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    Using computational methods for the prediction of drug vehicles

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    Publication date
    2014
    Author
    Mistry, Pritesh
    Palczewska, Anna Maria
    Neagu, Daniel
    Trundle, Paul R.
    Keyword
    Drugs; Vehicles; Radio frequency; Artificial neural networks; Support vector machines; Predictive models; Neurons
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    Abstract
    Drug vehicles are chemical carriers that aid a drug's passage through an organism. Whilst they possess no intrinsic efficacy they are designed to achieve desirable characteristics which can include improving a drug's permeability and or solubility, targeting a drug to a specific site or reducing a drug's toxicity. All of which are ideally achieved without compromising the efficacy of the drug. Whilst the majority of drug vehicle research is focused on the solubility and permeability issues of a drug, significant progress has been made on using vehicles for toxicity reduction. Achieving this can enable safer and more effective use of a potent drug against diseases such as cancer. From a molecular perspective, drugs activate or deactivate biochemical pathways through interactions with cellular macromolecules resulting in toxicity. For newly developed drugs such pathways are not always clearly understood but toxicity endpoints are still required as part of a drug's registration. An understanding of which vehicles could be used to ameliorate the unwanted toxicities of newly developed drugs would be highly desirable to the pharmaceutical industry. In this paper we demonstrate the use of different classifiers as a means to select vehicles best suited to avert a drug's toxic effects when no other information about a drug's characteristics is known. Through analysis of data acquired from the Developmental Therapeutics Program (DTP) we are able to establish a link between a drug's toxicity and vehicle used. We demonstrate that classification and selection of the appropriate vehicle can be made based on the similarity of drug choice.
    URI
    http://hdl.handle.net/10454/10745
    Version
    No full-text available in the repository
    Citation
    Mistry P, Palczewska A, Neagu D et al (2014) Using computational methods for the prediction of drug vehicles. In: 14th UK Workshop on Computational Intelligence (UKCI). 8-10 Sep 2014, Bradford, UK: 1-7.
    Link to publisher’s version
    https://doi.org/10.1109/UKCI.2014.6930194
    Type
    Conference Paper
    Collections
    Engineering and Informatics Publications

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