
Man-made xenobiotics, whose potential toxicological effects are not fully understood, are oversaturating the already-contaminated environment. Due to the rate of toxicant accumulation, unmanaged disposal, and unknown adverse effects to the environment and the human population, there is a crucial need to screen for environmental toxicants. Animal models and in vitro models are ineffective models in predicting in vivo responses due to inter-species difference and/or lack of physiologically-relevant 3D tissue environment. Such conventional screening assays possess limitations that prevent dynamic understanding of toxicants and their metabolites produced in the human body. Organ-on-a-chip systems can recapitulate in vivo like environment and subsequently in vivo like responses generating a realistic mock-up of human organs of interest, which can potentially provide human physiology-relevant models for studying environmental toxicology. Feasibility, tunability, and low-maintenance features of organ-on-chips can also make possible to construct an interconnected network of multiple-organs-on-chip toward a realistic human-on-a-chip system. Such interconnected organ-on-a-chip network can be efficiently utilized for toxicological studies by enabling the study of metabolism, collective response, and fate of toxicants through its journey in the human body. Further advancements can address the challenges of this technology, which potentiates high predictive power for environmental toxicology studies.
Manufactured Materials, Microfluidics, Models, Animal, Animals, Humans, Environmental Pollutants, Ecotoxicology, Models, Biological, Xenobiotics
Manufactured Materials, Microfluidics, Models, Animal, Animals, Humans, Environmental Pollutants, Ecotoxicology, Models, Biological, Xenobiotics
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 71 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
