TY - JOUR
T1 - Use of big data in drug development for precision medicine
T2 - an update
AU - Qian, Tongqi
AU - Zhu, Shijia
AU - Hoshida, Yujin
N1 - Funding Information:
This work is supported by U.S. NIH/NIDDK R01 DK099558, European Union ERCE2014EAdGE671231 HEPCIR, Irma T. Hirschl Trust, U.S. Department of Defense W81XWHE16E1E0363, and Cancer Prevention and Research Institute of Texas RR180016 (to Y Hoshida).
Publisher Copyright:
© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2019/5/4
Y1 - 2019/5/4
N2 - Introduction: Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios. Areas covered: Here, we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery. Expert opinion: In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g. individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.
AB - Introduction: Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios. Areas covered: Here, we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery. Expert opinion: In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g. individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.
KW - Big data
KW - drug development
KW - precision medicine
UR - http://www.scopus.com/inward/record.url?scp=85066404018&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066404018&partnerID=8YFLogxK
U2 - 10.1080/23808993.2019.1617632
DO - 10.1080/23808993.2019.1617632
M3 - Review article
C2 - 31286058
AN - SCOPUS:85066404018
SN - 2380-8993
VL - 4
SP - 189
EP - 200
JO - Expert Review of Precision Medicine and Drug Development
JF - Expert Review of Precision Medicine and Drug Development
IS - 3
ER -