Literature DB >> 27430024

Use of big data in drug development for precision medicine.

Rosa S Kim1, Nicolas Goossens2, Yujin Hoshida1.   

Abstract

Drug development has been a costly and lengthy process with an extremely low success rate and lack of consideration of individual diversity in drug response and toxicity. Over the past decade, an alternative "big data" approach has been expanding at an unprecedented pace based on the development of electronic databases of chemical substances, disease gene/protein targets, functional readouts, and clinical information covering inter-individual genetic variations and toxicities. This paradigm shift has enabled systematic, high-throughput, and accelerated identification of novel drugs or repurposed indications of existing drugs for pathogenic molecular aberrations specifically present in each individual patient. The exploding interest from the information technology and direct-to-consumer genetic testing industries has been further facilitating the use of big data to achieve personalized Precision Medicine. Here we overview currently available resources and discuss future prospects.

Entities:  

Keywords:  Big data; drug development; high-throughput screen; in silico drug discovery; precision medicine

Year:  2016        PMID: 27430024      PMCID: PMC4943760          DOI: 10.1080/23808993.2016.1174062

Source DB:  PubMed          Journal:  Expert Rev Precis Med Drug Dev        ISSN: 2380-8993


  88 in total

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Review 4.  Using physiologically-based pharmacokinetic-guided "body-on-a-chip" systems to predict mammalian response to drug and chemical exposure.

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Review 6.  Predicting drug metabolism: experiment and/or computation?

Authors:  Johannes Kirchmair; Andreas H Göller; Dieter Lang; Jens Kunze; Bernard Testa; Ian D Wilson; Robert C Glen; Gisbert Schneider
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Review 8.  A survey of current trends in computational drug repositioning.

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2.  Exploration of Protein Aggregations in Parkinson's Disease Through Computational Approaches and Big Data Analytics.

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3.  Use of big data in drug development for precision medicine: an update.

Authors:  Tongqi Qian; Shijia Zhu; Yujin Hoshida
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Review 4.  Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery.

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Journal:  Int J Mol Sci       Date:  2018-05-25       Impact factor: 5.923

5.  Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.

Authors:  Manish Kumar Tripathi; Abhigyan Nath; Tej P Singh; A S Ethayathulla; Punit Kaur
Journal:  Mol Divers       Date:  2021-06-23       Impact factor: 3.364

Review 6.  Generic chemoprevention of hepatocellular carcinoma.

Authors:  Sai Krishna Athuluri-Divakar; Yujin Hoshida
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7.  Cell type-specific pharmacological kinase inhibition for cancer chemoprevention.

Authors:  Manjeet Deshmukh; Shigeki Nakagawa; Takaaki Higashi; Adam Vincek; Anu Venkatesh; Marina Ruiz de Galarreta; Anna P Koh; Nicolas Goossens; Hadassa Hirschfield; C Billie Bian; Naoto Fujiwara; Atsushi Ono; Hiroki Hoshida; Mohamed El-Abtah; Noor B Ahmad; Amaia Lujambio; Roberto Sanchez; Bryan C Fuchs; Klaas Poelstra; Jai Prakash; Yujin Hoshida
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9.  An exploratory assessment of the applicability of direct-to-consumer genetic testing to translational research in Japan.

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  9 in total

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