China
Africa
Explore chapters and articles related to this topic
Practical Considerations When Selecting and Using Gloves for Chemical Protection
Published in Robert N. Phalen, Howard I. Maibach, Protective Gloves for Occupational Use, 2023
Ideally, the glove will only be required to protect against the residual hazard identified in the risk assessment for that task. It is essential to ensure that the hazard is that which arises when one or more chemicals are used. This is rarely the same as the hazard stated on the safety data sheet(s). In the UK this has been recognized by the Health and Safety Executive, whose guidance on risk assessment states:Employers should regard a substance as hazardous to health if it is hazardous in the form in which it may occur in the work activity. A substance hazardous to health need not be just a chemical compound, it can also include mixtures of compounds, micro-organisms or natural materials, such as flour, stone or wood dust. (HSE ACoP)6
The Role of Natural Products in COVID-19
Published in Hanadi Talal Ahmedah, Muhammad Riaz, Sagheer Ahmed, Marius Alexandru Moga, The Covid-19 Pandemic, 2023
Iqra Akhtar, Sumera Javad, Tehreema Iftikhar, Amina Tariq, Hammad Majeed, Asma Ahmad, Muhammad Arfan, M. Zia-Ul-Haq
There are a number of computational tools available now which accelerate the drug discovery and decrease the budget of discovery. These tools help us to understand the fact that a chemical compound can be used in a drug or not. We can also make an estimation of the mechanism of drug action and its possible safe dose. These computational techniques involve Chemoinformatics, Network Pharmacology, Molecular Similarity, Pharmacogenomics De Novo Design, Quantitative Structure-Activity Relation and Molecular Docking, etc. [68–70].
Approaches for Identification and Validation of Antimicrobial Compounds of Plant Origin: A Long Way from the Field to the Market
Published in Mahendra Rai, Chistiane M. Feitosa, Eco-Friendly Biobased Products Used in Microbial Diseases, 2022
Lívia Maria Batista Vilela, Carlos André dos Santos-Silva, Ricardo Salas Roldan-Filho, Pollyanna Michelle da Silva, Marx de Oliveira Lima, José Rafael da Silva Araújo, Wilson Dias de Oliveira, Suyane de Deus e Melo, Madson Allan de Luna Aragão, Thiago Henrique Napoleão, Patrícia Maria Guedes Paiva, Ana Christina Brasileiro-Vidal, Ana Maria Benko-Iseppon
The main functionalities of these tools fall into five main areas of application (Fig. 10.3): (i) database search and integration, (ii) comparison of structures prioritizing similar compounds, (iii) search for a compound in different ways (chemical formula or two-dimensional structure), (iv) the grouping of compounds correlating characteristics of biological activity through the conversion of a similarity matrix by a distance matrix and prediction of chemical properties, considering that physicochemical properties data are essential to predict bioactivity and (v) other properties of small molecules recognized using machine learning approaches (Backman et al. 2011). These procedures allowing a more in-depth understanding of chemical compounds’ nature is highly relevant, promoting knowledge for both, to understand fundamental biological processes and to develop new therapeutic strategies (Dobson 2004).
Training recurrent neural networks as generative neural networks for molecular structures: how does it impact drug discovery?
Published in Expert Opinion on Drug Discovery, 2022
Sofia D’Souza, Prema Kv, Seetharaman Balaji
The discovery of novel chemical compounds with desired activities against the target of interest is crucial in drug discovery. The design, development, and testing of a compound to bring it to market is laborious, expensive, and takes a long time. Computational methods such as virtual screening and de novo design of focused libraries have enabled the speed up of the drug discovery process by reducing the number of compounds that have to be screened in wet-lab experiments. Advances in high throughput screening have significantly improved the discovery and development of small molecule drugs [1]. The success rate of lead molecules in clinical trials is falling owing to the nonfulfillment of the physicochemical properties required for pharmaceutical drugs [2] which requires exploration of newer molecules. Despite the progress made in the virtual and non-virtual screening of molecules, it is known that only a small region of the chemical space is sampled. To keep the discovery pipeline going, scientists face the arduous task of identifying novel compounds that could be synthesized and could enter the drug discovery pipeline.
Recent progress and challenges in drug development to fight hand, foot and mouth disease
Published in Expert Opinion on Drug Discovery, 2020
Ze Qin Lim, Qing Yong Ng, Justin Wei Qing Ng, Vikneswari Mahendran, Sylvie Alonso
The discovery of new drugs typically involves the extensive screening of defined libraries of chemical compounds. In the field of infectious diseases, historically, most of the antibacterial drugs currently in the market have been identified through phenotypic screen whereby hits were screened based on their killing activity against the whole microorganism without knowing the actual target [76]. In contrast, in the field of viral diseases, target-based screening had been the method of choice for the past two decades, whereby drugs were selected based on their binding activity to a specific viral protein target. However, there has been a paradigm shift toward phenotypic screens to identify antiviral drugs [77]. In this context, screening of antiviral drugs against EV-A71 has been based on the inhibition of the virus-induced cytopathic effects (CPE) compared to infected untreated control. Over the years, progress has been made not only in the technological/engineering aspects (for example, the development of HTS), but also in the availability of better-quality compounds libraries, as well as the utilization of optimized screening methods [78]. Selection of the compound libraries to screen is also very important and represents a determining factor to identify successful hits.
Transforming cancer drug discovery with Big Data and AI
Published in Expert Opinion on Drug Discovery, 2019
Paul Workman, Albert A. Antolin, Bissan Al-Lazikani
But increasingly, the information that the researcher would like to access and distil is hidden within vast, sparse data (Figure 2(b)). For example, such data include all chemical compounds, atomic interactions and synthetic reaction pathways in medicinal chemistry. It is in these huge-scale Big Data areas that complex DL algorithms come into their own. In contrast to ML, DL can discover key patterns hidden in a multidimensional data space through the layered abstraction of diverse data [25]. DL methods have enabled breakthroughs in several disciplines, for example the use of convolutional neural networks (CNNs) in image processing and of recurrent neural networks (RNNs) in text and speech [25]. However, they suffer from challenges such as high computational cost and difficulty in handling ‘messy’ and sparse biological data. AI encompasses these approaches, especially when the algorithms change and adapt in response to new information.