Lysosomal trapping of 4-dimethylamino-1-{3-(1-methyl-1H-imidazole-2-yl)propanoyl}piperidine, a hydrophilic and weakly basic amine, in human aortic vascular smooth muscle cells

Some weakly basic compounds lead to cell death accompanied by cellular vacuolation. The novel analgesic agent, 4-dimethylamino-1-{3-(1-methyl-1H-imidazole-2-yl)propanoyl}piperidine (DMIP), is a hydrophilic and weakly basic compound that induces vacuolation in the vascular smooth muscle cells in dogs. Here, we investigated the vacuolation mechanism and the potential cytotoxicity of DMIP using human aortic vascular smooth muscle cells. When cells were treated with DMIP (0.1, 0.3, and 1 mM) for 6, 24, and 48 h, clear cytoplasmic vacuolation was observed at 1 mM after 24 and 48 h, along with an increase in the intracellular DMIP concentration. The vacuolation and intracellular DMIP were markedly reduced by bafilomycin A1, a vacuolar H⁺-ATPase inhibitor. The late endosome marker Rab7 and lysosome marker LAMP-2 were highly expressed but the early endosome marker Rab5 and autophagosome marker LC3 were not expressed specifically on the vacuolar membranes. These results suggested that the most vacuoles were enlarged late endosomes/lysosomes, resulting from the accumulation of DMIP by ion trapping. Moreover, DMIP did not affect lysosomal membrane integrity and was less cytotoxic than chloroquine, an inducer of phospholipidosis. The current study provides further insight into the mechanisms of vacuolation and lysosomal trapping induced by the hydrophilic and weakly basic amine DMIP.

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

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.