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Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine
Author(s) -
Singh Ajay Vikram,
Ansari Mohammad Hasan Dad,
Rosenkranz Daniel,
Maharjan Romi Singh,
Kriegel Fabian L.,
Gandhi Kaustubh,
Kanase Anurag,
Singh Rishabh,
Laux Peter,
Luch Andreas
Publication year - 2020
Publication title -
advanced healthcare materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.288
H-Index - 90
eISSN - 2192-2659
pISSN - 2192-2640
DOI - 10.1002/adhm.201901862
Subject(s) - nanomedicine , adme , physiologically based pharmacokinetic modelling , nanotoxicology , in silico , computer science , nanotechnology , biochemical engineering , engineering , medicine , pharmacokinetics , chemistry , pharmacology , nanoparticle , materials science , gene , biochemistry
Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial‐based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)‐based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure–activity relationships at nanoscale (nano‐QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.