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Adv Protein Chem Struct Biol. 2016;102:147-79. doi: 10.1016/bs.apcsb.2015.09.005. Epub 2015 Oct 29.

Computational Approaches to Accelerating Novel Medicine and Better Patient Care from Bedside to Benchtop.

Author information

1
Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
2
Department of Biomedical Sciences, School of Medicine & Health Sciences, University of North Dakota, Grand Forks, North Dakota, USA.
3
Astrazeneca, Discovery Sciences, Group of Quantitative Biology, Molndal, Sweden.
4
Molecular and Cellular Biology, Liberal Arts and Sciences, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, USA.
5
School of Mechanical Engineering, National Technical University of Athens, Athens, Greece. ProtAtonce Ltd., Athens, Greece.
6
Astrazeneca, Discovery Sciences, Reagents and Assay Development, Transgenics Group, Molndal, Sweden.
7
Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA. Electronic address: jane.bai@fda.hhs.gov.

Abstract

Some successes have been achieved in the war on cancer over the past 30 years with recent efforts on protein kinase inhibitors. Nonetheless, we are still facing challenges due to cancer evolution. Cancers are complex and heterogeneous due to primary and secondary mutations, with phenotypic and molecular heterogeneity manifested among patients of a cancer, and within an individual patient throughout the disease course. Our understanding of cancer genomes has been facilitated by advances in omics and in bioinformatics technologies; major areas in cancer research are advancing in parallel on many fronts. Computational methods have been developed to decipher the molecular complexity of cancer and to identify driver mutations in cancers. Utilizing the identified driver mutations to develop effective therapy would require biological linkages from cellular context to clinical implication; for this purpose, computational mining of biomedical literature facilitates utilization of a huge volume of biomedical research data and knowledge. In addition, frontier technologies, such as genome editing technologies, are facilitating investigation of cancer mutations, and opening the door for developing novel treatments to treat diseases. We will review and highlight the challenges of treating cancers, which behave like moving targets due to mutation and evolution, and the current state-of-the-art research in the areas mentioned above.

KEYWORDS:

Computation; Driver mutations; Genome editing; Tumor heterogeneity

PMID:
26827605
DOI:
10.1016/bs.apcsb.2015.09.005
[Indexed for MEDLINE]

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