Details

Autor(en) / Beteiligte

• 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

Titel

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

Ist Teil von

• Advanced healthcare materials, 2020-09, Vol.9 (17), p.e1901862-n/a

Ort / Verlag

Germany: Wiley Subscription Services, Inc

Erscheinungsjahr

2020

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. Artificial intelligence and machine learning in computational nanomedicine are making great strides in nanoparticle‐modified theranostics design and development. The quantitative structure–activity relationships at nanoscale, physiologically based pharmacokinetic, absorption, distribution, metabolism, and excretion, and dosimetry‐based advanced computational models further help in understanding the in vitro physiological and pharmacokinetics and correlate the data with in vivo scenario. This review focuses on recent advances in computational modeling unlocking and empowering nanotoxicology.