Life science
Medical & healthcare, Drug development

Evidence-based prediction of cellular toxicity for amorphoussilica nanoparticles
Graduate School of Pharmaceutical Science
Doctoral Student Martin					https://researchmap.jp/martinj?lang=en

Life science

Institute for Protein Research, Laboratory for Computational Biology
Assistant Professor Reiko Watanabe

https://researchmap.jp/ozkrk?lang=en

Professor Kenji Mizuguchi			https://researchmap.jp/kenji_mizuguchi?lang=en

Abstract
Our research group has established a new method using artificial intelligence for safer design of nanosilicas, a type of nanoparticle, by combining literature data mining and machine learning. The
method established in this study is expected to contribute to thedevelopment of safer nanoparticle materials in various industrialfields.

▍ Background & Results
The exponential growth in scientific literature, exemplified by theannual increase in publications, including last year’s 34,000 papers
in the nanomaterials field alone, as indexed in PubMed, underscores the sheer magnitude of knowledge being generated. This
surge in information has reached a point where it transcends thecapacity of any single individual to efficiently read and comprehensively understand. To harness the immense potential of this wealthof literature for nanomaterials, Artificial Intelligence (AI) emerges
as a powerful tool that can significantly accelerate the process ofdesigning safe and functional nanoparticles.
Our group has established a new method using AI to design
nanosilicas (SiO2-NPs), a type of nanoparticle with diameter lessthan 100 nanometers or less, more safely. Specifically, by carefullycollecting information from over 100 scientific papers and utilizing
an advanced AI algorithm (CatBoost), we established a genericin silico prediction model for evaluating the safety of nanosilicas
by combining literature data mining and machine learning. This
research revealed several important factors that affect the safetyof nanosilicas, including concentration, presence of serum, size,exposure time, and surface properties. The results showed thatmodifying the surface of nanosilicas and using them at low concentrations can significantly improve their safety.

▍ Significance of the research and Future perspective
The motivation behind this research is to address the critical
gap in considering the safety implications of nanoparticles’ designconditions. While their widespread use has revolutionized various
industries, including cosmetics, paints, textiles, and electronics,
safety has not received adequate attention. By establishing a newevidence-based method using AI, researchers can distinguish between cytotoxic and noncytotoxic nanoparticles, thereby bearing
significant implications to improve the safety of nanoparticles andenhance consumer confidence in everyday uses of nanomaterials.
Through generation of nano-databases by literature data mining,
future research can conduct high-throughput in silico screening
to massive targeted discovery of functional nanoparticles such as
selectivity towards cancer cells.

P a t e n t
Tr e a t i s e

Martin; Watanabe, Reiko; Mizuguchi, Kenji et al. Evidence-based prediction of cellular toxicity for amorphous silica nanoparticles. ACS Nano. 2023,
17(11), 9987–9999. doi: 10.1021/acsnano.2c11968

URL
K e y w o r d AI, data mining, nanoparticles, toxicity