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

Abstract

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 Empower Advanced Biomedical Material Design to Toxicity Prediction

“…The first concept of such a model combination can be found in recent studies. [67] Table 1. Principal ML tools for adopting a statistical approach to combine the data derived from a systematic review for a meta-analysis of different classes of nanomaterials and their example application areas.…”

Inorganic Nanomaterials for Photothermal‐Based Cancer Theranostics

“…[268] Fourth, new technologies such as machine learning can be utilized to optimize the design of novel nanomedicines based on inorganic PTAs, enhancing therapeutic efficacy and decreasing side effects. [269] Nonetheless, one can predict that photothermal-based theranostics based on inorganic NPs is an emerging field as a minimally invasive option for clinical cancer treatments, making significant contribution in improving the quality of life of cancer patients.…”

Simulation Analysis of the Influence of Nozzle Structure Parameters on Material Controllability

“…In conclusion, we presented a novel flow-focusing nozzle, which can achieve material controlled distribution under the action of multi-parameter coordination. The significant results can not only achieve high controllability of biomaterials, but also lay the foundation of machine learning based simulation into 3D bioprinting and modelling of next generation nanoscaffolds and organs [ 28 ], which has the potential to provide better advantage to nanomedicine and medicine in general.…”