Toward a systematic exploration of nano-bio interactions.
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.
School of Chemistry and Chemical Engineering, Shandong University, Jinan, China.
Xue
X
Bai
School of Chemistry and Chemical Engineering, Shandong University, Jinan, China.
Fang
F
Liu
School of Chemistry and Chemical Engineering, Shandong University, Jinan, China.
Yin
Y
Liu
School of Chemistry and Chemical Engineering, Shandong University, Jinan, China.
Cong
C
Li
School of Chemistry and Chemical Engineering, Shandong University, Jinan, China.
Shenqing
S
Wang
School of Environmental Science and Technology, Shandong University, Jinan, China.
Hongyu
H
Zhou
Department of Chemistry, Rutgers University, Camden, NJ, United States; The Rutgers Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States.
Wenyi
W
Wang
Department of Chemistry, Rutgers University, Camden, NJ, United States; The Rutgers Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States.
Hao
H
Zhu
CSIRO Manufacturing, Bag 10, Clayton South MDC 3169, Australia; Monash Institute of Pharmaceutical Sciences, 392 Royal Parade, Parkville 3052, Australia; La Trobe Institute for Molecular Science, La Trobe University, Kingsbury Drive, Melbourne 3086, Australia; School of Chemical and Physical Sciences, Flinders University, Bedford Park 5042, Australia. Electronic address: d.winkler@latrobe.edu.au.
David A
DA
Winkler
School of Chemistry and Chemical Engineering, Shandong University, Jinan, China; School of Environmental Science and Technology, Shandong University, Jinan, China. Electronic address: drbingyan@yahoo.com.
Bing
B
Yan
D000465:Algorithms; D000826:Animal Testing Alternatives; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D049329:Nanostructures / Q000737:chemistry / Q000633:toxicity*; D036103:Nanotechnology / Q000379:methods*; D018570:Risk Assessment; D018675:Toxicity Tests / Q000379:methods*