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Enzyme Kinetics and Drugs as Enzyme Inhibitors
Published in Peter Grunwald, Pharmaceutical Biocatalysis, 2019
Enzymes are prominent targets for drug design because their catalytic activity may be pathogenic and associated to a large variety of diseases. This can be seen from the fact that almost 50% of all drugs are enzyme inhibitors (Hopkin and Groom, 2002). According to a bcc Research analysis the global market for enzyme inhibitors was valued US$ 104.4 billion for 2010 and estimated to reach about US$ 127.4 billion by 2016 (Dewan, 2012). As the number of published enzyme crystal structures steadily increases the search for structure-based drug/inhibitor design via computational screening of huge chemical databases has become possible (Śledź and Caflisch, 2018; Supuran, 2017). In this chapter, the main types of enzyme inhibition have been described and examples for their application in areas such as diabetes, cardiovascular diseases, cancer, or psychiatric disorders are given. Against the background of an improved health care for an ageing population, research activities with the aim to develop new drugs for treatment of complex biological malfunctions will remain of utmost importance in the future including the design of novel multi-target-directed ligands (Ramsay and Tipton, 2017; Jankowska et al., 2018).
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.
Deep learning for predicting toxicity of chemicals: a mini review
Published in Journal of Environmental Science and Health, Part C, 2018
Weihao Tang, Jingwen Chen, Zhongyu Wang, Hongbin Xie, Huixiao Hong
Modern chemical toxicity screening programs have generated an impressive amount of biological activity data. These data are available in several public databases. PubChem was established in 2004 by the National Center for Biotechnology Information. It is the largest public chemical database consisting of toxicity, pharmaceutical and genomic information.70–72 After over 10 years of development, there were more than 220 million substances and over 120 million assays were included in the database as of October 2016.18,73