaoyu Wang, Hao Wu, Haixia Wang, Yihan Wang, Zhenmei Lu
Another momentous application of GEMs in microbial ecology and environmental bioremediation is metabolic interaction modeling (Figure 3c, d). A study of GEMs in modeling interactions showed that interspecies microbial interactions could be driven by costless secretions (Pacheco et al., 2019). Iterative processing of FBA was used to mimic the paired growth of 24 species in various carbon sources with or without oxygen until no more metabolites were produced. Over 2 million simulations were carried out in silico, which would require an unreasonably excessive amount of labor in vitro. As a result, mathematical models enable in silico high-throughput simulations that are challenging to duplicate experimentally. Sun et al. used a two-species GEM to demonstrate that metabolic facilitation between an inoculant (Bacillus velezensis SQR9) and a plant-beneficial indigenous strain (Pseudomonas stutzeri XL272) promoted the growth of cucumber and alleviated salt stress in cucumber (Sun et al., 2022). The prediction of the GEM identified that branched-chain amino acids were potentially shared by B. velezensis SQR9 and P. stutzeri XL272 and participated in syntrophic cooperation of these two species; this result helps illustrate a synergistic interaction mechanism between a biocontrol bacterium and a partner species.
In silico prediction of the full United Nations Globally Harmonized System eye irritation categories of liquid chemicals by IATA-like bottom-up approach of random forest method
Published in Journal of Toxicology and Environmental Health, Part A, 2021
Yeonsoo Kang, Boram Jeong, Doo-Hyeon Lim, Donghwan Lee, Kyung-Min Lim
To replace animal testing, various alternative approaches were studied employing in silico, ex vivo, and in vitro methods (Benfenati et al. 2011; Kim et al. 2019a; Krewski et al. 2010; Moldenhauer 2003; Nepal et al. 2019; Ramos et al. 2019). Among these methods, in silico models have drawn attention because these might predict the properties of chemicals fast and cheaply. In silico prediction methods, such as structure–activity relationship (SAR), quantitative structure–activity relationship, quantitative structure–property relationship (QSPR), expert judgment system, read-across, grouping, physiochemical rules, molecular modeling, are playing important roles in the assessment of chemical safety, filling the critical gaps in the toxicological data of chemicals (Gerner, Liebsch, and Spielmann 2005; Kim et al. 2019b; Luechtefeld et al. 2016; Verma and Matthews 2015a).
Multisite pacing and myocardial scars: a computational study
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2020
Mohammad Albatat, Jacob Bergsland, Hermenegild Arevalo, Hans Henrik Odland, Samuel Wall, Joakim Sundnes, Ilangko Balasingham
The main benefit of in silico models is the ability to understand complex processes and to study the effect of different factors separately. This approach, however, require assumptions and simplifications that may influence results compared to what is seen clinically. In this study, scar tissue was assumed to be a uniform and nonconductive medium, and changes in electrophysiology of border zones or the so-called grey-zones were not taken into account. Border zones primarily affects arrhythmogenesis (Mendonca Costa et al. 2018), and since our purpose was to study mechanical functions of the LV, we chose to neglect this aspect of the border zone. Similarly, the effect of mechano-electric feedback was neglected, to reduce complexity and computational time, as it has a negligible effect on the relative function of the LV (Wall et al. 2011).