Predictive Systems Toxicology

Handle URI:
http://hdl.handle.net/10754/626882
Title:
Predictive Systems Toxicology
Authors:
Kiani, Narsis A.; Shang, Ming-Mei; Zenil, Hector; Tegner, Jesper ( 0000-0002-9568-5588 )
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.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Bioscience Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
arXiv
Issue Date:
15-Jan-2018
ARXIV:
arXiv:1801.05058
Type:
Preprint
Additional Links:
http://arxiv.org/abs/1801.05058v1; http://arxiv.org/pdf/1801.05058v1
Appears in Collections:
Other/General Submission; Bioscience Program; Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKiani, Narsis A.en
dc.contributor.authorShang, Ming-Meien
dc.contributor.authorZenil, Hectoren
dc.contributor.authorTegner, Jesperen
dc.date.accessioned2018-01-28T07:01:38Z-
dc.date.available2018-01-28T07:01:38Z-
dc.date.issued2018-01-15en
dc.identifier.urihttp://hdl.handle.net/10754/626882-
dc.description.abstractIn 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.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1801.05058v1en
dc.relation.urlhttp://arxiv.org/pdf/1801.05058v1en
dc.rightsArchived with thanks to arXiven
dc.titlePredictive Systems Toxicologyen
dc.typePreprinten
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentBioscience Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.eprint.versionPre-printen
dc.contributor.institutionScience for Life Laboratory, 171 21 Solna, Swedenen
dc.contributor.institutionAlgorithmic Dynamics Lab, Center for Molecular Medicine, Karolinska Institutet, Stockholm, 171 76, Swedenen
dc.contributor.institutionUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna, Karolinska Institutet, Stockholm, 171 76, Swedenen
dc.identifier.arxividarXiv:1801.05058en
kaust.authorTegner, Jesperen
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