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Methods Mol Biol. 2019;1939:181-198. doi: 10.1007/978-1-4939-9089-4_10.

A Computational Platform and Guide for Acceleration of Novel Medicines and Personalized Medicine.

Author information

1
Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
2
Translational Bioinformatics, UCB Pharma, Slough, UK.
3
Department of Pathology, New York University School of Medicine, New York, USA.
4
Department of Biomedical Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND, USA.
5
School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece.
6
College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA.
7
Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA. jane.bai@fda.hhs.gov.

Abstract

In the era of big data and informatics, computational integration of data across the hierarchical structures of human biology enables discovery of new druggable targets of disease and new mode of action of a drug. We present herein a computational framework and guide of integrating drug targets, gene expression data, transcription factors, and prior knowledge of protein interactions to computationally construct the signaling network (mode of action) of a drug. In a similar manner, a disease network is constructed using its disease targets. And then, drug candidates are computationally prioritized by computationally ranking the closeness between a disease network and a drug's signaling network. Furthermore, we describe the use of the most perturbed HLA genes to assess the safety risk for immune-mediated adverse reactions such as Stevens-Johnson syndrome/toxic epidermal necrolysis.

KEYWORDS:

Drug targets; Gene expression; HLA genes; Integer linear programming; Network protein interactions

PMID:
30848462
DOI:
10.1007/978-1-4939-9089-4_10
[Indexed for MEDLINE]

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