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

ABSTRACT

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.

KEYWORDS:

• Big data
• drug development
• precision medicine

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Article highlights

• The accumulating drug knowledge bases, multi-omics data, and clinical data, comprise the cross-domain big data, facilitating the systematical drug discovery.

• The increasing informatics infrastructure now enables big-data analysis to explore new drug therapeutics from multiple perspectives, such as genomics, proteomics, GWAS, pathway, EHR, and pheWAS.

• Numerous well-defined and high-quality clinical phenotypic information available greatly facilitate the construction of in silico drug-safety modeling in the early stage of drug discovery to screen low-toxicity compounds, to some extent, bypassing less-reliable rodent models.

• Rapidly developing machine learning techniques have drastically accelerated the big-data-based drug-discovery approach, further promoting the performance for both de novo drug discovery and drug repurposing.

• Incorporation of direct-to-consumer genetic testing and information technology industries has drastically strengthened the big-data-based approach, further enhancing individualized precision medicine.

• This box summarizes key points contained in the article.

Additional information

Funding

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).