Toward a systematic exploration of nano-bio interactions

Xue Bai, Fang Liu, Yin Liu, Cong Li, Shenqing Wang, Hongyu Zhou, Wenyi Wang, Hao Zhu, David A. Winkler, Bing Yan

Research output: Contribution to journalReview ArticleResearchpeer-review

25 Citations (Scopus)

Abstract

Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability. They can provide a viable method of reducing or eliminating animal testing. However, careful experimental design with the modelling of the results in mind is a proven and efficient way of exploring large materials spaces. This approach, coupled with high speed automated experimental synthesis and characterization technologies now appearing, is the fastest route to developing models that regulatory bodies may find useful. We advocate greatly increased focus on systematic modification of physicochemical properties of nanoparticles combined with comprehensive biological evaluation and computational analysis. This is essential to obtain better mechanistic understanding of nano-bio interactions, and to derive quantitatively predictive and robust models for the properties of nanomaterials that have useful domains of applicability.

Original languageEnglish
Pages (from-to)66-73
Number of pages8
JournalToxicology and Applied Pharmacology
Volume323
DOIs
Publication statusPublished - 15 May 2017

Keywords

  • nanoparticles
  • safety
  • biological effects
  • Machine Learning
  • QSAR, neural networks, deep learning, drug design

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