Using Large Datasets to Understand Nanotechnology

• Published in: Advanced materials (Weinheim) Vol. 31; no. 43; pp. e1902798 - n/a
• Main Authors: Paunovska, Kalina, Loughrey, David, Sago, Cory D., Langer, Robert, Dahlman, James E.
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.10.2019
• Subjects: Animals; big data; Biological activity; Biomolecules; Databases, Factual; Datasets; drug delivery; Drug Delivery Systems; High-Throughput Nucleotide Sequencing; Humans; Nanomaterials; Nanotechnology; Nanotechnology - methods; next‐generation sequencing; Organic chemistry; Proteomics; Toxicity; nanotechnology; drug delivery; big data; next-generation sequencing
• ISSN: 0935-9648; 1521-4095
• DOI: 10.1002/adma.201902798

Advances in sequencing technologies have made studying biological processes with genomics, transcriptomics, and proteomics commonplace. As a result, this suite of increasingly integrated techniques is well positioned to study drug delivery, a process that is influenced by many biomolecules working in concert. Omics‐based approaches can be used to study the vast nanomaterial chemical space as well as the biological factors that affect the safety, toxicity, and efficacy of nanotechnologies. The generation and analysis of large datasets, methods to interpret them, and dataset applications to nanomaterials to date, are demonstrated here. Finally, new approaches for how sequencing‐generated datasets can answer fundamental questions in nanotechnology based drug delivery are proposed. Studying biological processes with genomics, transcriptomics, and proteomics has become commonplace. Omics‐based approaches can help study the vast nanomaterial chemical space as well as biological factors that affect the safety, toxicity, and efficacy of nanotechnologies. The generation and analysis of large datasets and their application to answer fundamental questions in nanotechnology‐based drug delivery are reviewed.