Nicolas Widmer

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Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that(More)
BACKGROUND/AIMS Transactivated hepatic stellate cells (HSCs) represent the key source of extra cellular matrix (ECM) in fibrotic liver. Imatinib, a potent inhibitor of the PDGF receptor tyrosine kinase, reduces HSC proliferation and fibrogenesis when treatment is initiated before fibrosis has developed. We tested the antifibrotic potential of imatinib in(More)
—Drug delivery is one of the most common clinical routines in hospitals, and is critical to patients' health and recovery. It includes a decision making process in which a medical doctor decides the amount (dose) and frequency (dose interval) on the basis of a set of available patients' feature data and the doctor's clinical experience (a priori(More)
—Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients'(More)
The benzothiazinone lead compound, BTZ043, kills Mycobacterium tuberculosis by inhibiting the essential flavo-enzyme DprE1, decaprenylphosphoryl-beta-D-ribose 2-epimerase. Here, we synthesized a new series of piperazine-containing benzothiazinones (PBTZ) and show that, like BTZ043, the preclinical candidate PBTZ169 binds covalently to DprE1. The crystal(More)
  • Nancy Perrottet, Françoise Brunner-Ferber, Eric Grouzmann, François Spertini, Jérôme Biollaz, Thierry Buclin +1 other
  • 2016
BACKGROUND During clinical trials, researchers rarely question nominal doses specified on labels of investigational products, overlooking the potential for inaccuracies that may result when calculating pharmacokinetic and pharmacodynamic parameters. This study evaluated the disparity between nominal doses and the doses actually administered in two Phase I(More)
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