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.

In an in vitro nanotoxicity system, cell−nanoparticle (NP) interaction leads to the surface adsorption, uptake, and changes into nuclei/cell phenotype and chemistry, as an indicator of oxidative stress, genotoxicity, and carcinogenicity. Different types of nanomaterials and their chemical composition or "corona" have been widely studied in context with nanotoxicology. However, rare reports are available, which delineate the details of the cell shape index (CSI) and nuclear area factors (NAFs) as a descriptor of the type of nanomaterials. In this paper, we propose a machine-learning-based graph modeling and correlation-establishing approach using tight junction protein ZO-1-mediated alteration in the cell/nuclei phenotype to quantify and propose it as indices of cell−NP interactions. We believe that the phenotypic variation (CSI and NAF) in the epithelial cell is governed by the physicochemical descriptors (e.g., shape, size, zeta potential, concentration, diffusion coefficients, polydispersity, and so on) of the different classes of nanomaterials, which critically determines the intracellular uptake or cell membrane interactions when exposed to the epithelial cells at sub-lethal concentrations. The intrinsic and extrinsic physicochemical properties of the representative nanomaterials (NMs) were measured using optical (dynamic light scattering, NP tracking analysis) methods to create a set of nanodescriptors contributing to cell−NM interactions via phenotype adjustments. We used correlation function as a machine-learning algorithm to successfully predict cell and nuclei shapes and polarity functions as phenotypic markers for five different classes of nanomaterials studied herein this report. The CSI and NAF as nanodescriptors can be used as intuitive cell phenotypic parameters to define the safety of nanomaterials extensively used in consumer products and nanomedicine.

Daniel Rosenkranz received his B.Sc. degree in 2011 and his M.Sc. degree in 2014 from the University of Oldenburg. He later worked as a junior researcher at the University of Oldenburg till 2015, funded by the foundation of the metal industry in the northwest Germany. Since 2016, he is working as a Ph.D. scholar at the German Federal Institute of Risk Assessment and the German Federal Institute for Material Research and Testing. His research interests include nanomaterial characterization, fundamentals in analytics, matrix matched measurement strategies, low volume injection systems, protein-nanomaterial interactions.

Nanobiomaterials application into tissue repair and ulcer management is experiencing its golden age due to spurring diversity of translational opportunity to clinics. Over the past years, research in clinical science has seen a dramatic increase in medicinal materials at nanoscale those significantly contributed to tissue repair. This chapter outlines the new biomaterials at nanoscale those contribute state of the art clinical practices in ulcer management and wound healing due to their superior properties over traditional dressing materials. Designing new recipes for nanobiomaterials for tissue engineering practices spanning from micro to nano-dimension provided an edge over traditional wound care materials those mimic tissue in vivo. Clinical science stepped into design of artificial skin and extracellular matrix components emulating the innate structures with higher degree of precision. Advances in materials sciences polymer chemistry have yielded an entire class of new nanobiomaterials ranging from dendrimer to novel electrospun polymer with biodegradable chemistries and controlled molecular compositions assisting wound healing adhesives, bandages and controlled of therapeutics in specialized wound care. Moreover, supportive regenerative medicine is transforming into rational, real and successful component of modern clinics providing viable cell therapy of tissue remodeling. Soft nanotechnology involving hydrogel scaffold revolutionized the wound management supplementing physicobiochemical and mechanical considerations of tissue regeneration. Moreover, this chapter also reviews the current challenges and opportunities in specialized nanobiomaterials formulations those are desirable for optimal localized wound care considering their in situ physiological microenvironment.

Computational Nanotoxicology Machine learning tools are making great strides in advancing computational nanotoxicology via in‐silico modeling and ab‐initio simulations to understand the nano‐bio interactions from environmental and health safety perspectives. In article number http://doi.wiley.com/10.1002/aisy.202000084, Ajay Vikram Singh and co‐workers describe the potential, reality, challenges, and future advances that artifi cial intelligence (AI) and machine learning (ML) present in advanced material design and toxicity predictions.