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    Predictive Systems Toxicology

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    1801.05058.pdf
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    909.7Kb
    Format:
    PDF
    Description:
    Preprint
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    Type
    Preprint
    Authors
    Kiani, Narsis A. cc
    Shang, Ming-Mei
    Zenil, Hector cc
    Tegner, Jesper cc
    KAUST Department
    Biological and Environmental Sciences and Engineering (BESE) Division
    Bioscience Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2018-01-15
    Permanent link to this record
    http://hdl.handle.net/10754/626882
    
    Metadata
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    Abstract
    In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine point-of-view. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predictive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e. equipping models with numerous molecular features. This challenge also requires patient-specific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patient-specific models.
    Publisher
    arXiv
    arXiv
    1801.05058
    Additional Links
    http://arxiv.org/abs/1801.05058v1
    http://arxiv.org/pdf/1801.05058v1
    Collections
    Biological and Environmental Science and Engineering (BESE) Division; Preprints; Bioscience Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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